Does experience of the 'occult' predict use of complementary medicine? Experience of, and beliefs about, both complementary medicine and ways of telling the future.

PubMed

Furnham, A

2000-12-01

This study looked at the relationship between ratings of the perceived effectiveness of 24 methods for telling the future, 39 complementary therapies (CM) and 12 specific attitude statements about science and medicine. A total of 159 participants took part. The results showed that the participants were deeply sceptical of the effectiveness of the methods for telling the future which factored into meaningful and interpretable factors. Participants were much more positive about particular, but not all, specialties of complementary medicine (CM). These also factored into a meaningful factor structure. Finally, the 12 attitude to science/medicine statements revealed four factors: scepticism of medicine; the importance of psychological factors; patient protection; and the importance of scientific evaluation. Regressional analysis showed that belief in the total effectiveness of different ways of predicting the future was best predicted by beliefs in the effectiveness of the CM therapies. Although interest in the occult was associated with interest in CM, participants were able to distinguish between the two, and displayed scepticism about the effectiveness of methods of predicting the future and some CM therapies. Copyright 2000 Harcourt Publishers Ltd.

Commentary: Factors predicting family court decisions in high-conflict divorce.

PubMed

Stover, Carla Smith

2013-01-01

Factors that predict custody and visitation decisions are an important area of research, especially in the context of high-conflict divorce. In these cases, youths are at significantly higher risk for exposure to ongoing conflict, violence, and triangulation in their parents' disputes. What variables courts and evaluation clinics use to make custody decisions and whether they are the most salient requires further study. The work by Raub and colleagues in this issue extends our understanding of important factors considered by the courts and custody evaluators in high-conflict divorce and points to directions for future research in this area.

Examining Factors Predicting Students' Digital Competence

ERIC Educational Resources Information Center

Hatlevik, Ove Edvard; Guðmundsdóttir, Gréta Björk; Loi, Massimo

2015-01-01

The purpose of this study was to examine factors predicting lower secondary school students' digital competence and to explore differences between students when it comes to digital competence. Results from a digital competence test and survey in lower secondary school will be presented. It is important to learn more about and investigate what…

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela; ...

2017-07-01

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

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

PubMed

Clyde, M A; Parmigiani, G

1998-07-01

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

The two-factor model of psychopathic personality: evidence from the psychopathic personality inventory.

PubMed

Marcus, David K; Fulton, Jessica J; Edens, John F

2013-01-01

Psychopathy or psychopathic personality disorder represents a constellation of traits characterized by superficial charm, egocentricity, irresponsibility, fearlessness, persistent violation of social norms, and a lack of empathy, guilt, and remorse. Factor analyses of the Psychopathic Personality Inventory (PPI)typically yield two factors: Fearless Dominance (FD) and Self-Centered Impulsivity (SCI). Additionally, the Coldheartedness (CH) subscale typically does not load on either factor. The current paper includes a meta-analysis of studies that have examined theoretically important correlates of the two PPI factors and CH. Results suggest that (a) FD and SCI are orthogonal or weakly correlated, (b) each factor predicts distinct (and sometimes opposite) correlates, and (c) the FD factor is not highly correlated with most other measures of psychopathy. This pattern of results raises important questions about the relation between FD and SCI and the role of FD in conceptualizations of psychopathy. Our findings also indicate the need for future studies using the two-factor model of the PPI to conduct moderational analyses to examine potential interactions between FD and SCI in the prediction of important criterion measures.

Memory Resilience to Alzheimer's Genetic Risk: Sex Effects in Predictor Profiles.

PubMed

McDermott, Kirstie L; McFall, G Peggy; Andrews, Shea J; Anstey, Kaarin J; Dixon, Roger A

2017-10-01

Apolipoprotein E (APOE) ɛ4 and Clusterin (CLU) C alleles are risk factors for Alzheimer's disease (AD) and episodic memory (EM) decline. Memory resilience occurs when genetically at-risk adults perform at high and sustained levels. We investigated whether (a) memory resilience to AD genetic risk is predicted by biological and other risk markers and (b) the prediction profiles vary by sex and AD risk variant. Using a longitudinal sample of nondemented adults (n = 642, aged 53-95) we focused on memory resilience (over 9 years) to 2 AD risk variants (APOE, CLU). Growth mixture models classified resilience. Random forest analysis, stratified by sex, tested the predictive importance of 22 nongenetic risk factors from 5 domains (n = 24-112). For both sexes, younger age, higher education, stronger grip, and everyday novel cognitive activity predicted memory resilience. For women, 9 factors from functional, health, mobility, and lifestyle domains were also predictive. For men, only fewer depressive symptoms was an additional important predictor. The prediction profiles were similar for APOE and CLU. Although several factors predicted resilience in both sexes, a greater number applied only to women. Sex-specific mechanisms and intervention targets are implied. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Psychopathy and Violence: The Importance of Factor Level Interactions

ERIC Educational Resources Information Center

Walsh, Zach; Kosson, David S.

2008-01-01

The power of scales based on the Psychopathy Checklist (PCL; R. D. Hare, 1980) for prediction of violent behavior is well established. Although evidence suggests that this relationship is chiefly due to the impulsive and antisocial lifestyle component (Factor 2), the predictive power of psychopathy for violence may also reflect the multiplicative…

Factors Predicting Turkish and Korean Students' Science and Mathematics Achievement in TIMSS 2011

ERIC Educational Resources Information Center

Topçu, Mustafa Sami; Erbilgin, Evrim; Arikan, Serkan

2016-01-01

This study makes an important contribution to an expanding body of international comparative studies by exploring factors predicting differences in science and mathematics achievement by students in Turkey and the Republic of Korea on the 2011 TIMSS assessment. While these countries are similar with regards to population size, cultural beliefs…

Individual Characteristics, Family Factors, and Classroom Experiences as Predictors of Low-Income Kindergarteners' Social Skills.

PubMed

Griffith, Shayl; Arnold, David; Voegler-Lee, Mary-Ellen; Kupersmidt, Janis

2016-01-01

There has been increasing awareness of the need for research and theory to take into account the intersection of individual characteristics and environmental contexts when examining predictors of child outcomes. The present longitudinal, multi-informant study examined the cumulative and interacting contributions of child characteristics (language skills, inattention/hyperactivity, and aggression) and preschool and family contextual factors in predicting kindergarten social skills in 389 low-income preschool children. Child characteristics and classroom factors, but not family factors, predicted teacher-rated kindergarten social skills, while child characteristics alone predicted change in teacher-rated social skills from preschool to kindergarten. Child characteristics and family factors, but not classroom factors, predicted parent-rated kindergarten social skills. Family factors alone predicted change in parent-rated social skills from preschool to kindergarten. Individual child characteristics did not interact with family or classroom factors in predicting parent- or teacher-rated social skills, and support was therefore found for an incremental, rather than an interactive, predictive model of social skills. The findings underscore the importance of assessing outcomes in more than one context, and of considering the impact of both individual and environmental contextual factors on children's developing social skills when designing targeted intervention programs to prepare children for kindergarten.

Individual Characteristics, Family Factors, and Classroom Experiences as Predictors of Low-Income Kindergarteners’ Social Skills

PubMed Central

Griffith, Shayl; Arnold, David; Voegler-Lee, Mary-Ellen; Kupersmidt, Janis

2017-01-01

There has been increasing awareness of the need for research and theory to take into account the intersection of individual characteristics and environmental contexts when examining predictors of child outcomes. The present longitudinal, multi-informant study examined the cumulative and interacting contributions of child characteristics (language skills, inattention/hyperactivity, and aggression) and preschool and family contextual factors in predicting kindergarten social skills in 389 low-income preschool children. Child characteristics and classroom factors, but not family factors, predicted teacher-rated kindergarten social skills, while child characteristics alone predicted change in teacher-rated social skills from preschool to kindergarten. Child characteristics and family factors, but not classroom factors, predicted parent-rated kindergarten social skills. Family factors alone predicted change in parent-rated social skills from preschool to kindergarten. Individual child characteristics did not interact with family or classroom factors in predicting parent- or teacher-rated social skills, and support was therefore found for an incremental, rather than an interactive, predictive model of social skills. The findings underscore the importance of assessing outcomes in more than one context, and of considering the impact of both individual and environmental contextual factors on children’s developing social skills when designing targeted intervention programs to prepare children for kindergarten. PMID:28804528

As of 2012, what are the key predictive risk factors for pressure ulcers? Developing French guidelines for clinical practice.

PubMed

Michel, J-M; Willebois, S; Ribinik, P; Barrois, B; Colin, D; Passadori, Y

2012-10-01

An evaluation of predictive risk factors for pressure ulcers is essential in development of a preventive strategy on admission to hospitals and/or nursing homes. Identification of the predictive factors for pressure ulcers as of 2012. Systematic review of the literature querying the databases PASCAL Biomed, Cochrane Library and PubMed from 2000 through 2010. Immobility should be considered as a predictive risk factor for pressure ulcers (grade B). Undernutrition/malnutrition may also be a predictive risk factor for pressure ulcers (grade C). Even if the level of evidence is low, once these risk factors have been detected, management is essential. Sensitizing and mobilizing health care teams requires training in ways of tracking and screening. According to the experts, risk scales should be used. As decision aids, they should always be balanced and complemented by the clinical judgment of the treatment team. According to experts, it is important to know and predictively evaluate risk of pressure ulcers at the time of hospital admission. The predictive risk factors found in this study are identical to those highlighted at the 2001 consensus conference of which was PERSE was the promoter. Copyright © 2012. Published by Elsevier Masson SAS.

Rescue workers and trauma: Assessing interaction among risk factors after a firework factory explosion.

PubMed

Romano, Eugenia; Elklit, Ask

This study investigates which factors had the biggest impact on developing distress in rescue workers who were involved in a firework factory explosion. Four hundred sixty-five rescuers were assessed using items investigating demographic factors, organizational variables, social support, personality variables, and distress symptoms. Correlation and regression analyses were performed. Our final model provided 70 percent of the predictive model for post-traumatic stress disorder (PTSD) severity. Waiting time, lack of rest, problems at work, and perceived level of danger seemed to have the highest impact on protective factors. In addition to perceived life danger and personality, small organizational factors seem to play an important role in the prediction of PTSD. The importance of such factors needs further investigation in future research, contributing to a better organization in the field of disaster management.

The Effect and Relative Importance of Neutral Genetic Diversity for Predicting Parasitism Varies across Parasite Taxa

PubMed Central

Ruiz-López, María José; Monello, Ryan J.; Gompper, Matthew E.; Eggert, Lori S.

2012-01-01

Understanding factors that determine heterogeneity in levels of parasitism across individuals is a major challenge in disease ecology. It is known that genetic makeup plays an important role in infection likelihood, but the mechanism remains unclear as does its relative importance when compared to other factors. We analyzed relationships between genetic diversity and macroparasites in outbred, free-ranging populations of raccoons (Procyon lotor). We measured heterozygosity at 14 microsatellite loci and modeled the effects of both multi-locus and single-locus heterozygosity on parasitism using an information theoretic approach and including non-genetic factors that are known to influence the likelihood of parasitism. The association of genetic diversity and parasitism, as well as the relative importance of genetic diversity, differed by parasitic group. Endoparasite species richness was better predicted by a model that included genetic diversity, with the more heterozygous hosts harboring fewer endoparasite species. Genetic diversity was also important in predicting abundance of replete ticks (Dermacentor variabilis). This association fit a curvilinear trend, with hosts that had either high or low levels of heterozygosity harboring fewer parasites than those with intermediate levels. In contrast, genetic diversity was not important in predicting abundance of non-replete ticks and lice (Trichodectes octomaculatus). No strong single-locus effects were observed for either endoparasites or replete ticks. Our results suggest that in outbred populations multi-locus diversity might be important for coping with parasitism. The differences in the relationships between heterozygosity and parasitism for the different parasites suggest that the role of genetic diversity varies with parasite-mediated selective pressures. PMID:23049796

Can we "predict" long-term outcome for ambulatory transcutaneous electrical nerve stimulation in patients with chronic pain?

PubMed

Köke, Albère J; Smeets, Rob J E M; Perez, Roberto S; Kessels, Alphons; Winkens, Bjorn; van Kleef, Maarten; Patijn, Jacob

2015-03-01

Evidence for effectiveness of transcutaneous electrical nerve stimulation (TENS) is still inconclusive. As heterogeneity of chronic pain patients might be an important factor for this lack of efficacy, identifying factors for a successful long-term outcome is of great importance. A prospective study was performed to identify variables with potential predictive value for 2 outcome measures on long term (6 months); (1) continuation of TENS, and (2) a minimally clinical important pain reduction of ≥ 33%. At baseline, a set of risk factors including pain-related variables, psychological factors, and disability was measured. In a multiple logistic regression analysis, higher patient's expectations, neuropathic pain, no severe pain (< 80 mm visual analogue scale [VAS]) were independently related to long-term continuation of TENS. For the outcome "minimally clinical important pain reduction," the multiple logistic regression analysis indicated that no multisited pain (> 2 pain locations) and intermittent pain were positively and independently associated with a minimally clinical important pain reduction of ≥ 33%. The results showed that factors associated with a successful outcome in the long term are dependent on definition of successful outcome. © 2014 World Institute of Pain.

Risk factors for Apgar score using artificial neural networks.

PubMed

Ibrahim, Doaa; Frize, Monique; Walker, Robin C

2006-01-01

Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.

Highly Improved Predictability in the Forecasting of the East Asian Summer Monsoon

NASA Astrophysics Data System (ADS)

Lee, E.; Chase, T. N.; Rajagopalan, B.

2007-12-01

The East Asian summer monsoon greatly influences the lives and property of about a quarter of all the people in the world. However, the predictability of the monsoon is very low in comparison with that of Indian summer monsoon because of the complexity of the system which involves both tropical and sub-tropical climates. Previous monsoon prediction models emphasized ocean factors as the primary monsoon forcing. Here we show that pre-season land surface cover is at least as important as ocean indices. A new statistical forecast model of the East Asian summer monsoon using land cover conditions in addition to ocean heat sources doubles the predictability relative to a model using ocean factors alone. This work highlights the, as yet, undocumented importance of seasonal land cover in monsoon prediction and the role of the biosphere in the climate system as a whole. We also detail the physical mechanisms involved in these land surface forcings.

Biological and Sociocultural Factors during the School Years Predicting Women's Lifetime Educational Attainment

ERIC Educational Resources Information Center

Hendrick, C. Emily; Cohen, Alison K.; Deardorff, Julianna; Cance, Jessica D.

2016-01-01

Background: Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In this study, we examine the roles of sociocultural factors in youth and an understudied biological life event, pubertal timing, in predicting women's lifetime educational attainment. Methods: Using data from the National…

Predicting Social Support for Grieving Persons: A Theory of Planned Behavior Perspective

ERIC Educational Resources Information Center

Bath, Debra M.

2009-01-01

Research has consistently reported that social support from family, friends, and colleagues is an important factor in the bereaved person's ability to cope after the loss of a loved one. This study used a Theory of Planned Behavior framework to identify those factors that predict a person's intention to interact with, and support, a grieving…

The High School Experience of Students with Disabilities: Factors That Influence Their Predicted Likelihood of College Graduation

ERIC Educational Resources Information Center

Schechter, Julia F.

2017-01-01

The purpose of this research is to study college planning practices, skills and activities in the secondary school experience of undergraduate students with disabilities that were important to their self-predicted college graduation. This study is limited to exploring factors within the transition programming domains of student-focused planning,…

Success-factors in transition to university mathematics

NASA Astrophysics Data System (ADS)

Bengmark, S.; Thunberg, H.; Winberg, T. M.

2017-11-01

This study examines different factors' relative importance for students' performance in the transition to university mathematics. Students' characteristics (motivation, actions and beliefs) were measured when entering the university and at the end of the first year. Principal component analysis revealed four important constructs: Self-efficacy, Motivation type, Study habits and Views of mathematics. Subsequently, orthogonal partial least squares (OPLS) analysis was used for measuring the constructs' ability to predict students' university mathematics grades. No individual constructs measured at the time of entrance predicted more than 5% of the variation. On the other hand, jointly they predicted 14%, which is almost in pair with upper secondary grades predicting 17%. Constructs measured at the end of the first year were stronger predictors, jointly predicting 37% of the variation in university grades, with Self-efficacy (21%) and Motivation (12%) being the two strongest individual predictors. In general, Study habits were not important for predicting university achievement. However, for students with low upper secondary grades, the textbook and interaction with peers, rather than internet-based resources, contributed positively to achievement. The association between Views of mathematics and performance was weak for all groups and non-existing for students with low grades.

Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences.

PubMed

Andrabi, Munazah; Hutchins, Andrew Paul; Miranda-Saavedra, Diego; Kono, Hidetoshi; Nussinov, Ruth; Mizuguchi, Kenji; Ahmad, Shandar

2017-06-22

DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist, Minor groove width and Roll. The contributions of other shape features e.g. Shift, Slide and Opening cannot be evaluated using DNAshape. Here, we report a novel method DynaSeq, which predicts molecular dynamics-derived ensembles of a more exhaustive set of DNA shape features. We compared the DNAshape and DynaSeq predictions for the common features and applied both to predict the genome-wide binding sites of 1312 TFs available from protein interaction quantification (PIQ) data. The results indicate a good agreement between the two methods for the common shape features and point to advantages in using DynaSeq. Predictive models employing ensembles from individual conformational parameters revealed that base-pair opening - known to be important in strand separation - was the best predictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape. Of note, TFBS could be predicted not only from the features at the target motif sites, but also from those as far as 200 nucleotides away from the motif.

Information to Act: Household Characteristics are Predictors of Domestic Infestation with the Chagas Vector Triatoma dimidiata in Central America

PubMed Central

Zamora, Dulce María Bustamante; Hernández, Marianela Menes; Torres, Nuria; Zúniga, Concepción; Sosa, Wilfredo; de Abrego, Vianney; Escobar, María Carlota Monroy

2015-01-01

The interruption of vectorial transmission of Chagas disease by Triatoma dimidiata in central America is a public health challenge that cannot be resolved by insecticide application alone. In this study, we collected information on previously known household risk factors for infestation in 11 villages and more than 2,000 houses in Guatemala, Honduras, and El Salvador, and we constructed multivariate models and used multimodel inference to evaluate their importance as predictors of infestation in the region. The models had moderate ability to predict infested houses (sensitivity, 0.32–0.54) and excellent ability to predict noninfested houses (specificity higher than 0.90). Predictive ability was improved by including random village effects and presence of signs of infestation (insect feces, eggs, and exuviae) as fixed effects. Multimodel inference results varied depending on factors included, but house wall materials (adobe, bajareque, and palopique) and signs of infestation were among the most important predictive factors. Reduced models were not supported suggesting that all factors contributed to predictions. Previous knowledge and information from this study show that we have evidence to prioritize rural households for improvement to prevent house infestation with Triatoma dimidiata in Central America. House improvement will most likely have other health co-benefits. PMID:25870430

Information to act: household characteristics are predictors of domestic infestation with the Chagas vector Triatoma dimidiata in Central America.

PubMed

Bustamante Zamora, Dulce María; Hernández, Marianela Menes; Torres, Nuria; Zúniga, Concepción; Sosa, Wilfredo; de Abrego, Vianney; Monroy Escobar, María Carlota

2015-07-01

The interruption of vectorial transmission of Chagas disease by Triatoma dimidiata in central America is a public health challenge that cannot be resolved by insecticide application alone. In this study, we collected information on previously known household risk factors for infestation in 11 villages and more than 2,000 houses in Guatemala, Honduras, and El Salvador, and we constructed multivariate models and used multimodel inference to evaluate their importance as predictors of infestation in the region. The models had moderate ability to predict infested houses (sensitivity, 0.32-0.54) and excellent ability to predict noninfested houses (specificity higher than 0.90). Predictive ability was improved by including random village effects and presence of signs of infestation (insect feces, eggs, and exuviae) as fixed effects. Multimodel inference results varied depending on factors included, but house wall materials (adobe, bajareque, and palopique) and signs of infestation were among the most important predictive factors. Reduced models were not supported suggesting that all factors contributed to predictions. Previous knowledge and information from this study show that we have evidence to prioritize rural households for improvement to prevent house infestation with Triatoma dimidiata in Central America. House improvement will most likely have other health co-benefits. © The American Society of Tropical Medicine and Hygiene.

Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

NASA Astrophysics Data System (ADS)

Zhao, Chang; Song, Guojun

2017-08-01

Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

On the incremental validity of irrational beliefs to predict subjective well-being while controlling for personality factors.

PubMed

Spörrle, Matthias; Strobel, Maria; Tumasjan, Andranik

2010-11-01

This research examines the incremental validity of irrational thinking as conceptualized by Albert Ellis to predict diverse aspects of subjective well-being while controlling for the influence of personality factors. Rational-emotive behavior therapy (REBT) argues that irrational beliefs result in maladaptive emotions leading to reduced well-being. Although there is some early scientific evidence for this relation, it has never been investigated whether this connection would still persist when statistically controlling for the Big Five personality factors, which were consistently found to be important determinants of well-being. Regression analyses revealed significant incremental validity of irrationality over personality factors when predicting life satisfaction, but not when predicting subjective happiness. Results are discussed with respect to conceptual differences between these two aspects of subjective well-being.

Prediction of disease course in inflammatory bowel diseases.

PubMed

Lakatos, Peter Laszlo

2010-06-07

Clinical presentation at diagnosis and disease course of both Crohn's disease (CD) and ulcerative colitis are heterogeneous and variable over time. Since most patients have a relapsing course and most CD patients develop complications (e.g. stricture and/or perforation), much emphasis has been placed in the recent years on the determination of important predictive factors. The identification of these factors may eventually lead to a more personalized, tailored therapy. In this TOPIC HIGHLIGHT series, we provide an update on the available literature regarding important clinical, endoscopic, fecal, serological/routine laboratory and genetic factors. Our aim is to assist clinicians in the everyday practical decision-making when choosing the treatment strategy for their patients suffering from inflammatory bowel diseases.

Impacts of West Nile Virus on wildlife

USGS Publications Warehouse

Saito, E.K.; Wild, M.A.

2004-01-01

The recent epidemic of West Nile virus in the United States proved to be unexpectedly active and was the largest epidemic of the virus ever recorded. Much remains to be discovered about the ecology and epidemiology of West Nile virus in the United States, including which species are important in maintaining the virus in nature, why some species are more susceptible to lethal infection, and what environmental factors are important in predicting future epidemics. These factors will likely vary regionally, depending on local ecological characteristics. Until scientists better understand the virus and factors influencing its activity, predicting its effects for future seasons is impossible. However, experts are certain about one thing: West Nile virus is here to stay.

Predicting probability of occurrence and factors affecting distribution and abundance of three Ozark endemic crayfish species at multiple spatial scales

USGS Publications Warehouse

Nolen, Matthew S.; Magoulick, Daniel D.; DiStefano, Robert J.; Imhoff, Emily M.; Wagner, Brian K.

2014-01-01

We found that a range of environmental variables were important in predicting crayfish distribution and abundance at multiple spatial scales and their importance was species-, response variable- and scale dependent. We would encourage others to examine the influence of spatial scale on species distribution and abundance patterns.

The association between imported factors and prisoners' mental health: Implications for adaptation and intervention.

PubMed

Bowler, Nicholas; Phillips, Ceri; Rees, Paul

In the United Kingdom (UK) the prison population has increased by around one third since the turn of the millennium amid growing concern over the correctional mission of prisons, the number of prisoners exhibiting mental health difficulties and high levels of recidivism. This study aims to explore the relationship between 'imported' (pre-prison) factors and prisoner mental health status. Prisoners (N = 756) from two UK prisons completed an established measure of mental health (General Health Questionnaire: GHQ-12) and a bespoke survey on pre-prison characteristics and experiences (for example, dispositions, childhood abuse, substance misuse, learning difficulties and employment). Prevalence of mental health difficulties was high, with 40.3% reaching the 'caseness' threshold. Binary logistic regression and odds ratio analyses were used to explore the ability of imported factors to predict mental health 'caseness' and the direction of influence. Collectively, the imported factors correctly predicted the caseness category of 76.5% of participants (p < .001). Pre-prison dispositions proved to be strong predictors of caseness as did childhood sexual abuse and learning difficulties at school. We found the direction of influence of three imported factors differed from all others: unemployment, prior experience of prison and a history of substance misuse. These three factors are associated with a lower rate of mental health caseness. It is of concern that, on release, these same factors are likely to militate against re-integration into society. Imported factors can serve as powerful predictors of 'within-prison' mental health status, but practitioners need to be cognisant of the relative importance and direction of influence of factors, as evidenced by these findings. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Modelling oxygen transfer using dynamic alpha factors.

PubMed

Jiang, Lu-Man; Garrido-Baserba, Manel; Nolasco, Daniel; Al-Omari, Ahmed; DeClippeleir, Haydee; Murthy, Sudhir; Rosso, Diego

2017-11-01

Due to the importance of wastewater aeration in meeting treatment requirements and due to its elevated energy intensity, it is important to describe the real nature of an aeration system to improve design and specification, performance prediction, energy consumption, and process sustainability. Because organic loadings drive aeration efficiency to its lowest value when the oxygen demand (energy) is the highest, the implications of considering their dynamic nature on energy costs are of utmost importance. A dynamic model aimed at identifying conservation opportunities is presented. The model developed describes the correlation between the COD concentration and the α factor in activated sludge. Using the proposed model, the aeration efficiency is calculated as a function of the organic loading (i.e. COD). This results in predictions of oxygen transfer values that are more realistic than the traditional method of assuming constant α values. The model was applied to two water resource recovery facilities, and was calibrated and validated with time-sensitive databases. Our improved aeration model structure increases the quality of prediction of field data through the recognition of the dynamic nature of the alpha factor (α) as a function of the applied oxygen demand. For the cases presented herein, the model prediction of airflow improved by 20-35% when dynamic α is used. The proposed model offers a quantitative tool for the prediction of energy demand and for minimizing aeration design uncertainty. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigating the Importance of Various Individual, Interpersonal, Organisational and Demographic Variables when Predicting Job Burnout in Disability Support Workers

ERIC Educational Resources Information Center

Vassos, Maria V.; Nankervis, Karen L.

2012-01-01

Previous research has highlighted that factors such as large workload, role ambiguity, lack of support from colleagues, and challenging behaviour are associated with higher levels of burnout within the disability support worker (DSW) population. The aim of this research was to investigate which factors contribute the most to the prediction of the…

Enhancing We11-Being During Breast Cancer Recurrence.

DTIC Science & Technology

1999-07-01

concerns. Among the factors that predicted higher distress were more symptoms, lack of social support, less hope , and being younger. Cella, Mahon...medical problems and existential concerns. Among the factors that predicted higher distress were more symptoms, lack of social support, (ess hope ...own oral history), the importance of hope . Stress management: approaches that may be helpful, including relaxation, visualization, exercise (with

Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

PubMed

Sovány, Tamás; Papós, Kitti; Kása, Péter; Ilič, Ilija; Srčič, Stane; Pintye-Hódi, Klára

2013-06-01

The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary.

Use of prescription drugs and future delinquency among adolescent offenders.

PubMed

Drazdowski, Tess K; Jäggi, Lena; Borre, Alicia; Kliewer, Wendy L

2015-01-01

Non-medical use of prescription drugs (NMUPD) by adolescents is a significant public health concern. The present study investigated the profile of NMUPD in 1349 adolescent offenders from the Pathways to Desistance project, and whether NMUPD predicted future delinquency using longitudinal data. Results indicated that increased frequency and recency of NMUPD in adolescent offenders are related to some demographic factors, as well as increased risk for violence exposure, mental health diagnoses, other drug use, and previous delinquency, suggesting that severity of NMUPD is important to consider. However, ANCOVA analyses found that NMUPD was not a significant predictor of drug-related, non-aggressive, or aggressive delinquency 12 months later beyond other known correlates of delinquency. Age, sex, exposure to violence, lower socioeconomic status, more alcohol use, and having delinquency histories were more important than NMUPD in predicting future delinquency. These findings suggest that although NMUPD is an important risk factor relating to many correlates of delinquency, it does not predict future delinquency beyond other known risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Validating regulatory predictions from diverse bacteria with mutant fitness data

DOE PAGES

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.; ...

2017-05-24

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Validating regulatory predictions from diverse bacteria with mutant fitness data

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

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

PubMed

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Prediction of soil urea conversion and quantification of the importance degrees of influencing factors through a new combinatorial model based on cluster method and artificial neural network.

PubMed

Lei, Tao; Guo, Xianghong; Sun, Xihuan; Ma, Juanjuan; Zhang, Shaowen; Zhang, Yong

2018-05-01

Quantitative prediction of soil urea conversion is crucial in determining the mechanism of nitrogen transformation and understanding the dynamics of soil nutrients. This study aimed to establish a combinatorial prediction model (MCA-F-ANN) for soil urea conversion and quantify the relative importance degrees (RIDs) of influencing factors with the MCA-F-ANN method. Data samples were obtained from laboratory culture experiments, and soil nitrogen content and physicochemical properties were measured every other day. Results showed that when MCA-F-ANN was used, the mean-absolute-percent error values of NH 4 + -N, NO 3 - -N, and NH 3 contents were 3.180%, 2.756%, and 3.656%, respectively. MCA-F-ANN predicted urea transformation under multi-factor coupling conditions more accurately than traditional models did. The RIDs of reaction time (RT), electrical conductivity (EC), temperature (T), pH, nitrogen application rate (F), and moisture content (W) were 32.2%-36.5%, 24.0%-28.9%, 12.8%-15.2%, 9.8%-12.5%, 7.8%-11.0%, and 3.5%-6.0%, respectively. The RIDs of the influencing factors in a descending order showed the pattern RT > EC > T > pH > F > W. RT and EC were the key factors in the urea conversion process. The prediction accuracy of urea transformation process was improved, and the RIDs of the influencing factors were quantified. Copyright © 2018 Elsevier Ltd. All rights reserved.

The role of fear in predicting sexually transmitted infection screening.

PubMed

Shepherd, Lee; Smith, Michael A

2017-07-01

This study assessed the extent to which social-cognitive factors (attitude, subjective norm and perceived control) and the fear of a positive test result predict sexually transmitted infection (STI) screening intentions and subsequent behaviour. Study 1 (N = 85) used a longitudinal design to assess the factors that predict STI screening intention and future screening behaviour measured one month later at Time 2. Study 2 (N = 102) used an experimental design to determine whether the relationship between fear and screening varied depending on whether STI or HIV screening was being assessed both before and after controlling for social-cognitive factors. Across the studies the outcome measures were sexual health screening. In both studies, the fear of having an STI positively predicted STI screening intention. In Study 1, fear, but not the social-cognitive factors, also predicted subsequent STI screening behaviour. In Study 2, the fear of having HIV did not predict HIV screening intention, but attitude negatively and response efficacy positively predicted screening intention. This study highlights the importance of considering the nature of the health condition when assessing the role of fear on health promotion.

Personal Capacity Building for the Human Services: The Roles of Curriculum and Individual Differences in Predicting Self-Concept in College/University Students

ERIC Educational Resources Information Center

Parker, Philip D.; Martin, Andrew J.

2008-01-01

While much research has outlined the importance of intra-psychic factors in predicting workplace success, it is rare that attention is given to the development of these factors in training for human service professions (e.g. psychology, clergy, nursing). Accordingly, the present study explores differences in self-concept, a key intra-psychic…

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

PubMed

Carvajal, Thaddeus M; Viacrusis, Katherine M; Hernandez, Lara Fides T; Ho, Howell T; Amalin, Divina M; Watanabe, Kozo

2018-04-17

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

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

PubMed

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

2015-07-24

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

Predictive factors for work capacity in patients with musculoskeletal disorders.

PubMed

Lydell, Marie; Baigi, Amir; Marklund, Bertil; Månsson, Jörgen

2005-09-01

To identify predictive factors for work capacity in patients with musculoskeletal disorders. A descriptive, evaluative, quantitative study. The study was based on 385 patients who participated in a rehabilitation programme. Patients were divided into 2 groups depending on their ability to work. The groups were compared with each other with regard to sociodemographic factors, diagnoses, disability pension and number of sick days. The patient's level of exercise habits, ability to undertake activities, physical capacity, pain and quality of life were compared further using logistic regression analysis. Predictive factors for work capacity, such as ability to undertake activities, quality of life and fitness on exercise, were identified as important independent factors. Other well-known factors, i.e. gender, age, education, pain and earlier sickness certification periods, were also identified. Factors that were not significantly different between the groups were employment status, profession, diagnosis and levels of exercise habits. Identifying predictors for ability to return to work is an essential task for deciding on suitable individual rehabilitation. This study identified new predictive factors, such as ability to undertake activities, quality of life and fitness on exercise.

Prediction of disease course in inflammatory bowel diseases

PubMed Central

Lakatos, Peter Laszlo

2010-01-01

Clinical presentation at diagnosis and disease course of both Crohn’s disease (CD) and ulcerative colitis are heterogeneous and variable over time. Since most patients have a relapsing course and most CD patients develop complications (e.g. stricture and/or perforation), much emphasis has been placed in the recent years on the determination of important predictive factors. The identification of these factors may eventually lead to a more personalized, tailored therapy. In this TOPIC HIGHLIGHT series, we provide an update on the available literature regarding important clinical, endoscopic, fecal, serological/routine laboratory and genetic factors. Our aim is to assist clinicians in the everyday practical decision-making when choosing the treatment strategy for their patients suffering from inflammatory bowel diseases. PMID:20518078

Social aspects of suicidal behavior and prevention in early life: a review.

PubMed

Amitai, Maya; Apter, Alan

2012-03-01

The present review summarizes the updated literature on the social aspects of suicidal behavior and prevention in adolescents. The predictive role of psychiatric disorders and past history are well recognized in adolescent suicide, but the role of social and cultural factors is less clear. Studies have focused on the importance of ethnicity, gender, family characteristics, and socioeconomic status. More recently, attention has been addressed to broader social risk factors, such as bullying in adolescents, suicide contagion, sexual orientation, and the popular media. Further empirical evidence is needed to advance our understanding of suicidal youth, develop better assessment tools, and formulate effective prevention and treatment programs. Suicidal behavior remains an important clinical problem and major cause of death in youth. Social factors may be at least as important as genetics. Advancing our understanding of underlying cultural and sociological issues in youth suicide will help clinicians achieve more efficient prediction, prevention and treatment.

Prediction of functional loss in glaucoma from progressive optic disc damage.

PubMed

Medeiros, Felipe A; Alencar, Luciana M; Zangwill, Linda M; Bowd, Christopher; Sample, Pamela A; Weinreb, Robert N

2009-10-01

To evaluate the ability of progressive optic disc damage detected by assessment of longitudinal stereophotographs to predict future development of functional loss in those with suspected glaucoma. The study included 639 eyes of 407 patients with suspected glaucoma followed up for an average of 8.0 years with annual standard automated perimetry visual field and optic disc stereophotographs. All patients had normal and reliable standard automated perimetry results at baseline. Conversion to glaucoma was defined as development of 3 consecutive abnormal visual fields during follow-up. Presence of progressive optic disc damage was evaluated by grading longitudinally acquired simultaneous stereophotographs. Other predictive factors included age, intraocular pressure, central corneal thickness, pattern standard deviation, and baseline stereophotograph grading. Hazard ratios for predicting visual field loss were obtained by extended Cox models, with optic disc progression as a time-dependent covariate. Predictive accuracy was evaluated using a modified R(2) index. Progressive optic disc damage had a hazard ratio of 25.8 (95% confidence interval, 16.0-41.7) and was the most important risk factor for development of visual field loss with an R(2) of 79%. The R(2)s for other predictive factors ranged from 6% to 26%. Presence of progressive optic disc damage on stereophotographs was a highly predictive factor for future development of functional loss in glaucoma. These findings suggest the importance of careful monitoring of the optic disc appearance and a potential role for longitudinal assessment of the optic disc as an end point in clinical trials and as a reference for evaluation of diagnostic tests in glaucoma.

Vulnerability of recently recharged groundwater in principal aquifers of the United States to nitrate contamination

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.

2012-01-01

Recently recharged water (defined here as <60 years old) is generally the most vulnerable part of a groundwater resource to nonpoint-source nitrate contamination. Understanding at the appropriate scale the interactions of natural and anthropogenic controlling factors that influence nitrate occurrence in recently recharged groundwater is critical to support best management and policy decisions that are often made at the aquifer to subaquifer scale. New logistic regression models were developed using data from the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program and National Water Information System for 17 principal aquifers of the U.S. to identify important source, transport, and attenuation factors that control nonpoint source nitrate concentrations greater than relative background levels in recently recharged groundwater and were used to predict the probability of detecting elevated nitrate in areas beyond the sampling network. Results indicate that dissolved oxygen, crops and irrigated cropland, fertilizer application, seasonally high water table, and soil properties that affect infiltration and denitrification are among the most important factors in predicting elevated nitrate concentrations. Important differences in controlling factors and spatial predictions were identified in the principal aquifer and national-scale models and support the conclusion that similar spatial scales are needed between informed groundwater management and model development.

Modeling student success in engineering education

NASA Astrophysics Data System (ADS)

Jin, Qu

In order for the United States to maintain its global competitiveness, the long-term success of our engineering students in specific courses, programs, and colleges is now, more than ever, an extremely high priority. Numerous studies have focused on factors that impact student success, namely academic performance, retention, and/or graduation. However, there are only a limited number of works that have systematically developed models to investigate important factors and to predict student success in engineering. Therefore, this research presents three separate but highly connected investigations to address this gap. The first investigation involves explaining and predicting engineering students' success in Calculus I courses using statistical models. The participants were more than 4000 first-year engineering students (cohort years 2004 - 2008) who enrolled in Calculus I courses during the first semester in a large Midwestern university. Predictions from statistical models were proposed to be used to place engineering students into calculus courses. The success rates were improved by 12% in Calculus IA using predictions from models developed over traditional placement method. The results showed that these statistical models provided a more accurate calculus placement method than traditional placement methods and help improve success rates in those courses. In the second investigation, multi-outcome and single-outcome neural network models were designed to understand and to predict first-year retention and first-year GPA of engineering students. The participants were more than 3000 first year engineering students (cohort years 2004 - 2005) enrolled in a large Midwestern university. The independent variables include both high school academic performance factors and affective factors measured prior to entry. The prediction performances of the multi-outcome and single-outcome models were comparable. The ability to predict cumulative GPA at the end of an engineering student's first year of college was about a half of a grade point for both models. The predictors of retention and cumulative GPA while being similar differ in that high school academic metrics play a more important role in predicting cumulative GPA with the affective measures playing a more important role in predicting retention. In the last investigation, multi-outcome neural network models were used to understand and to predict engineering students' retention, GPA, and graduation from entry to departure. The participants were more than 4000 engineering students (cohort years 2004 - 2006) enrolled in a large Midwestern university. Different patterns of important predictors were identified for GPA, retention, and graduation. Overall, this research explores the feasibility of using modeling to enhance a student's educational experience in engineering. Student success modeling was used to identify the most important cognitive and affective predictors for a student's first calculus course retention, GPA, and graduation. The results suggest that the statistical modeling methods have great potential to assist decision making and help ensure student success in engineering education.

Social-Emotional Competence: An Essential Factor for Promoting Positive Adjustment and Reducing Risk in School Children

ERIC Educational Resources Information Center

Domitrovich, Celene E.; Durlak, Joseph A.; Staley, Katharine C.; Weissberg, Roger P.

2017-01-01

Social-emotional competence is a critical factor to target with universal preventive interventions that are conducted in schools because the construct (a) associates with social, behavioral, and academic outcomes that are important for healthy development; (b) predicts important life outcomes in adulthood; (c) can be improved with feasible and…

What Is Most Important: Social Factors, Health Selection, and Adolescent Educational Achievement

ERIC Educational Resources Information Center

Roos, Leslie L.; Hiebert, Brett; Manivong, Phongsack; Edgerton, Jason; Walld, Randy; MacWilliam, Leonard; de Rocquigny, Janelle

2013-01-01

This paper explores the relative importance of social factors and health measures in predicting educational achievement in early and late adolescence using population-based administrative data. The sample was made up of 41,943 children born in Manitoba, Canada between 1982 and 1989 and remaining in the province until age 18. Multilevel modeling…

Psychological Factors Associated with Genetic Test Decision-Making among Parents of Children with Autism Spectrum Disorders in Taiwan

ERIC Educational Resources Information Center

Xu, Lei; Richman, Alice R.

2015-01-01

Making decisions to undergo Autism Spectrum Disorders (ASD) genetic testing can be challenging. It is important to understand how the perceptions of affected individuals might influence testing decision-making. Although evidence has shown that psychological factors are important in predicting testing decisions, affect-type variables have been…

Performance considerations in long-term spaceflight

NASA Technical Reports Server (NTRS)

Akins, F. R.

1979-01-01

Maintenance of skilled performance during extended space flight is of critical importance to both the health and safety of crew members and to the overall success of mission goals. An examination of long term effects and performance requirements is therefore a factor of immense importance to the planning of future missions. Factors that were investigated include: definition of performance categories to be investigated; methods for assessing and predicting performance levels; in-flight factors which can affect performance; and factors pertinent to the maintenance of skilled performance.

A Reexamination of Sex Differences in Job Preferences.

ERIC Educational Resources Information Center

Siegfried, William D.; And Others

1981-01-01

Both male and female college students rated motivators as important, but females also placed importance on environmental factors. The subject's sex could be predicted by both the importance for self and importance for opposite sex ratings. Females' job preferences were related to their mothers' educational achievement. (Author)

Can Childhood Factors Predict Workplace Deviance?

PubMed Central

Piquero, Nicole Leeper; Moffitt, Terrie E.

2013-01-01

Compared to the more common focus on street crime, empirical research on workplace deviance has been hampered by highly select samples, cross-sectional research designs, and limited inclusion of relevant predictor variables that bear on important theoretical debates. A key debate concerns the extent to which childhood conduct-problem trajectories influence crime over the life-course, including adults’ workplace crime, whether childhood low self-control is a more important determinant than trajectories, and/or whether each or both of these childhood factors relate to later criminal activity. This paper provides evidence on this debate by examining two types of workplace deviance: production and property deviance separately for males and females. We use data from the Dunedin Multidisciplinary Health and Development Study, a birth cohort followed into adulthood, to examine how childhood factors (conduct-problem trajectories and low self-control) and then adult job characteristics predict workplace deviance at age 32. Analyses revealed that none of the childhood factors matter for predicting female deviance in the workplace but that conduct-problem trajectories did account for male workplace deviance. PMID:24882937

Social-cognitive antecedents of hand washing: Action control bridges the planning-behaviour gap.

PubMed

Reyes Fernández, Benjamín; Knoll, Nina; Hamilton, Kyra; Schwarzer, Ralf

2016-08-01

To examine motivational and volitional factors for hand washing in young adults, using the Health Action Process Approach (HAPA) as a theoretical framework. In a longitudinal design with two measurement points, six weeks apart, university students (N = 440) completed paper-based questionnaires. Prior hand washing frequency, self-efficacy, outcome expectancies, intention and action planning were measured at baseline, and coping planning, action control and hand washing frequency were measured at follow-up. A theory-based structural equation model was specified. In line with the HAPA, the motivational factors of self-efficacy and outcome expectancies predicted intention, whereas the volitional factors of planning and action control mediated between intention and changes in hand washing frequency. Action control was confirmed as the most proximal factor on hand washing behaviour, thus representing a bridge of the planning-behaviour gap. Both motivational and volitional processes are important to consider in the improvement of hand hygiene practices. Moreover, the statistically significant effects for planning and action control illustrate the importance of these key self-regulatory factors in the prediction of hand hygiene. The current study highlights the importance of adopting models that account for motivational and volitional factors to better understand hand washing behaviour.

Pornography use and sexual aggression: the impact of frequency and type of pornography use on recidivism among sexual offenders.

PubMed

Kingston, Drew A; Fedoroff, Paul; Firestone, Philip; Curry, Susan; Bradford, John M

2008-01-01

In this study, we examined the unique contribution of pornography consumption to the longitudinal prediction of criminal recidivism in a sample of 341 child molesters. We specifically tested the hypothesis, based on predictions informed by the confluence model of sexual aggression that pornography will be a risk factor for recidivism only for those individuals classified as relatively high risk for re-offending. Pornography use (frequency and type) was assessed through self-report and recidivism was measured using data from a national database from the Royal Canadian Mounted Police. Indices of recidivism, which were assessed up to 15 years after release, included an overall criminal recidivism index, as well as subcategories focusing on violent (including sexual) recidivism and sexual recidivism alone. Results for both frequency and type of pornography use were generally consistent with our predictions. Most importantly, after controlling for general and specific risk factors for sexual aggression, pornography added significantly to the prediction of recidivism. Statistical interactions indicated that frequency of pornography use was primarily a risk factor for higher-risk offenders, when compared with lower-risk offenders, and that content of pornography (i.e., pornography containing deviant content) was a risk factor for all groups. The importance of conceptualizing particular risk factors (e.g., pornography), within the context of other individual characteristics is discussed.

Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.

PubMed

Zitnik, Marinka; Zupan, Blaž

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.

Enhancing Well-Being During Breast Cancer Recurrence

DTIC Science & Technology

2005-07-01

concerns. Among the factors that predicted higher distress were more symptoms, lack of social support, less hope , and being younger. Celia, Mahon, and...existential concerns. Among the factors that predicted higher distress were more symptoms, lack of social support, less hope , and being younger. CelIa, Mahon...spiritual concerns, activities that -mayle )Ipful (e.g., recording one’s own oral history), the importance of hope .\\ Stress management: approaches that mayv

Prediction of cadmium enrichment in reclaimed coastal soils by classification and regression tree

NASA Astrophysics Data System (ADS)

Ru, Feng; Yin, Aijing; Jin, Jiaxin; Zhang, Xiuying; Yang, Xiaohui; Zhang, Ming; Gao, Chao

2016-08-01

Reclamation of coastal land is one of the most common ways to obtain land resources in China. However, it has long been acknowledged that the artificial interference with coastal land has disadvantageous effects, such as heavy metal contamination. This study aimed to develop a prediction model for cadmium enrichment levels and assess the importance of affecting factors in typical reclaimed land in Eastern China (DFCL: Dafeng Coastal Land). Two hundred and twenty seven surficial soil/sediment samples were collected and analyzed to identify the enrichment levels of cadmium and the possible affecting factors in soils and sediments. The classification and regression tree (CART) model was applied in this study to predict cadmium enrichment levels. The prediction results showed that cadmium enrichment levels assessed by the CART model had an accuracy of 78.0%. The CART model could extract more information on factors affecting the environmental behavior of cadmium than correlation analysis. The integration of correlation analysis and the CART model showed that fertilizer application and organic carbon accumulation were the most important factors affecting soil/sediment cadmium enrichment levels, followed by particle size effects (Al2O3, TFe2O3 and SiO2), contents of Cl and S, surrounding construction areas and reclamation history.

Research on time series data prediction based on clustering algorithm - A case study of Yuebao

NASA Astrophysics Data System (ADS)

Lu, Xu; Zhao, Tianzhong

2017-08-01

Forecasting is the prerequisite for making scientific decisions, it is based on the past information of the research on the phenomenon, and combined with some of the factors affecting this phenomenon, then using scientific methods to forecast the development trend of the future, it is an important way for people to know the world. This is particularly important in the prediction of financial data, because proper financial data forecasts can provide a great deal of help to financial institutions in their strategic implementation, strategic alignment and risk control. However, the current forecasts of financial data generally use the method of forecast of overall data, which lack of consideration of customer behavior and other factors in the financial data forecasting process, and they are important factors influencing the change of financial data. Based on this situation, this paper analyzed the data of Yuebao, and according to the user's attributes and the operating characteristics, this paper classified 567 users of Yuebao, and made further predicted the data of Yuebao for every class of users, the results showed that the forecasting model in this paper can meet the demand of forecasting.

Comparative validity of brief to medium-length Big Five and Big Six Personality Questionnaires.

PubMed

Thalmayer, Amber Gayle; Saucier, Gerard; Eigenhuis, Annemarie

2011-12-01

A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five inventories when conducting a study and are faced with a variety of options as to inventory length. Furthermore, a 6-factor model has been proposed to extend and update the Big Five model, in part by adding a dimension of Honesty/Humility or Honesty/Propriety. In this study, 3 popular brief to medium-length Big Five measures (NEO Five Factor Inventory, Big Five Inventory [BFI], and International Personality Item Pool), and 3 six-factor measures (HEXACO Personality Inventory, Questionnaire Big Six Scales, and a 6-factor version of the BFI) were placed in competition to best predict important student life outcomes. The effect of test length was investigated by comparing brief versions of most measures (subsets of items) with original versions. Personality questionnaires were administered to undergraduate students (N = 227). Participants' college transcripts and student conduct records were obtained 6-9 months after data was collected. Six-factor inventories demonstrated better predictive ability for life outcomes than did some Big Five inventories. Additional behavioral observations made on participants, including their Facebook profiles and cell-phone text usage, were predicted similarly by Big Five and 6-factor measures. A brief version of the BFI performed surprisingly well; across inventory platforms, increasing test length had little effect on predictive validity. Comparative validity of the models and measures in terms of outcome prediction and parsimony is discussed.

Neurobiological factors as predictors of cognitive-behavioral therapy outcome in individuals with antisocial behavior: a review of the literature.

PubMed

Cornet, Liza J M; de Kogel, Catharina H; Nijman, Henk L I; Raine, Adrian; van der Laan, Peter H

2014-11-01

This review focuses on the predictive value of neurobiological factors in relation to cognitive-behavioral therapy outcome among individuals with antisocial behavior. Ten relevant studies were found. Although the literature on this topic is scarce and diverse, it appears that specific neurobiological characteristics, such as physiological arousal levels, can predict treatment outcome. The predictive value of neurobiological factors is important as it could give more insight into the causes of variability in treatment outcome among individuals with antisocial behavior. Furthermore, results can contribute to improvement in current treatment selection procedures and to the development of alternative treatment options. © The Author(s) 2013.

California Community College Administrators' Use of Predictive Modeling to Improve Student Course Completions

ERIC Educational Resources Information Center

Grogan, Rita D.

2017-01-01

Purpose: The purpose of this case study was to determine the impact of utilizing predictive modeling to improve successful course completion rates for at-risk students at California community colleges. A secondary purpose of the study was to identify factors of predictive modeling that have the most importance for improving successful course…

Which factor is most important for occurrence of cutout complications in patients treated with proximal femoral nail antirotation? Retrospective analysis of 298 patients.

PubMed

Turgut, Ali; Kalenderer, Önder; Karapınar, Levent; Kumbaracı, Mert; Akkan, Hasan Ali; Ağuş, Haluk

2016-05-01

Mechanical complications, such as cut-out of the head-neck fixation device, are the most common causes of morbidity after trochanteric femur fracture treatment. The causes of cut-out complications are well defined in patients who are treated with sliding hip screws and biaxial cephalomedullary nails but there are few reports about the patients who are treated with proximal femoral nail antirotation. The purpose of this study was to evaluate the most important factor about occurance of cutout complication and also to evaluate the risks of the combination of each possible factors. Overally 298 patients were enrolled in the study. Medical records were reviewed for patients' age, fracture type, gender, anesthesia type and occurance of cut-out complication. Postoperatively taken radiographs were reviewed for tip-apex distance, obtained collo-diaphyseal angle, the quadrant of the helical blade and Ikuta reduction subgroup. The most important factor (s) and also predicted probability of cut-out complication was calculated for each combination of factors. Cut-out complication was observed in 14 patients (4.7 %). The most important factor about occurrence of the cut-out complication was found as varus reduction (p: 0.01), the second important factor was found as implantation of the helical blade in the improper quadrant (p: 0.02). Tip-apex distance was found as third important factor (p: 0.10). The predicted probability of cut-out complication was calculated as 45.6 % when whole of the four surgeon dependent factors were improperly obtained. Althought obtaining proper tip-apex distance is important to prevent cutout complication in these fractures, if the fracture is not reduced in varus position and helical blade is inserted in the proper quadrant, possibility of cut-out complication is very low even in the patients with high tip-apex distance.

Traditional Cardiovascular Risk Factors as Predictors of Cardiovascular Events in the U.S. Astronaut Corps

NASA Technical Reports Server (NTRS)

Halm, M. K.; Clark, A.; Wear, M. L.; Murray, J. D.; Polk, J. D.; Amirian, E.

2009-01-01

Risk prediction equations from the Framingham Heart Study are commonly used to predict the absolute risk of myocardial infarction (MI) and coronary heart disease (CHD) related death. Predicting CHD-related events in the U.S. astronaut corps presents a monumental challenge, both because astronauts tend to live healthier lifestyles and because of the unique cardiovascular stressors associated with being trained for and participating in space flight. Traditional risk factors may not hold enough predictive power to provide a useful indicator of CHD risk in this unique population. It is important to be able to identify individuals who are at higher risk for CHD-related events so that appropriate preventive care can be provided. This is of special importance when planning long duration missions since the ability to provide advanced cardiac care and perform medical evacuation is limited. The medical regimen of the astronauts follows a strict set of clinical practice guidelines in an effort to ensure the best care. The purpose of this study was to evaluate the utility of the Framingham risk score (FRS), low-density lipoprotein (LDL) and high-density lipoprotein levels, blood pressure, and resting pulse as predictors of CHD-related death and MI in the astronaut corps, using Cox regression. Of these factors, only two, LDL and pulse at selection, were predictive of CHD events (HR(95% CI)=1.12 (1.00-1.25) and HR(95% CI)=1.70 (1.05-2.75) for every 5-unit increase in LDL and pulse, respectively). Since traditional CHD risk factors may lack the specificity to predict such outcomes in astronauts, the development of a new predictive model, using additional measures such as electron-beam computed tomography and carotid intima-media thickness ultrasound, is planned for the future.

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Examining Overgeneral Autobiographical Memory as a Risk Factor for Adolescent Depression

ERIC Educational Resources Information Center

Rawal, Adhip; Rice, Frances

2012-01-01

Objective: Identifying risk factors for adolescent depression is an important research aim. Overgeneral autobiographical memory (OGM) is a feature of adolescent depression and a candidate cognitive risk factor for future depression. However, no study has ascertained whether OGM predicts the onset of adolescent depressive disorder. OGM was…

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

Coronary heart disease risk stratification: pitfalls and possibilities.

PubMed

Negi, Smita; Nambi, Vijay

Atherosclerosis of the coronary arteries, or coronary heart disease (CHD), is the most common cause of mortality in U.S. adults. The pathobiology of atherosclerosis and its complications is a continuum. At one end of the spectrum are young individuals without atherosclerotic disease who have not yet been exposed to lifestyle or other risk factors, and at the other end are patients with manifest atherosclerosis - myocardial infarction, stroke, and disabling peripheral arterial disease - where risk of recurrent disease and death is driven by the same factors initially responsible for the emergence of disease. However, it is clear that while risk factors are important in the development of CHD, not everyone with risk factors develops the disease and not everyone with CHD has risk factors. Furthermore, even similar degrees of exposure to a risk factor leads to disease in some individuals and not in others. Risk prediction, which is crucial in predicting and hence preventing disease, therefore becomes very challenging. In this article we review the currently available risk stratification tools for predicting CHD risk and discuss potential ways to improve risk prediction.

Occupancy by key transcription factors is a more accurate predictor of enhancer activity than histone modifications or chromatin accessibility

DOE PAGES

Dogan, Nergiz; Wu, Weisheng; Morrissey, Christapher S.; ...

2015-04-23

Regulated gene expression controls organismal development, and variation in regulatory patterns has been implicated in complex traits. Thus accurate prediction of enhancers is important for further understanding of these processes. Genome-wide measurement of epigenetic features, such as histone modifications and occupancy by transcription factors, is improving enhancer predictions, but the contribution of these features to prediction accuracy is not known. Given the importance of the hematopoietic transcription factor TAL1 for erythroid gene activation, we predicted candidate enhancers based on genomic occupancy by TAL1 and measured their activity. Contributions of multiple features to enhancer prediction were evaluated based on the resultsmore » of these and other studies. Results: TAL1-bound DNA segments were active enhancers at a high rate both in transient transfections of cultured cells (39 of 79, or 56%) and transgenic mice (43 of 66, or 65%). The level of binding signal for TAL1 or GATA1 did not help distinguish TAL1-bound DNA segments as active versus inactive enhancers, nor did the density of regulation-related histone modifications. A meta-analysis of results from this and other studies (273 tested predicted enhancers) showed that the presence of TAL1, GATA1, EP300, SMAD1, H3K4 methylation, H3K27ac, and CAGE tags at DNase hypersensitive sites gave the most accurate predictors of enhancer activity, with a success rate over 80% and a median threefold increase in activity. Chromatin accessibility assays and the histone modifications H3K4me1 and H3K27ac were sensitive for finding enhancers, but they have high false positive rates unless transcription factor occupancy is also included. Conclusions: Occupancy by key transcription factors such as TAL1, GATA1, SMAD1, and EP300, along with evidence of transcription, improves the accuracy of enhancer predictions based on epigenetic features.« less

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

PubMed

Su, Chao-Ton; Wang, Pa-Chun; Chen, Yan-Cheng; Chen, Li-Fei

2012-08-01

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

The relationship between wisdom and abstinence behaviors in women in recovery from substance abuse.

PubMed

Digangi, Julia A; Jason, Leonard A; Mendoza, Leslie; Miller, Steve A; Contreras, Richard

2013-01-01

Wisdom is theorized to be an important construct in recovery from substance abuse. In order to explore the role of wisdom in substance abuse recovery behaviors, the present study had two goals. First, it sought to examine the factor structure of a wisdom scale, the Foundational Value Scale (FVS) in a community sample of women in recovery from substance abuse. Second, the study examined how wisdom predicted the women's beliefs about their ability to abstain from future substance use. 116 women in recovery from substance abuse disorders were recruited from self-run recovery homes and a substance abuse recovery convention. Results from an exploratory factor analysis indicated that a modified version of the FVS has good internal consistency reliability and is composed of three wisdom-related dimensions. The three factors were then used to create a higher-order wisdom factor in a structural equation model (SEM) that was used to predict abstinence self-efficacy behaviors. Results from the SEM showed that the wisdom factor was predictive of greater abstinence self-efficacy behaviors. The FVS was found to be a reliable measure with women in recovery from substance abuse. In addition, wisdom predicted beliefs about self-efficacy such that those who reported higher levels of wisdom felt more confident in their abilities to abstain from alcohol. The results of this study indicate that wisdom is an important construct in the abstinence behaviors of women who are in recovery from substance abuse disorders.

Early Seizure Frequency and Aetiology Predict Long-Term Medical Outcome in Childhood-Onset Epilepsy

ERIC Educational Resources Information Center

Sillanpaa, Matti; Schmidt, Dieter

2009-01-01

In clinical practice, it is important to predict as soon as possible after diagnosis and starting treatment, which children are destined to develop medically intractable seizures and be at risk of increased mortality. In this study, we determined factors predictive of long-term seizure and mortality outcome in a population-based cohort of 102…

Using the Criterion-Predictor Factor Model to Compute the Probability of Detecting Prediction Bias with Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Culpepper, Steven Andrew

2012-01-01

The study of prediction bias is important and the last five decades include research studies that examined whether test scores differentially predict academic or employment performance. Previous studies used ordinary least squares (OLS) to assess whether groups differ in intercepts and slopes. This study shows that OLS yields inaccurate inferences…

Multivariate predictors of music perception and appraisal by adult cochlear implant users.

PubMed

Gfeller, Kate; Oleson, Jacob; Knutson, John F; Breheny, Patrick; Driscoll, Virginia; Olszewski, Carol

2008-02-01

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music.

Job stress models for predicting burnout syndrome: a review.

PubMed

Chirico, Francesco

2016-01-01

In Europe, the Council Directive 89/391 for improvement of workers' safety and health has emphasized the importance of addressing all occupational risk factors, and hence also psychosocial and organizational risk factors. Nevertheless, the construct of "work-related stress" elaborated from EU-OSHA is not totally corresponding with the "psychosocial" risk, that is a broader category of risk, comprising various and different psychosocial risk factors. The term "burnout", without any binding definition, tries to integrate symptoms as well as cause of the burnout process. In Europe, the most important methods developed for the work related stress risk assessment are based on the Cox's transactional model of job stress. Nevertheless, there are more specific models for predicting burnout syndrome. This literature review provides an overview of job burnout, highlighting the most important models of job burnout, such as the Job Strain, the Effort/Reward Imbalance and the Job Demands-Resources models. The difference between these models and the Cox's model of job stress is explored.

Social Aspects of Suicidal Behavior and Prevention in Early Life: A Review

PubMed Central

Amitai, Maya; Apter, Alan

2012-01-01

Purpose: The present review summarizes the updated literature on the social aspects of suicidal behavior and prevention in adolescents. Recent findings: The predictive role of psychiatric disorders and past history are well recognized in adolescent suicide, but the role of social and cultural factors is less clear. Studies have focused on the importance of ethnicity, gender, family characteristics, and socioeconomic status. More recently, attention has been addressed to broader social risk factors, such as bullying in adolescents, suicide contagion, sexual orientation, and the popular media. Further empirical evidence is needed to advance our understanding of suicidal youth, develop better assessment tools, and formulate effective prevention and treatment programs. Summary: Suicidal behavior remains an important clinical problem and major cause of death in youth. Social factors may be at least as important as genetics. Advancing our understanding of underlying cultural and sociological issues in youth suicide will help clinicians achieve more efficient prediction, prevention and treatment. PMID:22690178

Sensitivity study on durability variables of marine concrete structures

NASA Astrophysics Data System (ADS)

Zhou, Xin'gang; Li, Kefei

2013-06-01

In order to study the influence of parameters on durability of marine concrete structures, the parameter's sensitivity analysis was studied in this paper. With the Fick's 2nd law of diffusion and the deterministic sensitivity analysis method (DSA), the sensitivity factors of apparent surface chloride content, apparent chloride diffusion coefficient and its time dependent attenuation factor were analyzed. The results of the analysis show that the impact of design variables on concrete durability was different. The values of sensitivity factor of chloride diffusion coefficient and its time dependent attenuation factor were higher than others. Relative less error in chloride diffusion coefficient and its time dependent attenuation coefficient induces a bigger error in concrete durability design and life prediction. According to probability sensitivity analysis (PSA), the influence of mean value and variance of concrete durability design variables on the durability failure probability was studied. The results of the study provide quantitative measures of the importance of concrete durability design and life prediction variables. It was concluded that the chloride diffusion coefficient and its time dependent attenuation factor have more influence on the reliability of marine concrete structural durability. In durability design and life prediction of marine concrete structures, it was very important to reduce the measure and statistic error of durability design variables.

Predicting adolescents' disclosure of personal information in exchange for commercial incentives: an application of an extended theory of planned behavior.

PubMed

Heirman, Wannes; Walrave, Michel; Ponnet, Koen

2013-02-01

This study adopts a global theoretical framework to predict adolescents' disclosure of personal information in exchange for incentives offered by commercial Websites. The study postulates and tests the validity of a model based on the theory of planned behavior (TPB), including antecedent factors of attitude and perceived behavioral control (PBC). A survey was conducted among 1,042 respondents. Results from SEM analyses show that the hypothesized model fits the empirical data well. The model accounts for 61.9 percent of the variance in adolescents' intention to disclose and 43.7 percent of the variance in self-reported disclosure. Perceived social pressure exerted by significant others (subjective norm) is the most important TPB factor in predicting intention to disclose personal information in exchange for incentives. This finding suggests that in discussions of adolescents' information privacy, the importance of social factors outweighs the individually oriented TPB factors of attitude and PBC. Moreover, privacy concern and trust propensity are significant predictors of respondents' attitudes toward online disclosure in exchange for commercial incentives, whereas the frequency of Internet use significantly affects their level of PBC.

Red-light running violation prediction using observational and simulator data.

PubMed

Jahangiri, Arash; Rakha, Hesham; Dingus, Thomas A

2016-11-01

In the United States, 683 people were killed and an estimated 133,000 were injured in crashes due to running red lights in 2012. To help prevent/mitigate crashes caused by running red lights, these violations need to be identified before they occur, so both the road users (i.e., drivers, pedestrians, etc.) in potential danger and the infrastructure can be notified and actions can be taken accordingly. Two different data sets were used to assess the feasibility of developing red-light running (RLR) violation prediction models: (1) observational data and (2) driver simulator data. Both data sets included common factors, such as time to intersection (TTI), distance to intersection (DTI), and velocity at the onset of the yellow indication. However, the observational data set provided additional factors that the simulator data set did not, and vice versa. The observational data included vehicle information (e.g., speed, acceleration, etc.) for several different time frames. For each vehicle approaching an intersection in the observational data set, required data were extracted from several time frames as the vehicle drew closer to the intersection. However, since the observational data were inherently anonymous, driver factors such as age and gender were unavailable in the observational data set. Conversely, the simulator data set contained age and gender. In addition, the simulator data included a secondary (non-driving) task factor and a treatment factor (i.e., incoming/outgoing calls while driving). The simulator data only included vehicle information for certain time frames (e.g., yellow onset); the data did not provide vehicle information for several different time frames while vehicles were approaching an intersection. In this study, the random forest (RF) machine-learning technique was adopted to develop RLR violation prediction models. Factor importance was obtained for different models and different data sets to show how differently the factors influence the performance of each model. A sensitivity analysis showed that the factor importance to identify RLR violations changed when data from different time frames were used to develop the prediction models. TTI, DTI, the required deceleration parameter (RDP), and velocity at the onset of a yellow indication were among the most important factors identified by both models constructed using observational data and simulator data. Furthermore, in addition to the factors obtained from a point in time (i.e., yellow onset), valuable information suitable for RLR violation prediction was obtained from defined monitoring periods. It was found that period lengths of 2-6m contributed to the best model performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Integrating the ICF with positive psychology: Factors predicting role participation for mothers with multiple sclerosis.

PubMed

Farber, Ruth S; Kern, Margaret L; Brusilovsky, Eugene

2015-05-01

Being a mother has become a realizable life role for women with disabilities and chronic illnesses, including multiple sclerosis (MS). Identifying psychosocial factors that facilitate participation in important life roles-including motherhood-is essential to help women have fuller lives despite the challenge of their illness. By integrating the International Classification of Functioning, Disability, and Health (ICF) and a positive psychology perspective, this study examined how environmental social factors and positive personal factors contribute to daily role participation and satisfaction with parental participation. One hundred and 11 community-dwelling mothers with MS completed Ryff's Psychological Well-Being Scales, the Medical Outcome Study Social Support Survey, the Short Form-36, and the Parental Participation Scale. Hierarchical regression analyses examined associations between social support and positive personal factors (environmental mastery, self-acceptance, purpose in life) with daily role participation (physical and emotional) and satisfaction with parental participation. One-way ANOVAs tested synergistic combinations of social support and positive personal factors. Social support predicted daily role participation (fewer limitations) and greater satisfaction with parental participation. Positive personal factors contributed additional unique variance. Positive personal factors and social support synergistically predicted better function and greater satisfaction than either alone. Integrating components of the ICF and positive psychology provides a useful model for understanding how mothers with MS can thrive despite challenge or impairment. Both positive personal factors and environmental social factors were important contributors to positive role functioning. Incorporating these paradigms into treatment may help mothers with MS participate more fully in meaningful life roles. (c) 2015 APA, all rights reserved).

Psychosocial work environment factors and weight change: a prospective study among Danish health care workers.

PubMed

Gram Quist, Helle; Christensen, Ulla; Christensen, Karl Bang; Aust, Birgit; Borg, Vilhelm; Bjorner, Jakob B

2013-01-17

Lifestyle variables may serve as important intermediate factors between psychosocial work environment and health outcomes. Previous studies, focussing on work stress models have shown mixed and weak results in relation to weight change. This study aims to investigate psychosocial factors outside the classical work stress models as potential predictors of change in body mass index (BMI) in a population of health care workers. A cohort study, with three years follow-up, was conducted among Danish health care workers (3982 women and 152 men). Logistic regression analyses examined change in BMI (more than +/- 2 kg/m(2)) as predicted by baseline psychosocial work factors (work pace, workload, quality of leadership, influence at work, meaning of work, predictability, commitment, role clarity, and role conflicts) and five covariates (age, cohabitation, physical work demands, type of work position and seniority). Among women, high role conflicts predicted weight gain, while high role clarity predicted both weight gain and weight loss. Living alone also predicted weight gain among women, while older age decreased the odds of weight gain. High leadership quality predicted weight loss among men. Associations were generally weak, with the exception of quality of leadership, age, and cohabitation. This study of a single occupational group suggested a few new risk factors for weight change outside the traditional work stress models.

Predictive factors for pharyngocutaneous fistulization after total laryngectomy: a Dutch Head and Neck Society audit.

PubMed

Lansaat, Liset; van der Noort, Vincent; Bernard, Simone E; Eerenstein, Simone E J; Plaat, Boudewijn E C; Langeveld, Ton A P M; Lacko, Martin; Hilgers, Frans J M; de Bree, Remco; Takes, Robert P; van den Brekel, Michiel W M

2018-03-01

Incidences of pharyngocutaneous fistulization (PCF) after total laryngectomy (TL) reported in the literature vary widely, ranging from 2.6 to 65.5%. Comparison between different centers might identify risk factors, but also might enable improvements in quality of care. To enable this on a national level, an audit in the 8 principle Dutch Head and Neck Centers (DHNC) was initiated. A retrospective chart review of all 324 patients undergoing laryngectomy in a 2-year (2012 and 2013) period was performed. Overall PCF%, PCF% per center and factors predictive for PCF were identified. Furthermore, a prognostic model predicting the PCF% per center was developed. To provide additional data, a survey among the head and neck surgeons of the participating centers was carried out. Overall PCF% was 25.9. The multivariable prediction model revealed that previous treatment with (chemo)radiotherapy in combination with a long interval between primary treatment and TL, previous tracheotomy, near total pharyngectomy, neck dissection, and BMI < 18 were the best predictors for PCF. Early oral intake did not influence PCF rate. PCF% varied quite widely between centers, but for a large extend this could be explained with the prediction model. PCF performance rate (difference between the PCF% and the predicted PCF%) per DHNC, though, shows that not all differences are explained by factors established in the prediction model. However, these factors explain enough of the differences that, compensating for these factors, hospital is no longer independently predictive for PCF. This nationwide audit has provided valid comparative PCF data confirming the known risk factors from the literature which are important for counseling on PCF risks. Data show that variations in PCF% in the DHNCs (in part) are explainable by the variations in these predictive factors. Since elective neck dissection is a major risk factor for PCF, it only should be performed on well funded indication.

MATRIX FACTORIZATION-BASED DATA FUSION FOR GENE FUNCTION PREDICTION IN BAKER’S YEAST AND SLIME MOLD

PubMed Central

ŽITNIK, MARINKA; ZUPAN, BLAŽ

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker’s yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps. PMID:24297565

Using a Comprehensive Model to Test and Predict the Factors of Online Learning Effectiveness

ERIC Educational Resources Information Center

He, Minyan

2013-01-01

As online learning is an important part of higher education, the effectiveness of online learning has been tested with different methods. Although the literature regarding online learning effectiveness has been related to various factors, a more comprehensive review of the factors may result in broader understanding of online learning…

[Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].

PubMed

Wu, Chang-Guang; Li, Sheng; Ren, Hua-Dong; Yao, Xiao-Hua; Huang, Zi-Jie

2012-06-01

Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations

PubMed Central

Saigi-Morgui, Núria; Quteineh, Lina; Bochud, Pierre-Yves; Crettol, Severine; Kutalik, Zoltán; Wojtowicz, Agnieszka; Bibert, Stéphanie; Beckmann, Sonja; Mueller, Nicolas J; Binet, Isabelle; van Delden, Christian; Steiger, Jürg; Mohacsi, Paul; Stirnimann, Guido; Soccal, Paola M.; Pascual, Manuel; Eap, Chin B

2016-01-01

Background Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (nA = 995, nB = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors. Results w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m2 per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation. Conclusions This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation. PMID:27788139

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations.

PubMed

Saigi-Morgui, Núria; Quteineh, Lina; Bochud, Pierre-Yves; Crettol, Severine; Kutalik, Zoltán; Wojtowicz, Agnieszka; Bibert, Stéphanie; Beckmann, Sonja; Mueller, Nicolas J; Binet, Isabelle; van Delden, Christian; Steiger, Jürg; Mohacsi, Paul; Stirnimann, Guido; Soccal, Paola M; Pascual, Manuel; Eap, Chin B

2016-01-01

Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (nA = 995, nB = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors. w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m2 per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation. This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation.

Retrospective study of long-term outcomes of enzyme replacement therapy in Fabry disease: Analysis of prognostic factors

PubMed Central

Biegstraaten, Marieke; Hughes, Derralynn A.; Mehta, Atul; Elliott, Perry M.; Oder, Daniel; Watkinson, Oliver T.; Vaz, Frédéric M.; van Kuilenburg, André B. P.; Wanner, Christoph; Hollak, Carla E. M.

2017-01-01

Despite enzyme replacement therapy, disease progression is observed in patients with Fabry disease. Identification of factors that predict disease progression is needed to refine guidelines on initiation and cessation of enzyme replacement therapy. To study the association of potential biochemical and clinical prognostic factors with the disease course (clinical events, progression of cardiac and renal disease) we retrospectively evaluated 293 treated patients from three international centers of excellence. As expected, age, sex and phenotype were important predictors of event rate. Clinical events before enzyme replacement therapy, cardiac mass and eGFR at baseline predicted an increased event rate. eGFR was the most important predictor: hazard ratios increased from 2 at eGFR <90 ml/min/1.73m2 to 4 at eGFR <30, compared to patients with an eGFR >90. In addition, men with classical disease and a baseline eGFR <60 ml/min/1.73m2 had a faster yearly decline (-2.0 ml/min/1.73m2) than those with a baseline eGFR of >60. Proteinuria was a further independent risk factor for decline in eGFR. Increased cardiac mass at baseline was associated with the most robust decrease in cardiac mass during treatment, while presence of cardiac fibrosis predicted a stronger increase in cardiac mass (3.36 gram/m2/year). Of other cardiovascular risk factors, hypertension significantly predicted the risk for clinical events. In conclusion, besides increasing age, male sex and classical phenotype, faster disease progression while on enzyme replacement therapy is predicted by renal function, proteinuria and to a lesser extent cardiac fibrosis and hypertension. PMID:28763515

A maintenance time prediction method considering ergonomics through virtual reality simulation.

PubMed

Zhou, Dong; Zhou, Xin-Xin; Guo, Zi-Yue; Lv, Chuan

2016-01-01

Maintenance time is a critical quantitative index in maintainability prediction. An efficient maintenance time measurement methodology plays an important role in early stage of the maintainability design. While traditional way to measure the maintenance time ignores the differences between line production and maintenance action. This paper proposes a corrective MOD method considering several important ergonomics factors to predict the maintenance time. With the help of the DELMIA analysis tools, the influence coefficient of several factors are discussed to correct the MOD value and the designers can measure maintenance time by calculating the sum of the corrective MOD time of each maintenance therbligs. Finally a case study is introduced, by maintaining the virtual prototype of APU motor starter in DELMIA, designer obtains the actual maintenance time by the proposed method, and the result verifies the effectiveness and accuracy of the proposed method.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Making predictions of mangrove deforestation: a comparison of two methods in Kenya.

PubMed

Rideout, Alasdair J R; Joshi, Neha P; Viergever, Karin M; Huxham, Mark; Briers, Robert A

2013-11-01

Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined. © 2013 Blackwell Publishing Ltd.

Using implicit attitudes of exercise importance to predict explicit exercise dependence symptoms and exercise behaviors.

PubMed

Forrest, Lauren N; Smith, April R; Fussner, Lauren M; Dodd, Dorian R; Clerkin, Elise M

2016-01-01

"Fast" (i.e., implicit) processing is relatively automatic; "slow" (i.e., explicit) processing is relatively controlled and can override automatic processing. These different processing types often produce different responses that uniquely predict behaviors. In the present study, we tested if explicit, self-reported symptoms of exercise dependence and an implicit association of exercise as important predicted exercise behaviors and change in problematic exercise attitudes. We assessed implicit attitudes of exercise importance and self-reported symptoms of exercise dependence at Time 1. Participants reported daily exercise behaviors for approximately one month, and then completed a Time 2 assessment of self-reported exercise dependence symptoms. Undergraduate males and females (Time 1, N = 93; Time 2, N = 74) tracked daily exercise behaviors for one month and completed an Implicit Association Test assessing implicit exercise importance and subscales of the Exercise Dependence Questionnaire (EDQ) assessing exercise dependence symptoms. Implicit attitudes of exercise importance and Time 1 EDQ scores predicted Time 2 EDQ scores. Further, implicit exercise importance and Time 1 EDQ scores predicted daily exercise intensity while Time 1 EDQ scores predicted the amount of days exercised. Implicit and explicit processing appear to uniquely predict exercise behaviors and attitudes. Given that different implicit and explicit processes may drive certain exercise factors (e.g., intensity and frequency, respectively), these behaviors may contribute to different aspects of exercise dependence.

Using implicit attitudes of exercise importance to predict explicit exercise dependence symptoms and exercise behaviors

PubMed Central

Forrest, Lauren N.; Smith, April R.; Fussner, Lauren M.; Dodd, Dorian R.; Clerkin, Elise M.

2015-01-01

Objectives ”Fast” (i.e., implicit) processing is relatively automatic; “slow” (i.e., explicit) processing is relatively controlled and can override automatic processing. These different processing types often produce different responses that uniquely predict behaviors. In the present study, we tested if explicit, self-reported symptoms of exercise dependence and an implicit association of exercise as important predicted exercise behaviors and change in problematic exercise attitudes. Design We assessed implicit attitudes of exercise importance and self-reported symptoms of exercise dependence at Time 1. Participants reported daily exercise behaviors for approximately one month, and then completed a Time 2 assessment of self-reported exercise dependence symptoms. Method Undergraduate males and females (Time 1, N = 93; Time 2, N = 74) tracked daily exercise behaviors for one month and completed an Implicit Association Test assessing implicit exercise importance and subscales of the Exercise Dependence Questionnaire (EDQ) assessing exercise dependence symptoms. Results Implicit attitudes of exercise importance and Time 1 EDQ scores predicted Time 2 EDQ scores. Further, implicit exercise importance and Time 1 EDQ scores predicted daily exercise intensity while Time 1 EDQ scores predicted the amount of days exercised. Conclusion Implicit and explicit processing appear to uniquely predict exercise behaviors and attitudes. Given that different implicit and explicit processes may drive certain exercise factors (e.g., intensity and frequency, respectively), these behaviors may contribute to different aspects of exercise dependence. PMID:26195916

Logistic regression analysis of psychosocial correlates associated with recovery from schizophrenia in a Chinese community.

PubMed

Tse, Samson; Davidson, Larry; Chung, Ka-Fai; Yu, Chong Ho; Ng, King Lam; Tsoi, Emily

2015-02-01

More mental health services are adopting the recovery paradigm. This study adds to prior research by (a) using measures of stages of recovery and elements of recovery that were designed and validated in a non-Western, Chinese culture and (b) testing which demographic factors predict advanced recovery and whether placing importance on certain elements predicts advanced recovery. We examined recovery and factors associated with recovery among 75 Hong Kong adults who were diagnosed with schizophrenia and assessed to be in clinical remission. Data were collected on socio-demographic factors, recovery stages and elements associated with recovery. Logistic regression analysis was used to identify variables that could best predict stages of recovery. Receiver operating characteristic curves were used to detect the classification accuracy of the model (i.e. rates of correct classification of stages of recovery). Logistic regression results indicated that stages of recovery could be distinguished with reasonable accuracy for Stage 3 ('living with disability', classification accuracy = 75.45%) and Stage 4 ('living beyond disability', classification accuracy = 75.50%). However, there was no sufficient information to predict Combined Stages 1 and 2 ('overwhelmed by disability' and 'struggling with disability'). It was found that having a meaningful role and age were the most important differentiators of recovery stage. Preliminary findings suggest that adopting salient life roles personally is important to recovery and that this component should be incorporated into mental health services. © The Author(s) 2014.

The Relative Importance of Family History, Gender, Mode of Onset, and Age at Onsetin Predicting Clinical Features of First-Episode Psychotic Disorders.

PubMed

Compton, Michael T; Berez, Chantal; Walker, Elaine F

Family history of psychosis, gender, mode of onset, and age at onset are considered prognostic factors important to clinicians evaluating first-episode psychosis; yet, clinicians have little guidance as to how these four factors differentially predict early-course substance abuse, symptomatology, and functioning. We conducted a "head-to-head comparison" of these four factors regarding their associations with key clinical features at initial hospitalization. We also assessed potential interactions between gender and family history with regard to age at onset of psychosis and symptom severity. Consecutively admitted first-episode patients (n=334) were evaluated in two studies that rigorously assessed a number of early-course variables. Associations among variables of interest were examined using Pearson correlations, χ 2 tests, Student's t-tests, and 2×2 factorial analyses of variance. Substance (nicotine, alcohol, and cannabis) abuse and positive symptom severity were predicted only by male gender. Negative symptom severity and global functioning impairments were predicted by earlier age at onset of psychosis. General psychopathology symptom severity was predicted by both mode of onset and age at onset. Interaction effects were not observed with regard to gender and family history in predicting age at onset or symptom severity. The four prognostic features have differential associations with substance abuse, domains of symptom severity, and global functioning. Gender and age at onset of psychosis appear to be more predictive of clinical features at the time of initial evaluation (and thus presumably longer term outcomes) than the presence of a family history of psychosis and a more gradual mode of onset.

Memorable Messages as Guides to Self-Assessment of Behavior: The Role of Instrumental Values.

ERIC Educational Resources Information Center

Smith, Sandi W.; Ellis, Jennifer Butler; Yoo, Hyo-Jin

2001-01-01

Uses control theory to predict how important instrumental values and internalized memorable messages work together when undergraduate students self-assess their previous behavior. Finds none of the four higher-order value factors predicted the hypothesized relationships among values, messages, and behaviors; but the value of…

Improving Outcomes for Workers with Mental Retardation

ERIC Educational Resources Information Center

Fornes, Sandra; Rocco, Tonette S.; Rosenberg, Howard

2008-01-01

This research presents an analysis of factors predicting job retention, job satisfaction, and job performance of workers with mental retardation. The findings highlight self-determination as a critical skill in predicting the three important employee outcomes. The study examined a hypothesized job retention model and the outcome of the three…

Use of Isolated Trout Hepatocytes to Predict Measured Hepatic Clearance and Whole-animal Bioconcentration Factors for Six Polyaromatic Hydrocarbons

EPA Science Inventory

Hepatic metabolism is an important determinant of chemical bioaccumulation in fish. Consequently, measured in vitro hepatic metabolism may improve model predictions of bioaccumulation. In this study, fresh and cryopreserved trout hepatocytes were used to measure in vitro intrin...

Don't panic: interpretation bias is predictive of new onsets of panic disorder.

PubMed

Woud, Marcella L; Zhang, Xiao Chi; Becker, Eni S; McNally, Richard J; Margraf, Jürgen

2014-01-01

Psychological models of panic disorder postulate that interpretation of ambiguous material as threatening is an important maintaining factor for the disorder. However, demonstrations of whether such a bias predicts onset of panic disorder are missing. In the present study, we used data from the Dresden Prediction Study, in which a epidemiologic sample of young German women was tested at two time points approximately 17 months apart, allowing the study of biased interpretation as a potential risk factor. At time point one, participants completed an Interpretation Questionnaire including two types of ambiguous scenarios: panic-related and general threat-related. Analyses revealed that a panic-related interpretation bias predicted onset of panic disorder, even after controlling for two established risk factors: anxiety sensitivity and fear of bodily sensations. This is the first prospective study demonstrating the incremental validity of interpretation bias as a predictor of panic disorder onset. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predictive Factors of Nivolumab-induced Hypothyroidism in Patients with Non-small Cell Lung Cancer.

PubMed

Maekura, Toshiya; Naito, Maiko; Tahara, Masahiro; Ikegami, Naoya; Kimura, Yohei; Sonobe, Shoko; Kobayashi, Takehiko; Tsuji, Taisuke; Minomo, Shojiro; Tamiya, Akihiro; Atagi, Shinji

2017-01-01

Although immune checkpoint inhibitors play an important role in the therapy of lung cancer, they are associated with various immune-related adverse events and predictive factors of them are unclear. In this study, we investigated predictive factors of nivolumab-induced hypothyroidism which is one of the adverse events in patients with lung cancer. Patients with non-small-cell lung cancer who were administered nivolumab at our hospital between December 2015 and May 2016 were retrospectively enrolled. The thyroid-stimulating hormone, free triiodothyronine, free thyroxine, thyroid peroxidase (TPO) antibody, and thyroglobulin antibody levels of each patient were analyzed. Of the 64 patients enrolled, 5 (7.8%) developed hypothyroidism after treatment with nivolumab. The TPO and thyroglobulin antibodies were significantly positive in patients who developed primary hypothyroidism. TPO and thyroglobulin antibody levels at baseline may be predictive of hypothyroidism. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Predictive factors for moderate or severe exacerbations in asthma patients receiving outpatient care.

PubMed

Gutiérrez, Francisco Javier Álvarez; Galván, Marta Ferrer; Gallardo, Juan Francisco Medina; Mancera, Marta Barrera; Romero, Beatriz Romero; Falcón, Auxiliadora Romero

2017-05-02

Asthma exacerbations are important events that affect disease control, but predictive factors for severe or moderate exacerbations are not known. The objective was to study the predictive factors for moderate (ME) and severe (SE) exacerbations in asthma patients receiving outpatient care. Patients aged > 12 years with asthma were included in the study and followed-up at 4-monthly intervals over a 12-month period. Clinical (severity, level of control, asthma control test [ACT]), atopic, functional, inflammatory, SE and ME parameters were recorded. Univariate analysis was used to compare data from patients presenting at least 1 SE or ME during the follow-up period vs no exacerbations. Statistically significant (p <0.1) factors were then subjected to multiple analysis by binary logistic regression. A total of 330 patients completed the study, most of whom were atopic (76%), women (nearly 70%), with moderate and mild persistent asthma (>80%). Twenty-seven patients (8%) had a SE and 183 had a ME (58.5%) during follow-up. In the case of SEs, the only predictive factor identified in the multiple analysis was previous SE (baseline visit OR 4.218 95% CI 1.53-11.58, 4-month follow-up OR 6.88 95% CI 2.018-23.51) and inhalation technique (OR 3.572 95% CI 1.324-9.638). In the case of MEs, the only predictive factor found in the multiple analysis were previous ME (baseline visit OR 2.90 95% CI 1.54-5.48, 4-month follow- up OR 1.702 95% CI 1.146-2.529). The primary predictive factor for SE or ME is prior SE or ME, respectively. SEs seem to constitute a specific patient "phenotype", in which the sole predictive factor is prior SEs.

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection.

PubMed

Zipkin, Elise F; Grant, Evan H Campbell; Fagan, William F

2012-10-01

The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multispecies hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions about species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation data set. We found that wetland hydroperiod (the length of time that a wetland holds water), as well as the occurrence state in the prior year, were generally the most important factors in determining occupancy. The model with habitat-only covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multispecies models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection

USGS Publications Warehouse

Zipkin, Elise F.; Grant, Evan H. Campbell; Fagan, William F.

2012-01-01

The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multi-species hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions of species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation dataset. We found that wetland hydroperiod (the length of time that a wetland holds water) as well as the occurrence state in the prior year were generally the most important factors in determining occupancy. The model with only habitat covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multi-species models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.

Running away from home: a longitudinal study of adolescent risk factors and young adult outcomes.

PubMed

Tucker, Joan S; Edelen, Maria Orlando; Ellickson, Phyllis L; Klein, David J

2011-05-01

Little is known about the adolescent risk factors and young adult health-related outcomes associated with running away from home. We examined these correlates of running away using longitudinal data from 4,329 youth (48% female, 85% white) who were followed from Grade 9 to age 21. Nearly 14% of the sample reported running away in the past year at Grade 10 and/or Grade 11. Controlling for demographics and general delinquency, running away from home was predicted by lack of parental support, school disengagement, greater depressive affect, and heavier substance use at Grade 9. In turn, runaways had higher drug dependence scores and more depressive symptoms at age 21 than non-runaways, even after taking these antecedent risk factors into account. Runaway status did not predict alcohol dependence risk at age 21. Results highlight the importance of substance use and depression, both as factors propelling adolescents to run away and as important long-term consequences of running away.

The Importance of Biotic vs. Abiotic Drivers of Local Plant Community Composition Along Regional Bioclimatic Gradients

PubMed Central

Klanderud, Kari; Vandvik, Vigdis; Goldberg, Deborah

2015-01-01

We assessed if the relative importance of biotic and abiotic factors for plant community composition differs along environmental gradients and between functional groups, and asked which implications this may have in a warmer and wetter future. The study location is a unique grid of sites spanning regional-scale temperature and precipitation gradients in boreal and alpine grasslands in southern Norway. Within each site we sampled vegetation and associated biotic and abiotic factors, and combined broad- and fine-scale ordination analyses to assess the relative explanatory power of these factors for species composition. Although the community responses to biotic and abiotic factors did not consistently change as predicted along the bioclimatic gradients, abiotic variables tended to explain a larger proportion of the variation in species composition towards colder sites, whereas biotic variables explained more towards warmer sites, supporting the stress gradient hypothesis. Significant interactions with precipitation suggest that biotic variables explained more towards wetter climates in the sub alpine and boreal sites, but more towards drier climates in the colder alpine. Thus, we predict that biotic interactions may become more important in alpine and boreal grasslands in a warmer future, although more winter precipitation may counteract this trend in oceanic alpine climates. Our results show that both local and regional scales analyses are needed to disentangle the local vegetation-environment relationships and their regional-scale drivers, and biotic interactions and precipitation must be included when predicting future species assemblages. PMID:26091266

The Importance of Biotic vs. Abiotic Drivers of Local Plant Community Composition Along Regional Bioclimatic Gradients.

PubMed

Klanderud, Kari; Vandvik, Vigdis; Goldberg, Deborah

2015-01-01

We assessed if the relative importance of biotic and abiotic factors for plant community composition differs along environmental gradients and between functional groups, and asked which implications this may have in a warmer and wetter future. The study location is a unique grid of sites spanning regional-scale temperature and precipitation gradients in boreal and alpine grasslands in southern Norway. Within each site we sampled vegetation and associated biotic and abiotic factors, and combined broad- and fine-scale ordination analyses to assess the relative explanatory power of these factors for species composition. Although the community responses to biotic and abiotic factors did not consistently change as predicted along the bioclimatic gradients, abiotic variables tended to explain a larger proportion of the variation in species composition towards colder sites, whereas biotic variables explained more towards warmer sites, supporting the stress gradient hypothesis. Significant interactions with precipitation suggest that biotic variables explained more towards wetter climates in the sub alpine and boreal sites, but more towards drier climates in the colder alpine. Thus, we predict that biotic interactions may become more important in alpine and boreal grasslands in a warmer future, although more winter precipitation may counteract this trend in oceanic alpine climates. Our results show that both local and regional scales analyses are needed to disentangle the local vegetation-environment relationships and their regional-scale drivers, and biotic interactions and precipitation must be included when predicting future species assemblages.

Multiscale processing of loss of metal: a machine learning approach

NASA Astrophysics Data System (ADS)

De Masi, G.; Gentile, M.; Vichi, R.; Bruschi, R.; Gabetta, G.

2017-07-01

Corrosion is one of the principal causes of degradation to failure of marine structures. In practice, localized corrosion is the most dangerous mode of attack and can result in serious failures, in particular in marine flowlines and inter-field lines, arousing serious concerns relatively to environmental impact. The progress in time of internal corrosion, the location along the route and across the pipe section, the development pattern and the depth of the loss of metal are a very complex issue: the most important factors are products characteristics, transport conditions over the operating lifespan, process fluid-dynamics, and pipeline geometrical configuration. Understanding which factors among them play the most important role is a key step to develop a model able to predict with enough accuracy the sections more exposed to risk of failure. Some factors play a crucial role at certain spatial scales while other factors at other scales. The Mutual Information Theory, intimately related to the concept of Shannon Entropy in Information theory, has been applied to detect the most important variables at each scale. Finally, the variables emerged from this analysis at each scale have been integrated in a predicting data driven model sensibly improving its performance.

Fuzzy Regression Prediction and Application Based on Multi-Dimensional Factors of Freight Volume

NASA Astrophysics Data System (ADS)

Xiao, Mengting; Li, Cheng

2018-01-01

Based on the reality of the development of air cargo, the multi-dimensional fuzzy regression method is used to determine the influencing factors, and the three most important influencing factors of GDP, total fixed assets investment and regular flight route mileage are determined. The system’s viewpoints and analogy methods, the use of fuzzy numbers and multiple regression methods to predict the civil aviation cargo volume. In comparison with the 13th Five-Year Plan for China’s Civil Aviation Development (2016-2020), it is proved that this method can effectively improve the accuracy of forecasting and reduce the risk of forecasting. It is proved that this model predicts civil aviation freight volume of the feasibility, has a high practical significance and practical operation.

Predictive factors of telemedicine service acceptance and behavioral intention of physicians.

PubMed

Rho, Mi Jung; Choi, In Young; Lee, Jaebeom

2014-08-01

Despite the proliferation of telemedicine technology, telemedicine service acceptance has been slow in actual healthcare settings. The purpose of this research is to develop a theoretical model for explaining the predictive factors influencing physicians' willingness to use telemedicine technology to provide healthcare services. We developed the Telemedicine Service Acceptance model based on the technology acceptance model (TAM) with the inclusion of three predictive constructs from the previously published telemedicine literature: (1) accessibility of medical records and of patients as clinical factors, (2) self-efficacy as an individual factor and (3) perceived incentives as regulatory factors. A survey was conducted, and structural equation modeling was applied to evaluate the empirical validity of the model and causal relationships within the model using the data collected from 183 physicians. Our results confirmed the validity of the original TAM constructs: the perceived usefulness of telemedicine directly impacted the behavioral intention to use it, and the perceived ease of use directly impacted both the perceived usefulness and the behavioral intention to use it. In addition, new predictive constructs were found to have ramifications on TAM variables: the accessibility of medical records and of patients directly impacted the perceived usefulness of telemedicine, self-efficacy had a significant positive effect on both the perceived ease of use and the perceived usefulness of telemedicine, and perceived incentives were found to be important with respect to the intention to use telemedicine technology. This study demonstrated that the Telemedicine Service Acceptance model was feasible and could explain the acceptance of telemedicine services by physicians. These results identified important factors for increasing the involvement of physicians in telemedicine practice. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Risk factors for dating violence versus cohabiting violence: Results from the third generation of the Cambridge Study in Delinquent Development.

PubMed

Theobald, Delphine; Farrington, David P; Ttofi, Maria M; Crago, Rebecca V

2016-10-01

Dating violence is an important problem. Evidence suggests that women are more likely to perpetrate dating violence. The present study investigates the prevalence of dating violence compared with cohabiting violence in a community sample of men and women and assesses to what extent child and adolescent explanatory factors predict this behaviour. A secondary aim is to construct a risk score for dating violence based on the strongest risk factors. The Cambridge Study in Delinquent Development is a prospective longitudinal survey of 411 men (generation 2) born in the 1950s in an inner London area. Most recently, their sons and daughters [generation 3 (G3)] have been interviewed regarding their perpetration of dating and cohabiting violence, utilising the Conflict Tactics Scale. Risk factors were measured in four domains (family, parental, socio-economic and individual). A larger proportion of women than men perpetrated at least one act of violence towards their dating partner (36.4 vs 21.7%). There was a similar pattern for cohabiting violence (39.6 vs 21.4%). A number of risk factors were significantly associated with the perpetration of dating violence. For G3 women, these included a convicted father, parental conflict, large family size and poor housing. For G3 men, these included having a young father or mother, separation from the father before age 16, early school leaving, frequent truancy and having a criminal conviction. A risk score for both men and women, based on 10 risk factors, significantly predicted dating violence. Risk factors from four domains were important in predicting dating violence, but they were different for G3 men and women. It may be important to consider different risk factors and different risk assessments for male compared with female perpetration of dating violence. Early identification and interventions are recommended. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model.

PubMed

Wen, Kuang-Yi; Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

2010-01-01

To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Two of the seven factors, 'organizational motivation' and 'meeting user needs,' were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term.

Which psychological factors exacerbate irritable bowel syndrome? Development of a comprehensive model.

PubMed

van Tilburg, Miranda A L; Palsson, Olafur S; Whitehead, William E

2013-06-01

There is evidence that psychological factors affect the onset, severity and duration of irritable bowel syndrome (IBS). However, it is not clear which psychological factors are the most important and how they interact. The aims of the current study are to identify the most important psychological factors predicting IBS symptom severity and to investigate how these psychological variables are related to each other. Study participants were 286 IBS patients who completed a battery of psychological questionnaires including neuroticism, abuse history, life events, anxiety, somatization and catastrophizing. IBS severity measured by the IBS Severity Scale was the dependent variable. Path analysis was performed to determine the associations among the psychological variables, and IBS severity. Although the hypothesized model showed adequate fit, post hoc model modifications were performed to increase prediction. The final model was significant (Chi(2)=2.2; p=0.82; RMSEA<.05) predicting 36% of variance in IBS severity. Catastrophizing (standardized coefficient (β)=0.33; p<.001) and somatization (β=0.20; p<.001) were the only two psychological variables directly associated with IBS severity. Anxiety had an indirect effect on IBS symptoms through catastrophizing (β=0.80; p<.001); as well as somatization (β=0.37; p<.001). Anxiety, in turn, was predicted by neuroticism (β=0.66; p<.001) and stressful life events (β=0.31; p<.001). While cause-and-effect cannot be determined from these cross-sectional data, the outcomes suggest that the most fruitful approach to curb negative effects of psychological factors on IBS is to reduce catastrophizing and somatization. Copyright © 2013 Elsevier Inc. All rights reserved.

Risk Factors for Internet Gaming Disorder: Psychological Factors and Internet Gaming Characteristics.

PubMed

Rho, Mi Jung; Lee, Hyeseon; Lee, Taek-Ho; Cho, Hyun; Jung, Dong Jin; Kim, Dai-Jin; Choi, In Young

2017-12-27

Background : Understanding the risk factors associated with Internet gaming disorder (IGD) is important to predict and diagnose the condition. The purpose of this study is to identify risk factors that predict IGD based on psychological factors and Internet gaming characteristics; Methods : Online surveys were conducted between 26 November and 26 December 2014. There were 3568 Korean Internet game users among a total of 5003 respondents. We identified 481 IGD gamers and 3087 normal Internet gamers, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5) criteria. Logistic regression analysis was applied to identify significant risk factors for IGD; Results : The following eight risk factors were found to be significantly associated with IGD: functional and dysfunctional impulsivity (odds ratio: 1.138), belief self-control (1.034), anxiety (1.086), pursuit of desired appetitive goals (1.105), money spent on gaming (1.005), weekday game time (1.081), offline community meeting attendance (2.060), and game community membership (1.393; p < 0.05 for all eight risk factors); Conclusions : These risk factors allow for the prediction and diagnosis of IGD. In the future, these risk factors could also be used to inform clinical services for IGD diagnosis and treatment.

Risk Factors for Internet Gaming Disorder: Psychological Factors and Internet Gaming Characteristics

PubMed Central

Lee, Hyeseon; Lee, Taek-Ho; Cho, Hyun; Kim, Dai-Jin; Choi, In Young

2017-01-01

Background: Understanding the risk factors associated with Internet gaming disorder (IGD) is important to predict and diagnose the condition. The purpose of this study is to identify risk factors that predict IGD based on psychological factors and Internet gaming characteristics; Methods: Online surveys were conducted between 26 November and 26 December 2014. There were 3568 Korean Internet game users among a total of 5003 respondents. We identified 481 IGD gamers and 3087 normal Internet gamers, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5) criteria. Logistic regression analysis was applied to identify significant risk factors for IGD; Results: The following eight risk factors were found to be significantly associated with IGD: functional and dysfunctional impulsivity (odds ratio: 1.138), belief self-control (1.034), anxiety (1.086), pursuit of desired appetitive goals (1.105), money spent on gaming (1.005), weekday game time (1.081), offline community meeting attendance (2.060), and game community membership (1.393; p < 0.05 for all eight risk factors); Conclusions: These risk factors allow for the prediction and diagnosis of IGD. In the future, these risk factors could also be used to inform clinical services for IGD diagnosis and treatment. PMID:29280953

Factors influencing U.S. canine heartworm (Dirofilaria immitis) prevalence.

PubMed

Wang, Dongmei; Bowman, Dwight D; Brown, Heidi E; Harrington, Laura C; Kaufman, Phillip E; McKay, Tanja; Nelson, Charles Thomas; Sharp, Julia L; Lund, Robert

2014-06-06

This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates. The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence. All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist. The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence.

Factors influencing U.S. canine heartworm (Dirofilaria immitis) prevalence

PubMed Central

2014-01-01

Background This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates. Methods The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence. Results All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist. Conclusions The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence. PMID:24906567

[Forest lighting fire forecasting for Daxing'anling Mountains based on MAXENT model].

PubMed

Sun, Yu; Shi, Ming-Chang; Peng, Huan; Zhu, Pei-Lin; Liu, Si-Lin; Wu, Shi-Lei; He, Cheng; Chen, Feng

2014-04-01

Daxing'anling Mountains is one of the areas with the highest occurrence of forest lighting fire in Heilongjiang Province, and developing a lightning fire forecast model to accurately predict the forest fires in this area is of importance. Based on the data of forest lightning fires and environment variables, the MAXENT model was used to predict the lightning fire in Daxing' anling region. Firstly, we studied the collinear diagnostic of each environment variable, evaluated the importance of the environmental variables using training gain and the Jackknife method, and then evaluated the prediction accuracy of the MAXENT model using the max Kappa value and the AUC value. The results showed that the variance inflation factor (VIF) values of lightning energy and neutralized charge were 5.012 and 6.230, respectively. They were collinear with the other variables, so the model could not be used for training. Daily rainfall, the number of cloud-to-ground lightning, and current intensity of cloud-to-ground lightning were the three most important factors affecting the lightning fires in the forest, while the daily average wind speed and the slope was of less importance. With the increase of the proportion of test data, the max Kappa and AUC values were increased. The max Kappa values were above 0.75 and the average value was 0.772, while all of the AUC values were above 0.5 and the average value was 0. 859. With a moderate level of prediction accuracy being achieved, the MAXENT model could be used to predict forest lightning fire in Daxing'anling Mountains.

Implementation of predictive data mining techniques for identifying risk factors of early AVF failure in hemodialysis patients.

PubMed

Rezapour, Mohammad; Khavanin Zadeh, Morteza; Sepehri, Mohammad Mehdi

2013-01-01

Arteriovenous fistula (AVF) is an important vascular access for hemodialysis (HD) treatment but has 20-60% rate of early failure. Detecting association between patient's parameters and early AVF failure is important for reducing its prevalence and relevant costs. Also predicting incidence of this complication in new patients is a beneficial controlling procedure. Patient safety and preservation of early AVF failure is the ultimate goal. Our research society is Hasheminejad Kidney Center (HKC) of Tehran, which is one of Iran's largest renal hospitals. We analyzed data of 193 HD patients using supervised techniques of data mining approach. There were 137 male (70.98%) and 56 female (29.02%) patients introduced into this study. The average of age for all the patients was 53.87 ± 17.47 years. Twenty eight patients had smoked and the number of diabetic patients and nondiabetics was 87 and 106, respectively. A significant relationship was found between "diabetes mellitus," "smoking," and "hypertension" with early AVF failure in this study. We have found that these mentioned risk factors have important roles in outcome of vascular surgery, versus other parameters such as "age." Then we predicted this complication in future AVF surgeries and evaluated our designed prediction methods with accuracy rates of 61.66%-75.13%.

Development of novel prediction model for drug-induced mitochondrial toxicity by using naïve Bayes classifier method.

PubMed

Zhang, Hui; Yu, Peng; Ren, Ji-Xia; Li, Xi-Bo; Wang, He-Li; Ding, Lan; Kong, Wei-Bao

2017-12-01

Mitochondrial dysfunction has been considered as an important contributing factor in the etiology of drug-induced organ toxicity, and even plays an important role in the pathogenesis of some diseases. The objective of this investigation was to develop a novel prediction model of drug-induced mitochondrial toxicity by using a naïve Bayes classifier. For comparison, the recursive partitioning classifier prediction model was also constructed. Among these methods, the prediction performance of naïve Bayes classifier established here showed best, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set were 95 ± 0.6% and 81 ± 1.1%, respectively. In addition, four important molecular descriptors and some representative substructures of toxicants produced by ECFP_6 fingerprints were identified. We hope the established naïve Bayes prediction model can be employed for the mitochondrial toxicity assessment, and these obtained important information of mitochondrial toxicants can provide guidance for medicinal chemists working in drug discovery and lead optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

How to quantify exposure to traumatic stress? Reliability and predictive validity of measures for cumulative trauma exposure in a post-conflict population.

PubMed

Wilker, Sarah; Pfeiffer, Anett; Kolassa, Stephan; Koslowski, Daniela; Elbert, Thomas; Kolassa, Iris-Tatjana

2015-01-01

While studies with survivors of single traumatic experiences highlight individual response variation following trauma, research from conflict regions shows that almost everyone develops posttraumatic stress disorder (PTSD) if trauma exposure reaches extreme levels. Therefore, evaluating the effects of cumulative trauma exposure is of utmost importance in studies investigating risk factors for PTSD. Yet, little research has been devoted to evaluate how this important environmental risk factor can be best quantified. We investigated the retest reliability and predictive validity of different trauma measures in a sample of 227 Ugandan rebel war survivors. Trauma exposure was modeled as the number of traumatic event types experienced or as a score considering traumatic event frequencies. In addition, we investigated whether age at trauma exposure can be reliably measured and improves PTSD risk prediction. All trauma measures showed good reliability. While prediction of lifetime PTSD was most accurate from the number of different traumatic event types experienced, inclusion of event frequencies slightly improved the prediction of current PTSD. As assessing the number of traumatic events experienced is the least stressful and time-consuming assessment and leads to the best prediction of lifetime PTSD, we recommend this measure for research on PTSD etiology.

Multivariate Predictors of Music Perception and Appraisal by Adult Cochlear Implant Users

PubMed Central

Gfeller, Kate; Oleson, Jacob; Knutson, John F.; Breheny, Patrick; Driscoll, Virginia; Olszewski, Carol

2009-01-01

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music. PMID:18669126

Does Positive Youth Development Predict Adolescent Attitudes about Sexuality?

ERIC Educational Resources Information Center

Chapman, Erin N.; Werner-Wilson, Ronald Jay

2008-01-01

The purpose of this study was to explore the relationships among individual factors, parental factors, involvement in activities, and adolescent attitudes regarding sex (the outcome variable). We suggest that Positive Youth Development (PYD) research and programming should include promoting healthy sexuality as an important developmental outcome…

Specificity of disgust domains in the prediction of contamination anxiety and avoidance: a multimodal examination.

PubMed

Olatunji, Bunmi O; Ebesutani, Chad; Haidt, Jonathan; Sawchuk, Craig N

2014-07-01

Although core, animal-reminder, and contamination disgust are viewed as distinct "types" of disgust vulnerabilities, the extent to which individual differences in the three disgust domains uniquely predict contamination-related anxiety and avoidance remains unclear. Three studies were conducted to fill this important gap in the literature. Study 1 was conducted to first determine if the three types of disgust could be replicated in a larger and more heterogeneous sample. Confirmatory factor analysis revealed that a bifactor model consisting of a "general disgust" dimension and the three distinct disgust dimensions yielded a better fit than a one-factor model. Structural equation modeling in Study 2 showed that while latent core, animal-reminder, and contamination disgust factors each uniquely predicted a latent "contamination anxiety" factor above and beyond general disgust, only animal-reminder uniquely predicted a latent "non-contamination anxiety" factor above and beyond general disgust. However, Study 3 found that only contamination disgust uniquely predicted behavioral avoidance in a public restroom where contamination concerns are salient. These findings suggest that although the three disgust domains are associated with contamination anxiety and avoidance, individual differences in contamination disgust sensitivity appear to be most uniquely predictive of contamination-related distress. The implications of these findings for the development and maintenance of anxiety-related disorders marked by excessive contamination concerns are discussed. Copyright © 2014. Published by Elsevier Ltd.

Using Conversation Topics for Predicting Therapy Outcomes in Schizophrenia

PubMed Central

Howes, Christine; Purver, Matthew; McCabe, Rose

2013-01-01

Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation. PMID:23943658

Parenting and Child "DRD4" Genotype Interact to Predict Children's Early Emerging Effortful Control

ERIC Educational Resources Information Center

Smith, Heather J.; Sheikh, Haroon I.; Dyson, Margaret W.; Olino, Thomas M.; Laptook, Rebecca S.; Durbin, C. Emily; Hayden, Elizabeth P.; Singh, Shiva M.; Klein, Daniel N.

2012-01-01

Effortful control (EC), or the trait-like capacity to regulate dominant responses, has important implications for children's development. Although genetic factors and parenting likely influence EC, few studies have examined whether they interact to predict its development. This study examined whether the "DRD4" exon III variable number tandem…

Predicting Academic Success in Higher Education: What's More Important than Being Smart?

ERIC Educational Resources Information Center

Kappe, Rutger; van der Flier, Henk

2012-01-01

This study investigated the combined predictive validity of intelligence and personality factors on multiple measures of academic achievement. Students in a college of higher education in the Netherlands (N = 137) completed a survey that measured intelligence, the Big Five personality traits, motivation, and four specific personality traits.…

Evolution of competitive ability within Lonicera japonica's invaded range

Treesearch

Gregory A. Evans; Francis F. Kilkenny; Laura F. Galloway

2013-01-01

Factors influencing invasive taxa may change during the course of an invasion. For example, intraspecific competition is predicted to be more important in areas with older stands of dense monospecific invaders than at the margins of an invaded range. We evaluated evolution in response to predicted changes in competition by comparing the intraspecific competitive...

Curriculum Track and Its Influences on Predicting High School Dropout Likelihood

ERIC Educational Resources Information Center

Mohd Kamalludeen, Rosemaliza

2012-01-01

Dropping out of school is a major concern as high school graduation credentials have been used as an important measurement tool to define post-secondary success. Numerous researchers presented a multitude of factors that predict dropouts at individual and school levels. Curriculum track choice, or high school course-taking sequence, defines…

Perceptions of Crowding: Predicting at the Residence, Neighborhood, and City Levels.

ERIC Educational Resources Information Center

Schmidt, Donald E.; And Others

1979-01-01

Details the results of a large-scale field study aimed at testing two theories on human crowding. Found that psychological factors are increasingly important for the prediction of crowding as one moved from the immediate residence to the less immediate city level. Implications, limitations and further results are discussed. (Author/MA)

Becoming a Cigarette Smoker: A Social-Psychological Perspective.

ERIC Educational Resources Information Center

Sherman, Steven J.; And Others

Research in the area of social cognition has suggesed that actual stimuli don't predict later judgments and responses as well as cognitive representations of and cognitive responses to those stimuli, in the form of attitudes, impressions, or causal attributions. To identify the factors most important in predicting adolescent smoking, a 4-year…

Emotional Intelligence as a Predictor for Success in Online Learning

ERIC Educational Resources Information Center

Berenson, Robin; Boyles, Gary; Weaver, Ann

2008-01-01

As students increasingly opt for online classes, it becomes more important for administrators to predict levels of potential academic success. This study examined the intrinsic factors of emotional intelligence (EI) and personality to determine the extent to which they predict grade point average (GPA), a measure of academic success, among…

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

USGS Publications Warehouse

Jennings, Cecil A.; Sundmark, Aaron P.

2017-01-01

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

Per Aspera ad Astra: Through Complex Population Modeling to Predictive Theory.

PubMed

Topping, Christopher J; Alrøe, Hugo Fjelsted; Farrell, Katharine N; Grimm, Volker

2015-11-01

Population models in ecology are often not good at predictions, even if they are complex and seem to be realistic enough. The reason for this might be that Occam's razor, which is key for minimal models exploring ideas and concepts, has been too uncritically adopted for more realistic models of systems. This can tie models too closely to certain situations, thereby preventing them from predicting the response to new conditions. We therefore advocate a new kind of parsimony to improve the application of Occam's razor. This new parsimony balances two contrasting strategies for avoiding errors in modeling: avoiding inclusion of nonessential factors (false inclusions) and avoiding exclusion of sometimes-important factors (false exclusions). It involves a synthesis of traditional modeling and analysis, used to describe the essentials of mechanistic relationships, with elements that are included in a model because they have been reported to be or can arguably be assumed to be important under certain conditions. The resulting models should be able to reflect how the internal organization of populations change and thereby generate representations of the novel behavior necessary for complex predictions, including regime shifts.

Predicting College Success: The Relative Contributions of Five Social/Personality Factors, Five Cognitive/Learning Factors, and SAT Scores

PubMed Central

Hannon, Brenda

2014-01-01

To-date, studies have examined simultaneously the relative predictive powers of two or three factors on GPA. The present study examines the relative powers of five social/personality factors, five cognitive/learning factors, and SAT scores to predict freshmen and non-freshmen (sophomores, juniors, seniors) academic success (i.e., GPA). The results revealed many significant predictors of GPA for both freshmen and non-freshmen. However, subsequent regressions showed that only academic self-efficacy, epistemic belief of learning, and high-knowledge integration explained unique variance in GPA (19%-freshmen, 23.2%-non-freshmen). Further for freshmen, SAT scores explained an additional unique 10.6% variance after the influences attributed to these three predictors was removed whereas for non-freshmen, SAT scores failed to explain any additional variance. These results highlight the unique and important contributions of academic self-efficacy, epistemic belief of learning and high-knowledge integration to GPA beyond other previously-identified predictors. PMID:25568884

Cognitive domains that predict time to diagnosis in prodromal Huntington disease.

PubMed

Harrington, Deborah Lynn; Smith, Megan M; Zhang, Ying; Carlozzi, Noelle E; Paulsen, Jane S

2012-06-01

Prodromal Huntington's disease (prHD) is associated with a myriad of cognitive changes but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials. The present study sought to characterise cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict time to diagnosis. Participants included gene negative and gene positive prHD participants who were enrolled in the PREDICT-HD study. The CAG-age product (CAP) score was the measure of an individual's genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis. Six factors were identified: (1) speed/inhibition, (2) verbal working memory, (3) motor planning/speed, (4) attention-information integration, (5) sensory-perceptual processing and (6) verbal learning/memory. Factor scores were sensitive to worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms. Conclusions The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive Huntington's disease trials where they may be more sensitive than individual tests.

Risk assessment and management to prevent preterm birth.

PubMed

Koullali, B; Oudijk, M A; Nijman, T A J; Mol, B W J; Pajkrt, E

2016-04-01

Preterm birth is the most important cause of neonatal mortality and morbidity worldwide. In this review, we review potential risk factors associated with preterm birth and the subsequent management to prevent preterm birth in low and high risk women with a singleton or multiple pregnancy. A history of preterm birth is considered the most important risk factor for preterm birth in subsequent pregnancy. General risk factors with a much lower impact include ethnicity, low socio-economic status, maternal weight, smoking, and periodontal status. Pregnancy-related characteristics, including bacterial vaginosis and asymptomatic bacteriuria, appear to be of limited value in the prediction of preterm birth. By contrast, a mid-pregnancy cervical length measurement is independently associated with preterm birth and could be used to identify women at risk of a premature delivery. A fetal fibronectin test may be of additional value in the prediction of preterm birth. The most effective methods to prevent preterm birth depend on the obstetric history, which makes the identification of women at risk of preterm birth an important task for clinical care providers. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Study on factors influencing survival in patients with advanced gastric carcinoma after resection by Cox's proportional hazard model].

PubMed

Wang, S; Sun, Z; Wang, S

1996-11-01

A prospective follow-up study of 539 advanced gastric carcinoma patients after resection was undertaken between 1 January 1980 and 31 December 1989, with a follow-up rate of 95.36%. A multivariate analysis of possible factors influencing survival of these patients was performed, and their predicting models of survival rates was established by Cox proportional hazard model. The results showed that the major significant prognostic factors influencing survival of these patients were rate and station of lymph node metastases, type of operation, hepatic metastases, size of tumor, age and location of tumor. The most important factor was the rate of lymph node metastases. According to their regression coefficients, the predicting value (PV) of each patient was calculated, then all patients were divided into five risk groups according to PV, their predicting models of survival rates after resection were established in groups. The goodness-fit of estimated predicting models of survival rates were checked by fitting curve and residual plot, and the estimated models tallied with the actual situation. The results suggest that the patients with advanced gastric cancer after resection without lymph node metastases and hepatic metastases had a better prognosis, and their survival probability may be predicted according to the predicting model of survival rates.

Psychosocial and nonclinical factors predicting hospital utilization in patients of a chronic disease management program: a prospective observational study.

PubMed

Tran, Mark W; Weiland, Tracey J; Phillips, Georgina A

2015-01-01

Psychosocial factors such as marital status (odds ratio, 3.52; 95% confidence interval, 1.43-8.69; P = .006) and nonclinical factors such as outpatient nonattendances (odds ratio, 2.52; 95% confidence interval, 1.22-5.23; P = .013) and referrals made (odds ratio, 1.20; 95% confidence interval, 1.06-1.35; P = .003) predict hospital utilization for patients in a chronic disease management program. Along with optimizing patients' clinical condition by prescribed medical guidelines and supporting patient self-management, addressing psychosocial and nonclinical issues are important in attempting to avoid hospital utilization for people with chronic illnesses.

Systematic Review of Prognostic Factors for Return to Work in Workers with Sub Acute and Chronic Low Back Pain.

PubMed

Steenstra, Ivan A; Munhall, Claire; Irvin, Emma; Oranye, Nelson; Passmore, Steven; Van Eerd, Dwayne; Mahood, Quenby; Hogg-Johnson, Sheilah

2017-09-01

Purpose We systematically reviewed the evidence on factors that predict duration of sick leave in workers after 6 weeks low back pain (LBP) related sick leave. We hypothesized that different factors affect the duration of the leave depending on the time away from work. Methods The review occurred in seven phases: (1) developing the central question, (2) conducting the literature search, (3) identifying relevant publications, (4) quality appraisal, (5) data extraction, (6) evidence synthesis, and (7) knowledge translation. We searched for studies that reported episodes of LBP and sick leave that lasted more than 6 weeks. All included studies reported at least one prognostic factor where return to work was the outcome. Results We identified twenty-two relevant publications. The impact of pain, functional status and radiating pain seems to change with duration of work disability. Workers' recovery expectations remain important after 6 weeks. Modified duties are rarely studied in later phases of work disability. Depression/mental health did not appear to be an important factor in later phases. Workplace physical factors remain important. There is insufficient evidence that pain catastrophising and fear avoidance are predictive factors in later phases. There was moderate evidence for age in the later phases. Functional capacity and claim related factors were supported by some evidence. Discusion Physical demands in the workplace are preventing workers from getting back to work in a timely fashion across phases. The psychosocial work environment is understudied in later phases. Overall, we cannot conclude that prognostic factors change over time.

Lack of Effect of Sleep Apnea on Oxidative Stress in Obstructive Sleep Apnea Syndrome (OSAS) Patients

PubMed Central

Simiakakis, M.; Kapsimalis, F.; Chaligiannis, E.; Loukides, S.; Sitaras, N.; Alchanatis, M.

2012-01-01

Purpose The aim of this study was to evaluate markers of systemic oxidative stress and antioxidant capacity in subjects with and without OSAS in order to investigate the most important factors that determine the oxidant–antioxidant status. Methods A total of 66 subjects referred to our Sleep laboratory were examined by full polysomnography. Oxidative stress and antioxidant activity were assessed by measurement of the derivatives of reactive oxygen metabolites (d-ROMs) and the biological antioxidant capacity (BAP) in blood samples taken in the morning after the sleep study. Known risk factors for oxidative stress, such as age, sex, obesity, smoking, hypelipidemia, and hypertension, were investigated as possible confounding factors. Results 42 patients with OSAS (Apnea-Hypopnea index >15 events/hour) were compared with 24 controls (AHI<5). The levels of d-ROMS were significantly higher (p = 0.005) in the control group but the levels of antioxidant capacity were significantly lower (p = 0.004) in OSAS patients. The most important factors predicting the variance of oxidative stress were obesity, smoking habit, and sex. Parameters of sleep apnea severity were not associated with oxidative stress. Minimal oxygen desaturation and smoking habit were the most important predicting factors of BAP levels. Conclusion Obesity, smoking, and sex are the most important determinants of oxidative stress in OSAS subjects. Sleep apnea might enhance oxidative stress by the reduction of antioxidant capacity of blood due to nocturnal hypoxia. PMID:22761732

USE OF A CONVECTION-DIFFUSION MODEL TO UNDERSTAND GASTROINTESTINAL ABSORPTION OF ENVIRONMENTALLY-RELEVANT CHEMICALS

EPA Science Inventory

Understanding the factors that affect the gastrointestinal absorption of chemicals is important to predicting the delivered systemic dose of chemicals following exposure in food, water, and other media. Two factors of particular interest are the effects of a matrix to which th...

Abiotic factors influencing deer browsing in West Virginia

Treesearch

Tyler A. Campbell; Benjamin R. Laseter; W. Mark Ford; Richard H. Odom; Karl V. Miller

2006-01-01

We present a comparison of woody browse availability and white-tailed deer (Odocoileus virginianus) use among clearcut interiors, skidder trail edges, and mature forest and an evaluation of the relative importance of aboitic factors in predicting browsing pressure within regenerating clearcuts in the central Appalachians of West Virginia. We sampled...

Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters

USDA-ARS?s Scientific Manuscript database

Measuring and monitoring controls on wind erosion can facilitate detection and prediction of soil degradation important for food security. Ground cover is widely recognised as an important factor for controlling soil erosion by wind and water. Consequently, maintaining ground cover (e.g., vegetation...

Randomization in clinical trials: stratification or minimization? The HERMES free simulation software.

PubMed

Fron Chabouis, Hélène; Chabouis, Francis; Gillaizeau, Florence; Durieux, Pierre; Chatellier, Gilles; Ruse, N Dorin; Attal, Jean-Pierre

2014-01-01

Operative clinical trials are often small and open-label. Randomization is therefore very important. Stratification and minimization are two randomization options in such trials. The first aim of this study was to compare stratification and minimization in terms of predictability and balance in order to help investigators choose the most appropriate allocation method. Our second aim was to evaluate the influence of various parameters on the performance of these techniques. The created software generated patients according to chosen trial parameters (e.g., number of important prognostic factors, number of operators or centers, etc.) and computed predictability and balance indicators for several stratification and minimization methods over a given number of simulations. Block size and proportion of random allocations could be chosen. A reference trial was chosen (50 patients, 1 prognostic factor, and 2 operators) and eight other trials derived from this reference trial were modeled. Predictability and balance indicators were calculated from 10,000 simulations per trial. Minimization performed better with complex trials (e.g., smaller sample size, increasing number of prognostic factors, and operators); stratification imbalance increased when the number of strata increased. An inverse correlation between imbalance and predictability was observed. A compromise between predictability and imbalance still has to be found by the investigator but our software (HERMES) gives concrete reasons for choosing between stratification and minimization; it can be downloaded free of charge. This software will help investigators choose the appropriate randomization method in future two-arm trials.

The mathematical limits of genetic prediction for complex chronic disease.

PubMed

Keyes, Katherine M; Smith, George Davey; Koenen, Karestan C; Galea, Sandro

2015-06-01

Attempts at predicting individual risk of disease based on common germline genetic variation have largely been disappointing. The present paper formalises why genetic prediction at the individual level is and will continue to have limited utility given the aetiological architecture of most common complex diseases. Data were simulated on one million populations with 10 000 individuals in each populations with varying prevalences of a genetic risk factor, an interacting environmental factor and the background rate of disease. The determinant risk ratio and risk difference magnitude for the association between a gene variant and disease is a function of the prevalence of the interacting factors that activate the gene, and the background rate of disease. The risk ratio and total excess cases due to the genetic factor increase as the prevalence of interacting factors increase, and decrease as the background rate of disease increases. Germline genetic variations have high predictive capacity for individual disease only under conditions of high heritability of particular genetic sequences, plausible only under rare variant hypotheses. Under a model of common germline genetic variants that interact with other genes and/or environmental factors in order to cause disease, the predictive capacity of common genetic variants is determined by the prevalence of the factors that interact with the variant and the background rate. A focus on estimating genetic associations for the purpose of prediction without explicitly grounding such work in an understanding of modifiable (including environmentally influenced) factors will be limited in its ability to yield important insights about the risk of disease. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Histologic assessment of tumor budding in preoperative biopsies to predict nodal metastasis in squamous cell carcinoma of the tongue and floor of the mouth.

PubMed

Seki, Mai; Sano, Takaaki; Yokoo, Satoshi; Oyama, Tetsunari

2016-04-01

In squamous cell carcinoma (SCC) of the tongue and the floor of the mouth (FOM), it is important to predict lymph node metastasis, including occult metastasis, before operating. The purpose of this study was for us to determine practical histopathologic parameters as predictive factors for lymph node metastasis in preoperative SCC biopsy specimens. We examined 91 cases of SCC for conventional histopathologic assessment and a new factor, tumor budding, and their relationship with lymph node metastasis. Significant factors via univariate analysis (p < .01) were budding (score ≥3) and tumor depth (≥3 mm) and these were associated with lymph node metastasis. Moreover, both budding and tumor depth significantly correlated with relapse-free survival; however, evaluating biopsy specimens often proved inaccurate for predicting true tumor depth of cancer invasion. Tumor budding using immunohistochemistry for cytokeratin should be added to routine histologic assessments as a new criterion factoring into the decision as to whether neck dissection is indicated. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1582-E1590, 2016. © 2015 Wiley Periodicals, Inc.

The role of dispersal mode and habitat specialization for metacommunity structure of shallow beach invertebrates.

PubMed

Rodil, Iván F; Lucena-Moya, Paloma; Jokinen, Henri; Ollus, Victoria; Wennhage, Håkan; Villnäs, Anna; Norkko, Alf

2017-01-01

Metacommunity ecology recognizes the interplay between local and regional patterns in contributing to spatial variation in community structure. In aquatic systems, the relative importance of such patterns depends mainly on the potential connectivity of the specific system. Thus, connectivity is expected to increase in relation to the degree of water movement, and to depend on the specific traits of the study organism. We examined the role of environmental and spatial factors in structuring benthic communities from a highly connected shallow beach network using a metacommunity approach. Both factors contributed to a varying degree to the structure of the local communities suggesting that environmental filters and dispersal-related mechanisms played key roles in determining abundance patterns. We categorized benthic taxa according to their dispersal mode (passive vs. active) and habitat specialization (generalist vs. specialist) to understand the relative importance of environment and dispersal related processes for shallow beach metacommunities. Passive dispersers were predicted by a combination of environmental and spatial factors, whereas active dispersers were not spatially structured and responded only to local environmental factors. Generalists were predicted primarily by spatial factors, while specialists were only predicted by local environmental factors. The results suggest that the role of the spatial component in metacommunity organization is greater in open coastal waters, such as shallow beaches, compared to less-connected environmentally controlled aquatic systems. Our results also reveal a strong environmental role in structuring the benthic metacommunity of shallow beaches. Specifically, we highlight the sensitivity of shallow beach macrofauna to environmental factors related to eutrophication proxies.

The role of dispersal mode and habitat specialization for metacommunity structure of shallow beach invertebrates

PubMed Central

Lucena-Moya, Paloma; Jokinen, Henri; Ollus, Victoria; Wennhage, Håkan; Villnäs, Anna; Norkko, Alf

2017-01-01

Metacommunity ecology recognizes the interplay between local and regional patterns in contributing to spatial variation in community structure. In aquatic systems, the relative importance of such patterns depends mainly on the potential connectivity of the specific system. Thus, connectivity is expected to increase in relation to the degree of water movement, and to depend on the specific traits of the study organism. We examined the role of environmental and spatial factors in structuring benthic communities from a highly connected shallow beach network using a metacommunity approach. Both factors contributed to a varying degree to the structure of the local communities suggesting that environmental filters and dispersal-related mechanisms played key roles in determining abundance patterns. We categorized benthic taxa according to their dispersal mode (passive vs. active) and habitat specialization (generalist vs. specialist) to understand the relative importance of environment and dispersal related processes for shallow beach metacommunities. Passive dispersers were predicted by a combination of environmental and spatial factors, whereas active dispersers were not spatially structured and responded only to local environmental factors. Generalists were predicted primarily by spatial factors, while specialists were only predicted by local environmental factors. The results suggest that the role of the spatial component in metacommunity organization is greater in open coastal waters, such as shallow beaches, compared to less-connected environmentally controlled aquatic systems. Our results also reveal a strong environmental role in structuring the benthic metacommunity of shallow beaches. Specifically, we highlight the sensitivity of shallow beach macrofauna to environmental factors related to eutrophication proxies. PMID:28196112

Explaining use of food parenting practices: the importance of predisposing factors and parental cognitions.

PubMed

Gevers, Dorus Wm; van Assema, Patricia; de Vries, Nanne K; Kremers, Stef Pj

2017-09-01

The high energy intake from energy-dense foods among children in developed countries is undesirable. Improving food parenting practices has the potential to lower snack intakes among children. To inform the development of interventions, we aimed to predict food parenting practice patterns around snacking (i.e. 'high covert control and rewarding', 'low covert control and non-rewarding', 'high involvement and supportive' and 'low involvement and indulgent'). A cross-sectional survey was conducted. To predict the patterns of food parenting practices, multinomial logistic regression analyses were run with 888 parents. Predictors included predisposing factors (i.e. parents' and children's demographics and BMI, parents' personality, general parenting, and parenting practices used by their own parents) and parents' cognitions (i.e. perceived behaviour of other parents, subjective norms, attitudes, self-efficacy and outcome expectations). The Netherlands (October-November 2014). Dutch parents of children aged 4-12 years old. After backward elimination, nineteen factors had a statistically significant contribution to the model (Nagelkerke R 2=0·63). Overall, self-efficacy and outcome expectations were among the strongest explanatory factors. Considering the predisposing factors only, the general parenting factor nurturance most strongly predicted the food parenting clusters. Nurturance particularly distinguished highly involved parents from parents employing a pattern of low involvement. Parental cognitions and nurturance are important factors to explain the use of food parenting practices around snacking. The results suggest that intervention developers should attempt to increase self-efficacy and educate parents about what constitute effective and ineffective parenting practices. Promoting nurturance might be a prerequisite to achieve prolonged change.

Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

PubMed Central

Wang, Xin; Wang, Ying; Sun, Hongbin

2016-01-01

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework. PMID:27034651

Therapygenetics: Using genetic markers to predict response to psychological treatment for mood and anxiety disorders

PubMed Central

2013-01-01

Considerable variation is evident in response to psychological therapies for mood and anxiety disorders. Genetic factors alongside environmental variables and gene-environment interactions are implicated in the etiology of these disorders and it is plausible that these same factors may also be important in predicting individual differences in response to psychological treatment. In this article, we review the evidence that genetic variation influences psychological treatment outcomes with a primary focus on mood and anxiety disorders. Unlike most past work, which has considered prediction of response to pharmacotherapy, this article reviews recent work in the field of therapygenetics, namely the role of genes in predicting psychological treatment response. As this is a field in its infancy, methodological recommendations are made and opportunities for future research are identified. PMID:23388219

High-severity fire: evaluating its key drivers and mapping its probability across western US forests

NASA Astrophysics Data System (ADS)

Parks, Sean A.; Holsinger, Lisa M.; Panunto, Matthew H.; Jolly, W. Matt; Dobrowski, Solomon Z.; Dillon, Gregory K.

2018-04-01

Wildland fire is a critical process in forests of the western United States (US). Variation in fire behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire severity, however, is poorly understood. Here, we explore the drivers of high-severity fire for forested ecoregions in the western US over the period 2002–2015. Fire severity was quantified using a satellite-inferred index of severity, the relativized burn ratio. For each ecoregion, we used boosted regression trees to model high-severity fire as a function of live fuel, topography, climate, and fire weather. We found that live fuel, on average, was the most important factor driving high-severity fire among ecoregions (average relative influence = 53.1%) and was the most important factor in 14 of 19 ecoregions. Fire weather was the second most important factor among ecoregions (average relative influence = 22.9%) and was the most important factor in five ecoregions. Climate (13.7%) and topography (10.3%) were less influential. We also predicted the probability of high-severity fire, were a fire to occur, using recent (2016) satellite imagery to characterize live fuel for a subset of ecoregions in which the model skill was deemed acceptable (n = 13). These ‘wall-to-wall’ gridded ecoregional maps provide relevant and up-to-date information for scientists and managers who are tasked with managing fuel and wildland fire. Lastly, we provide an example of the predicted likelihood of high-severity fire under moderate and extreme fire weather before and after fuel reduction treatments, thereby demonstrating how our framework and model predictions can potentially serve as a performance metric for land management agencies tasked with reducing hazardous fuel across large landscapes.

Specific Disgust Sensitivities Differentially Predict Interest in Careers of Varying Procedural-Intensity among Medical Students

ERIC Educational Resources Information Center

Consedine, Nathan S.; Windsor, John A.

2014-01-01

Mismatches between the needs of public health systems and student interests have led to renewed study on the factors predicting career specializations among medical students. While most work examines career and lifestyle values, emotional proclivities may be important; disgust sensitivity may help explain preferences for careers with greater and…

Role of Parent Literacy and Numeracy Expectations and Activities in Predicting Early Numeracy Skills

ERIC Educational Resources Information Center

Segers, Eliane; Kleemans, Tijs; Verhoeven, Ludo

2015-01-01

The home numeracy environment (i.e., parents' numeracy expectations and activities), is related to early numeracy in young children. As recent studies have shown that both cognitive and linguistic factors play an important role in predicting numeracy development, it may be assumed that rather than the home "numeracy" environment, the…

The Role of Resilience, Delayed Gratification and Stress in Predicting Academic Performance

ERIC Educational Resources Information Center

Cheng, Vivienne; Catling, Jonathan

2015-01-01

Transition to university is an important and potentially stressful life event for students. Previous studies have shown that resilience, delay of gratification and stress can affect the academic performance of students. However, none have shown the effect of these factors in predicting academic performance, hence the current study aimed to look at…

Improvement of Quench Factor Analysis in Phase and Hardness Prediction of a Quenched Steel

NASA Astrophysics Data System (ADS)

Kianezhad, M.; Sajjadi, S. A.

2013-05-01

The accurate prediction of alloys' properties introduced by heat treatment has been considered by many researchers. The advantages of such predictions are reduction of test trails and materials' consumption as well as time and energy saving. One of the most important methods to predict hardness in quenched steel parts is Quench Factor Analysis (QFA). Classical QFA is based on the Johnson-Mehl-Avrami-Kolmogorov (JMAK) equation. In this study, a modified form of the QFA based on the work by Rometsch et al. is compared with the classical QFA, and they are applied to prediction of hardness of steels. For this purpose, samples of CK60 steel were utilized as raw material. They were austenitized at 1103 K (830 °C). After quenching in different environments, they were cut and their hardness was determined. In addition, the hardness values of the samples were fitted using the classical and modified equations for the quench factor analysis and the results were compared. Results showed a significant improvement in fitted values of the hardness and proved the higher efficiency of the new method.

To Communicate or Not to Communicate: Factors Predicting Passengers' Intentions to Ask a Driver to Stop Text Messaging While Driving.

PubMed

Wang, Xiao

2016-01-01

Interpersonal communication is important in health campaigns. This research examined factors that are associated with passengers' intentions to communicate no texting with a texting driver in a scenario where the driver is their friend. Based on survey data collected from 546 college students, results showed that students' attitudes toward communication about no texting while driving were predicted by their utilitarian (i.e., safety), value-expressive, and ego-defensive motivations, in addition to being predicted by self-efficacy and norms. Additional results revealed that empathic concern was correlated with the value-expressive motivation and anticipated guilt. Anticipated guilt, together with attitudes, norms, and efficacy, predicted communication intentions. Results revealed that including attitude functions (motivations) in the reasoned action model could help propose and test theory-based predictions in interpersonal communication and health behaviors.

Evaluating shortened versions of the AUDIT as screeners for alcohol use problems in a general population study

PubMed Central

Nayak, Madhabika B.; Bond, Jason C.; Greenfield, Thomas K.

2015-01-01

Background Efficient alcohol screening measures are important to prevent or treat alcohol use disorders (AUDs). Objectives We studied different versions of the Alcohol Use Disorders Identification Test (AUDIT) comparing their performance to the full AUDIT and an AUD measure as screeners for alcohol use problems in Goa, India. Methods Data from a general population study on 743 male drinkers aged 18 to 49 years are reported. Drinkers completed the AUDIT and an AUD measure. We created shorter versions of the AUDIT by a) collapsing AUDIT item responses into 3 and 2 categories and b) deleting 2 items with the lowest factor loadings. Each version was evaluated using factor, reliability and validity, and differential item functioning (DIF) analysis by age, education, standard of living index (SLI), and area of residence. Results A single factor solution was found for each version with lower factor loadings for items on guilt and concern. There were no significant differences among the different AUDIT versions in predicting AUD. No significant DIF was found by education, SLI or area of residence. DIF was observed for the alcohol frequency item by age. Conclusions/Importance The AUDIT may be used with dichotomized response options without loss of predictive validity. A shortened 8-item dichotomized scale can adequately screen for AUDs in Goa when brevity is of paramount importance, although with lower predictive validity. Although the frequency item was endorsed more by older men, there is no evidence that the AUDIT items perform differently in other groups of male drinkers in Goa. PMID:26549791

Negative Social Relationships Predict Posttraumatic Stress Symptoms Among War-Affected Children Via Posttraumatic Cognitions.

PubMed

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

2016-07-01

Post traumatic cognitions (PTCs) are important determinants of post traumatic stress symptoms (PTS symptoms). We tested whether risk factors of PTS symptoms (trauma, demographics, social and family-related factors) predict PTCs and whether PTCs mediate the association between risk factors and PTS symptoms among war-affected children. The participants were 240 Palestinian children 10-12 years old, half boys and half girls, and their parents. Children reported about psychological maltreatment, sibling and peer relations, war trauma, PTCs, PTS symptoms, and depression. Parents reported about their socioeconomic status and their own PTS symptoms. The associations between the variables were estimated in structural equation models. In models which included all the variables, PTCs were predicted by and mediated the effects of psychological maltreatment, war trauma, sibling conflict, and peer unpopularity on PTS symptoms. Other predictors had statistically non-significant effects. Psychological maltreatment had the largest indirect effect (b* = 0.29, p = 0.002) and the indirect effects of war trauma (b* = 0.10, p = 0.045), sibling conflict (b* = 0.10, p = 0.045), and peer unpopularity (b* = 0.10, p = 0.094) were lower and about the same size. Age-salient social relationships are potentially important in the development of both PTCs and PTS symptoms among preadolescents. Furthermore, PTCs mediate the effects of the risk factors of PTS symptoms. The causality of the associations among the variables is not established but it could be studied in the future with interventions which improve the negative aspects of traumatized children's important social relationships.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

[The role of some psychological, psychosocial and obstetrical factors in the intensity of postpartum blues].

PubMed

Séjourné, N; Denis, A; Theux, G; Chabrol, H

2008-04-01

Within days following birth, most women show signs of mood changes, commonly named baby blues. Baby blues can result in postpartum depression. Hence it appears important to explore in more details the clinical background related to the intensity of postpartum blues. The aim of this study is to investigate the contribution of psychological, psychosocial and obstetrical factors to the intensity of postpartum blues. One hundred and forty-eight women participated in the study and completed questionnaires three days after delivery. A questionnaire was built to collect information on psychosocial and obstetrical factors. The Maternity Blues (Kennerley and Gath, 1989) was used to assess postpartum blues. Psychological factors were measured with the Maternal Self-Report Inventory (Shea et Tronick, 1988), the Perceived Stress Scale (Cohen, Kamarch et Mermelstein, 1983) and the Sarason's Social Support Questionnaire (1983). Four multiple regression analyses were conducted to predict the intensity of postpartum blues by entering psychosocial factors, history of depression, obstetrical factors and psychological and relational factors. Significant predictors (maternal self-esteem, marital status, previous psychotherapeutic treatment, previous antidepressant treatment) were entered in a multiple regression analysis predicting the intensity of postpartum blues. This model accounted for 31% of the variance in the intensity of postpartum blues (F(4, 143)=17.9; P<0.001). Maternal self-esteem (beta=-0.37; P<0.001), marital situation (beta=-0.16; P=0.02) were significant predictors. Previous antidepressant treatment (beta=0.13; P=0.05) was almost a significant predictor. The preventive implication of this study is important. Some psychological and psychosocial variables predicted the intensity of postpartum blues and may be used in order to detect women who exhibit risk factors.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

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

2016-04-16

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

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

On their best behavior: how animal behavior can help determine the combined effects of species interactions and climate change.

PubMed

Harmon, Jason P; Barton, Brandon T

2013-09-01

The increasingly appreciated link between climate change and species interactions has the potential to help us understand and predict how organisms respond to a changing environment. As this connection grows, it becomes even more important to appreciate the mechanisms that create and control the combined effect of these factors. However, we believe one such important set of mechanisms comes from species' behavior and the subsequent trait-mediated interactions, as opposed to the more often studied density-mediated effects. Behavioral mechanisms are already well appreciated for mitigating the separate effects of the environment and species interactions. Thus, they could be at the forefront for understanding the combined effects. In this review, we (1) show some of the known behaviors that influence the individual and combined effects of climate change and species interactions; (2) conceptualize general ways behavior may mediate these combined effects; and (3) illustrate the potential importance of including behavior in our current tools for predicting climate change effects. In doing so, we hope to promote more research on behavior and other mechanistic factors that may increase our ability to accurately predict climate change effects. © 2013 New York Academy of Sciences.

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

PubMed Central

Sun, Jimeng; Hu, Jianying; Luo, Dijun; Markatou, Marianthi; Wang, Fei; Edabollahi, Shahram; Steinhubl, Steven E.; Daar, Zahra; Stewart, Walter F.

2012-01-01

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. Methods: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. Results: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003–2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. Conclusion: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors. PMID:23304365

The relative importance of avoidance and restoration-oriented stressors for grief and depression in bereaved parents.

PubMed

Harper, Mairi; O'Connor, Rory C; O'Carroll, Ronan E

2015-01-01

Previous research has identified a number of individual risk factors for parental bereavement including the sex of the parent, the sex of the child, avoidance-focussed coping style and time since death. These factors emerged from research where variables were tested univariately and their relative importance is currently unknown. The current research, therefore, aims to investigate which risk factors are important, multivariately, for the outcomes of grief and depression in parents following the death of their child. Psychosocial measures were completed by 106 bereaved parents four years post-loss, recruited from death records in Scotland. The cause of the child's death included long-term illness and stillbirths as well as sudden and violent deaths. In multivariate regression analyses, depression was predicted by higher avoidance-focussed coping and higher number of restoration-oriented stressors such as relationship difficulties, problems at work and financial issues. Grief was predicted by higher avoidance, restoration stressors and level of continuing bonds. The present study adds to the knowledge about the phenomenon of parental bereavement with participants recruited directly from death records rather than through support, clinical or obituary sources. Factors previously found to be associated with outcomes when tested univariately such as sudden, violent death or sex of the parent were not significant when tested multivariately. This study highlights that different vulnerability factors exist for grief and depression in bereaved parents.

Predictive Factors of Regular Physical Activity among Middle-Aged Women in the West of Iran, Hamadan: Application of PRECEDE Model.

PubMed

Emdadi, Shohreh; Hazavehie, Seyed Mohammad Mehdi; Soltanian, Alireza; Bashirian, Saeed; Heidari Moghadam, Rashid

2015-01-01

Regular physical activity is important for midlife women. Models and theories help better understanding this behavior among middle-aged women and better planning for change behavior in target group. This study aimed to investigate predictive factors of regular physical activity among middle-aged women based on PRECEDE model as a theoretical framework. This descriptive-analytical study was performed on 866 middle-aged women of Hamadan City western Iran, recruited with a proportional stratified sampling method in 2015. The participants completed a self-administered questionnaire including questions on demographic characteristics and PRECEDE model constructs and IPAQ questionnaire. Data were then analyzed by SPSS-16 and AMOS-16 using the Pearson correlation test and the pathway analysis method. Overall, 57% of middle-aged women were inactive (light level) or not sufficiently active. With SEM (Structural Equation Modeling) analysis, knowledge b=0.84, P<0.001, attitude b=0.799, P<0.001, self-efficacy b=0.633, P<0.001 as predisposing factor and social support as reinforcing factor b=0.2, P<0.001 were the most important predictors for physical activity among middle-aged women in Hamadan. The framework of the PRECEDE model is useful in understanding regular physical activity among middle-aged women. Furthermore, results showed the importance of predisposing and reinforcing factors when planning educational interventions.

Predicting Influential Factors of Secondary Career and Technical Education Teachers' Intent to Stay in the Profession

ERIC Educational Resources Information Center

Dainty, Julie D.

2012-01-01

Retaining highly qualified career and technical education teachers is important in maintaining and growing quality secondary career and technical education programs. Therefore, the purpose of this study was to identify factors contributing to teacher retention specifically in the area of career and technical education (CTE) and determine…

Predicting pre-planting risk of Stagonospora nodorum blotch in winter wheat using machine learning models

USDA-ARS?s Scientific Manuscript database

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this know...

Intelligence and Scientific-Creative Thinking: Their Convergence in the Explanation of Students' Academic Performance

ERIC Educational Resources Information Center

Ruiz, Maria Jose; Bermejo, Rosario; Ferrando, Mercedes; Prieto, Maria Dolores; Sainz, Marta

2014-01-01

Introduction: Academic performance is usually generally explained by student's intelligence, although other factors such as personality and motivation also account for it. Factors associated with a more complex thought process in adolescence are also beginning to gain importance in the prediction of academic performance. Among these forms of…

Analysis of clinically important factors on the performance of advanced hydraulic, microprocessor-controlled exo-prosthetic knee joints based on 899 trial fittings

PubMed Central

Hahn, Andreas; Lang, Michael; Stuckart, Claudia

2016-01-01

Abstract The objective of this work is to evaluate whether clinically important factors may predict an individual's capability to utilize the functional benefits provided by an advanced hydraulic, microprocessor-controlled exo-prosthetic knee component. This retrospective cross-sectional cohort analysis investigated the data of above knee amputees captured during routine trial fittings. Prosthetists rated the performance indicators showing the functional benefits of the advanced maneuvering capabilities of the device. Subjects were asked to rate their perception. Simple and multiple linear and logistic regression was applied. Data from 899 subjects with demographics typical for the population were evaluated. Ability to vary gait speed, perform toileting, and ascend stairs were identified as the most sensitive performance predictors. Prior C-Leg users showed benefits during advanced maneuvering. Variables showed plausible and meaningful effects, however, could not claim predictive power. Mobility grade showed the largest effect but also failed to be predictive. Clinical parameters such as etiology, age, mobility grade, and others analyzed here do not suffice to predict individual potential. Daily walking distance may pose a threshold value and be part of a predictive instrument. Decisions based solely on single parameters such as mobility grade rating or walking distance seem to be questionable. PMID:27828871

Analysis of clinically important factors on the performance of advanced hydraulic, microprocessor-controlled exo-prosthetic knee joints based on 899 trial fittings.

PubMed

Hahn, Andreas; Lang, Michael; Stuckart, Claudia

2016-11-01

The objective of this work is to evaluate whether clinically important factors may predict an individual's capability to utilize the functional benefits provided by an advanced hydraulic, microprocessor-controlled exo-prosthetic knee component.This retrospective cross-sectional cohort analysis investigated the data of above knee amputees captured during routine trial fittings. Prosthetists rated the performance indicators showing the functional benefits of the advanced maneuvering capabilities of the device. Subjects were asked to rate their perception. Simple and multiple linear and logistic regression was applied.Data from 899 subjects with demographics typical for the population were evaluated. Ability to vary gait speed, perform toileting, and ascend stairs were identified as the most sensitive performance predictors. Prior C-Leg users showed benefits during advanced maneuvering. Variables showed plausible and meaningful effects, however, could not claim predictive power. Mobility grade showed the largest effect but also failed to be predictive.Clinical parameters such as etiology, age, mobility grade, and others analyzed here do not suffice to predict individual potential. Daily walking distance may pose a threshold value and be part of a predictive instrument. Decisions based solely on single parameters such as mobility grade rating or walking distance seem to be questionable.

Using a Prediction Model to Manage Cyber Security Threats.

PubMed

Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

2015-01-01

Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

Using a Prediction Model to Manage Cyber Security Threats

PubMed Central

Muthu Sivashanmugam, Premapriya

2015-01-01

Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization. PMID:26065024

Biological and Sociocultural Factors During the School Years Predicting Women's Lifetime Educational Attainment.

PubMed

Hendrick, C Emily; Cohen, Alison K; Deardorff, Julianna; Cance, Jessica D

2016-03-01

Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In this study, we examine the roles of sociocultural factors in youth and an understudied biological life event, pubertal timing, in predicting women's lifetime educational attainment. Using data from the National Longitudinal Survey of Youth 1997 cohort (N = 3889), we conducted sequential multivariate linear regression analyses to investigate the influences of macro-level and family-level sociocultural contextual factors in youth (region of country, urbanicity, race/ethnicity, year of birth, household composition, mother's education, and mother's age at first birth) and early menarche, a marker of early pubertal development, on women's educational attainment after age 24. Pubertal timing and all sociocultural factors in youth, other than year of birth, predicted women's lifetime educational attainment in bivariate models. Family factors had the strongest associations. When family factors were added to multivariate models, geographic region in youth, and pubertal timing were no longer significant. Our findings provide additional evidence that family factors should be considered when developing comprehensive and inclusive interventions in childhood and adolescence to promote lifetime educational attainment among girls. © 2016, American School Health Association.

Risk and protective factors as predictors of outcome in adolescents with psychiatric disorder and aggression.

PubMed

Vance, J Eric; Bowen, Natasha K; Fernandez, Gustavo; Thompson, Shealy

2002-01-01

To identify predictors of behavioral outcomes in high-risk adolescents with aggression and serious emotional disturbance (SED). Three hundred thirty-seven adolescents from a statewide North Carolina treatment program for aggressive youths with SED were followed between July 1995 and June 1999 from program entry (T1) to approximately 1 year later (T2). Historical and current psychosocial risk and protective factors as well as psychiatric symptom severity at T1 were tested as predictors of high and low behavioral functioning at T2. Behavioral functioning was a composite based on the frequency of risk-taking, self-injurious, threatening, and assaultive behavior. Eleven risk and protective factors were predictive of T2 behavioral functioning, while none of the measured T1 psychiatric symptoms was predictive. A history of aggression and negative parent-child relationships in childhood was predictive of worse T2 behavior, as was lower IQ. Better T2 behavioral outcomes were predicted by a history of consistent parental employment and positive parent-child relations, higher levels of current family support, contact with prosocial peers, higher reading level, good problem-solving abilities, and superior interpersonal skills. Among high-risk adolescents with aggression and SED, psychiatric symptom severity may be a less important predictor of behavioral outcomes than certain risk and protective factors. Several factors predictive of good behavioral functioning represent feasible intervention targets.

Predicting influenza vaccination uptake among health care workers: what are the key motivators?

PubMed

Corace, Kimberly; Prematunge, Chatura; McCarthy, Anne; Nair, Rama C; Roth, Virginia; Hayes, Thomas; Suh, Kathryn N; Balfour, Louise; Garber, Gary

2013-08-01

Health care worker (HCW) vaccination was critical to protecting HCW during the H1N1 pandemic. However, vaccine uptake rates fell below recommended targets. This study examined motivators and barriers influencing HCW pH1N1 vaccination to identify modifiable factors that can improve influenza vaccine uptake. A cross-sectional survey was conducted at a large Canadian tertiary care hospital. HCW (N = 3,275) completed measures of demographics, vaccination history, influenza risk factors, and attitudes toward pH1N1 vaccination. Self-reported vaccination was verified with staff vaccination records. Of the total sample, 2,862 (87.4%) HCW received the pH1N1 vaccine. Multiple logistic regression analyses were used to predict HCW vaccination. HCW attitudes toward vaccination significantly predicted vaccination, even after adjusting for demographics, vaccine history, and influenza risk factors. This model correctly predicted 95% (confidence interval [CI]: 0.93-0.96) of HCW vaccination. Key modifiable factors driving HCW vaccination include (1) desire to protect family members and patients, (2) belief that vaccination is important even if one is healthy, (3) confidence in vaccine safety, and (4) supervisor and physician encouragement. This research identified fundamental reasons why HCW get vaccinated and provides direction for future influenza vaccination programs. To enhance vaccine uptake, it is important to target HCW attitudes in influenza vaccine campaigns and create a culture of vaccine promotion in the workplace, including strong messaging from supervisors and physicians. Copyright © 2013 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

Stress concentration factors for circular, reinforced penetrations in pressurized cylindrical shells. Ph.D. Thesis - Virginia Univ.

NASA Technical Reports Server (NTRS)

Ramsey, J. W., Jr.

1975-01-01

The effect on stresses in a cylindrical shell with a circular penetration subject to internal pressure was investigated in thin, shallow linearly, elastic cylindrical shells. Results provide numerical predictions of peak stress concentration factors around nonreinforced and reinforced penetrations in pressurized cylindrical shells. Analytical results were correlated with published formulas, as well as theoretical and experimental results. An accuracy study was made of the finite element program for each of the configurations considered important in pressure vessel technology. A formula is developed to predict the peak stress concentration factor for analysis and/or design in conjunction with the ASME Boiler and Pressure Vessel Code.

Available clinical markers of treatment outcome integrated in mathematical models to guide therapy in HIV infection.

PubMed

Vergu, Elisabeta; Mallet, Alain; Golmard, Jean-Louis

2004-02-01

Because treatment failure in many HIV-infected persons may be due to multiple causes, including resistance to antiretroviral agents, it is important to better tailor drug therapy to individual patients. This improvement requires the prediction of treatment outcome from baseline immunological or virological factors, and from results of resistance tests. Here, we review briefly the available clinical factors that have an impact on therapy outcome, and discuss the role of a predictive modelling approach integrating these factors proposed in a previous work. Mathematical and statistical models could become essential tools to address questions that are difficult to study clinically and experimentally, thereby guiding decisions in the choice of individualized drug regimens.

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

NASA Astrophysics Data System (ADS)

Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

2017-07-01

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

Risk Factors for Postoperative Respiratory Mortality and Morbidity in Patients Undergoing Coronary Artery Bypass Grafting

PubMed Central

Rajaei, Samira; Dabbagh, Ali

2012-01-01

ABSTRACT Nowadays, coronary artery bypass grafting (CABG) is considered to be one of the most common surgical procedures. This procedure has been the main topic in many clinical research studies, which have assessed the effect of the procedure on patients’ outcomes. Like other surgical procedures, this procedure is also accompanied by a number of unwanted complications, including those of the respiratory system. Since the respiratory system plays an integral role in defining the clinical outcome of patients, improvements in studies that can assess and predict clinical outcomes of the respiratory system, assume greater importance. There are a number of predictive models which can assess patients in the preoperative period and introduce a number of risk factors, which could be considered as prognostic factors for patients undergoing CABG. The respiratory system is among the clinical systems that are assessed in many prediction scoring systems. This review assesses the main studies which have evaluated the possible risk factors for postoperative respiratory mortality and morbidity, in patients undergoing CABG. PMID:24223339

CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data

PubMed Central

O'Connor, Timothy; Bodén, Mikael

2017-01-01

Abstract Identifying the genomic regions and regulatory factors that control the transcription of genes is an important, unsolved problem. The current method of choice predicts transcription factor (TF) binding sites using chromatin immunoprecipitation followed by sequencing (ChIP-seq), and then links the binding sites to putative target genes solely on the basis of the genomic distance between them. Evidence from chromatin conformation capture experiments shows that this approach is inadequate due to long-distance regulation via chromatin looping. We present CisMapper, which predicts the regulatory targets of a TF using the correlation between a histone mark at the TF's bound sites and the expression of each gene across a panel of tissues. Using both chromatin conformation capture and differential expression data, we show that CisMapper is more accurate at predicting the target genes of a TF than the distance-based approaches currently used, and is particularly advantageous for predicting the long-range regulatory interactions typical of tissue-specific gene expression. CisMapper also predicts which TF binding sites regulate a given gene more accurately than using genomic distance. Unlike distance-based methods, CisMapper can predict which transcription start site of a gene is regulated by a particular binding site of the TF. PMID:28204599

Factors predicting labor induction success: a critical analysis.

PubMed

Crane, Joan M G

2006-09-01

Because of the risk of failed induction of labor, a variety of maternal and fetal factors as well as screening tests have been suggested to predict labor induction success. Certain characteristics of the woman (including parity, age, weight, height and body mass index), and of the fetus (including birth weight and gestational age) are associated with the success of labor induction; with parous, young women who are taller and lower weight having a higher rate of induction success. Fetuses with a lower birth weight or increased gestational age are also associated with increased induction success. The condition of the cervix at the start of induction is an important predictor, with the modified Bishop score being a widely used scoring system. The most important element of the Bishop score is dilatation. Other predictors, including transvaginal ultrasound (TVUS) and biochemical markers [including fetal fibronectin (fFN)] have been suggested. Meta-analyses of studies identified from MEDLINE, PubMed, and EMBASE and published from 1990 to October 2005 were performed evaluating the use of TVUS and fFN in predicting labor induction success in women at term with singleton gestations. Both TVUS and Bishop score predicted successful induction [likelihood ratio (LR)=1.82, 95% confidence interval (CI)=1.51-2.20 and LR=2.10, 95%CI=1.67-2.64, respectively]. As well, fFN and Bishop score predicted successful induction (LR=1.49, 95%CI=1.20-1.85, and LR=2.62, 95%CI=1.88-3.64, respectively). Although TVUS and fFN predicted successful labor induction, neither has been shown to be superior to Bishop score. Further research is needed to evaluate these potential predictors and insulin-like growth factor binding protein-1 (IGFBP-1), another potential biochemical marker.

Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence.

PubMed

Asadi, Hamed; Kok, Hong Kuan; Looby, Seamus; Brennan, Paul; O'Hare, Alan; Thornton, John

2016-12-01

To identify factors influencing outcome in brain arteriovenous malformations (BAVM) treated with endovascular embolization. We also assessed the feasibility of using machine learning techniques to prognosticate and predict outcome and compared this to conventional statistical analyses. A retrospective study of patients undergoing endovascular treatment of BAVM during a 22-year period in a national neuroscience center was performed. Clinical presentation, imaging, procedural details, complications, and outcome were recorded. The data was analyzed with artificial intelligence techniques to identify predictors of outcome and assess accuracy in predicting clinical outcome at final follow-up. One-hundred ninety-nine patients underwent treatment for BAVM with a mean follow-up duration of 63 months. The commonest clinical presentation was intracranial hemorrhage (56%). During the follow-up period, there were 51 further hemorrhagic events, comprising spontaneous hemorrhage (n = 27) and procedural related hemorrhage (n = 24). All spontaneous events occurred in previously embolized BAVMs remote from the procedure. Complications included ischemic stroke in 10%, symptomatic hemorrhage in 9.8%, and mortality rate of 4.7%. Standard regression analysis model had an accuracy of 43% in predicting final outcome (mortality), with the type of treatment complication identified as the most important predictor. The machine learning model showed superior accuracy of 97.5% in predicting outcome and identified the presence or absence of nidal fistulae as the most important factor. BAVMs can be treated successfully by endovascular techniques or combined with surgery and radiosurgery with an acceptable risk profile. Machine learning techniques can predict final outcome with greater accuracy and may help individualize treatment based on key predicting factors. Copyright © 2016 Elsevier Inc. All rights reserved.

Integrated CFD modeling of gas turbine combustors

NASA Technical Reports Server (NTRS)

Fuller, E. J.; Smith, C. E.

1993-01-01

3D, curvilinear, multi-domain CFD analysis is becoming a valuable tool in gas turbine combustor design. Used as a supplement to experimental testing. CFD analysis can provide improved understanding of combustor aerodynamics and used to qualitatively assess new combustor designs. This paper discusses recent advancements in CFD combustor methodology, including the timely integration of the design (i.e. CAD) and analysis (i.e. CFD) processes. Allied Signal's F124 combustor was analyzed at maximum power conditions. The assumption of turbulence levels at the nozzle/swirler inlet was shown to be very important in the prediction of combustor exit temperatures. Predicted exit temperatures were compared to experimental rake data, and good overall agreement was seen. Exit radial temperature profiles were well predicted, while the predicted pattern factor was 25 percent higher than the harmonic-averaged experimental pattern factor.

The relative importance of regional, local, and evolutionary factors structuring cryptobenthic coral-reef assemblages

NASA Astrophysics Data System (ADS)

Ahmadia, Gabby N.; Tornabene, Luke; Smith, David J.; Pezold, Frank L.

2018-03-01

Factors shaping coral-reef fish species assemblages can operate over a wide range of spatial scales (local versus regional) and across both proximate and evolutionary time. Niche theory and neutral theory provide frameworks for testing assumptions and generating insights about the importance of local versus regional processes. Niche theory postulates that species assemblages are an outcome of evolutionary processes at regional scales followed by local-scale interactions, whereas neutral theory presumes that species assemblages are formed by largely random processes drawing from regional species pools. Indo-Pacific cryptobenthic coral-reef fishes are highly evolved, ecologically diverse, temporally responsive, and situated on a natural longitudinal diversity gradient, making them an ideal group for testing predictions from niche and neutral theories and effects of regional and local processes on species assemblages. Using a combination of ecological metrics (fish density, diversity, assemblage composition) and evolutionary analyses (testing for phylogenetic niche conservatism), we demonstrate that the structure of cryptobenthic fish assemblages can be explained by a mixture of regional factors, such as the size of regional species pools and broad-scale barriers to gene flow/drivers of speciation, coupled with local-scale factors, such as the relative abundance of specific microhabitat types. Furthermore, species of cryptobenthic fishes have distinct microhabitat associations that drive significant differences in assemblage community structure between microhabitat types, and these distinct microhabitat associations are phylogenetically conserved over evolutionary timescales. The implied differential fitness of cryptobenthic fishes across varied microhabitats and the conserved nature of their ecology are consistent with predictions from niche theory. Neutral theory predictions may still hold true for early life-history stages, where stochastic factors may be more important in explaining recruitment. Overall, through integration of ecological and evolutionary techniques, and using multiple spatial scales, our study offers a unique perspective on factors determining coral-reef fish assemblages.

Lifestyle and Genetic Predictors of Stiffness Index in Community-dwelling Elderly Korean Men and Women.

PubMed

Park, Kyung-Ae; Park, Yeon-Hwan; Suh, Min-Hee; Choi-Kwon, Smi

2015-09-01

Differing lifestyle, nutritional, and genetic factors may lead to a differing stiffness index (SI) determined by quantitative ultrasound in elderly men and women. The purpose of this study was to determine SI and the gender-specific factors associated with low SI in a Korean elderly cohort. This was a cross-sectional descriptive study identifying the gender-specific factors related to SI in 252 men and women aged 65 years and greater from local senior centers in Seoul, Korea between January and February 2009. The mean SI of elderly men was significantly higher than that of the women's. A multiple regression analysis reveals that age, nutritional status, and physical activity were predictive factors of lower SI in men, whereas age, alcohol consumption, educational level, and genetic polymorphism were predictive factors for elderly women. Low SI was common in both elderly men and women. We found gender differences in factors linked to low SI. In multiple regression analysis, nutritional status and physical activity were more important factors in men, whereas alcohol consumption, educational level, and genetic polymorphism were significant factors predicting low SI in women. Gender-specific modifiable risk factors associated with low SI should be considered when developing osteoporosis prevention programs for the elderly. Copyright © 2015. Published by Elsevier B.V.

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Nellis, M. Duane

1996-01-01

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

Cryptic chytridiomycosis linked to climate and genetic variation in amphibian populations of the southeastern United States

PubMed Central

Hoffman, Eric A.; Tye, Matthew R.; Hether, Tyler D.; Savage, Anna E.

2017-01-01

North American amphibians have recently been impacted by two major emerging pathogens, the fungus Batrachochytrium dendrobatidis (Bd) and iridoviruses in the genus Ranavirus (Rv). Environmental factors and host genetics may play important roles in disease dynamics, but few studies incorporate both of these components into their analyses. Here, we investigated the role of environmental and genetic factors in driving Bd and Rv infection prevalence and severity in a biodiversity hot spot, the southeastern United States. We used quantitative PCR to characterize Bd and Rv dynamics in natural populations of three amphibian species: Notophthalmus perstriatus, Hyla squirella and Pseudacris ornata. We combined pathogen data, genetic diversity metrics generated from neutral markers, and environmental variables into general linear models to evaluate how these factors impact infectious disease dynamics. Occurrence, prevalence and intensity of Bd and Rv varied across species and populations, but only one species, Pseudacris ornata, harbored high Bd intensities in the majority of sampled populations. Genetic diversity and climate variables both predicted Bd prevalence, whereas climatic variables alone predicted infection intensity. We conclude that Bd is more abundant in the southeastern United States than previously thought and that genetic and environmental factors are both important for predicting amphibian pathogen dynamics. Incorporating both genetic and environmental information into conservation plans for amphibians is necessary for the development of more effective management strategies to mitigate the impact of emerging infectious diseases. PMID:28448517

Investigation of a Suicide Ideation Risk Profile in People With Co-occurring Depression and Substance Use Disorder.

PubMed

Handley, Tonelle E; Kay-Lambkin, Frances J; Baker, Amanda L; Lewin, Terry J; Kelly, Brian J; Inder, Kerry J; Attia, John R; Kavanagh, David J

2016-11-01

Disengagement from services is common before suicide, hence identifying factors at treatment presentation that predict future suicidality is important. This article explores risk profiles for suicidal ideation among treatment seekers with depression and substance misuse. Participants completed assessments at baseline and 6 months. Baseline demographics, psychiatric history, and current symptoms were entered into a decision tree to predict suicidal ideation at follow-up. Sixty-three percent of participants at baseline and 43.5% at follow-up reported suicidal ideation. Baseline ideation most salient when psychiatric illness began before adulthood, increasing the rate of follow-up ideation by 16%. Among those without baseline ideation, dysfunctional attitudes were the most important risk factor, increasing rates of suicidal ideation by 35%. These findings provide evidence of factors beyond initial diagnoses that increase the likelihood of suicidal ideation and are worthy of clinical attention. In particular, providing suicide prevention resources to those with high dysfunctional attitudes may be beneficial.

Perinatal and sociodemographic factors at birth predicting conduct problems and violence to age 18 years: comparison of Brazilian and British birth cohorts.

PubMed

Murray, Joseph; Maughan, Barbara; Menezes, Ana M B; Hickman, Matthew; MacLeod, John; Matijasevich, Alicia; Gonçalves, Helen; Anselmi, Luciana; Gallo, Erika A G; Barros, Fernando C

2015-08-01

Many low- and middle-income countries have high levels of violence. Research in high-income countries shows that risk factors in the perinatal period are significant precursors of conduct problems which can develop into violence. It is not known whether the same early influences are important in lower income settings with higher rates of violence. This study compared perinatal and sociodemographic risk factors between Brazil and Britain, and their role in explaining higher rates of conduct problems and violence in Brazil. Prospective population-based birth cohort studies were conducted in Pelotas, Brazil (N = 3,618) and Avon, Britain (N = 4,103). Eleven perinatal and sociodemographic risk factors were measured in questionnaires completed by mothers during the perinatal period. Conduct problems were measured in questionnaires completed by mothers at age 11, and violence in self-report questionnaires completed by adolescents at age 18. Conduct problems were predicted by similar risk factors in Brazil and Britain. Female violence was predicted by several of the same risk factors in both countries. However, male violence in Brazil was associated with only one risk factor, and several risk factor associations were weaker in Brazil than in Britain for both females and males. Almost 20% of the higher risk for conduct problems in Brazil compared to Britain was explained by differential exposure to risk factors. The percentage of the cross-national difference in violence explained by early risk factors was 15% for females and 8% for males. A nontrivial proportion of cross-national differences in antisocial behaviour are related to perinatal and sociodemographic conditions at the start of life. However, risk factor associations are weaker in Brazil than in Britain, and influences in other developmental periods are probably of particular importance for understanding male youth violence in Brazil. © 2014 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

PubMed

Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

2018-04-11

Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree and negative degree; (3) though the binary DDI network contains no information about enhancive and degressive DDIs at all, it implies some of their relationship in the comprehensive DDI matrix; (4) the occurrence of signs indicating enhancive and degressive DDIs is not random because the comprehensive DDI network is equipped with a structural balance.

Level and Change in Perceived Control Predict 19-Year Mortality: Findings from the Americans' Changing Lives Study

ERIC Educational Resources Information Center

Infurna, Frank J.; Ram, Nilam; Gerstorf, Denis

2013-01-01

Perceived control plays an important role for health across adulthood and old age. However, little is known about the factors that account for such associations and whether changes in control (or control trajectory) uniquely predict major health outcomes over and above mean levels of control. Using data from the nationwide Americans' Changing…

Using the Theory of Planned Behavior to Understand Cervical Cancer Screening among Latinas

ERIC Educational Resources Information Center

Roncancio, Angelica M.; Ward, Kristy K.; Sanchez, Ingrid A.; Cano, Miguel A.; Byrd, Theresa L.; Vernon, Sally W.; Fernandez-Esquer, Maria Eugenia; Fernandez, Maria E.

2015-01-01

To reduce the high incidence of cervical cancer among Latinas in the United States it is important to understand factors that predict screening behavior. The aim of this study was to test the utility of theory of planned behavior in predicting cervical cancer screening among a group of Latinas. A sample of Latinas (N = 614) completed a baseline…

Perceived Reasons for Living at Index Hospitalization and Future Suicide Attempt

PubMed Central

Lizardi, Dana; Currier, Diane; Galfalvy, Hanga; Sher, Leo; Burke, Ainsley; Mann, John; Oquendo, Maria

2013-01-01

It is unclear why certain individuals choose not to engage in suicidal behavior. Although important, protective factors against suicidal behavior have seldom been studied. The Reasons for Living Inventory is a measure of putative protective factors that is inversely related to a history of suicide attempts, but its predictive utility remains relatively untested. This study sought to determine whether the Reasons for Living Inventory predicts future suicide attempts over a 2-year period. Depressed inpatients were assessed for reasons for living and were followed for 2 years. Follow-up interviews took place at 3 months, 1 year, and 2 years after discharge from the index hospitalization. Survival analysis indicates a high score on the Reasons for Living Inventory predicted fewer future suicide attempts within a 2-year period in women but not in men. Perceived reasons for living serve as protective factors against suicide attempt in women and not in men. PMID:17502812

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing, China.

PubMed

Xu, Chunyu; Li, Na; Yang, Yibing; Li, Yunpu; Liu, Zhe; Wang, Qin; Zheng, Tongzhang; Civitarese, Anna; Xu, Dongqun

2017-06-01

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM 2.5 ) and to develop models to predict PM 2.5 Finf in Beijing. Eighty-eight paired indoor-outdoor PM 2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM 2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM 2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM 2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM 2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM 2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification. Failure to consider the differences between indoor and outdoor PM 2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM 2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM 2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM 2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM 2.5 infiltration factor were also developed.

Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees.

PubMed

Cheong, Yoon Ling; Leitão, Pedro J; Lakes, Tobia

2014-07-01

The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Effects of Residency and Body Size on Contest Initiation and Outcome in the Territorial Dragon, Ctenophorus decresii

PubMed Central

Umbers, Kate D. L.; Osborne, Louise; Keogh, J. Scott

2012-01-01

Empirical studies of the determinants of contests have been attempting to unravel the complexity of animal contest behaviour for decades. This complexity requires that experiments incorporate multiple determinants into studies to tease apart their relative effects. In this study we examined the complex contest behaviour of the tawny dragon (Ctenophorus decresii), a territorial agamid lizard, with the specific aim of defining the factors that determine contest outcome. We manipulated the relative size and residency status of lizards in contests to weight their importance in determining contest outcome. We found that size, residency and initiating a fight were all important in determining outcomes of fights. We also tested whether residency or size was important in predicting the status of lizard that initiated a fight. We found that residency was the most important factor in predicting fight initiation. We discuss the effects of size and residency status in context of previous studies on contests in tawny dragons and other animals. Our study provides manipulative behavioural data in support of the overriding effects of residency on initiation fights and winning them. PMID:23077558

Recent advances in recurrent urinary tract infection from pathogenesis and biomarkers to prevention.

PubMed

Jhang, Jia-Fong; Kuo, Hann-Chorng

2017-01-01

Recurrent urinary tract infection (UTI) might be one of the most common problems in urological clinics. Recent research has revealed novel evidence about recurrent UTI and it should be considered a different disease from the first infection. The pathogenesis of recurrent UTI might include two mechanisms, bacterial factors and deficiencies in host defense. Bacterial survival in the urinary bladder after antibiotic treatment and progression to form intracellular bacterial communities might be the most important bacterial factors. In host defense deficiency, a defect in pathogen recognition and urothelial barrier function impairment play the most important roles. Immunodeficiency and urogenital tract anatomical abnormalities have been considered the essential risk factors for recurrent UTI. In healthy women, voiding dysfunction and behavioral factors also increase the risk of recurrent UTI. Sexual intercourse and estrogen deficiency in postmenopausal women might have the strongest association with recurrent UTI. Traditional lifestyle factors such as fluid intake and diet are not considered independent risk factors now. Serum and urine biomarkers to predict recurrent UTI from the first infection have also attracted a wide attention recently. Current clinical evidence suggests that serum macrophage colony-stimulating factor and urinary nerve growth factor have potential predictive value for recurrent UTI. Clinical trials have proven the efficacy of the oral immunoactive agent OM-89 for the prevention of UTI. Vaccines for recurrent UTI are recommended by the latest guidelines and are available on the market.

Predicting self-care behaviours of patients with type 2 diabetes: the importance of beliefs about behaviour, not just beliefs about illness.

PubMed

French, David P; Wade, Alisha N; Farmer, Andrew J

2013-04-01

There is evidence that perceptions of treatment may be more predictive than illness perceptions, e.g. medication adherence is often better predicted by beliefs about medication than by beliefs about illness. The present study aims to assess the generality of this finding, by comparing the extent to which self-care behaviours of patients with type 2 diabetes are predicted by patients' beliefs about those behaviours, compared with their illness perceptions. This study is a one year prospective cohort analysis of 453 patients recruited to a randomised trial of blood glucose self-monitoring. Behaviour was assessed by the medication adherence report scale (MARS) and diabetes self-care activities (DSCA) scales; illness perceptions by IPQ-R; study-specific scales of beliefs about diet and physical activity were constructed by factor analysing items based on beliefs elicited in an earlier interview study involving patients with type 2 diabetes. Past behaviour, trial group allocation, and clinical and demographic factors predicted between 16% and 35% variance in medication adherence, exercise, and diet scales. Illness perceptions added between 0.9% and 4.5% additional variance; beliefs about behaviour added a further 1.1% to 6.4% additional variance. Beliefs regarding, respectively, the importance of exercise in controlling diabetes, the need to east less, and enjoyment from eating sweet or fatty food, added unique variance. Beliefs about behaviour are at least as important as beliefs about illness in predicting several health-related behaviours. This suggests the possibility that behaviour change interventions with patient groups would be more effective by targeting beliefs about behaviour, rather than beliefs about illness. Copyright © 2012 Elsevier Inc. All rights reserved.

Use of factor scores for predicting body weight from linear body measurements in three South African indigenous chicken breeds.

PubMed

Malomane, Dorcus Kholofelo; Norris, David; Banga, Cuthbert B; Ngambi, Jones W

2014-02-01

Body weight and weight of body parts are of economic importance. It is difficult to directly predict body weight from highly correlated morphological traits through multiple regression. Factor analysis was carried out to examine the relationship between body weight and five linear body measurements (body length, body girth, wing length, shank thickness, and shank length) in South African Venda (VN), Naked neck (NN), and Potchefstroom koekoek (PK) indigenous chicken breeds, with a view to identify those factors that define body conformation. Multiple regression was subsequently performed to predict body weight, using orthogonal traits derived from the factor analysis. Measurements were obtained from 210 chickens, 22 weeks of age, 70 chickens per breed. High correlations were obtained between body weight and all body measurements except for wing length in PK. Two factors extracted after varimax rotation explained 91, 95, and 83% of total variation in VN, NN, and PK, respectively. Factor 1 explained 73, 90, and 64% in VN, NN, and PK, respectively, and was loaded on all body measurements except for wing length in VN and PK. In a multiple regression, these two factors accounted for 72% variation in body weight in VN, while only factor 1 accounted for 83 and 74% variation in body weight in NN and PK, respectively. The two factors could be used to define body size and conformation of these breeds. Factor 1 could predict body weight in all three breeds. Body measurements can be better selected jointly to improve body weight in these breeds.

Saturday night's alright for fighting: antisocial traits, fighting, and weapons carrying in a large sample of youth.

PubMed

Ferguson, Christopher J; Cricket Meehan, D

2010-12-01

The current study examines risk and protective factors for youth antisocial personality and behavior from a multivariate format. It is hoped that this research will elucidate those risk and protective factors most important for focus of future prevention and intervention efforts. The current study examines multiple factors associated with youth antisocial traits and behavior in a sample of 8,256 youth (mean age 14), with the goal of identifying the strongest and most consistent risk or protective factors. Data was collected from the Ohio version of the Youth Risk Behavior Surveillance System's (YRBSS) school-based Youth Risk Behavior Survey (YRBS) developed by the Centers for Disease Control (CDC). Hierarchical multiple regression analyses identified peer delinquency, drug use and negative community influences as predictive of antisocial traits. Schools and families functioned as protective factors. Youth who fought frequently tended to be male, antisocial, dug using, depressed, and associated with delinquent peers. Weapons carrying was most common among drug using, antisocial males. Television and video game use were not predictive of antisocial, fighting or weapons carrying outcomes. Developmental patterns across age ranges regarding the relative importance of specific risk factors were also examined. Strategies for intervention and prevention of youth violence that focus on peers, neighborhoods, depression, and families may be particularly likely to bear fruit.

Heart rate variability as predictive factor for sudden cardiac death.

PubMed

Sessa, Francesco; Anna, Valenzano; Messina, Giovanni; Cibelli, Giuseppe; Monda, Vincenzo; Marsala, Gabriella; Ruberto, Maria; Biondi, Antonio; Cascio, Orazio; Bertozzi, Giuseppe; Pisanelli, Daniela; Maglietta, Francesca; Messina, Antonietta; Mollica, Maria P; Salerno, Monica

2018-02-23

Sudden cardiac death (SCD) represents about 25% of deaths in clinical cardiology. The identification of risk factors for SCD is the philosopher's stone of cardiology and the identification of non-invasive markers of risk of SCD remains one of the most important goals for the scientific community.The aim of this review is to analyze the state of the art around the heart rate variability (HRV) as a predictor factor for SCD.HRV is probably the most analyzed index in cardiovascular risk stratification technical literature, therefore an important number of models and methods have been developed.Nowadays, low HRV has been shown to be independently predictive of increased mortality in post- myocardial infarction patients, heart failure patients, in contrast with the data of the general population.Contrariwise, the relationship between HRV and SCD has received scarce attention in low-risk cohorts. Furthermore, in general population the attributable risk is modest and the cost/benefit ratio is not always convenient.The HRV evaluation could become an important tool for health status in risks population, even though the use of HRV alone for risk stratification of SCD is limited and further studies are needed.

[Quality of life in ankylosing spondylitis].

PubMed

Younes, Mohamed; Jalled, Anis; Aydi, Zohra; Younes, Kaouthar; Jguirim, Mahbouba; Zrour, Saoussen; Ben Salah, Zohra; Bejia, Ismail; Touzi, Mongi; Bergaoui, Naceur

2011-04-01

Ankylosing Spondylarthritis (AS) involves by its frequency and its repercussion on the functional capacity an important handicap and deterioration of the patients quality of life. To evaluate the handicap and the quality of life during the AS and to seek the predictive factors of the deterioration of this quality of life. A prospective study relating to 50 patients recruited in the Department of Rheumatology of F. B. Hospital of Monastir during 6 months period (Mars to September 2008). The studied parameters were the quality of life evaluated by a specific sore (ASQOL) and a generic score (SF-12). Also the physical, social and economic felt handicap was evaluated using a qualitative scale. Predictive factors (clinical, biological and radiological) of the quality of life were carried out. Our patients are divided in 42 men and 8 women with an average age of 38.9 ± 10.7 years. The average duration of AS is of 11.9 ± 7.6 years. The average of ASQOL is of 11.9 ± 4 (extremes: 0- 17). The average of physical SF12 is of 29.8 ± 6 (21.7-53.2) and of mental SF-12 of 35.3 ± 6.6 (22.5-55.8). The physical, social and economic felt handicap was considered to be average or important in respectively 88%, 72% and 86% of the cases. The predictive factors of a high ASQOL (faded quality of life) are absence of occupation, high BASMI, a high number of painful articulations and high BASFI, BASDAI, BASG, BASRI and EVA total pain. The factors associated to the alteration of the quality of life according to SF-12'S are male sex, professional statute, high number of painful articulations and high BASDAI, BASFI and BASRI. Our study shows the important deterioration of the quality of life in AS patients. The existence of the predictive factors of quality of life primarily related to the functional capacity of the patients and to the disease activity implicates an early and adequate disease management in order to decrease this repercussion.

A New Criterion for Prediction of Hot Tearing Susceptibility of Cast Alloys

NASA Astrophysics Data System (ADS)

Nasresfahani, Mohamad Reza; Niroumand, Behzad

2014-08-01

A new criterion for prediction of hot tearing susceptibility of cast alloys is suggested which takes into account the effects of both important mechanical and metallurgical factors and is believed to be less sensitive to the presence of volume defects such as bifilms and inclusions. The criterion was validated by studying the hot tearing tendency of Al-Cu alloy. In conformity with the experimental results, the new criterion predicted reduction of hot tearing tendency with increasing the copper content.

Analyzing of economic growth based on electricity consumption from different sources

NASA Astrophysics Data System (ADS)

Maksimović, Goran; Milosavljević, Valentina; Ćirković, Bratislav; Milošević, Božidar; Jović, Srđan; Alizamir, Meysam

2017-10-01

Economic growth could be influenced by different factors. In this study was analyzed the economic growth based on the electricity consumption form different sources. As economic growth indicator gross domestic product (GDP) was used. ANFIS (adaptive neuro fuzzy inference system) methodology was applied to determine the most important factors from the given set for the GDP growth prediction. Six inputs were used: electricity production from coal, hydroelectric, natural gas, nuclear, oil and renewable sources. Results shown that the electricity consumption from renewable sources has the highest impact on the economic or GDP growth prediction.

Linking extinction-colonization dynamics to genetic structure in a salamander metapopulation.

PubMed

Cosentino, Bradley J; Phillips, Christopher A; Schooley, Robert L; Lowe, Winsor H; Douglas, Marlis R

2012-04-22

Theory predicts that founder effects have a primary role in determining metapopulation genetic structure. However, ecological factors that affect extinction-colonization dynamics may also create spatial variation in the strength of genetic drift and migration. We tested the hypothesis that ecological factors underlying extinction-colonization dynamics influenced the genetic structure of a tiger salamander (Ambystoma tigrinum) metapopulation. We used empirical data on metapopulation dynamics to make a priori predictions about the effects of population age and ecological factors on genetic diversity and divergence among 41 populations. Metapopulation dynamics of A. tigrinum depended on wetland area, connectivity and presence of predatory fish. We found that newly colonized populations were more genetically differentiated than established populations, suggesting that founder effects influenced genetic structure. However, ecological drivers of metapopulation dynamics were more important than age in predicting genetic structure. Consistent with demographic predictions from metapopulation theory, genetic diversity and divergence depended on wetland area and connectivity. Divergence was greatest in small, isolated wetlands where genetic diversity was low. Our results show that ecological factors underlying metapopulation dynamics can be key determinants of spatial genetic structure, and that habitat area and isolation may mediate the contributions of drift and migration to divergence and evolution in local populations.

Diabetes and Hypertension Consistently Predict the Presence and Extent of Coronary Artery Calcification in Symptomatic Patients: A Systematic Review and Meta-Analysis

PubMed Central

Nicoll, Rachel; Zhao, Ying; Ibrahimi, Pranvera; Olivecrona, Gunilla; Henein, Michael

2016-01-01

Background: The relationship of conventional cardiovascular risk factors (age, gender, ethnicity, diabetes, dyslipidaemia, hypertension, obesity, exercise, and the number of risk factors) to coronary artery calcification (CAC) presence and extent has never before been assessed in a systematic review and meta-analysis. Methods: We included only English language studies that assessed at least three conventional risk factors apart from age, gender, and ethnicity, but excluded studies in which all patients had another confirmed condition such as renal disease. Results: In total, 10 studies, comprising 15,769 patients, were investigated in the systematic review and seven studies, comprising 12,682 patients, were included in the meta-analysis, which demonstrated the importance of diabetes and hypertension as predictors of CAC presence and extent, with age also predicting CAC presence. Male gender, dyslipidaemia, family history of coronary artery disease, obesity, and smoking were overall not predictive of either CAC presence or extent, despite dyslipidaemia being a key risk factor for coronary artery disease (CAD). Conclusion: Diabetes and hypertension consistently predict the presence and extent of CAC in symptomatic patients. PMID:27608015

Different Fit Perceptions in an Academic Environment: Attitudinal and Behavioral Outcomes

ERIC Educational Resources Information Center

Li, Yixuan; Yao, Xiang; Chen, Kun; Wang, Yi

2013-01-01

This study examines whether students perceive three different types of fit in an academic environment (i.e., interest-major [I-M] fit, demands-abilities [D-A] fit, and needs-supplies [N-S] fit) and whether these factors predict important academic and well-being criteria using a Chinese student sample. Results from confirmatory factor analyses…

Predicting General Academic Performance and Identifying the Differential Contribution of Participating Variables Using Artificial Neural Networks

ERIC Educational Resources Information Center

Musso, Mariel F.; Kyndt, Eva; Cascallar, Eduardo C.; Dochy, Filip

2013-01-01

Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students'…

Revealing Word Order: Using Serial Position in Binomials to Predict Properties of the Speaker

ERIC Educational Resources Information Center

Iliev, Rumen; Smirnova, Anastasia

2016-01-01

Three studies test the link between word order in binomials and psychological and demographic characteristics of a speaker. While linguists have already suggested that psychological, cultural and societal factors are important in choosing word order in binomials, the vast majority of relevant research was focused on general factors and on broadly…

Social cure, what social cure? The propensity to underestimate the importance of social factors for health.

PubMed

Haslam, S Alexander; McMahon, Charlotte; Cruwys, Tegan; Haslam, Catherine; Jetten, Jolanda; Steffens, Niklas K

2018-02-01

Recent meta-analytic research indicates that social support and social integration are highly protective against mortality, and that their importance is comparable to, or exceeds, that of many established behavioural risks such as smoking, high alcohol consumption, lack of exercise, and obesity that are the traditional focus of medical research (Holt-Lunstad et al., 2010). The present study examines perceptions of the contribution of these various factors to life expectancy within the community at large. American and British community respondents (N = 502) completed an on-line survey assessing the perceived importance of social and behavioural risk factors for mortality. As hypothesized, while respondents' perceptions of the importance of established behavioural risks was positively and highly correlated with their actual importance, social factors were seen to be far less important for health than they actually are. As a result, overall, there was a small but significant negative correlation between the perceived benefits and the actual benefits of different social and behavioural factors. Men, younger participants, and participants with a lower level of education were more likely to underestimate the importance of social factors for health. There was also evidence that underestimation was predicted by a cluster of ideological factors, the most significant of which was respondents' respect for prevailing convention and authorities as captured by Right-Wing Authoritarianism. Findings suggest that while people generally underestimate the importance of social factors for health this also varies as a function of demographic and ideological factors. They point to a range of challenges confronting those who seek to promote greater awareness of the importance of social factors for health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol.

PubMed

Jerosch-Herold, Christina; Shepstone, Lee; Wilson, Edward C F; Dyer, Tony; Blake, Julian

2014-02-07

Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions.

Predictive model of thrombospondin-1 and vascular endothelial growth factor in breast tumor tissue.

PubMed

Rohrs, Jennifer A; Sulistio, Christopher D; Finley, Stacey D

2016-01-01

Angiogenesis, the formation of new blood capillaries from pre-existing vessels, is a hallmark of cancer. Thus far, strategies for reducing tumor angiogenesis have focused on inhibiting pro-angiogenic factors, while less is known about the therapeutic effects of mimicking the actions of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important endogenous inhibitor of angiogenesis that has been investigated as an anti-angiogenic agent. TSP1 impedes the growth of new blood vessels in many ways, including crosstalk with pro-angiogenic factors. Due to the complexity of TSP1 signaling, a predictive systems biology model would provide quantitative understanding of the angiogenic balance in tumor tissue. Therefore, we have developed a molecular-detailed, mechanistic model of TSP1 and vascular endothelial growth factor (VEGF), a promoter of angiogenesis, in breast tumor tissue. The model predicts the distribution of the angiogenic factors in tumor tissue, revealing that TSP1 is primarily in an inactive, cleaved form due to the action of proteases, rather than bound to its cellular receptors or to VEGF. The model also predicts the effects of enhancing TSP1's interactions with its receptors and with VEGF. To provide additional predictions that can guide the development of new anti-angiogenic drugs, we simulate administration of exogenous TSP1 mimetics that bind specific targets. The model predicts that the CD47-binding TSP1 mimetic dramatically decreases the ratio of receptor-bound VEGF to receptor-bound TSP1, in favor of anti-angiogenesis. Thus, we have established a model that provides a quantitative framework to study the response to TSP1 mimetics.

Predictive factors of tumor control and survival after radiosurgery for local failures of nasopharyngeal carcinoma.

PubMed

Chua, Daniel T T; Sham, Jonathan S T; Hung, Kwan-Ngai; Leung, Lucullus H T; Au, Gordon K H

2006-12-01

Stereotactic radiosurgery has been employed as a salvage treatment of local failures of nasopharyngeal carcinoma (NPC). To identify patients that would benefit from radiosurgery, we reviewed our data with emphasis on factors that predicted treatment outcome. A total of 48 patients with local failures of NPC were treated by stereotactic radiosurgery between March 1996 and February 2005. Radiosurgery was administered using a modified linear accelerator with single or multiple isocenters to deliver a median dose of 12.5 Gy to the target periphery. Median follow-up was 54 months. Five-year local failure-free probability after radiosurgery was 47.2% and 5-year overall survival rate was 46.9%. Neuroendocrine complications occurred in 27% of patients but there were no treatment-related deaths. Time interval from primary radiotherapy, retreatment T stage, prior local failures and tumor volume were significant predictive factors of local control and/or survival whereas age was of marginal significance in predicting survival. A radiosurgery prognostic scoring system was designed based on these predictive factors. Five-year local failure-free probabilities in patients with good, intermediate and poor prognostic scores were 100%, 42.5%, and 9.6%. The corresponding five-year overall survival rates were 100%, 51.1%, and 0%. Important factors that predicted tumor control and survival after radiosurgery were identified. Patients with good prognostic score should be treated by radiosurgery in view of the excellent results. Patients with intermediate prognostic score may also be treated by radiosurgery but those with poor prognostic score should receive other salvage treatments.

Parental aggression as a predictor of boys’ hostile attribution across the transition to middle school

PubMed Central

Yaros, Anna; Lochman, John E.; Wells, Karen C.

2015-01-01

Aggression among youth is public health problem that is often studied in the context of how youth interpret social information. Social cognitive factors, especially hostile attribution biases, have been identified as risk factors for the development of youth aggression, particularly across the transition to middle school. Parental behaviors, including parental aggression to children in the form of corporal punishment and other aggressive behavior, have also been linked to aggressive behavior in children at these ages. Despite the important role played by these two risk factors, the connection between the two has not been fully studied in the literature. This study examined the link between parental aggression and children’ hostile attributions longitudinally among a diverse sample of 123 boys as they entered middle school. Results support acceptance of a model in which parental aggression to children prior to entering middle school predicted children’s hostile attributions after the transition to middle school above and beyond that which was predicted by previous levels of hostile attributions. As expected, hostile attributions also predicted change in parent- and teacher-rated child aggression. These findings provides important evidence of the role that parental behavior plays in youth social cognition at this critical age, which has implications for understanding the development of aggressive behavior. PMID:27647945

Initial evidence that polymorphisms in neurotransmitter-regulating genes contribute to being born small for gestational age

PubMed Central

Morgan, Angharad R.; Thompson, John M.D.; Waldie, Karen E.; Cornforth, Christine M.; Turic, Darko; Sonuga-Barke, Edmund J.S.; Lam, Wen-Jiun; Ferguson, Lynnette R.; Mitchell, Edwin A.

2012-01-01

Being born small for gestational age (SGA) is a putative risk factor for the development of later cognitive and psychiatric health problems. While the inter-uterine environment has been shown to play an important role in predicting birth weight, little is known about the genetic factors that might be important. Here we test the hypothesis that neurotransmitter-regulating genes implicated in psychiatric disorders previously shown to be associated with SGA (such as attention-deficit hyperactivity disorder) are themselves predictive of SGA. DNA was collected from 227 SGA and 319 appropriate for gestational age children taking part in the Auckland Birthweight Collaborative Study. Candidate single nucleotide polymorphisms in genes regulating activity within dopamine, serotonin, glutamate and gamma-aminobutyric acid pathways were genotyped. Multiple regression analysis, controlling for potentially confounding factors, supported nominally significant associations between SGA and single nucleotide polymorphisms in COMT, HTR2A, SLC1A1 and SLC6A1. This is the first evidence that genes implicated in psychiatric disorders previously linked to SGA status themselves predict SGA. This highlights the possibility that the link between SGA and psychiatric disorders such as attention-deficit hyperactivity disorder may in part be genetically determined – that SGA marks pre-existing genetic risk for later problems. PMID:27625810

Cognitive and emotional factors predicting decisional conflict among high-risk breast cancer survivors who receive uninformative BRCA1/2 results.

PubMed

Rini, Christine; O'Neill, Suzanne C; Valdimarsdottir, Heiddis; Goldsmith, Rachel E; Jandorf, Lina; Brown, Karen; DeMarco, Tiffani A; Peshkin, Beth N; Schwartz, Marc D

2009-09-01

To investigate high-risk breast cancer survivors' risk reduction decision making and decisional conflict after an uninformative BRCA1/2 test. Prospective, longitudinal study of 182 probands undergoing BRCA1/2 testing, with assessments 1-, 6-, and 12-months postdisclosure. Primary predictors were health beliefs and emotional responses to testing assessed 1-month postdisclosure. Main outcomes included women's perception of whether they had made a final risk management decision (decision status) and decisional conflict related to this issue. There were four patterns of decision making, depending on how long it took women to make a final decision and the stability of their decision status across assessments. Late decision makers and nondecision makers reported the highest decisional conflict; however, substantial numbers of women--even early and intermediate decision makers--reported elevated decisional conflict. Analyses predicting decisional conflict 1- and 12-months postdisclosure found that, after accounting for control variables and decision status, health beliefs and emotional factors predicted decisional conflict at different timepoints, with health beliefs more important 1 month after test disclosure and emotional factors more important 1 year later. Many of these women may benefit from decision making assistance. Copyright 2009 APA, all rights reserved.

Toward a cumulative ecological risk model for the etiology of child maltreatment

PubMed Central

MacKenzie, Michael J.; Kotch, Jonathan B.; Lee, Li-Ching

2011-01-01

The purpose of the current study was to further the integration of cumulative risk models with empirical research on the etiology of child maltreatment. Despite the well-established literature supporting the importance of the accumulation of ecological risk, this perspective has had difficulty infiltrating empirical maltreatment research and its tendency to focus on more limited risk factors. Utilizing a sample of 842 mother-infant dyads, we compared the capacity of individual risk factors and a cumulative index to predict maltreatment reports in a prospective longitudinal investigation over the first sixteen years of life. The total load of risk in early infancy was found to be related to maternal cognitions surrounding her new role, measures of social support and well-being, and indicators of child cognitive functioning. After controlling for total level of cumulative risk, most single factors failed to predict later maltreatment reports and no single variable provided odd-ratios as powerful as the predictive power of a cumulative index. Continuing the shift away from simplistic causal models toward an appreciation for the cumulative nature of risk would be an important step forward in the way we conceptualize intervention and support programs, concentrating them squarely on alleviating the substantial risk facing so many of society’s families. PMID:24817777

Research on Fault Rate Prediction Method of T/R Component

NASA Astrophysics Data System (ADS)

Hou, Xiaodong; Yang, Jiangping; Bi, Zengjun; Zhang, Yu

2017-07-01

T/R component is an important part of the large phased array radar antenna array, because of its large numbers, high fault rate, it has important significance for fault prediction. Aiming at the problems of traditional grey model GM(1,1) in practical operation, the discrete grey model is established based on the original model in this paper, and the optimization factor is introduced to optimize the background value, and the linear form of the prediction model is added, the improved discrete grey model of linear regression is proposed, finally, an example is simulated and compared with other models. The results show that the method proposed in this paper has higher accuracy and the solution is simple and the application scope is more extensive.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

[From "deadly quartet" to "metabolic syndrome". An analysis of its clinical relevance].

PubMed

Vancheri, Federico; Burgio, Antonio; Dovico, Rossana

2007-03-01

The metabolic syndrome denotes a clustering of specific risk factors for both cardiovascular disease and type 2 diabetes, whose underlying pathophysiology is believed to include insulin resistance. It has been widely reported that the syndrome is a simple clinical tool to identify people at high long term risk of cardiovascular disease and diabetes. However, its clinical importance is under debate. There are substantial uncertainties about the clinical definition of the syndrome, as to whether the risk factors clustering indicates a single unifying disorder, whether the risk conferred by the condition as a whole is higher risk than its individual components, and whether its predictive value of future cardiovascular events or diabetes is greater than established predicting models such as the Framingham Risk Score and the Diabetes Risk Score. We undertook an extensive review of the literature. Our analysis indicates that current definitions of the syndrome are incomplete or ambiguous, more than one pathophysiological process underlies the syndrome, although the combination of insulin resistance and hyperinsulinemia are related to most cases; the risk associated with the syndrome is no greater than that explained by the presence of its components, and the syndrome is less effective in predicting the future development of cardiovascular events and diabetes than established predicting models. Although the syndrome has some importance in understanding the pathophysiology of cardiovascular and diabetes risk factors clustering, its use as a clinical syndrome is not justified by current data.

Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

PubMed

Barlett, Christopher P

2015-06-01

The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Relationship between amputation and risk factors in individuals with diabetes mellitus: A study with Brazilian patients.

PubMed

Mantovani, Alessandra M; Fregonesi, Cristina E P T; Palma, Mariana R; Ribeiro, Fernanda E; Fernandes, Rômulo A; Christofaro, Diego G D

Individuals with diabetes develop lower extremity amputation for several reasons. Investigations into pathways to the development of complications are important both for treatment and prevention. To evaluate the relationship between amputation and risk factors in people with diabetes mellitus. All participants included in this study (n=165) were recruited from the Diabetic Foot Program, developed in a Brazilian University, over seven years (2007-2014) and all information for this study was extracted from their clinical records. The prevalence of amputation in patients with diabetes with four risk factors was up to 20% higher when compared to those with only one risk factor. The main predictive risk factors for amputation in this population were the presence of an ulcer and smoking. The risk factors for amputation can be predicted for people with diabetes mellitus and, in the present study, the main factors were the presence of an ulcer and the smoking habit. Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.

Predicting responses to climate change requires all life-history stages.

PubMed

Zeigler, Sara

2013-01-01

In Focus: Radchuk, V., Turlure, C. & Schtickzelle, N. (2013) Each life stage matters: the importance of assessing response to climate change over the complete life cycle in butterflies. Journal of Animal Ecology, 82, 275-285. Population-level responses to climate change depend on many factors, including unexpected interactions among life history attributes; however, few studies examine climate change impacts over complete life cycles of focal species. Radchuk, Turlure & Schtickzelle () used experimental and modelling approaches to predict population dynamics for the bog fritillary butterfly under warming scenarios. Although they found that warming improved fertility and survival of all stages with one exception, populations were predicted to decline because overwintering larvae, whose survival declined with warming, were disproportionately important contributors to population growth. This underscores the importance of considering all life history stages in analyses of climate change's effects on population dynamics. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Computational modeling of human oral bioavailability: what will be next?

PubMed

Cabrera-Pérez, Miguel Ángel; Pham-The, Hai

2018-06-01

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Psycholegal abilities and restoration of competence to stand trial.

PubMed

Morris, Douglas R; Deyoung, Nathaniel J

2012-01-01

Criminal defendants adjudicated incompetent to stand trial are typically hospitalized for competence restoration in state institutions. Prolonged restoration hospitalizations involve civil rights concerns and increasing financial costs, and there remains interest in determining which individuals are likely to be successfully restored. We retrospectively reviewed hospital records of 455 male defendants admitted to a forensic treatment center for competence restoration in an effort to determine whether psychiatric diagnoses, demographic factors, or psycholegal abilities were predictive of successful or failed restoration. At varying stages of restoration efforts, psychotic disorder, mental retardation, and previous state hospitalization predicted unsuccessful restoration, while substance use and personality disorders were predictive of successful restoration. Psycholegal abilities were predictive of successful restoration and appeared to form a continuum, with basic behavior and outlook, factual legal understanding, and rational attorney assistance factors demonstrating progressively increased importance in successful restoration. Copyright © 2012 John Wiley & Sons, Ltd.

Factors Predicting Ethiopian Anesthetists' Intention to Leave Their Job.

PubMed

Kols, Adrienne; Kibwana, Sharon; Molla, Yohannes; Ayalew, Firew; Teshome, Mihereteab; van Roosmalen, Jos; Stekelenburg, Jelle

2018-05-01

Ethiopia has rapidly expanded training programs for associate clinician anesthetists in order to address shortages of anesthesia providers. However, retaining them in the public health sector has proven challenging. This study aimed to determine anesthetists' intentions to leave their jobs and identify factors that predict turnover intentions. A nationally representative, cross-sectional survey of 251 anesthetists working in public-sector hospitals in Ethiopia was conducted in 2014. Respondents were asked whether they planned to leave the job in the next year and what factors they considered important when making decisions to quit. Bivariate and multivariable logistic regressions were conducted to investigate 16 potential predictors of turnover intentions, including personal and facility characteristics as well as decision-making factors. Almost half (n = 120; 47.8%) of anesthetists planned to leave their jobs in the next year, and turnover intentions peaked among those with 2-5 years of experience. Turnover intentions were not associated with the compulsory service obligation. Anesthetists rated salary and opportunities for professional development as the most important factors in decisions to quit. Five predictors of turnover intentions were significant in the multivariable model: younger age, working at a district rather than regional or referral hospital, the perceived importance of living conditions, opportunities for professional development, and conditions at the workplace. Human resources strategies focused on improving living conditions for anesthetists and expanding professional development opportunities may increase retention. Special attention should be focused on younger anesthetists and those posted at district hospitals.

Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence.

PubMed

Tseng, Chih-Jen; Lu, Chi-Jie; Chang, Chi-Chang; Chen, Gin-Den; Cheewakriangkrai, Chalong

2017-05-01

Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting school sense of community: students' perceptions at two Catholic universities.

PubMed

Bottom, Todd L; Ferrari, Joseph R; Matteo, Elizabeth; Todd, Nathan R

2013-01-01

Understanding the factors that predict sense of community (SOC) among college students has important implications for higher education policy and practice. The present study determined whether perceptions of inclusion and religious pluralism across 2,199 university students' (1,442 women, 757 men; M age = 23.42, SD =7.84) at two Catholic universities predicted levels of school sense of community (SSOC). As expected, results indicated that perceptions of both inclusion and religious pluralism significantly predicted SSOC. However, mixed results were found regarding the interaction of university setting with inclusion and religious pluralism. Limitations and future directions for research are discussed.

Memory for Emotional Experiences in the Context of Attachment and Social Interaction Style

ERIC Educational Resources Information Center

Alexander, Kristen Weede; O'Hara, Karen Davis; Bortfeld, Heidi V.; Anderson, Summerlynn J.; Newton, Emily K.; Kraft, Rosemarie H.

2010-01-01

Important dimensions of emotional experiences include the level of arousal elicited and the source of that arousal, yet memory for events differing on these constructs is often compared within and across studies. One important factor for emotional memory is attachment security, which predicts how parents and children relate to each other and to…

Some Simple Solutions to the Problem of Predicting Boundary-Layer Self-Induced Pressures

NASA Technical Reports Server (NTRS)

Bertram, Mitchel H.; Blackstock, Thomas A.

1961-01-01

Simplified theoretical approaches are shown, based on hypersonic similarity boundary-layer theory, which allow reasonably accurate estimates to be made of the surface pressures on plates on which viscous effects are important. The consideration of viscous effects includes the cases where curved surfaces, stream pressure gradients, and leadingedge bluntness are important factors.

Ionization Efficiency of Doubly Charged Ions Formed from Polyprotic Acids in Electrospray Negative Mode

NASA Astrophysics Data System (ADS)

Liigand, Piia; Kaupmees, Karl; Kruve, Anneli

2016-07-01

The ability of polyprotic acids to give doubly charged ions in negative mode electrospray was studied and related to physicochemical properties of the acids via linear discriminant analysis (LDA). It was discovered that the compound has to be strongly acidic (low p K a1 and p K a2) and to have high hydrophobicity (log P ow) to become multiply charged. Ability to give multiply charged ions in ESI/MS cannot be directly predicted from the solution phase acidities. Therefore, for the first time, a quantitative model to predict the charge state of the analyte in ESI/MS is proposed and validated for small anions. Also, a model to predict ionization efficiencies of these analytes was developed. Results indicate that acidity of the analyte, its octanol-water partition coefficient, and charge delocalization are important factors that influence ionization efficiencies as well as charge states of the analytes. The pH of the solvent was also found to be an important factor influencing the ionization efficiency of doubly charged ions.

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

[Lightning-caused fire, its affecting factors and prediction: a review].

PubMed

Zhang, Ji-Li; Bi, Wu; Wang, Xiao-Hong; Wang, Zi-Bo; Li, Di-Fei

2013-09-01

Lightning-caused fire is the most important natural fire source. Its induced forest fire brings enormous losses to human beings and ecological environment. Many countries have paid great attention to the prediction of lightning-caused fire. From the viewpoint of the main factors affecting the formation of lightning-caused fire, this paper emphatically analyzed the effects and action mechanisms of cloud-to-ground lightning, fuel, meteorology, and terrain on the formation and development process of lightning-caused fire, and, on the basis of this, summarized and reviewed the logistic model, K-function, and other mathematical methods widely used in prediction research of lightning-caused fire. The prediction methods and processes of lightning-caused fire in America and Canada were also introduced. The insufficiencies and their possible solutions for the present researches as well as the directions of further studies were proposed, aimed to provide necessary theoretical basis and literature reference for the prediction of lightning-caused fire in China.

Doing the counter-regulation shuffle: The importance of flexibility and hunger for predicting food consumption following a preload.

PubMed

Broadbent, Jaclyn; Fuller-Tyszkiewicz, Matthew; Dennerstein, Michelle; Greenwood, Jesse; Hancock, Naomi; Thavapalan, Nithyyaa; White, Melissa

This study utilised the preload paradigm to evaluate whether trait-like dieting attitudes and behaviours (dietary restraint and flexibility in dieting rules) and context-specific factors (negative mood and hunger) predict food consumption among male and female participants. Following a high calorie preload, 79 participants aged 18-40 completed a deceptive taste test in which they were encouraged to eat as much of the taste test foods as desired, and this ad libitum intake was measured. Although each predictor (except negative mood) predicted consumption when tested individually, regression analyses revealed that dieting flexibility and current hunger were the strongest unique predictors of intake. Mood failed to directly predict food consumption, nor did it moderate the relationship between restraint and food intake. Collectively, findings suggest that emphasis on dietary restraint in preload studies may be misplaced, as other proximal and stable factors may better predict food consumption. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Analysis of factors influencing hydration site prediction based on molecular dynamics simulations.

PubMed

Yang, Ying; Hu, Bingjie; Lill, Markus A

2014-10-27

Water contributes significantly to the binding of small molecules to proteins in biochemical systems. Molecular dynamics (MD) simulation based programs such as WaterMap and WATsite have been used to probe the locations and thermodynamic properties of hydration sites at the surface or in the binding site of proteins generating important information for structure-based drug design. However, questions associated with the influence of the simulation protocol on hydration site analysis remain. In this study, we use WATsite to investigate the influence of factors such as simulation length and variations in initial protein conformations on hydration site prediction. We find that 4 ns MD simulation is appropriate to obtain a reliable prediction of the locations and thermodynamic properties of hydration sites. In addition, hydration site prediction can be largely affected by the initial protein conformations used for MD simulations. Here, we provide a first quantification of this effect and further indicate that similar conformations of binding site residues (RMSD < 0.5 Å) are required to obtain consistent hydration site predictions.

Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.

PubMed

Ambale-Venkatesh, Bharath; Yang, Xiaoying; Wu, Colin O; Liu, Kiang; Hundley, W Gregory; McClelland, Robyn; Gomes, Antoinette S; Folsom, Aaron R; Shea, Steven; Guallar, Eliseo; Bluemke, David A; Lima, João A C

2017-10-13

Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies. To test the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes in comparison to standard cardiovascular risk scores. We included participants from the MESA (Multi-Ethnic Study of Atherosclerosis). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of cardiovascular disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across the United States were included. Seven-hundred thirty-five variables from imaging and noninvasive tests, questionnaires, and biomarker panels were obtained. We used the random survival forests technique to identify the top-20 predictors of each outcome. Imaging, electrocardiography, and serum biomarkers featured heavily on the top-20 lists as opposed to traditional cardiovascular risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary Artery Calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age, and ankle-brachial index were among the top predictors of atrial fibrillation. TNF-α (tissue necrosis factor-α) and IL (interleukin)-2 soluble receptors and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) levels were important across all outcomes. The random survival forests technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10%-25%). Machine learning in conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights on subclinical disease markers without apriori assumptions of causality. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00005487. © 2017 American Heart Association, Inc.

Predictive Factors for Insufficient Weight Loss After Bariatric Surgery: Does Obstructive Sleep Apnea Influence Weight Loss?

PubMed

de Raaff, Christel A L; Coblijn, Usha K; de Vries, Nico; Heymans, Martijn W; van den Berg, Bob T J; van Tets, Willem F; van Wagensveld, Bart A

2016-05-01

Important endpoints of bariatric surgery are weight loss and improvement of comorbidities, of which obstructive sleep apnea (OSA) is the highest accompanying comorbidity (70%). This study aimed to evaluate the influence of OSA on weight loss after bariatric surgery and to provide predictive factors for insufficient weight loss (defined as ≤50% excess weight loss (EWL)) at 1 year follow-up. All consecutive patients, who underwent primary laparoscopic Roux-en-Y gastric bypass or laparoscopic sleeve gastrectomy between 2006 and 2014 were retrospectively reviewed. Patients with data on preoperative apnea-hypopnea index (AHI) and pre- and postoperative body mass index (BMI) were included. After surgery, the percentage excess weight loss (%EWL) and BMI changes were compared between preoperatively diagnosed OSA-, subdivided in mild, moderate, and severe OSA, and non-OSA patients. Multivariable logistic regression analysis evaluated predictive factors for ≤50% EWL. A total of 816 patients, 522 (64%) with and 294 (36%) without OSA, were included. After 1 year, OSA patients achieved less %EWL than non-OSA patients (65.5 SD 20.7 versus 70.3 SD 21.0; p < 0.01). The lowest %EWL was seen in severe OSA patients (61.7 SD 20.2). However, when adjusted for waist circumference, BMI, and age, no effect of OSA was seen on %EWL or changes in BMI. Although AHI, gender, age, BMI, type of surgery, and type II diabetes were predictive factors for ≤50% EWL (area under the curve 0.778), the AHI as variable was of little importance. The presence of OSA does not individually impair weight loss after bariatric surgery.

Absence of back disorders in adults and work-related predictive factors in a 5-year perspective.

PubMed

Reigo, T; Tropp, H; Timpka, T

2001-06-01

Factors important for avoiding back disorders in different age-groups have seldom been compared and studied over time. We therefore set out to study age-related differences in socio-economic and work-related factors associated with the absence of back disorders in a 5-year comparative cohort study using a mailed questionnaire. Two subgroups (aged 25-34 and 54-59 years) derived from a representative sample of the Swedish population were followed at baseline, 1 year and 5 years. Questions were asked about the duration of back pain episodes, relapses, work changes and work satisfaction. A work adaptability, partnership, growth, affection, resolve (APGAR) score was included in the final questionnaire. Multivariate logistic regression was used to identify factors predicting the absence of back disorders. Absence of physically heavy work predicted an absence of back disorders [odds ratio (OR), 2.86; 95% confidence interval (CI), 1.3-6.3] in the older group. In the younger age-group, the absence of stressful work predicted absence of back disorders (OR, 2.0; 95% CI, 1.1-3.6). Thirty-seven per cent of the younger age-group and 43% of the older age-group did not experience any back pain episodes during the study period. The exploratory work APGAR scores indicated that back disorders were only associated with lower work satisfaction in the older group. The analyses point out the importance of avoiding perceived psychological stress in the young and avoiding perceived physically heavy work in the older age-group for avoiding back disorders. The results suggest a need for different programmes at workplaces to avoid back disorders depending on the age of the employees concerned.

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

NASA Astrophysics Data System (ADS)

Thomas Steven Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2016-11-01

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

Predictors of regular cigarette smoking among adolescent females: Does body image matter?

PubMed Central

Kaufman, Annette R.; Augustson, Erik M.

2013-01-01

This study examined how factors associated with body image predict regular smoking in adolescent females. Data were from the National Longitudinal Study of Adolescent Health (Add Health), a study of health-related behaviors in a nationally representative sample of adolescents in grades 7 through 12. Females in Waves I and II (n=6,956) were used for this study. Using SUDAAN to adjust for the sampling frame, univariate and multivariate analyses were performed to investigate if baseline body image factors, including perceived weight, perceived physical development, trying to lose weight, and self-esteem, were predictive of regular smoking status 1 year later. In univariate analyses, perceived weight (p<.01), perceived physical development (p<.0001), trying to lose weight (p<.05), and self-esteem (p<.0001) significantly predicted regular smoking 1 year later. In the logistic regression model, perceived physical development (p<.05), and self-esteem (p<.001) significantly predicted regular smoking. The more developed a female reported being in comparison to other females her age, the more likely she was to be a regular smoker. Lower self-esteem was predictive of regular smoking. Perceived weight and trying to lose weight failed to reach statistical significance in the multivariate model. This current study highlights the importance of perceived physical development and self-esteem when predicting regular smoking in adolescent females. Efforts to promote positive self-esteem in young females may be an important strategy when creating interventions to reduce regular cigarette smoking. PMID:18686177

Spatial prediction of ground subsidence susceptibility using an artificial neural network.

PubMed

Lee, Saro; Park, Inhye; Choi, Jong-Kuk

2012-02-01

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

Prognostic factors in prostate cancer.

PubMed

Braeckman, Johan; Michielsen, Dirk

2007-01-01

In the nineteenth century the main goal of medicine was predictive: diagnose the disease and achieve a satisfying prognosis of the patient's chances. Today the effort has shifted to cure the disease. Since the twentieth century, the word prognosis has also been used in nonmedical contexts, for example in corporate finance or elections. The most accurate form of prognosis is achieved statistically. Based on different prognostic factors it should be possible to tell patients how they are expected to do after prostate cancer has been diagnosed and how different treatments may change this outcome. A prognosis is a prediction. The word prognosis comes from the Greek word (see text) and means foreknowing. In the nineteenth century this was the main goal of medicine: diagnose the disease and achieve a satisfying prognosis of the patient's chances. Today the effort has shifted towards seeking a cure. Prognostic factors in (prostate) cancer are defined as "variables that can account for some of the heterogeneity associated with the expected course and outcome of a disease". Bailey defined prognosis as "a reasoned forecast concerning the course, pattern, progression, duration, and end of the disease. Prognostic factors are not only essential to understand the natural history and the course of the disease, but also to predict possible different outcomes of different treatments or perhaps no treatment at all. This is extremely important in a disease like prostate cancer where there is clear evidence that a substantial number of cases discovered by prostate-specific antigen (PSA) testing are unlikely ever to become clinically significant, not to mention mortal. Furthermore, prognostic factors are of paramount importance for correct interpretation of clinical trials and for the construction of future trials. Finally, according to WHO national screening committee criteria for implementing a national screening programme, widely accepted prognostic factors must be defined before assessing screening.

Biological and socio-cultural factors during the school years predicting women’s lifetime educational attainment

PubMed Central

Hendrick, C. Emily; Cohen, Alison K.; Deardorff, Julianna

2015-01-01

BACKGROUND Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In the current study, we examine the roles of socio-cultural factors in youth and an understudied biological life event, pubertal timing, in predicting women’s lifetime educational attainment. METHODS Using data from the National Longitudinal Survey of Youth 1997 cohort (N = 3889), we conducted sequential multivariate linear regression analyses to investigate the influences of macro-level and family-level socio-cultural contextual factors in youth (region of country, urbanicity, race/ethnicity, year of birth, household composition, mother’s education, mother’s age at first birth) and early menarche, a marker of early pubertal development, on women’s educational attainment after age 24. RESULTS Pubertal timing and all socio-cultural factors in youth, other than year of birth, predicted women’s lifetime educational attainment in bivariate models. Family factors had the strongest associations. When family factors were added to multivariate models, geographic region in youth and pubertal timing were no longer significant. CONCLUSION Our findings provide additional evidence that family factors should be considered when developing comprehensive and inclusive interventions in childhood and adolescence to promote lifetime educational attainment among girls. PMID:26830508

Predictors of Mental Health Symptoms, Automatic Thoughts, and Self-Esteem Among University Students.

PubMed

Hiçdurmaz, Duygu; İnci, Figen; Karahan, Sevilay

2017-01-01

University youth is a risk group regarding mental health, and many mental health problems are frequent in this group. Sociodemographic factors such as level of income and familial factors such as relationship with father are reported to be associated with mental health symptoms, automatic thoughts, and self-esteem. Also, there are interrelations between mental health problems, automatic thoughts, and self-esteem. The extent of predictive effect of each of these variables on automatic thoughts, self-esteem, and mental health symptoms is not known. We aimed to determine the predictive factors of mental health symptoms, automatic thoughts, and self-esteem in university students. Participants were 530 students enrolled at a university in Turkey, during 2014-2015 academic year. Data were collected using the student information form, the Brief Symptom Inventory, the Automatic Thoughts Questionnaire, and the Rosenberg Self-Esteem Scale. Mental health symptoms, self-esteem, perception of the relationship with the father, and level of income as a student significantly predicted automatic thoughts. Automatic thoughts, mental health symptoms, participation in family decisions, and age had significant predictive effects on self-esteem. Finally, automatic thoughts, self-esteem, age, and perception of the relationship with the father had significant predictive effects on mental health symptoms. The predictive factors revealed in our study provide important information to practitioners and researchers by showing the elements that need to be screened for mental health of university students and issues that need to be included in counseling activities.

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

PubMed

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

2017-06-01

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

The roles of deliberate practice and innate ability in developing expertise: evidence and implications.

PubMed

Kulasegaram, Kulamakan M; Grierson, Lawrence E M; Norman, Geoffrey R

2013-10-01

Medical education research focuses extensively on experience and deliberate practice (DP) as key factors in the development of expert performance. The research on DP minimises the role of individual ability in expert performance. This claim ignores a large body of research supporting the importance of innate individual cognitive differences. We review the relationship between DP and an innate individual ability, working memory (WM) capacity, to illustrate how both DP and individual ability predict expert performance. This narrative review examines the relationship between DP and WM in accounting for expert performance. Studies examining DP, WM and individual differences were identified through a targeted search. Although all studies support extensive DP as a factor in explaining expertise, much research suggests individual cognitive differences, such as WM capacity, predict expert performance after controlling for DP. The extent to which this occurs may be influenced by the nature of the task under study and the cognitive processes used by experts. The importance of WM capacity is greater for tasks that are non-routine or functionally complex. Clinical reasoning displays evidence of this task-dependent importance of individual ability. No single factor is both necessary and sufficient in explaining expertise, and individual abilities such as WM can be important. These individual abilities are likely to contribute to expert performance in clinical settings. Medical education research and practice should identify the individual differences in novices and experts that are important to clinical performance. © 2013 John Wiley & Sons Ltd.

Protective Factors Against Depression and Suicidal Behaviour in Adolescence

PubMed Central

Breton, Jean-Jacques; Labelle, Réal; Berthiaume, Claude; Royer, Chantal; St-Georges, Marie; Ricard, Dominique; Abadie, Pascale; Gérardin, Priscille; Cohen, David; Guilé, Jean-Marc

2015-01-01

Objectives: To examine whether protective factors in the Protection for Adolescent Depression Study (PADS) moderate the impact of stressful events on depression and suicidal behaviour in the community and a clinical setting; and to study the influence of sex. Method: Participants were 283 adolescents from the community and 119 from a mood disorder clinic in Montreal. The participants were evaluated on 6 instruments measuring individual risk and protective factors. Descriptive analyses and univariate and multiple logistic regression models were carried out. Results: Risk factors predicted higher levels of depression and presence of suicidal behaviour, and protective factors predicted lower levels of depression and absence of suicidal behaviour, as expected under the vulnerability-resilience stress model. Several sex differences were observed in terms of the predictive power of risk factors (for example, hopelessness among girls and keep to themselves among boys) and protective factors (for example, focusing on the positive among girls and self-discovery among boys). Conclusions: Findings from the PADS suggest that protective factors moderate the impact of stress on depression and suicidal behaviour. Developing protection appears important in the presence of chronic conditions, such as depressive disorders, to reduce the likelihood of further episodes. The influence of sex makes it all the more relevant to target different factors for boys and girls to increase protection and decrease risk in prevention and intervention programs. PMID:25886672

Effective detection of human leukocyte antigen risk alleles in celiac disease using tag single nucleotide polymorphisms.

PubMed

Monsuur, Alienke J; de Bakker, Paul I W; Zhernakova, Alexandra; Pinto, Dalila; Verduijn, Willem; Romanos, Jihane; Auricchio, Renata; Lopez, Ana; van Heel, David A; Crusius, J Bart A; Wijmenga, Cisca

2008-05-28

The HLA genes, located in the MHC region on chromosome 6p21.3, play an important role in many autoimmune disorders, such as celiac disease (CD), type 1 diabetes (T1D), rheumatoid arthritis, multiple sclerosis, psoriasis and others. Known HLA variants that confer risk to CD, for example, include DQA1*05/DQB1*02 (DQ2.5) and DQA1*03/DQB1*0302 (DQ8). To diagnose the majority of CD patients and to study disease susceptibility and progression, typing these strongly associated HLA risk factors is of utmost importance. However, current genotyping methods for HLA risk factors involve many reactions, and are complicated and expensive. We sought a simple experimental approach using tagging SNPs that predict the CD-associated HLA risk factors. Our tagging approach exploits linkage disequilibrium between single nucleotide polymorphism (SNPs) and the CD-associated HLA risk factors DQ2.5 and DQ8 that indicate direct risk, and DQA1*0201/DQB1*0202 (DQ2.2) and DQA1*0505/DQB1*0301 (DQ7) that attribute to the risk of DQ2.5 to CD. To evaluate the predictive power of this approach, we performed an empirical comparison of the predicted DQ types, based on these six tag SNPs, with those executed with current validated laboratory typing methods of the HLA-DQA1 and -DQB1 genes in three large cohorts. The results were validated in three European celiac populations. Using this method, only six SNPs were needed to predict the risk types carried by >95% of CD patients. We determined that for this tagging approach the sensitivity was >0.991, specificity >0.996 and the predictive value >0.948. Our results show that this tag SNP method is very accurate and provides an excellent basis for population screening for CD. This method is broadly applicable in European populations.

Population-level genetic variation and climate change in a biodiversity hotspot

PubMed Central

2017-01-01

Introduction Estimated future climate scenarios can be used to predict where hotspots of endemism may occur over the next century, but life history, ecological and genetic traits will be important in informing the varying responses within myriad taxa. Essential to predicting the consequences of climate change to individual species will be an understanding of the factors that drive genetic structure within and among populations. Here, I review the factors that influence the genetic structure of plant species in California, but are applicable elsewhere; existing levels of genetic variation, life history and ecological characteristics will affect the ability of an individual taxon to persist in the presence of anthropogenic change. Factors influencing the distribution of genetic variation Persistence in the face of climate change is likely determined by life history characteristics: dispersal ability, generation time, reproductive ability, degree of habitat specialization, plant–insect interactions, existing genetic diversity and availability of habitat or migration corridors. Existing levels of genetic diversity in plant populations vary based on a number of evolutionary scenarios that include endemism, expansion since the last glacial maximum, breeding system and current range sizes. Regional priorities and examples A number of well-documented examples are provided from the California Floristic Province. Some predictions can be made for the responses of plant taxa to rapid environmental changes based on geographic position, evolutionary history, existing genetic variation, and ecological amplitude. Conclusions, Solutions and Recommendations The prediction of how species will respond to climate change will require a synthesis drawing from population genetics, geography, palaeontology and ecology. The important integration of the historical factors that have shaped the distribution and existing genetic structure of California’s plant taxa will enable us to predict and prioritize the conservation of species and areas most likely to be impacted by rapid climate change, human disturbance and invasive species. PMID:28069633

A broad assessment of factors determining Culicoides imicola abundance: modelling the present and forecasting its future in climate change scenarios.

PubMed

Acevedo, Pelayo; Ruiz-Fons, Francisco; Estrada, Rosa; Márquez, Ana Luz; Miranda, Miguel Angel; Gortázar, Christian; Lucientes, Javier

2010-12-06

Bluetongue (BT) is still present in Europe and the introduction of new serotypes from endemic areas in the African continent is a possible threat. Culicoides imicola remains one of the most relevant BT vectors in Spain and research on the environmental determinants driving its life cycle is key to preventing and controlling BT. Our aim was to improve our understanding of the biotic and abiotic determinants of C. imicola by modelling its present abundance, studying the spatial pattern of predicted abundance in relation to BT outbreaks, and investigating how the predicted current distribution and abundance patterns might change under future (2011-2040) scenarios of climate change according to the Intergovernmental Panel on Climate Change. C. imicola abundance data from the bluetongue national surveillance programme were modelled with spatial, topoclimatic, host and soil factors. The influence of these factors was further assessed by variation partitioning procedures. The predicted abundance of C. imicola was also projected to a future period. Variation partitioning demonstrated that the pure effect of host and topoclimate factors explained a high percentage (>80%) of the variation. The pure effect of soil followed in importance in explaining the abundance of C. imicola. A close link was confirmed between C. imicola abundance and BT outbreaks. To the best of our knowledge, this study is the first to consider wild and domestic hosts in predictive modelling for an arthropod vector. The main findings regarding the near future show that there is no evidence to suggest that there will be an important increase in the distribution range of C. imicola; this contrasts with an expected increase in abundance in the areas where it is already present in mainland Spain. What may be expected regarding the future scenario for orbiviruses in mainland Spain, is that higher predicted C. imicola abundance may significantly change the rate of transmission of orbiviruses.

Family- and School-Related Factors in 9- to 15-Year-Olds Predicting Educational Attainment in Adulthood: A Prospective 27-Year Follow-Up Study

ERIC Educational Resources Information Center

Hintsanen, Mirka; Hintsa, Taina; Merjonen, Paivi; Leino, Mare; Keltikangas-Jarvinen, Liisa

2011-01-01

Introduction: This prospective longitudinal study examined several selected family- and school-related factors simultaneously in order to investigate the importance of well known and less examined predictors of educational attainment. Method: The participants were 844 (486 girls) nine-, 12-, and 15-years old comprehensive school students. Family-…

Buried above Ground: A University-Based Study of Risk/Protective Factors for Suicidality among Sexual Minority Youth in Canada

ERIC Educational Resources Information Center

Peter, Tracey; Taylor, Catherine

2014-01-01

This study examined differences in suicidal behavior between lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ) and non-LGBTQ university students as well as investigated the importance of risk and protective factors in the prediction of suicidality between these two groups. A total of 1,205 university students participated in the…

How Personality Affects Vulnerability among Israelis and Palestinians following the 2009 Gaza Conflict

PubMed Central

Canetti, Daphna; Kimhi, Shaul; Hanoun, Rasmiyah; Rocha, Gabriel A.; Galea, Sandro; Morgan, Charles A.

2016-01-01

Can the onset of PTSD symptoms and depression be predicted by personality factors and thought control strategies? A logical explanation for the different mental health outcomes of individuals exposed to trauma would seem to be personality factors and thought control strategies. Trauma exposure is necessary but not sufficient for the development of PTSD. To this end, we assess the role of personality traits and coping styles in PTSD vulnerability among Israeli and Palestinian students amid conflict. We also determine whether gender and exposure level to trauma impact the likelihood of the onset of PTSD symptoms. Five questionnaires assess previous trauma, PTSD symptoms, demographics, personality factors and thought control strategies, which are analyzed using path analysis. Findings show that the importance of personality factors and thought control strategies in predicting vulnerability increases in the face of political violence: the higher stress, the more important the roles of personality and thought control strategies. Thought control strategies associated with introverted and less emotionally stable personality-types correlate positively with higher levels of PTSD symptoms and depression, particularly among Palestinians. By extension, because mental health is key to reducing violence in the region, PTSD reduction in conflict zones warrants rethinking. PMID:27391240

What Predicts Exercise Maintenance and Well-Being? Examining The Influence of Health-Related Psychographic Factors and Social Media Communication.

PubMed

Zhou, Xin; Krishnan, Archana

2018-01-26

Habitual exercising is an important precursor to both physical and psychological well-being. There is, thus, a strong interest in identifying key factors that can best motivate individuals to sustain regular exercise regimen. In addition to the importance of psychographic factors, social media use may act as external motivator by allowing users to interact and communicate about exercise. In this study, we examined the influence of health consciousness, health-oriented beliefs, intrinsic motivation, as willingness to communicate about health on social media, social media activity on exercise, and online social support on exercise maintenance and well-being on a sample of 532 American adults. Employing structural equation modeling, we found that health-oriented beliefs mediated the effect of health consciousness on intrinsic motivation which in turn was a significant predictor of exercise maintenance. Exercise maintenance significantly predicted both physical and psychological well-being. Extrinsic motivators, as measured by willingness to communicate about health on social media, social media activity on exercise, and online social support did not however significantly influence exercise maintenance. These findings have implications for the design and implementation of exercise-promoting interventions by identifying underlying factors that influence exercise maintenance.

Uncertainties of isoprene emissions in the MEGAN model estimated for a coniferous and broad-leaved mixed forest in Southern China

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

Situ, S.; Wang, Xuemei; Guenther, Alex B.

2014-12-01

Using local observed emission factor, meteorological data, vegetation 5 information and dynamic MODIS LAI, MEGANv2.1 was constrained to predict the isoprene emission from Dinghushan forest in the Pearl River Delta region during a field campaign in November 2008, and the uncertainties in isoprene emission estimates were quantified by the Monte Carlo approach. The results indicate that MEGAN can predict the isoprene emission reasonably during the campaign, and the mean value of isoprene emission is 2.35 mg m-2 h-1 in daytime. There are high uncertainties associated with the MEGAN inputs and calculated parameters, and the relative error can be as highmore » as -89 to 111% for a 95% confidence interval. The emission factor of broadleaf trees and the activity factor accounting for light and temperature dependence are the most important contributors to the uncertainties in isoprene emission estimated for the Dinghushan forest during the campaign. The results also emphasize the importance of accurate observed PAR and temperature to reduce the uncertainties in isoprene emission estimated by model, because the MEGAN model activity factor accounting for light and temperature dependence is highly sensitive to PAR and temperature.« less

How Personality Affects Vulnerability among Israelis and Palestinians following the 2009 Gaza Conflict.

PubMed

Canetti, Daphna; Kimhi, Shaul; Hanoun, Rasmiyah; Rocha, Gabriel A; Galea, Sandro; Morgan, Charles A

2016-01-01

Can the onset of PTSD symptoms and depression be predicted by personality factors and thought control strategies? A logical explanation for the different mental health outcomes of individuals exposed to trauma would seem to be personality factors and thought control strategies. Trauma exposure is necessary but not sufficient for the development of PTSD. To this end, we assess the role of personality traits and coping styles in PTSD vulnerability among Israeli and Palestinian students amid conflict. We also determine whether gender and exposure level to trauma impact the likelihood of the onset of PTSD symptoms. Five questionnaires assess previous trauma, PTSD symptoms, demographics, personality factors and thought control strategies, which are analyzed using path analysis. Findings show that the importance of personality factors and thought control strategies in predicting vulnerability increases in the face of political violence: the higher stress, the more important the roles of personality and thought control strategies. Thought control strategies associated with introverted and less emotionally stable personality-types correlate positively with higher levels of PTSD symptoms and depression, particularly among Palestinians. By extension, because mental health is key to reducing violence in the region, PTSD reduction in conflict zones warrants rethinking.

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

Mink, S. E. de; Belczynski, K., E-mail: S.E.deMink@uva.nl, E-mail: kbelczyn@astrouw.edu.pl

The initial mass function (IMF), binary fraction, and distributions of binary parameters (mass ratios, separations, and eccentricities) are indispensable inputs for simulations of stellar populations. It is often claimed that these are poorly constrained, significantly affecting evolutionary predictions. Recently, dedicated observing campaigns have provided new constraints on the initial conditions for massive stars. Findings include a larger close binary fraction and a stronger preference for very tight systems. We investigate the impact on the predicted merger rates of neutron stars and black holes. Despite the changes with previous assumptions, we only find an increase of less than a factor ofmore » 2 (insignificant compared with evolutionary uncertainties of typically a factor of 10–100). We further show that the uncertainties in the new initial binary properties do not significantly affect (within a factor of 2) our predictions of double compact object merger rates. An exception is the uncertainty in IMF (variations by a factor of 6 up and down). No significant changes in the distributions of final component masses, mass ratios, chirp masses, and delay times are found. We conclude that the predictions are, for practical purposes, robust against uncertainties in the initial conditions concerning binary parameters, with the exception of the IMF. This eliminates an important layer of the many uncertain assumptions affecting the predictions of merger detection rates with the gravitational wave detectors aLIGO/aVirgo.« less

The Role of Parental Distress in Moderating the Influence of Child Neglect on Maladjustment.

PubMed

Berzenski, Sara R; Bennett, David S; Marini, Victoria A; Sullivan, Margaret Wolan; Lewis, Michael

2014-11-01

Despite pervasive evidence of the harmful impact of neglect on children's adjustment, individual differences in adaptation persist. This study examines parental distress as a contextual factor that may moderate the relation between neglect and child adjustment, while considering the specificity of the relation between neglect and internalizing versus externalizing problems. In a sample of 66 children (33 with a documented child protective services history of neglect prior to age six), neglect predicted internalizing, and to a lesser extent externalizing, problems as rated by teachers at age seven. Parental distress moderated the relation between neglect and internalizing, but not externalizing, problems. Specifically, higher levels of neglect predicted more internalizing problems only among children of distressed parents. These findings indicate that parent-level variables are important to consider in evaluating the consequences of neglect, and point to the importance of considering contextual factors when identifying those children most at risk following neglect.

miR-185 is an independent prognosis factor and suppresses tumor metastasis in gastric cancer.

PubMed

Tan, Zhiqin; Jiang, Hao; Wu, Youhua; Xie, Liming; Dai, Wenxiang; Tang, Hailin; Tang, Sanyuan

2014-01-01

miR-185 has been identified as an important factor in several cancers such as breast cancer, ovarial cancer, and prostate cancer. However, its effect and prognostic value in gastric cancer are still poorly known. In this study, we found that the expression levels of miR-185 were strongly downregulated in gastric cancer and associated with clinical stage and the presence of lymph node metastases. Moreover, miR-185 might independently predict OS and RFS in gastric cancer. We further found that upregulation of miR-185 inhibited the proliferation and metastasis of gastric cancer cells in vitro and in vivo. Taken together, our findings demonstrate that the miR-185 is important for gastric cancer initiation and progression and holds promise as a prognostic biomarker to predict survival and relapse in gastric cancer. It is also a potential therapeutic tool to improve clinical outcomes in the above disease.

Evaluating Shortened Versions of the AUDIT as Screeners for Alcohol Use Problems in a General Population Study.

PubMed

Nayak, Madhabika B; Bond, Jason C; Greenfield, Thomas K

2015-01-01

Efficient alcohol screening measures are important to prevent or treat alcohol use disorders (AUDs). We studied different versions of the Alcohol Use Disorders Identification Test (AUDIT) comparing their performance to the full AUDIT and an AUD measure as screeners for alcohol use problems in Goa, India. Data from a general population study on 743 male drinkers aged 18-49 years are reported. Drinkers completed the AUDIT and an AUD measure. We created shorter versions of the AUDIT by (a) collapsing AUDIT item responses into three and two categories and (b) deleting two items with the lowest factor loadings. Each version was evaluated using factor, reliability and validity, and differential item functioning (DIF) analysis by age, education, standard of living index (SLI), and area of residence. A single factor solution was found for each version with lower factor loadings for items on guilt and concern. There were no significant differences among the different AUDIT versions in predicting AUD. No significant DIF was found by education, SLI or area of residence. DIF was observed for the alcohol frequency item by age. The AUDIT may be used with dichotomized response options without loss of predictive validity. A shortened eight-item dichotomized scale can adequately screen for AUDs in Goa when brevity is of paramount importance, although with lower predictive validity. Although the frequency item was endorsed more by older men, there is no evidence that the AUDIT items perform differently in other groups of male drinkers in Goa.

Effects of forest fragmentation on nocturnal Asian birds: A case study from Xishuangbanna, China.

PubMed

K Dayananda, Salindra; Goodale, Eben; Lee, Myung-Bok; Liu, Jia-Jia; Mammides, Christos; O Pasion, Bonifacio; Quan, Rui-Chang; W Ferry Slik, J; Sreekar, Rachakonda; W Tomlinson, Kyle; Yasuda, Mika

2016-05-18

Owls have the potential to be keystone species for conservation in fragmented landscapes, as the absence of these predators could profoundly change community structure. Yet few studies have examined how whole communities of owls respond to fragmentation, especially in the tropics. When evaluating the effect of factors related to fragmentation, such as fragment area and distance to the edge, on these birds, it is also important in heterogeneous landscapes to ask how 'location factors' such as the topography, vegetation and soil of the fragment predict their persistence. In Xishuangbanna, southwest China, we established 43 transects (200 m×60 m) within 20 forest fragments to sample nocturnal birds, both visually and aurally. We used a multimodel inference approach to identify the factors that influence owl species richness, and generalized linear mixed models to predict the occurrence probabilities of each species. We found that fragmentation factors dominated location factors, with larger fragments having more species, and four of eight species were significantly more likely to occur in large fragments. Given the potential importance of these birds on regulating small mammal and other animal populations, and thus indirectly affecting seed dispersal, we suggest further protection of large fragments and programs to increase their connectivity to the remaining smaller fragments.

Risk factors for early adolescent drug use in four ethnic and racial groups.

PubMed

Vega, W A; Zimmerman, R S; Warheit, G J; Apospori, E; Gil, A G

1993-02-01

It is widely believed that risk factors identified in previous epidemiologic studies accurately predict adolescent drug use. Comparative studies are needed to determine how risk factors vary in prevalence, distribution, sensitivity, and pattern across the major US ethnic/racial groups. Baseline questionnaire data from a 3-year epidemiologic study of early adolescent development and drug use were used to conduct bivariate and multivariate risk factor analyses. Respondents (n = 6760) were sixth- and seventh-grade Cuban, other Hispanic, Black, and White non-Hispanic boys in the 48 middle schools of the greater Miami (Dade County) area. Findings indicate 5% lifetime illicit drug use, 4% lifetime inhalant use, 37% lifetime alcohol use, and 21% lifetime tobacco use, with important intergroup differences. Monotonic relationships were found between 10 risk factors and alcohol and illicit drug use. Individual risk factors were distributed disproportionately, and sensitivity and patterning of risk factors varied widely by ethnic/racial subsample. While the cumulative prevalence of risk factors bears a monotonic relationship to drug use, ethnic/racial differences in risk factor profiles, especially for Blacks, suggest differential predictive value based on cultural differences.

Thermal Modeling of Resistance Spot Welding and Prediction of Weld Microstructure

NASA Astrophysics Data System (ADS)

Sheikhi, M.; Valaee Tale, M.; Usefifar, GH. R.; Fattah-Alhosseini, Arash

2017-11-01

The microstructure of nuggets in resistance spot welding can be influenced by the many variables involved. This study aimed at examining such a relationship and, consequently, put forward an analytical model to predict the thermal history and microstructure of the nugget zone. Accordingly, a number of numerical simulations and experiments were conducted and the accuracy of the model was assessed. The results of this assessment revealed that the proposed analytical model could accurately predict the cooling rate in the nugget and heat-affected zones. Moreover, both analytical and numerical models confirmed that sheet thickness and electrode-sheet interface temperature were the most important factors influencing the cooling rate at temperatures lower than about T l/2. Decomposition of austenite is one of the most important transformations in steels occurring over this temperature range. Therefore, an easy-to-use map was designed against these parameters to predict the weld microstructure.

Prediction of interface residue based on the features of residue interaction network.

PubMed

Jiao, Xiong; Ranganathan, Shoba

2017-11-07

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictors of burnout and health status in Samaritans' listening volunteers.

PubMed

Roche, Adeline; Ogden, Jane

2017-12-01

Samaritan listening volunteers provide emotional support to people in distress or suicidal. Samaritans' has high volunteer turnover, which may be due to burnout. This study evaluated the role of demographic and psychosocial factors in predicting Samaritans listening volunteers' burnout and health status. Samaritans' listening volunteers (n = 216) from seven branches across UK completed an online survey to assess their levels of burnout (emotional exhaustion, depersonalisation, personal accomplishment), subjective health status, coping, empathy and social support. Overall, listeners showed low levels of burnout and good health. Regression analysis revealed that higher emotional exhaustion was predicted by younger age and avoidant coping style; higher depersonalisation was predicted by lower empathy fantasy and higher avoidant coping style; lower personal accomplishment scores were predicted by higher empathy personal distress and worse health status was predicted by more hours per week spent on listening duties, lower social support and higher avoidant coping style. Overall, different factors influenced different facets of burnout. However, higher use of avoidant coping style consistently predicted higher burnout and worse health status, suggesting avoidant coping is an important target for intervention.

What are Predictive Factors for Developing of Barrett’s Esophagus in Patients with Gerd–our Experience

PubMed Central

Gashi, Zaim; Ivkovski, Ljube; Shabani, Ragip; Haziri, Adem; Juniku-Shkololli, Argjira

2011-01-01

Introduction: Barrett’s esophagus (BE) is a condition in which the normal squamous epithelium of the esophagus is replaced with metaplastic intestinal-type epithelium. This epithelium can progress sequentially from metaplasia to low-grade dysplasia, then to high-grade dysplasia and finally to invasive adenocarcinoma. Many factors that appear to be risk factors for the presence of BE include obesity, the presence of hiatal hernia, and interestingly, the absence of Helicobacter pylori infection. The aim: The aim of this study was to determine the predictive factors for progression of gastroesophageal reflux disease (GERD) to BE. Methods: 42 patients with endoscopically diagnosed and histopathologically verified BE were included in this prospective study. We analysed predictive factors such as: age, sex, obesity, alcohol consumption and smoking, reflux symptom duration in this patients, prevalence of short and long segment of BE, and the presence of hiatal hernia. After endoscopic examination of these patients, the presence of BE was verified with histopathological examination and finally, infection with H. pylori was determined. Results: Among 42 subjects, 25 (59%) were males and 17 (41%) were females, with mean age of 52.8±3.28 years. Obesity was present in 24 of 42 patients (57%). 27 of 42 patients (64%) were smokers. Symptom duration in this patients was approximately 9.4 years. From total number of patients, 52% were with SSBE and 48% patients were with LSBE. Hiatal hernia was present in 64% of patients, of which 66% were with LSBE and 34% with SSBE. In these patients, prevalence of infection with H. pylori was present in 12% of cases, 9.5% in patients with SSBE and 2.5% in patients with LSBE. Conclusions: The important risk factors for appearance of BE in GERD patients were male sex, middle age, smooking and alcohol consumption. Obesity is an important factor for development of BE. Most of patients with BE also had hiatal hernia, in majority of cases these were patients with LSBE. The prevalence of infection with H. Pylori in patients with BE was lower and this may predict a protective role of this microorganism. PMID:23407541

Baseline Risk Factors that Predict the Development of Open-angle Glaucoma in a Population: The Los Angeles Latino Eye Study

PubMed Central

Jiang, Xuejuan; Varma, Rohit; Wu, Shuang; Torres, Mina; Azen, Stanley P; Francis, Brian A.; Chopra, Vikas; Nguyen, Betsy Bao-Thu

2012-01-01

Objective To determine which baseline socio-demographic, lifestyle, anthropometric, clinical, and ocular risk factors predict the development of open-angle glaucoma (OAG) in an adult population. Design A population-based, prospective cohort study. Participants A total of 3,772 self-identified Latinos aged 40 years and older from Los Angeles, California who were free of OAG at baseline. Methods Participants from the Los Angeles Latino Eye Study had standardized study visits at baseline and 4-year follow-up with structured interviews and a comprehensive ophthalmologic examination. OAG was defined as the presence of an open angle and a glaucomatous visual field abnormality and/or evidence of glaucomatous optic nerve damage in at least one eye. Multivariate logistic regression with stepwise selection was performed to determine which potential baseline risk factors independently predict the development of OAG. Main Outcome Measure Odds ratios for various risk factors. Results Over the 4-year follow-up, 87 participants developed OAG. The baseline risk factors that predict the development of OAG include: older age (odds ratio [OR] per decade, 2.19; 95% confidence intervals [CI], 1.74-2.75; P<0.001), higher intraocular pressure (OR per mmHg, 1.18; 95% CI, 1.10-1.26; P<0.001), longer axial length (OR per mm, 1.48; 95% CI, 1.22-1.80; P<0.001), thinner central cornea (OR per 40 μm thinner, 1.30; 95% CI, 1.00-1.70; P=0.050) higher waist to hip ratio (OR per 0.05 higher, 1.21; 95% CI, 1.05-1.39; P=0.007) and lack of vision insurance (OR, 2.08; 95% CI, 1.26-3.41; P=0.004). Conclusions Despite a mean baseline IOP of 14 mmHg in Latinos, higher intraocular pressure is an important risk factor for developing OAG. Biometric measures suggestive of less structural support such as longer axial length and thin CCT were identified as important risk factors. Lack of health insurance reduces access to eye care and increases the burden of OAG by reducing the likelihood of early detection and treatment of OAG. PMID:22796305

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm.

PubMed

Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh

2016-05-01

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for Cancer Research.

The dynamics of transmission and the dynamics of networks.

PubMed

Farine, Damien

2017-05-01

A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors, such as seasonality, led to consistent differences in the structure of social networks, using dynamic vs. static representations of networks generated differences in the predicted outbreak size of an emergent disease. These findings highlight some of the challenges associated with studying disease dynamics in animal populations, and the importance of continuing efforts to develop the network tools needed to study disease spread. © 2017 The Author. Journal of Animal Ecology © 2017 British Ecological Society.

Recent advances in recurrent urinary tract infection from pathogenesis and biomarkers to prevention

PubMed Central

Jhang, Jia-Fong; Kuo, Hann-Chorng

2017-01-01

Recurrent urinary tract infection (UTI) might be one of the most common problems in urological clinics. Recent research has revealed novel evidence about recurrent UTI and it should be considered a different disease from the first infection. The pathogenesis of recurrent UTI might include two mechanisms, bacterial factors and deficiencies in host defense. Bacterial survival in the urinary bladder after antibiotic treatment and progression to form intracellular bacterial communities might be the most important bacterial factors. In host defense deficiency, a defect in pathogen recognition and urothelial barrier function impairment play the most important roles. Immunodeficiency and urogenital tract anatomical abnormalities have been considered the essential risk factors for recurrent UTI. In healthy women, voiding dysfunction and behavioral factors also increase the risk of recurrent UTI. Sexual intercourse and estrogen deficiency in postmenopausal women might have the strongest association with recurrent UTI. Traditional lifestyle factors such as fluid intake and diet are not considered independent risk factors now. Serum and urine biomarkers to predict recurrent UTI from the first infection have also attracted a wide attention recently. Current clinical evidence suggests that serum macrophage colony-stimulating factor and urinary nerve growth factor have potential predictive value for recurrent UTI. Clinical trials have proven the efficacy of the oral immunoactive agent OM-89 for the prevention of UTI. Vaccines for recurrent UTI are recommended by the latest guidelines and are available on the market. PMID:28974905

Prediction of Happy-Sad Mood from Daily Behaviors and Previous Sleep History

PubMed Central

Sano, Akane; Yu, Amy; McHill, Andrew W.; Phillips, Andrew J. K.; Taylor, Sara; Jaques, Natasha; Klerman, Elizabeth B.; Picard, Rosalind W.

2016-01-01

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants, for 30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other factors in college students. We analyzed daily and monthly behavior and physiology and identified factors that affect mood, including how accurately sleep duration and sleep regularity for the past 1-5 days classified the participants into high/low mood using support vector machines. We found statistically significant associations among sad mood and poor health-related factors. Behavioral factors such as the percentage of neutral social interactions and the total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity was a more important discriminator of mood than sleep duration for most participants, although both variables predicted happy/sad mood with from 70-82% accuracy. The number of nights giving the best prediction of happy/sad mood varied for different groups of individuals. PMID:26737854

Suicidality and its relationship with depression, alcohol disorders and childhood experiences of violence: results from the ESEMeD study.

PubMed

Hardt, J; Bernert, S; Matschinger, H; Angermeier, M C; Vilagut, G; Bruffaerts, R; de Girolamo, G; de Graaf, R; Haro, J M; Kovess, V; Alonso, J

2015-04-01

Suicidality constitutes a major health concern in many countries. The aim of the present paper was to analyse 10 of its risk factors and their interdependence. Data on suicidality, mental disorders and experience of childhood violence was collected from 8796 respondents in the European Study of the Epidemiology of Mental Disorders (ESEMeD). The CIDI was used to assess mental disorders. Individuals were randomly divided into two subgroups. In one, a Graphical Markov model to predict suicidality was constructed, in the second, predictors were cross-validated. Lifetime suicidality was predicted mainly by lifetime depression and early experiences of violence, with a pseudo R-square of 12.8%. In addition, alcohol disorders predicted suicidality, but played a minor role compared with the other risk factors in this sample. In addition to depression, early experience of violence constitutes an important risk factor of suicidality. This is a cross-sectional and retrospective study assessing risk factors for suicidality, not for suicide itself. Copyright © 2015 Elsevier B.V. All rights reserved.

The relative importance of wife abuse as a risk factor for violence against children.

PubMed

Tajima, E A

2000-11-01

To investigate the relative importance of wife abuse as a risk factor for physical child abuse, physical punishment, and verbal child abuse. The study explored the importance of wife abuse relative to blocks of parent, child, and family characteristics and also relative to specific risk factors. This study re-analyzed a sub-sample (N = 2,733) of data from the 1985 National Family Violence Survey. Hierarchical logistic regressions were conducted, using five different criterion variables measuring physical child abuse, physical punishment, and verbal abuse separately and in combination. Blocks of parent, child, and family characteristics were more important predictors of violence towards children than was wife abuse, though the presence of wife abuse in the home was a consistently significant specific risk factor for all forms of violence against children. Of specific risk factors, a respondent's history of having been hit as an adolescent was a larger risk factor for physical child abuse than was wife abuse. Wife abuse was an important predictor of physical punishment. Non-violent marital discord was a greater factor in predicting likelihood of verbal child abuse than was wife abuse. Though this study confirms the association between wife abuse and violence towards children, it cautions us not to overlook the contribution of other factors in our attempts to understand the increased risk attributed to wife abuse.

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Cognitive factors predicting intentions to search for health information: an application of the theory of planned behaviour.

PubMed

Austvoll-Dahlgren, Astrid; Falk, Ragnhild S; Helseth, Sølvi

2012-12-01

Peoples' ability to obtain health information is a precondition for their effective participation in decision making about health. However, there is limited evidence describing which cognitive factors can predict the intention of people to search for health information. To test the utility of a questionnaire in predicting intentions to search for health information, and to identify important predictors associated with this intention such that these could be targeted in an Intervention. A questionnaire was developed based on the Theory of Planned Behaviour and tested on both a mixed population sample (n=30) and a sample of parents (n = 45). The questionnaire was explored by testing for internal consistency, calculating inter-correlations between theoretically-related constructs, and by using multiple regression analysis. The reliability and validity of the questionnaire were found to be satisfactory and consistent across the two samples. The questionnaires' direct measures prediction of intention was high and accounted for 47% and 55% of the variance in behavioural intentions. Attitudes and perceived behavioural control were identified as important predictors to intention for search for health information. The questionnaire may be a useful tool for understanding and evaluating behavioural intentions and beliefs related to searches for health information. © 2012 The authors. Health Information and Libraries Journal © 2012 Health Libraries Group.

Sexual Dating Aggression Across Grades 8 Through 12: Timing and Predictors of Onset

PubMed Central

Reyes, H. Luz McNaughton; Foshee, Vangie A.

2013-01-01

Investigators have identified a number of factors that increase risk for physical and psychological dating abuse perpetration during adolescence, but as yet little is known about the etiology of sexual dating aggression during this critical developmental period. This is an important gap in the literature given that research suggests that patterns of sexual dating violence that are established during this period may carry over into young adulthood. Using a sample of 459 male adolescents (76% White, 19% Black), the current study used survival analysis to examine the timing and predictors of sexual dating aggression perpetration onset across grades 8 through 12. Risk for sexual dating aggression onset increased across early adolescence, peaked in the 10th grade, and desisted thereafter. As predicted based on the Confluence Model of sexual aggression, associations between early physical aggression towards peers and dates and sexual aggression onset were stronger for teens reporting higher levels of rape myth acceptance. Contrary to predictions, inter-parental violence, prior victimization experiences, and parental monitoring knowledge did not predict sexual dating aggression onset. Findings support the notion that risk factors may work synergistically to predict sexual dating aggression and highlight the importance of rape myth acceptance as a construct that should be addressed by violence prevention programs. PMID:23180071

Four-Phase Dendritic Model for the Prediction of Macrosegregation, Shrinkage Cavity, and Porosity in a 55-Ton Ingot

NASA Astrophysics Data System (ADS)

Ge, Honghao; Ren, Fengli; Li, Jun; Han, Xiujun; Xia, Mingxu; Li, Jianguo

2017-03-01

A four-phase dendritic model was developed to predict the macrosegregation, shrinkage cavity, and porosity during solidification. In this four-phase dendritic model, some important factors, including dendritic structure for equiaxed crystals, melt convection, crystals sedimentation, nucleation, growth, and shrinkage of solidified phases, were taken into consideration. Furthermore, in this four-phase dendritic model, a modified shrinkage criterion was established to predict shrinkage porosity (microporosity) of a 55-ton industrial Fe-3.3 wt pct C ingot. The predicted macrosegregation pattern and shrinkage cavity shape are in a good agreement with experimental results. The shrinkage cavity has a significant effect on the formation of positive segregation in hot top region, which generally forms during the last stage of ingot casting. The dendritic equiaxed grains also play an important role on the formation of A-segregation. A three-dimensional laminar structure of A-segregation in industrial ingot was, for the first time, predicted by using a 3D case simulation.

A review on the factors affecting mite growth in stored grain commodities.

PubMed

Collins, D A

2012-03-01

A thorough review of the literature has identified the key factors and interactions that affect the growth of mite pests on stored grain commodities. Although many factors influence mite growth, the change and combinations of the physical conditions (temperature, relative humidity and/or moisture content) during the storage period are likely to have the greatest impact, with biological factors (e.g. predators and commodity) playing an important role. There is limited information on the effects of climate change, light, species interactions, local density dependant factors, spread of mycotoxins and action thresholds for mites. A greater understanding of these factors may identify alternative control techniques. The ability to predict mite population dynamics over a range of environmental conditions, both physical and biological, is essential in providing an early warning of mite infestations, advising when appropriate control measures are required and for evaluating control measures. This information may provide a useful aid in predicting and preventing mite population development as part of a risk based decision support system.

Suicide Among Soldiers: A Review of Psychosocial Risk and Protective Factors

PubMed Central

Nock, Matthew K.; Deming, Charlene A.; Fullerton, Carol S.; Gilman, Stephen E.; Goldenberg, Matthew; Kessler, Ronald C.; McCarroll, James E.; McLaughlin, Katie A.; Peterson, Christopher; Schoenbaum, Michael; Stanley, Barbara; Ursano, Robert J.

2014-01-01

Suicide is difficult to predict and prevent and remains a leading cause of death worldwide. Although soldiers historically have had a suicide rate well below that of the general population, the suicide rate among members of the U.S. Army has increased markedly over the past several years and now exceeds that of the general population. This paper reviews psychosocial factors known to be associated with the increased risk of suicidal behavior in general and describes how some of these factors may be especially important in understanding suicide among soldiers. Moving forward, the prevention of suicide requires additional research aimed at: (a) better describing when, where, and among whom suicidal behavior occurs, (b) using exploratory studies to discover new risk and protective factors, (c) developing new methods of predicting suicidal behavior that synthesize information about modifiable risk and protective factors from multiple domains, and (d) understanding the mechanisms and pathways through which suicidal behavior develops. Although the scope and severity of this problem is daunting, the increasing attention and dedication to this issue by the Armed Forces, scientists, and society provide hope for our ability to better predict and prevent these tragic outcomes in the future. PMID:23631542

Understanding social anxiety as a risk for alcohol use disorders: Fear of scrutiny, not social interaction fears, prospectively predicts alcohol use disorders

PubMed Central

Buckner, Julia D.; Schmidt, Norman B.

2009-01-01

Increasing evidence indicates that social anxiety may be a premorbid risk factor for alcohol use disorders (AUD). The aim of this study was to replicate and extend previous work examining whether social anxiety is a risk factor for AUD by evaluating both the temporal antecedence and non-spuriousness of this relationship. We also examined whether social anxiety first-order factors (social interaction anxiety, observation anxieties) served as specific predictors of AUD. A non-referred sample of 404 psychologically healthy young adults (i.e. free from current or past Axis I psychopathology) was prospectively followed over approximately two years. Social anxiety (but not depression or trait anxiety) at baseline significantly predicted subsequent AUD onset. The relationship between social anxiety and AUD remained even after controlling for relevant variables (gender, depression, trait anxiety). Further, social anxiety first-order factors differentially predicted AUD onset, such that observation anxieties (but not social interaction anxiety) were prospectively linked to AUD onset. This study provides further support that social anxiety (and fear of scrutiny specifically) appears to serve as an important and potentially specific AUD-related variable that deserves serious attention as a potential vulnerability factor. PMID:18547587

The Frank Stinchfield Award: the impact of socioeconomic factors on outcome after THA: a prospective, randomized study.

PubMed

Allen Butler, R; Rosenzweig, Seth; Myers, Leann; Barrack, Robert L

2011-02-01

Most studies of total hip arthroplasty (THA) focus on the effect of the type of implant on the clinical result. Relatively little data are available on the impact of the patient's preoperative status and socioeconomic factors on the clinical results following THA. We determined the relative importance of patient preoperative and socioeconomic status compared to implant and technique factors in predicting patient outcome as reflected by scores on commonly utilized rating scales (eg, Harris Hip Score, WOMAC, SF-12, degree of patient satisfaction, or presence or severity of thigh pain) following cementless THA. All patients during the study period were offered enrollment in a prospective, randomized study to receive either a titanium, tapered, proximally coated stem; or a Co-Cr, cylindrical, extensively coated stem; 102 patients were enrolled. We collected detailed patient data preoperatively including diagnosis, age, gender, insurance status, medical comorbidities, tobacco and alcohol use, household income, educational level, and history of treatment for lumbar spine pathology. Clinical evaluation included Harris Hip Score, SF-12, WOMAC, pain drawing, and UCLA activity rating and satisfaction questionnaire. Implant factors included stem type, stem size, fit in the canal, and stem-bone stiffness ratios. Minimum 2 year followup was obtained in 95% of the enrolled patients (102 patients). Patient demographics and preoperative status were more important than implant factors in predicting the presence of thigh pain, dissatisfaction, and a low hip score. The most predictive factors were ethnicity, educational level, poverty level, income, and a low preoperative WOMAC score or preoperative SF-12 mental component score. No implant parameter correlated with outcome or satisfaction. Socioeconomic factors and preoperative status have more impact on the clinical outcome of cementless THA than implant related factors. Level I, prospective, randomized clinical trial. See the guidelines online for a complete description of level of evidence.

Study of Psycho-Social Factors Affecting Traffic Accidents Among Young Boys in Tehran

PubMed Central

Javadi, Seyyed Mohammad Hossein; Fekr Azad, Hossein; Tahmasebi, Siyamak; Rafiei, Hassan; Rahgozar, Mehdi; Tajlili, Alireza

2015-01-01

Background: Unprecedented growth of fatalities due to traffic accidents in the recent years has raised great concerns and efforts of authorities in order to identify and control the causes of these accidents. Objectives: In the present study, the contribution of psychological, social, demographic, environmental and behavioral factors on traffic accidents was studied for young boys in Tehran, emphasizing the importance of psychosocial factors. Patients and Methods: The design of the present study was quantitative (correlational) in which a sample population including 253 boys from Tehran (Iran) with an age range of 18 to 24 who had been referred to insurance institutions, hospitals, correctional facilities as well as prisons, were selected using stratified cluster sampling during the year 2013.The subjects completed the following questionnaires: demographic, general health, lifestyle, Manchester Driving Behavior Questionnaire (MDBQ), young parenting, and NEO-Five Factor Inventory (NEO-FFI). For data analysis, descriptive statistics, correlation coefficient, and inferential statistics including simultaneous regression, stepwise regression, and structural equations modeling were used. Results: The findings indicated that in the psychosocial model of driving behavior (including lapses, mistakes, and intentional violations) and accidents, psychological factors, depression (P < 0.02), personality trait of conscientiousness (P < 0.02), failure schema due to the parenting style of mother (P = 0.001), and perception of police commands (P < 0.002), played an important role in predicting driving behavior. Among social factors, perception of police regulations (P = 0.003), had an important effect on violations and mistakes. Among environmental and behavioral factors, major factors such as driving age (P = 0.001), drug and alcohol use (P = 0.001), having driver’s license (P = 0.013), records of imprisonment or committing a crime (P = 0.012) were also able to predict occurrence of accidents. Conclusions: As the results of this study show, different factors contribute to different driving behaviors and accidents. The broad scope of these factors links accidents to other social issues and damages. PMID:26421169

Study of Psycho-Social Factors Affecting Traffic Accidents Among Young Boys in Tehran.

PubMed

Javadi, Seyyed Mohammad Hossein; Fekr Azad, Hossein; Tahmasebi, Siyamak; Rafiei, Hassan; Rahgozar, Mehdi; Tajlili, Alireza

2015-07-01

Unprecedented growth of fatalities due to traffic accidents in the recent years has raised great concerns and efforts of authorities in order to identify and control the causes of these accidents. In the present study, the contribution of psychological, social, demographic, environmental and behavioral factors on traffic accidents was studied for young boys in Tehran, emphasizing the importance of psychosocial factors. The design of the present study was quantitative (correlational) in which a sample population including 253 boys from Tehran (Iran) with an age range of 18 to 24 who had been referred to insurance institutions, hospitals, correctional facilities as well as prisons, were selected using stratified cluster sampling during the year 2013.The subjects completed the following questionnaires: demographic, general health, lifestyle, Manchester Driving Behavior Questionnaire (MDBQ), young parenting, and NEO-Five Factor Inventory (NEO-FFI). For data analysis, descriptive statistics, correlation coefficient, and inferential statistics including simultaneous regression, stepwise regression, and structural equations modeling were used. The findings indicated that in the psychosocial model of driving behavior (including lapses, mistakes, and intentional violations) and accidents, psychological factors, depression (P < 0.02), personality trait of conscientiousness (P < 0.02), failure schema due to the parenting style of mother (P = 0.001), and perception of police commands (P < 0.002), played an important role in predicting driving behavior. Among social factors, perception of police regulations (P = 0.003), had an important effect on violations and mistakes. Among environmental and behavioral factors, major factors such as driving age (P = 0.001), drug and alcohol use (P = 0.001), having driver's license (P = 0.013), records of imprisonment or committing a crime (P = 0.012) were also able to predict occurrence of accidents. As the results of this study show, different factors contribute to different driving behaviors and accidents. The broad scope of these factors links accidents to other social issues and damages.

Spatiotemporal Patterns of Evapotranspiration in Response to Multiple Environmental Factors Simulated by the Community Land Model

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

Shi, Xiaoying; Mao, Jiafu; Thornton, P.

Spatiotemporal patterns of evapotranspiration (ET) over the period from 1982 to 2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates. We find that climate dominates the predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, and replaces climate to function as the dominant factor controlling ET changes over the North America, South America and Asia regions. Comparedmore » to the effect of climate and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. The aerosol deposition contribution is the third most important factor for trends of ET over Europe, while it has the smallest impact over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.« less

Analysis of functional importance of binding sites in the Drosophila gap gene network model.

PubMed

Kozlov, Konstantin; Gursky, Vitaly V; Kulakovskiy, Ivan V; Dymova, Arina; Samsonova, Maria

2015-01-01

The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions. We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions. The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

On the State of Stress and Failure Prediction Near Planetary Surface Loads

NASA Astrophysics Data System (ADS)

Schultz, R. A.

1996-03-01

The state of stress surrounding planetary surface loads has been used extensively to predict failure of surface rocks and to invert this information for effective elastic thickness. As demonstrated previously, however, several factors can be important including an explicit comparison between model stresses and rock strength as well as the magnitude of calculated stress. As re-emphasized below, failure to take stress magnitudes into account can lead to erroneous predictions of near-surface faulting. This abstract results from discussions on graben formation at Fall 1995 AGU.

Flyover-noise measurement and prediction

NASA Technical Reports Server (NTRS)

Peart, Noel A.

1991-01-01

Details are presented for the measurement and prediction of aircraft flyover noise to be used for certification, research and development, community noise surveys, airport monitors, and pass fail criteria. Test details presented are applicable to all types of aircraft, both large and small, and the use of Federal Aviation Regulations (FAR) Part 36 (ref. 1) is emphasized. Accuracy of noise measurements is important. Thus, a pass-fail criterion should be used for all noise measurements. Finally, factors which influence the sound propagation and noise prediction procedures, such as atmospheric and ground effects, are also presented.

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

PubMed

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial "break in" period of the simulation.

The gendered nature of men's filial care.

PubMed

Campbell, Lori D; Martin-Matthews, Anne

2003-11-01

This paper investigates sociodemographic and family structure factors that predict men's involvement (n = 773) in different gendered dimensions of filial caregiving: traditionally male, gender neutral, and traditionally female care. The concepts that guide this research relate to family obligations or motivations to provide care, specifically, commitment to care, legitimate excuses, and caring by default. Data for this research come from the Work and Family Survey (1991-1993) conducted by the Work and Eldercare Research Group of CARNET: The Canadian Aging Research Network. Although such factors as geographic proximity and sibling network composition predict men's involvement independent of the type of task, the gendered nature of the task is important in how other factors, such as filial obligation, parental status, education, and income influence involvement in care. The findings suggest that, for traditionally male tasks, legitimate excuses or a commitment to care may play a more minor role in influencing men's involvement than is true for traditionally female tasks. Overall, this research demonstrates the importance of examining the gendered nature of the care tasks and highlights the value of the conceptual framework for explaining variations in men's filial care.

Temperamental factors in remitted depression: The role of effortful control and attentional mechanisms.

PubMed

Marchetti, Igor; Shumake, Jason; Grahek, Ivan; Koster, Ernst H W

2018-08-01

Temperamental effortful control and attentional networks are increasingly viewed as important underlying processes in depression and anxiety. However, it is still unknown whether these factors facilitate depressive and anxiety symptoms in the general population and, more specifically, in remitted depressed individuals. We investigated to what extent effortful control and attentional networks (i.e., Attention Network Task) explain concurrent depressive and anxious symptoms in healthy individuals (n = 270) and remitted depressed individuals (n = 90). Both samples were highly representative of the US population. Increased effortful control predicted a substantial decrease in symptoms of both depression and anxiety in the whole sample, whereas decreased efficiency of executive attention predicted a modest increase in depressive symptoms. Remitted depressed individuals did not show less effortful control nor less efficient attentional networks than healthy individuals. Moreover, clinical status did not moderate the relationship between temperamental factors and either depressive or anxiety symptoms. Limitations include the cross-sectional nature of the study. Our study shows that temperamental effortful control represents an important transdiagnostic process for depressive and anxiety symptoms in adults. Copyright © 2018 Elsevier B.V. All rights reserved.

Mental workload prediction based on attentional resource allocation and information processing.

PubMed

Xiao, Xu; Wanyan, Xiaoru; Zhuang, Damin

2015-01-01

Mental workload is an important component in complex human-machine systems. The limited applicability of empirical workload measures produces the need for workload modeling and prediction methods. In the present study, a mental workload prediction model is built on the basis of attentional resource allocation and information processing to ensure pilots' accuracy and speed in understanding large amounts of flight information on the cockpit display interface. Validation with an empirical study of an abnormal attitude recovery task showed that this model's prediction of mental workload highly correlated with experimental results. This mental workload prediction model provides a new tool for optimizing human factors interface design and reducing human errors.

Epidemiology and Long-term Clinical and Biologic Risk Factors for Pneumonia in Community-Dwelling Older Americans

PubMed Central

Alvarez, Karina; Loehr, Laura; Folsom, Aaron R.; Newman, Anne B.; Weissfeld, Lisa A.; Wunderink, Richard G.; Kritchevsky, Stephen B.; Mukamal, Kenneth J.; London, Stephanie J.; Harris, Tamara B.; Bauer, Doug C.; Angus, Derek C.

2013-01-01

Background: Preventing pneumonia requires better understanding of incidence, mortality, and long-term clinical and biologic risk factors, particularly in younger individuals. Methods: This was a cohort study in three population-based cohorts of community-dwelling individuals. A derivation cohort (n = 16,260) was used to determine incidence and survival and develop a risk prediction model. The prediction model was validated in two cohorts (n = 8,495). The primary outcome was 10-year risk of pneumonia hospitalization. Results: The crude and age-adjusted incidences of pneumonia were 6.71 and 9.43 cases/1,000 person-years (10-year risk was 6.15%). The 30-day and 1-year mortality were 16.5% and 31.5%. Although age was the most important risk factor (range of crude incidence rates, 1.69-39.13 cases/1,000 person-years for each 5-year increment from 45-85 years), 38% of pneumonia cases occurred in adults < 65 years of age. The 30-day and 1-year mortality were 12.5% and 25.7% in those < 65 years of age. Although most comorbidities were associated with higher risk of pneumonia, reduced lung function was the most important risk factor (relative risk = 6.61 for severe reduction based on FEV1 by spirometry). A clinical risk prediction model based on age, smoking, and lung function predicted 10-year risk (area under curve [AUC] = 0.77 and Hosmer-Lemeshow [HL] C statistic = 0.12). Model discrimination and calibration were similar in the internal validation cohort (AUC = 0.77; HL C statistic, 0.65) but lower in the external validation cohort (AUC = 0.62; HL C statistic, 0.45). The model also calibrated well in blacks and younger adults. C-reactive protein and IL-6 were associated with higher pneumonia risk but did not improve model performance. Conclusions: Pneumonia hospitalization is common and associated with high mortality, even in younger healthy adults. Long-term risk of pneumonia can be predicted in community-dwelling adults with a simple clinical risk prediction model. PMID:23744106

Background Predictors and Event-Specific Characteristics of Sexual Aggression Incidents: The Roles of Alcohol and Other Factors.

PubMed

Davis, Kelly Cue; Danube, Cinnamon L; Stappenbeck, Cynthia A; Norris, Jeanette; George, William H

2015-08-01

Sexual assault in the United States is an important public health concern. Using prospective longitudinal methods and responses from 217 community men, we examined whether background characteristics predicted subsequent sexual aggression (SA) perpetration during a 3-month follow-up period. We also examined event-specific characteristics of reported SA occurrences. Consistent with predictions, SA perpetration history, aggressive and impulsive personality traits, rape myth attitudes, and alcohol expectancies predicted SA (both non- and alcohol-involved) at follow-up. In addition, alcohol-involved assaults occurred more often with casual (vs. steady) partners but were more likely to involve condom use with casual (vs. steady) partners. Results suggest important avenues for future research and SA prevention efforts. © The Author(s) 2015.

The factor structure of complex posttraumatic stress disorder in traumatized refugees.

PubMed

Nickerson, Angela; Cloitre, Marylene; Bryant, Richard A; Schnyder, Ulrich; Morina, Naser; Schick, Matthis

2016-01-01

The construct of complex posttraumatic stress disorder (CPTSD) has attracted much research attention in previous years, however it has not been systematically evaluated in individuals exposed to persecution and displacement. Given that CPTSD has been proposed as a diagnostic category in the ICD-11, it is important that it be examined in refugee groups. In the current study, we proposed to test, for the first time, the factor structure of CPTSD proposed for the ICD-11 in a sample of resettled treatment-seeking refugees. The study sample consisted of 134 traumatized refugees from a variety of countries of origin, with approximately 93% of the sample having been exposed to torture. We used confirmatory factor analysis to examine the factor structure of CPTSD in this sample and examined the sensitivity, specificity, positive predictive power and negative predictive power of individual items in relation to the CPTSD diagnosis. Findings revealed that a two-factor higher-order model of CPTSD comprising PTSD and Difficulties in Self-Organization (χ 2 (47)=57.322, p =0.144, RMSEA=0.041, CFI=0.981, TLI=0.974) evidenced superior fit compared to a one-factor higher-order model of CPTSD (χ 2 (48)=65.745, p =0.045, RMSEA=0.053, CFI=0.968, TLI=0.956). Overall, items evidenced strong sensitivity and negative predictive power, moderate positive predictive power, and poor specificity. Findings provide preliminary evidence for the validity of the CPTSD construct with highly traumatized treatment-seeking refugees.

Application of Decision Tree in the Prediction of Periventricular Leukomalacia (PVL) Occurrence in Neonates After Neonatal Heart Surgery

PubMed Central

Jalali, Ali; Licht, Daniel J.; Nataraj, C.

2013-01-01

This paper is concerned with the prediction of the occurrence of Periventricular Leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after the cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. The decision tree classification technique has been selected as a tool for prediction of the PVL because of its capacity for discovering rules and novel associations in the data. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected from Children's Hospital of Philadelphia (CHOP) are used for this study. Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), blood hemoglobin (Hb), hemoglobin oxygen content (HbO2), oxygen saturation (SpO2) and relative cerebral blood flow (rCBF). Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and min and max of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in blood gas data, maximum rate of change in the concentration of bicarbonate ions in blood (HCO3) and minimum rate of change in the partial pressure of dissolved CO2 in the blood (PaCO2) are the most important factors for prediction of the PVL. Among vital features the kurtosis of HR and Hb are the most important parameters. PMID:23367279

Integrated deployment architecture for predictive real-time traffic routing incorporating human factors considerations.

DOT National Transportation Integrated Search

2014-05-01

As Advanced Traveler Information Systems (ATIS) are being more widely accessed by drivers, understanding drivers behavioral responses to real-time travel information through ATIS and its consequential benefits are important to the widespread deplo...

[Relationships between motivational regulation strategies, motivational factors, and learning behaviors outside the classroom].

PubMed

Umemoto, Takatoyo; Tanaka, Kenshiro

2017-04-01

This study examined the relationships among motivational regulation strategies, motivational factors, and learning behaviors outside the classroom. There are three subtypes of motivational regulation strategies: autonomous regulation strategies, cooperative strategies, and performance-focused strategies. Motivational factors included in the investigation were self-efficacy and task value, while behavioral and emotional engagement and study time were selected as learning behaviors outside the classroom. A self-report questionnaire was administered to 322 undergraduates from two universities. Multiple regression analysis revealed the use of autonomous regulation strategies, and that task value was positively correlated with engagement and study time. Moreover, self-efficacy positively predicted study time. In contrast, the use of performance strategies negatively predicted engagement. The use of cooperative strategies did not predict learning behaviors. These results indicate that motivation, as well as the regulation of motivation, were important for learning outside the classroom. The effects of regulation of motivation and motivation on learning outside the classroom are discussed in light of the current findings.

Prediction of residual shear strength of corroded reinforced concrete beams

NASA Astrophysics Data System (ADS)

Imam, Ashhad; Azad, Abul Kalam

2016-09-01

With the aim of providing experimental data on the shear capacity and behavior of corroded reinforced concrete beams that may help in the development of strength prediction models, the test results of 13 corroded and four un-corroded beams are presented. Corrosion damage was induced by accelerated corrosion induction through impressed current. Test results show that loss of shear strength of beams is mostly attributable to two important damage factors namely, the reduction in stirrups area due to corrosion and the corrosion-induced cracking of concrete cover to stirrups. Based on the test data, a method is proposed to predict the residual shear strength of corroded reinforced concrete beams in which residual shear strength is calculated first by using corrosion-reduced steel area alone, and then it is reduced by a proposed reduction factor, which collectively represents all other applicable corrosion damage factors. The method seems to yield results that are in reasonable agreement with the available test data.

The WRKY transcription factor family in Brachypodium distachyon.

PubMed

Tripathi, Prateek; Rabara, Roel C; Langum, Tanner J; Boken, Ashley K; Rushton, Deena L; Boomsma, Darius D; Rinerson, Charles I; Rabara, Jennifer; Reese, R Neil; Chen, Xianfeng; Rohila, Jai S; Rushton, Paul J

2012-06-22

A complete assembled genome sequence of wheat is not yet available. Therefore, model plant systems for wheat are very valuable. Brachypodium distachyon (Brachypodium) is such a system. The WRKY family of transcription factors is one of the most important families of plant transcriptional regulators with members regulating important agronomic traits. Studies of WRKY transcription factors in Brachypodium and wheat therefore promise to lead to new strategies for wheat improvement. We have identified and manually curated the WRKY transcription factor family from Brachypodium using a pipeline designed to identify all potential WRKY genes. 86 WRKY transcription factors were found, a total higher than all other current databases. We therefore propose that our numbering system (BdWRKY1-BdWRKY86) becomes the standard nomenclature. In the JGI v1.0 assembly of Brachypodium with the MIPS/JGI v1.0 annotation, nine of the transcription factors have no gene model and eleven gene models are probably incorrectly predicted. In total, twenty WRKY transcription factors (23.3%) do not appear to have accurate gene models. To facilitate use of our data, we have produced The Database of Brachypodium distachyon WRKY Transcription Factors. Each WRKY transcription factor has a gene page that includes predicted protein domains from MEME analyses. These conserved protein domains reflect possible input and output domains in signaling. The database also contains a BLAST search function where a large dataset of WRKY transcription factors, published genes, and an extensive set of wheat ESTs can be searched. We also produced a phylogram containing the WRKY transcription factor families from Brachypodium, rice, Arabidopsis, soybean, and Physcomitrella patens, together with published WRKY transcription factors from wheat. This phylogenetic tree provides evidence for orthologues, co-orthologues, and paralogues of Brachypodium WRKY transcription factors. The description of the WRKY transcription factor family in Brachypodium that we report here provides a framework for functional genomics studies in an important model system. Our database is a resource for both Brachypodium and wheat studies and ultimately projects aimed at improving wheat through manipulation of WRKY transcription factors.

The WRKY transcription factor family in Brachypodium distachyon

PubMed Central

2012-01-01

Background A complete assembled genome sequence of wheat is not yet available. Therefore, model plant systems for wheat are very valuable. Brachypodium distachyon (Brachypodium) is such a system. The WRKY family of transcription factors is one of the most important families of plant transcriptional regulators with members regulating important agronomic traits. Studies of WRKY transcription factors in Brachypodium and wheat therefore promise to lead to new strategies for wheat improvement. Results We have identified and manually curated the WRKY transcription factor family from Brachypodium using a pipeline designed to identify all potential WRKY genes. 86 WRKY transcription factors were found, a total higher than all other current databases. We therefore propose that our numbering system (BdWRKY1-BdWRKY86) becomes the standard nomenclature. In the JGI v1.0 assembly of Brachypodium with the MIPS/JGI v1.0 annotation, nine of the transcription factors have no gene model and eleven gene models are probably incorrectly predicted. In total, twenty WRKY transcription factors (23.3%) do not appear to have accurate gene models. To facilitate use of our data, we have produced The Database of Brachypodium distachyon WRKY Transcription Factors. Each WRKY transcription factor has a gene page that includes predicted protein domains from MEME analyses. These conserved protein domains reflect possible input and output domains in signaling. The database also contains a BLAST search function where a large dataset of WRKY transcription factors, published genes, and an extensive set of wheat ESTs can be searched. We also produced a phylogram containing the WRKY transcription factor families from Brachypodium, rice, Arabidopsis, soybean, and Physcomitrella patens, together with published WRKY transcription factors from wheat. This phylogenetic tree provides evidence for orthologues, co-orthologues, and paralogues of Brachypodium WRKY transcription factors. Conclusions The description of the WRKY transcription factor family in Brachypodium that we report here provides a framework for functional genomics studies in an important model system. Our database is a resource for both Brachypodium and wheat studies and ultimately projects aimed at improving wheat through manipulation of WRKY transcription factors. PMID:22726208

Ecological Trait Composition of Freshwater Fish Across Gradients of Environmental Variability in North-Eastern Australia

NASA Astrophysics Data System (ADS)

Kennard, M. J.; Pusey, B. J.; Arthington, A. H.

2005-05-01

North-eastern Australia encompasses 18o of latitude, monsoonal/tropical to sub-tropical/temperate climates, geomorphologically diverse rivers, and flow regimes with markedly varied seasonality, constancy and predictability. Fish assemblages in the region vary in relation to the predictability of aquatic habitat availability and other topographic, climatic and/or biogeographic factors. This paper examines how environmental, biogeographic and phylogenetic factors may constrain ecological trait composition at local and regional scales. We derived 17 categories of ecological traits to describe the morphology, behaviour, habitat, life history and trophic characteristics of 114 fish species from 64 river basins. Trait composition varied substantially across the region. The number of riffle dwelling species, maximum size and longevity of fishes was greater in the hydrologically predictable and constant rivers of the Wet Tropics region than in more unpredictable or seasonal environments. The importance of herbivory was also greater in the tropics. Historical biogeographic and phylogenetic factors may confound our ability to understand the role of environmental factors in determining spatial variation in ecological trait composition. Understanding the functional linkages between environmental drivers of fish species distributions via their ecological characteristics should provide a foundation for predicting future impacts of environmental change in a region of Australia subject to increasing human pressures.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

2018-01-01

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

Attitude to Substance Abuse: Do Personality and Socio-Demographic Factors Matter?

PubMed Central

Rahimian Boogar, Isaac; Tabatabaee, Sayed Mosa; Tosi, Jalileh

2014-01-01

Background: Substance abuse is a serious global problem that is affected by multiple psychosocial and socio-demographic factors. Objectives: This study aimed to investigate the leading factors in positive attitude and tendency toward substance abuse in terms of personality, socio-economic, and socio-demographic factors. Patients and Methods: In a cross-sectional study, 200 college students (105 females and 95 males) residing in Damghan University dormitory in northeast of Iran were recruited by random sampling from March to July 2013. The participants were instructed and asked to complete the NEO FIVE-factor Inventory, the attitude to substance abuse scale, and the demographic questionnaire. Then data were analyzed by stepwise multiple regression employing PASW 18. Results: Being male sex and neuroticism had a significant positive role in predicting positive attitude toward substance abuse in university students. In addition, agreeableness, conscientiousness, openness, and socio-economic status had a significant negative role in predicting tendency toward substance abuse (P < 0.001). Extraversion had no significant role in prediction of positive attitude to substance abuse (P > 0.05). Conclusions: Lower agreeableness, decreased conscientiousness, higher neuroticism, diminished openness, low socio-economic status, and male sex might make university students more inclined to substance abuse. Thus, it is reasonable to show the importance of these factors in tailored prevention programs. PMID:25593892

Gender differences in predicting high-risk drinking among undergraduate students.

PubMed

Wilke, Dina J; Siebert, Darcy Clay; Delva, Jorge; Smith, Michael P; Howell, Richard L

2005-01-01

The purpose of this study was to examine gender differences in college students' high-risk drinking as measured by an estimated blood alcohol concentration (eBAC) based on gender, height, weight, self-reported number of drinks, and hours spent drinking. Using a developmental/contextual framework, high-risk drinking is conceptualized as a function of relevant individual characteristics, interpersonal factors, and contextual factors regularly mentioned in the college drinking literature. Individual characteristics include race, gender, and age; interpersonal characteristics include number of sexual partners and having experienced forced sexual contact. Finally, contextual factors include Greek membership, living off-campus, and perception of peer drinking behavior. This study is a secondary data analysis of 1,422 students at a large university in the Southeast. Data were gathered from a probability sample of students through a mail survey. A three-step hierarchical logistic regression analysis showed gender differences in the pathway for high-risk drinking. For men, high-risk drinking was predicted by a combination of individual characteristics and contextual factors. For women, interpersonal factors, along with individual characteristics and contextual factors, predicted high-risk drinking, highlighting the importance of understanding female sexual relationships and raising questions about women's risk-taking behavior. Implications for prevention and assessment are discussed.

Attitude to substance abuse: do personality and socio-demographic factors matter?

PubMed

Rahimian Boogar, Isaac; Tabatabaee, Sayed Mosa; Tosi, Jalileh

2014-09-01

Substance abuse is a serious global problem that is affected by multiple psychosocial and socio-demographic factors. This study aimed to investigate the leading factors in positive attitude and tendency toward substance abuse in terms of personality, socio-economic, and socio-demographic factors. In a cross-sectional study, 200 college students (105 females and 95 males) residing in Damghan University dormitory in northeast of Iran were recruited by random sampling from March to July 2013. The participants were instructed and asked to complete the NEO FIVE-factor Inventory, the attitude to substance abuse scale, and the demographic questionnaire. Then data were analyzed by stepwise multiple regression employing PASW 18. Being male sex and neuroticism had a significant positive role in predicting positive attitude toward substance abuse in university students. In addition, agreeableness, conscientiousness, openness, and socio-economic status had a significant negative role in predicting tendency toward substance abuse (P < 0.001). Extraversion had no significant role in prediction of positive attitude to substance abuse (P > 0.05). Lower agreeableness, decreased conscientiousness, higher neuroticism, diminished openness, low socio-economic status, and male sex might make university students more inclined to substance abuse. Thus, it is reasonable to show the importance of these factors in tailored prevention programs.

Placenta growth factor not vascular endothelial growth factor A or C can predict the early recurrence after radical resection of hepatocellular carcinoma.

PubMed

Ho, Ming-Chih; Chen, Chiung-Nien; Lee, Hsinyu; Hsieh, Fon-Jou; Shun, Chia-Tung; Chang, Chi-Lun; Lai, Yeun-Tyng; Lee, Po-Huang

2007-06-08

The purpose of this study was to evaluate the relationship between the expression of PlGF in tumor tissue and clinical outcomes in HCC patients. Tumor PlGF and vascular endothelial growth factor (VEGF)-A and VEGF-C mRNA were analyzed. Results demonstrated that patients with PlGF expression levels higher than median tended to have early recurrence compared to patients with PlGF expression lower than median (P=.031). In patients with AJCC stage II-III disease, this difference was even more significant (P=.002). In contrast, VEGF-A and VEGF-C could not predict early recurrence-free survival. Since PlGF expression correlated with early recurrence of HCC, PlGF may be an important prognostic indicator in HCC.

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 modified fall risk assessment tool that is specific to physical function predicts falls in community-dwelling elderly people.

PubMed

Hirase, Tatsuya; Inokuchi, Shigeru; Matsusaka, Nobuou; Nakahara, Kazumi; Okita, Minoru

2014-01-01

Developing a practical fall risk assessment tool to predict the occurrence of falls in the primary care setting is important because investigators have reported deterioration of physical function associated with falls. Researchers have used many performance tests to predict the occurrence of falls. These performance tests predict falls and also assess physical function and determine exercise interventions. However, the need for such specialists as physical therapists to accurately conduct these tests limits their use in the primary care setting. Questionnaires for fall prediction offer an easy way to identify high-risk fallers without requiring specialists. Using an existing fall assessment questionnaire, this study aimed to identify items specific to physical function and determine whether those items were able to predict falls and estimate physical function of high-risk fallers. The analysis consisted of both retrospective and prospective studies and used 2 different samples (retrospective, n = 1871; prospective, n = 292). The retrospective study and 3-month prospective study comprised community-dwelling individuals aged 65 years or older and older adults using community day centers. The number of falls, risk factors for falls (15 risk factors on the questionnaire), and physical function determined by chair standing test (CST) and Timed Up and Go Test (TUGT) were assessed. The retrospective study selected fall risk factors related to physical function. The prospective study investigated whether the number of selected risk factors could predict falls. The predictive power was determined using the area under the receiver operating characteristic curve. Seven of the 15 risk factors were related to physical function. The area under the receiver operating characteristic curve for the sum of the selected risk factors of previous falls plus the other risk factors was 0.82 (P = .00). The best cutoff point was 4 risk factors, with sensitivity and specificity of 84% and 68%, respectively. The mean values for the CST and TUGT at the best cutoff point were 12.9 and 12.5 seconds, respectively. In the retrospective study, the values for the CST and TUGT corresponding to the best cutoff point from the prospective study were 13.2 and 11.4 seconds, respectively. This study confirms that a screening tool comprising 7 fall risk factors can be used to predict falls. The values for the CST and TUGT corresponding to the best cutoff point for the selected 7 risk factors determined in our prospective study were similar to the cutoff points for the CST and TUGT in previous studies for fall prediction. We propose that the sum of the selected risk factors of previous falls plus the other risk factors may be identified as the estimated value for physical function. These findings may contribute to earlier identification of high-risk fallers and intervention for fall prevention.

Identifying Environmental Risk Factors of Cholera in a Coastal Area with Geospatial Technologies

PubMed Central

Xu, Min; Cao, Chunxiang; Wang, Duochun; Kan, Biao

2014-01-01

Satellites contribute significantly to environmental quality and public health. Environmental factors are important indicators for the prediction of disease outbreaks. This study reveals the environmental factors associated with cholera in Zhejiang, a coastal province of China, using both Remote Sensing (RS) and Geographic information System (GIS). The analysis validated the correlation between the indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local cholera magnitude based on a ten-year monthly data from the year 1999 to 2008. Cholera magnitude has been strongly affected by the concurrent variables of SST and SSH, while OCC has a one-month time lag effect. A cholera prediction model has been established based on the sea environmental factors. The results of hot spot analysis showed the local cholera magnitude in counties significantly associated with the estuaries and rivers. PMID:25551518

Identifying environmental risk factors of cholera in a coastal area with geospatial technologies.

PubMed

Xu, Min; Cao, Chunxiang; Wang, Duochun; Kan, Biao

2014-12-29

Satellites contribute significantly to environmental quality and public health. Environmental factors are important indicators for the prediction of disease outbreaks. This study reveals the environmental factors associated with cholera in Zhejiang, a coastal province of China, using both Remote Sensing (RS) and Geographic information System (GIS). The analysis validated the correlation between the indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local cholera magnitude based on a ten-year monthly data from the year 1999 to 2008. Cholera magnitude has been strongly affected by the concurrent variables of SST and SSH, while OCC has a one-month time lag effect. A cholera prediction model has been established based on the sea environmental factors. The results of hot spot analysis showed the local cholera magnitude in counties significantly associated with the estuaries and rivers.

Geographic Profiling to Assess the Risk of Rare Plant Poaching in Natural Areas

NASA Astrophysics Data System (ADS)

Young, John A.; van Manen, Frank T.; Thatcher, Cindy A.

2011-09-01

We demonstrate the use of an expert-assisted spatial model to examine geographic factors influencing the poaching risk of a rare plant (American ginseng, Panax quinquefolius L.) in Shenandoah National Park, Virginia, USA. Following principles of the analytic hierarchy process (AHP), we identified a hierarchy of 11 geographic factors deemed important to poaching risk and requested law enforcement personnel of the National Park Service to rank those factors in a series of pair-wise comparisons. We used those comparisons to determine statistical weightings of each factor and combined them into a spatial model predicting poaching risk. We tested the model using 69 locations of previous poaching incidents recorded by law enforcement personnel. These locations occurred more frequently in areas predicted by the model to have a higher risk of poaching than random locations. The results of our study can be used to evaluate resource protection strategies and to target law enforcement activities.

Beyond Negative Pain-Related Psychological Factors: Resilience Is Related to Lower Pain Affect in Healthy Adults.

PubMed

Hemington, Kasey S; Cheng, Joshua C; Bosma, Rachael L; Rogachov, Anton; Kim, Junseok A; Davis, Karen D

2017-09-01

Resilience, a characteristic that enhances adaptation in response to stressful events, is a positive psychological factor that can predict and modulate health outcomes. However, resilience is rarely considered in pain research. Conversely, negative psychological factors (eg, anxiety, depression) are known to be related to the affective dimension of pain. It is critical to understand all potential psychological drivers of pain affect, a prominent component of chronic pain. We tested the hypothesis that higher resilience is associated with lower pain affect, above and beyond the predictive value of negative psychological factors. Healthy adults underwent psychophysical testing to acquire ratings of heat pain intensity and unpleasantness and completed the Resilience Scale, the State-Trait Anxiety Inventory (trait form), Beck Depression Inventory, Pain Catastrophizing Scale, and the Pain Vigilance and Attention Questionnaire. Multiple regression modeling (n = 68) showed resilience to be a negatively associated with pain affect (unpleasantness). Furthermore, in individuals with higher anxiety scores, resilience was protective against higher pain affect. This highlights the importance of resilience, a positive psychological factor, in the affective dimension of pain. This study is the first to assess a positive psychological factor and experimental pain affect, and has the potential to improve prediction of and treatment strategies for clinical pain. We report that resilience, a positive psychological factor, interacts with anxiety and is associated with heat pain affect (unpleasantness) in healthy individuals. Resilience may provide predictive value of chronic pain affect and treatment outcomes, and could be a target for behavioral therapy. Copyright © 2017 American Pain Society. Published by Elsevier Inc. All rights reserved.

Time Factor in the Theory of Anthropogenic Risk Prediction in Complex Dynamic Systems

NASA Astrophysics Data System (ADS)

Ostreikovsky, V. A.; Shevchenko, Ye N.; Yurkov, N. K.; Kochegarov, I. I.; Grishko, A. K.

2018-01-01

The article overviews the anthropogenic risk models that take into consideration the development of different factors in time that influence the complex system. Three classes of mathematical models have been analyzed for the use in assessing the anthropogenic risk of complex dynamic systems. These models take into consideration time factor in determining the prospect of safety change of critical systems. The originality of the study is in the analysis of five time postulates in the theory of anthropogenic risk and the safety of highly important objects. It has to be stressed that the given postulates are still rarely used in practical assessment of equipment service life of critically important systems. That is why, the results of study presented in the article can be used in safety engineering and analysis of critically important complex technical systems.

Trapped between the two cultures: Urban college students' attitudes toward science

NASA Astrophysics Data System (ADS)

Dawson, Roy Edward

Most Americans agree that science plays an important part in maintaining our leadership role in economics, health, and security. Yet when it comes to science and math we appear to be baffled. Only 25% of Americans understand the process of science well enough to make informed judgment about scientific research reported in the media (National Science Foundation, 1998). What is it that turns Americans away from science? Is it our culture, schools, families, or friends? This study investigates urban college students' attitudes toward science to determine what changes might promote increased participation in the questions, ethical implications and culture of science. Volunteers completed a science questionnaire which included multiple-choice and open-answer questions. The questions were divided into the categories of individual characteristics, home/family, peers, and school/teachers. The multiple-choice questions were analyzed with quantitative statistical techniques. The open-answer questions were used to rate each student's attitude toward science and then analyzed with qualitative methods. Thirteen factors were significant in predicting science attitude but none of them, by itself, explained a large amount of variation. A multiple regression model indicated that the significant factors (in order of importance) were watching science television with your family, having a father not employed in science, having friends who like science, and imagining yourself to be a successful student. A hierarchical multiple regression analysis indicated that the categories of individual characteristics, family, and peers were all significant contributors to the model's prediction of science attitude. School environment/teachers did not add significant predictive power to the model. The qualitative results indicated that the factors of (1) a student's previous experience in science classes and (2) the curriculum philosophy which his or her science teachers employed appeared to be the most important factors in determining a student's feelings toward science. Outliers to the science attitude profile were interviewed to determine how they maintained a positive attitude toward science when the profile predicted a negative attitude. These students appeared to be resilient and it is not clear if resiliency is a way of defeating the profile, or if resilient students incorrectly identified themselves as outliers to the profile.

Predicting Relapse among Young Adults: Psychometric Validation of the Advanced Warning of Relapse (AWARE) Scale

PubMed Central

Kelly, John F.; Hoeppner, Bettina B.; Urbanoski, Karen A.; Slaymaker, Valerie

2011-01-01

Objective Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure - the Advance WArning of RElapse scale (AWARE) scale (Miller and Harris, 2000) in an understudied but clinically important sample of young adults. Method Inpatient youth (N=303; Age 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Results Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. Conclusions The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. PMID:21700396

Predicting relapse among young adults: psychometric validation of the Advanced WArning of RElapse (AWARE) scale.

PubMed

Kelly, John F; Hoeppner, Bettina B; Urbanoski, Karen A; Slaymaker, Valerie

2011-10-01

Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure-the Advance WArning of RElapse (AWARE) scale (Miller & Harris, 2000) in an understudied but clinically important sample of young adults. Inpatient youth (N=303; Ages 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment.

PubMed

Mirroshandel, Seyed Abolghasem; Ghasemian, Fatemeh; Monji-Azad, Sara

2016-12-01

Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection. The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process. In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time. A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Pre-treatment factor structures of the Montgomery and Åsberg Depression Rating scale as predictors of response to escitalopram in Indian patients with non-psychotic major depressive disorder.

PubMed

Basu, Aniruddha; Chadda, Rakesh; Sood, Mamta; Rizwan, S A

2017-08-01

Major Depressive Disorder (MDD) is a broad heterogeneous construct resolving into several symptom-clusters by factor analysis. The aim was to find the factor structures of MDD as per Montgomery and Asberg Depression Rating Scale (MADRS) and whether they predict escitalopram response. In a longitudinal study at a tertiary institute in north India, 116 adult out-patients with non-psychotic unipolar MDD were assessed with MADRS before and after treatment with escitalopram (10-20mg) over 6-8 weeks for drug response. For total 116 patients pre-treatment four factor structures of MADRS extracted by principal component analysis with varimax rotation altogether explained a variance of 57%: first factor 'detachment' (concentration difficulty, lassitude, inability to feel); second factor 'psychic anxiety' (suicidal thoughts and inner tension); third 'mood-pessimism' (apparent sadness, reported sadness, pessimistic thoughts) and fourth 'vegetative' (decreased sleep, appetite). Eighty patients (68.9%) who completed the study had mean age 35.37±10.9 yrs, majority were male (57.5%), with mean pre-treatment MADRS score 28.77±5.18 and majority (65%) having moderate severity (MADRS <30). Among them 56 (70%) responded to escitalopram. At the end of the treatment there were significant changes in all the 4 factor structures (p<0.01). Vegetative function was an important predictor of response (p<0.01, odd's ratio: 1.3 [1.1-1.6] 95% CI). Melancholia significantly predicted non-response (p=0.04). Non-psychotic unipolar major depression having moderate severity in north Indian patients as per MADRS resolved into four factor-structures all significantly improved with adequate escitalopram treatment. Understanding the factor structure is important as they can be important predictor of escitalopram response. Copyright © 2017 Elsevier B.V. All rights reserved.

Ranking malaria risk factors to guide malaria control efforts in African highlands.

PubMed

Protopopoff, Natacha; Van Bortel, Wim; Speybroeck, Niko; Van Geertruyden, Jean-Pierre; Baza, Dismas; D'Alessandro, Umberto; Coosemans, Marc

2009-11-25

Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies. A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through "classification and regression trees", an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density. In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.

How coping styles, cognitive distortions, and attachment predict problem gambling among adolescents and young adults.

PubMed

Calado, Filipa; Alexandre, Joana; Griffiths, Mark D

2017-12-01

Background and aims Recent research suggests that youth problem gambling is associated with several factors, but little is known how these factors might influence or interact each other in predicting this behavior. Consequently, this is the first study to examine the mediation effect of coping styles in the relationship between attachment to parental figures and problem gambling. Methods A total of 988 adolescents and emerging adults were recruited to participate. The first set of analyses tested the adequacy of a model comprising biological, cognitive, and family variables in predicting youth problem gambling. The second set of analyses explored the relationship between family and individual variables in problem gambling behavior. Results The results of the first set of analyses demonstrated that the individual factors of gender, cognitive distortions, and coping styles showed a significant predictive effect on youth problematic gambling, and the family factors of attachment and family structure did not reveal a significant influence on this behavior. The results of the second set of analyses demonstrated that the attachment dimension of angry distress exerted a more indirect influence on problematic gambling, through emotion-focused coping style. Discussion This study revealed that some family variables can have a more indirect effect on youth gambling behavior and provided some insights in how some factors interact in predicting problem gambling. Conclusion These findings suggest that youth gambling is a multifaceted phenomenon, and that the indirect effects of family variables are important in estimating the complex social forces that might influence adolescent decisions to gamble.

Predictors and Patterns of Local, Regional, and Distant Failure in Squamous Cell Carcinoma of the Vulva.

PubMed

Bogani, Giorgio; Cromi, Antonella; Serati, Maurizio; Uccella, Stefano; Donato, Violante Di; Casarin, Jvan; Naro, Edoardo Di; Ghezzi, Fabio

2017-06-01

To identify factors predicting for recurrence in vulvar cancer patients undergoing surgical treatment. We retrospectively evaluated data of consecutive patients with squamous cell vulvar cancer treated between January 1, 1990 and December 31, 2013. Basic descriptive statistics and multivariable analysis were used to design predicting models influencing outcomes. Five-year disease-free survival (DFS) and overall survival (OS) were analyzed using the Cox model. The study included 101 patients affected by vulvar cancer: 64 (63%) stage I, 12 (12%) stage II, 20 (20%) stage III, and 5 (5%) stage IV. After a mean (SD) follow-up of 37.6 (22.1) months, 21 (21%) recurrences occurred. Local, regional, and distant failures were recorded in 14 (14%), 6 (6%), and 3 (3%) patients, respectively. Five-year DFS and OS were 77% and 82%, respectively. At multivariate analysis only stromal invasion >2 mm (hazard ratio: 4.9 [95% confidence interval, 1.17-21.1]; P=0.04) and extracapsular lymph node involvement (hazard ratio: 9.0 (95% confidence interval, 1.17-69.5); P=0.03) correlated with worse DFS, although no factor independently correlated with OS. Looking at factors influencing local and regional failure, we observed that stromal invasion >2 mm was the only factor predicting for local recurrence, whereas lymph node extracapsular involvement predicted for regional recurrence. Stromal invasion >2 mm and lymph node extracapsular spread are the most important factors predicting for local and regional failure, respectively. Studies evaluating the effectiveness of adjuvant treatment in high-risk patients are warranted.

Pathways from parental knowledge and warmth to adolescent marijuana use: an extension to the theory of planned behavior.

PubMed

Lac, Andrew; Alvaro, Eusebio M; Crano, William D; Siegel, Jason T

2009-03-01

Despite research indicating that effective parenting plays an important protective role in adolescent risk behaviors, few studies have applied theory to examine this link with marijuana use, especially with national data. In the current study (N = 2,141), we hypothesized that parental knowledge (of adolescent activities and whereabouts) and parental warmth are antecedents of adolescents' marijuana beliefs-attitudes, subjective norms, and perceived behavioral control-as posited by the Theory of Planned Behavior (TPB; Ajzen 1991). These three types of beliefs were hypothesized to predict marijuana intention, which in turn was hypothesized to predict marijuana consumption. Results of confirmatory factor analyses corroborated the psychometric properties of the two-factor parenting structure as well as the five-factor structure of the TPB. Further, the proposed integrative predictive framework, estimated with a latent structural equation model, was largely supported. Parental knowledge inversely predicted pro-marijuana attitudes, subjective norms, and perceived behavioral control; parental warmth inversely predicted pro-marijuana attitudes and subjective norms, ps < .001. Marijuana intention (p < .001), but not perceived behavioral control, predicted marijuana use 1 year later. In households with high parental knowledge, parental warmth also was perceived to be high (r = .54, p < .001). Owing to the analysis of nationally representative data, results are generalizable to the United States population of adolescents 12-18 years of age.

Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Nikolić, Vlastimir; Petković, Dalibor; Lazov, Lyubomir; Milovančević, Miloš

2016-07-01

Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R2 = 0.9653). The worst prediction was observed for dross area per unit length (R2 = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter.

Molecular Biomarkers of Cancer Stem/Progenitor Cells Associated with Progression, Metastases, and Treatment Resistance of Aggressive Cancers

PubMed Central

Mimeault, Murielle; Batra, Surinder K.

2014-01-01

The validation of novel diagnostic, prognostic, and predictive biomarkers and therapeutic targets in tumor cells is of critical importance for optimizing the choice and efficacy of personalized therapies. Importantly, recent advances have led to the identification of gene-expression signatures in cancer cells, including cancer stem/progenitor cells, in the primary tumors, exosomes, circulating tumor cells (CTC), and disseminated cancer cells at distant metastatic sites. The gene-expression signatures may help to improve the accuracy of diagnosis and predict the therapeutic responses and overall survival of patients with cancer. Potential biomarkers in cancer cells include stem cell–like markers [CD133, aldehyde dehydrogenase (ALDH), CD44, and CD24], growth factors, and their cognate receptors [epidermal growth factor receptor (EGFR), EGFRvIII, and HER2], molecules associated with epithelial–mesenchymal transition (EMT; vimentin, N-cadherin, snail, twist, and Zeb1), regulators of altered metabolism (phosphatidylinositol-3′ kinase/Akt/mTOR), and drug resistance (multidrug transporters and macrophage inhibitory cytokine-1). Moreover, different pluripotency-associated transcription factors (Oct3/4, Nanog, Sox2, and Myc) and microRNAs that are involved in the epigenetic reprogramming and acquisition of stem cell–like properties by cancer cells during cancer progression may also be exploited as molecular biomarkers to predict the risk of metastases, systemic treatment resistance, and disease relapse of patients with cancer. PMID:24273063

Investigating the importance of various individual, interpersonal, organisational and demographic variables when predicting job burnout in disability support workers.

PubMed

Vassos, Maria V; Nankervis, Karen L

2012-01-01

Previous research has highlighted that factors such as large workload, role ambiguity, lack of support from colleagues, and challenging behaviour are associated with higher levels of burnout within the disability support worker (DSW) population. The aim of this research was to investigate which factors contribute the most to the prediction of the three facets of burnout--feeling exhausted and overextended by one's work (emotional exhaustion), detached and callous responses towards work (depersonalisation) and a lack of achievement and productivity within one's role (personal accomplishment). The factors chosen for analysis within this research were analysed within four categories linked to theories of burnout development (individual, interpersonal, organisational and demographic). A sample of 108 DSWs completed a questionnaire booklet that contained standardised measures of burnout and job stressors related to disability work. Results highlighted the importance of predictors such as challenging behaviour (interpersonal), workload (individual), supervisor support (individual), work-home conflict (individual), job feedback (individual), role ambiguity (organisational), low job status (organisational), role conflict (organisational), gender (demographic) and work hours (demographic) when predicting one or more of the facets of burnout. In conclusion, disability services and organisations may benefit from focusing on remodelling their staff-related organisational practices in order to prevent the development of burnout in their DSWs (e.g., increase supervision and support practices). Copyright © 2012 Elsevier Ltd. All rights reserved.

Satisfaction with Health Care among Latinas

PubMed Central

Abraído-Lanza, Ana F.; Céspedes, Amarilis; Daya, Shaira; Flórez, Karen R.; White, Kellee

2013-01-01

Despite growing interest in disparities in access to health care, relatively little is known about different facets of care among Latinas, their satisfaction with the care they receive, and the predictors of satisfaction. This study examined whether various health care access and context factors, the quality of the patient-physician interaction, and medical mistrust predict satisfaction with health care among Latina immigrants in New York City. Structured interviews were conducted with 220 Latinas predominantly from the Dominican Republic and aged 40 years or over. Of the access to health care variables examined, greater waiting time predicted dissatisfaction with health care. Greater quality of the patient-physician interaction predicted less dissatisfaction. The effect of the patient-physician interaction on dissatisfaction was mediated, in part, by waiting time. The results illustrate the important role of specific health care factors in satisfaction with care. PMID:21551929

A Superior Kirchhoff Method for Aeroacoustic Noise Prediction: The Ffowcs Williams-Hawkings Equation

NASA Technical Reports Server (NTRS)

Brentner, Kenneth S.

1997-01-01

The prediction of aeroacoustic noise is important; all new aircraft must meet noise certification requirements. Local noise standards can be even more stringent. The NASA noise reduction goal is to reduce perceived noise levels by a factor of two in 10 years. The objective of this viewgraph presentation is to demonstrate the superiority of the FW-H approach over the Kirchoff method for aeroacoustics, both analytically and numerically.

Development of Rail Temperature Prediction Model : Research Results

DOT National Transportation Integrated Search

2008-06-01

Preventing track buckling is important to the railroad industry's goal of operational safety. It is a common practice for railroads to impose slow orders during hot weather when the risk of track buckling is high. Numerous factors affect track buckli...

Predictors of suicidal ideation in older people: a decision tree analysis.

PubMed

Handley, Tonelle E; Hiles, Sarah A; Inder, Kerry J; Kay-Lambkin, Frances J; Kelly, Brian J; Lewin, Terry J; McEvoy, Mark; Peel, Roseanne; Attia, John R

2014-11-01

Suicide among older adults is a major public health issue worldwide. Although studies have identified psychological, physical, and social contributors to suicidal thoughts in older adults, few have explored the specific interactions between these factors. This article used a novel statistical approach to explore predictors of suicidal ideation in a community-based sample of older adults. Prospective cohort study. Participants aged 55-85 years were randomly selected from the Hunter Region, a large regional center in New South Wales, Australia. Baseline psychological, physical, and social factors, including psychological distress, physical functioning, and social support, were used to predict suicidal ideation at the 5-year follow-up. Classification and regression tree modeling was used to determine specific risk profiles for participants depending on their individual well-being in each of these key areas. Psychological distress was the strongest predictor, with 25% of people with high distress reporting suicidal ideation. Within high psychological distress, lower physical functioning significantly increased the likelihood of suicidal ideation, with high distress and low functioning being associated with ideation in 50% of cases. A substantial subgroup reported suicidal ideation in the absence of psychological distress; dissatisfaction with social support was the most important predictor among this group. The performance of the model was high (area under the curve: 0.81). Decision tree modeling enabled individualized "risk" profiles for suicidal ideation to be determined. Although psychological factors are important for predicting suicidal ideation, both physical and social factors significantly improved the predictive ability of the model. Assessing these factors may enhance identification of older people at risk of suicidal ideation. Copyright © 2014. Published by Elsevier Inc.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

PubMed

Chi, Chih-Lin; Zeng, Wenjun; Oh, Wonsuk; Borson, Soo; Lenskaia, Tatiana; Shen, Xinpeng; Tonellato, Peter J

2017-12-01

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Depression is more than the sum-score of its parts: individual DSM symptoms have different risk factors

PubMed Central

Fried, Eiko I.; Nesse, Randolph M.; Zivin, Kara; Guille, Constance; Sen, Srijan

2014-01-01

Background For diagnostic purposes, the nine symptoms that compose the DSM-5 criteria for Major Depressive Disorder (MDD) are assumed to be interchangeable indicators of one underlying disorder, implying that they should all have similar risk factors. The present study investigates this hypothesis, utilizing a population cohort that shifts from low to elevated depression levels. Methods We assessed the nine DSM-5 MDD criterion symptoms and seven depression risk factors (personal and family MDD history, sex, childhood stress, neuroticism, work hours, and stressful life events) in a longitudinal study of medical interns prior to and throughout internship (n=1289). We tested if risk factors varied across symptoms, and whether a latent disease model could account for heterogeneity between symptoms. Results All MDD symptoms increased significantly during residency training. Four risk factors predicted increases in unique subsets of PHQ-9 symptoms over time (depression history, childhood stress, sex, and stressful life events), while neuroticism and work hours predicted increases in all symptoms, albeit to varying magnitudes. MDD family history did not predict increases in any symptom. The strong heterogeneity of associations persisted after controlling for a latent depression factor. Conclusions The influence of risk factors varies substantially across DSM depression criterion symptoms. Since symptoms are etiologically heterogeneous, considering individual symptoms in addition to depression diagnosis might offer important insights obfuscated by symptom sum-scores. PMID:24289852

CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes

PubMed Central

Hestand, Matthew S; van Galen, Michiel; Villerius, Michel P; van Ommen, Gert-Jan B; den Dunnen, Johan T; 't Hoen, Peter AC

2008-01-01

Background The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments. Results We have developed a novel tool, "CORE_TF" (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor binding sites in promoters of co-regulated genes. To improve upon existing binding site predictions, the tool searches for position weight matrices from the TRANSFACR database that are over-represented in an experimental set compared to a random set of promoters and identifies cross-species conservation of the predicted transcription factor binding sites. The algorithm has been evaluated with expression and chromatin-immunoprecipitation on microarray data. We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content, which is a unique feature of our tool. Conclusion The program CORE_TF is accessible in a user friendly web interface at . It provides a table of over-represented transcription factor binding sites in the users input genes' promoters and a graphical view of evolutionary conserved transcription factor binding sites. In our test data sets it successfully predicts target transcription factors and their binding sites. PMID:19036135

Student Buy-In Toward Formative Assessments: The Influence of Student Factors and Importance for Course Success †

PubMed Central

Brazeal, Kathleen R.; Couch, Brian A.

2017-01-01

Formative assessment (FA) techniques, such as pre-class assignments, in-class activities, and post-class homework, have been shown to improve student learning. While many students find these techniques beneficial, some students may not understand how they support learning or may resist their implementation. Improving our understanding of FA buy-in has important implications, since buy-in can potentially affect whether students fully engage with and learn from FAs. We investigated FAs in 12 undergraduate biology courses to understand which student characteristics influenced buy-in toward FAs and whether FA buy-in predicted course success. We administered a mid-semester survey that probed student perceptions toward several different FA types, including activities occurring before, during, and after class. The survey included closed-ended questions aligned with a theoretical framework outlining key FA objectives. We used factor analysis to calculate an overall buy-in score for each student and general linear models to determine whether certain characteristics were associated with buy-in and whether buy-in predicted exam scores and course grades. We found that unfixed student qualities, such as perceptions, behaviors, and beliefs, consistently predicted FA buy-in, while fixed characteristics, including demographics, previous experiences, and incoming performance metrics, had more limited effects. Importantly, we found that higher buy-in toward most FA types predicted higher exam scores and course grades, even when controlling for demographic characteristics and previous academic performance. We further discuss steps that instructors can take to maximize student buy-in toward FAs. PMID:28512523

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.

Sediment compaction and pore pressure prediction in deepwater basin of the South China Sea: Estimation from ODP and IODP drilling well data

NASA Astrophysics Data System (ADS)

Xie, Yangbing; Wu, Tuoyu; Sun, Jin; Zhang, Hanyu; Wang, Jiliang; Gao, Jinwei; Chen, Chuanxu

2018-02-01

Overpressure in deepwater basins not only causes serious soft sediment deformation, but also significantly affects the safety of drilling operations. Therefore, prediction of overpressure in sediments has become an important task in deepwater oil exploration and development. In this study, we analyze the drilling data from ODP Leg 184 Sites 1144, 1146, and 1148, and IODP Leg 349 Sites U1431, U1432, U1433, and U1435 to study the sediment compaction and controls in the northern South China Sea. Sedimentation rate, sediment content, distribution area, and buried depth are the factors that influence sediment compaction in the deepwater basin of the South China Sea. Among these factors, the sediment content is the most important. The fitted normal compacted coefficients and mudline porosity for an interval of 50 m shows disciplinary variation versus depth. The pore pressure predicted from different fitted results shows varying overpressure situations. The normal compaction trend from Site 1144 reflects the porosity variation trend in stable deposition basins in the northern South China Sea. The predicted pore pressure shows overpressure at Site 1144, which is attributed to compaction disequilibrium. Nevertheless, the mixed lithology column may influence the predicted over-pressure at Site 1148, which is responsible for the confusing result. Above all, we find that sediment compaction should serve as a proxy for pore pressure in the deepwater basin of the South China Sea.

What is worse? A hierarchy of family-related risk factors predicting alcohol use in adolescence.

PubMed

Kuntsche, Emmanuel N; Kuendig, Hervé

2006-01-01

The aim of the present study was to determine if family structure, perception of excessive drinking in the family, and family bonding hold a graduated importance in predicting adolescent alcohol use and their association with peers who drink excessively. Three nested linear structural models were calculated separately for frequent and excessive drinking, based on a sample of 3,127 eighth and ninth graders in Switzerland (mean age 15.3, SD 0.8) surveyed in spring 2002 in the context of the "Health Behavior in School-Aged Children (HBSC)" study. The results confirm that the perception of excessive drinking in the family is more closely related to both frequent and excessive drinking than family structure, and family bonding is more closely related than drinking perception. Adjusting for both socio-demographic variables and the association with peers who drink excessively only slightly changed the results. To predict an association with the latter, family structure was more important than the perception of drinking, but family bonding remained the predominant predictor. The results stress the graduated importance of family-related risk factors: by listening to their children's worries, by spending their free time with them, and by providing help when needed, parents might have the possibility to actively minimize the risk of frequent and excessive drinking regardless of whether they are frequent excessive drinkers or live without a partner.

Design of Critical Components

NASA Technical Reports Server (NTRS)

Hendricks, Robert C.; Zaretsky, Erwin V.

2001-01-01

Critical component design is based on minimizing product failures that results in loss of life. Potential catastrophic failures are reduced to secondary failures where components removed for cause or operating time in the system. Issues of liability and cost of component removal become of paramount importance. Deterministic design with factors of safety and probabilistic design address but lack the essential characteristics for the design of critical components. In deterministic design and fabrication there are heuristic rules and safety factors developed over time for large sets of structural/material components. These factors did not come without cost. Many designs failed and many rules (codes) have standing committees to oversee their proper usage and enforcement. In probabilistic design, not only are failures a given, the failures are calculated; an element of risk is assumed based on empirical failure data for large classes of component operations. Failure of a class of components can be predicted, yet one can not predict when a specific component will fail. The analogy is to the life insurance industry where very careful statistics are book-kept on classes of individuals. For a specific class, life span can be predicted within statistical limits, yet life-span of a specific element of that class can not be predicted.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

PubMed

Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

2017-11-01

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

The temporal stability and predictive validity of pupils' causal attributions for difficult classroom behaviour.

PubMed

Lambert, Nathan; Miller, Andy

2010-12-01

Recent studies have investigated the causal attributions for difficult pupil behaviour made by teachers, pupils, and parents but none have investigated the temporal stability or predictive validity of these attributions. This study examines the causal attributions made for difficult classroom behaviour by students on two occasions 30 months apart. The longitudinal stability of these attributions is considered as is the predictive validity of the first set of attributions in relation to teachers' later judgments about individual students' behaviour. Two hundred and seventeen secondary school age pupils (114 males, 103 females) provided data on the two occasions. Teachers also rated each student's behaviour at the two times. A questionnaire listing 63 possible causes of classroom misbehaviour was delivered to pupils firstly when they were in Year 7 (aged 11-12) and then again, 30 months later. Responses were analysed through exploratory factor analysis (EFA). Additionally, teachers were asked to rate the standard of behaviour of each of the students on the two occasions. EFA of the Years 7 and 10 data indicated that pupils' attributions yielded broadly similar five-factor models with the perceived relative importance of these factors remaining the same. Analysis also revealed a predictive relationship between pupils' attributions regarding the factor named culture of misbehaviour in Year 7, and teachers' judgments of their standard of behaviour in Year 10. The present study suggests that young adolescents' causal attributions for difficult classroom behaviour remain stable over time and are predictive of teachers' later judgments about their behaviour.

Child Maltreatment and Risky Sexual Behavior.

PubMed

Thompson, Richard; Lewis, Terri; Neilson, Elizabeth C; English, Diana J; Litrownik, Alan J; Margolis, Benyamin; Proctor, Laura; Dubowitz, Howard

2017-02-01

Risky sexual behavior is a serious public health problem. Child sexual abuse is an established risk factor, but other forms of maltreatment appear to elevate risky behavior. The mechanisms by which child maltreatment influence risk are not well understood. This study used data from 859 high-risk youth, followed through age 18. Official reports of each form of maltreatment were coded. At age 16, potential mediators (trauma symptoms and substance use) were assessed. At age 18, risky sexual behavior (more than four partners, unprotected sex, unassertiveness in sexual refusal) was assessed. Neglect significantly predicted unprotected sex. Substance use predicted unprotected sex and four or more partners but did not mediate the effects of maltreatment. Trauma symptoms predicted unprotected sex and mediated effects of emotional maltreatment on unprotected sex and on assertiveness in sexual refusal and the effects of sexual abuse on unprotected sex. Both neglect and emotional maltreatment emerged as important factors in risky sexual behavior. Trauma symptoms appear to be an important pathway by which maltreatment confers risk for risky sexual behavior. Interventions to reduce risky sexual behavior should include assessment and treatment for trauma symptoms and for history of child maltreatment in all its forms.

Fine motor skills and early comprehension of the world: two new school readiness indicators.

PubMed

Grissmer, David; Grimm, Kevin J; Aiyer, Sophie M; Murrah, William M; Steele, Joel S

2010-09-01

Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and quantifying their importance by determining which of children's developing skills measured around kindergarten entrance would predict later reading and math achievement. This article extends Duncan et al.'s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified kindergarten math and reading readiness and attention as the primary long-term predictors but found no effects from social skills or internalizing and externalizing behavior. We incorporated motor skills measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later achievement. Using one of the data sets, we also predicted later science scores and incorporated an additional early test of general knowledge of the social and physical world as a predictor. We found that the test of general knowledge was by far the strongest predictor of science and reading and also contributed significantly to predicting later math, making the content of this test another important kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much stronger overall predictors of later math, reading, and science scores than early math and reading scores alone.

The Power of Personality

PubMed Central

Roberts, Brent W.; Kuncel, Nathan R.; Shiner, Rebecca; Caspi, Avshalom; Goldberg, Lewis R.

2015-01-01

The ability of personality traits to predict important life outcomes has traditionally been questioned because of the putative small effects of personality. In this article, we compare the predictive validity of personality traits with that of socioeconomic status (SES) and cognitive ability to test the relative contribution of personality traits to predictions of three critical outcomes: mortality, divorce, and occupational attainment. Only evidence from prospective longitudinal studies was considered. In addition, an attempt was made to limit the review to studies that controlled for important background factors. Results showed that the magnitude of the effects of personality traits on mortality, divorce, and occupational attainment was indistinguishable from the effects of SES and cognitive ability on these outcomes. These results demonstrate the influence of personality traits on important life outcomes, highlight the need to more routinely incorporate measures of personality into quality of life surveys, and encourage further research about the developmental origins of personality traits and the processes by which these traits influence diverse life outcomes. PMID:26151971

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Analysis of Factors Influencing Hydration Site Prediction Based on Molecular Dynamics Simulations

PubMed Central

2015-01-01

Water contributes significantly to the binding of small molecules to proteins in biochemical systems. Molecular dynamics (MD) simulation based programs such as WaterMap and WATsite have been used to probe the locations and thermodynamic properties of hydration sites at the surface or in the binding site of proteins generating important information for structure-based drug design. However, questions associated with the influence of the simulation protocol on hydration site analysis remain. In this study, we use WATsite to investigate the influence of factors such as simulation length and variations in initial protein conformations on hydration site prediction. We find that 4 ns MD simulation is appropriate to obtain a reliable prediction of the locations and thermodynamic properties of hydration sites. In addition, hydration site prediction can be largely affected by the initial protein conformations used for MD simulations. Here, we provide a first quantification of this effect and further indicate that similar conformations of binding site residues (RMSD < 0.5 Å) are required to obtain consistent hydration site predictions. PMID:25252619

Longitudinal predictors of subjective recovery in psychosis.

PubMed

Law, Heather; Shryane, Nick; Bentall, Richard P; Morrison, Anthony P

2016-07-01

Research has highlighted the importance of recovery as defined by the service user, and suggests a link to negative emotion, although little is known about the role of negative emotion in predicting subjective recovery. To investigate longitudinal predictors of variability in recovery scores with a focus on the role of negative emotion. Participants (n = 110) with experience of psychosis completed measures of psychiatric symptoms, social functioning, subjective recovery, depression, hopelessness and self-esteem at baseline and 6 months later. Path analysis was used to examine predictive factors for recovery and negative emotion. Subjective recovery scores were predicted by negative emotion, positive self-esteem and hopelessness, and to a lesser extent by symptoms and functioning. Current recovery score was not predicted by past recovery score after accounting for past symptoms, current hopelessness and current positive self-esteem. Psychosocial factors and negative emotion appear to be the strongest longitudinal predictors of variation in subjective recovery, rather than psychiatric symptoms. © The Royal College of Psychiatrists 2016.

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol

PubMed Central

2014-01-01

Background Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. Methods/Design In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. Discussion This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions. PMID:24507749

A Concept Mapping Study of Physicians' Perceptions of Factors Influencing Management and Control of Hypertension in Sub-Saharan Africa

PubMed Central

Iwelunmor, Juliet; Blackstone, Sarah; Gyamfi, Joyce; Airhihenbuwa, Collins; Plange-Rhule, Jacob; Tayo, Bamidele; Adanu, Richard; Ogedegbe, Gbenga

2015-01-01

Hypertension, once a rare problem in Sub-Saharan Africa (SSA), is predicted to be a major cause of death by 2020 with mortality rates as high as 75%. However, comprehensive knowledge of provider-level factors that influence optimal management is limited. The objective of the current study was to discover physicians' perceptions of factors influencing optimal management and control of hypertension in SSA. Twelve physicians attending the Cardiovascular Research Training (CaRT) Institute at the University of Ghana, College of Health Sciences, were invited to complete a concept mapping process that included brainstorming the factors influencing optimal management and control of hypertension in patients, sorting and organizing the factors into similar domains, and rating the importance and feasibility of efforts to address these factors. The highest ranked important and feasible factors include helping patients accept their condition and availability of adequate equipment to enable the provision of needed care. The findings suggest that patient self-efficacy and support, physician-related factors, policy factors, and economic factors are important aspects that must be addressed to achieve optimal hypertension management. Given the work demands identified by physicians, future research should investigate cost-effective strategies of shifting physician responsibilities to well-trained no-physician clinicians in order to improve hypertension management. PMID:26550488

Validation of the German version of the short form of the dysfunctional beliefs and attitudes about sleep scale (DBAS-16).

PubMed

Lang, Christin; Brand, Serge; Holsboer-Trachsler, Edith; Pühse, Uwe; Colledge, Flora; Gerber, Markus

2017-06-01

Research shows that dysfunctional sleep-related cognitions play an important role in the development, maintenance and exacerbation of insomnia. This study examines the factorial validity, psychometric properties and both concurrent and predictive validity of the German version of the 16-item DBAS (dysfunctional beliefs and attitudes about sleep) scale. Data was collected in 864 vocational students from the German-speaking part of Switzerland (43% females, M age  = 17.9 years). Data collection took place twice within a 10-month interval. The students completed a German translation of the DBAS-16, the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), and provided information about their psychological functioning. Descriptive statistics, factorial validity, internal consistency, gender differences, concurrent, and predictive validity were examined. Confirmatory factor analysis supported the 4-factor structure of the DBAS-16. All factors (consequences, worry/helplessness, expectations, medication) were positively correlated and had acceptable psychometric properties. Females reported higher scores across all DBAS measures. Weak-to-moderate correlations were found between dysfunctional sleep-related beliefs, insomnia and poor sleep quality. Dysfunctional sleep-related beliefs were also associated with decreased psychological functioning, and consistently predicted insomnia and poor psychological functioning at follow-up, even after controlling for socio-demographic background and baseline levels. The present study provides support for the validity and psychometric properties of the German version of the DBAS-16. Most importantly, it corroborates the relevance of cognitive-emotional factors in the onset and maintenance of insomnia and psychological symptoms among young people.

Major psychological factors affecting acceptance of gene-recombination technology.

PubMed

Tanaka, Yutaka

2004-12-01

The purpose of this study was to verify the validity of a causal model that was made to predict the acceptance of gene-recombination technology. A structural equation model was used as a causal model. First of all, based on preceding studies, the factors of perceived risk, perceived benefit, and trust were set up as important psychological factors determining acceptance of gene-recombination technology in the structural equation model. An additional factor, "sense of bioethics," which I consider to be important for acceptance of biotechnology, was added to the model. Based on previous studies, trust was set up to have an indirect influence on the acceptance of gene-recombination technology through perceived risk and perceived benefit in the model. Participants were 231 undergraduate students in Japan who answered a questionnaire with a 5-point bipolar scale. The results indicated that the proposed model fits the data well, and showed that acceptance of gene-recombination technology is explained largely by four factors, that is, perceived risk, perceived benefit, trust, and sense of bioethics, whether the technology is applied to plants, animals, or human beings. However, the relative importance of the four factors was found to vary depending on whether the gene-recombination technology was applied to plants, animals, or human beings. Specifically, the factor of sense of bioethics is the most important factor in acceptance of plant gene-recombination technology and animal gene-recombination technology, and the factors of trust and perceived risk are the most important factors in acceptance of human being gene-recombination technology.

Contraceptive Embarrassment and Contraceptive Behavior among Young Single Women.

ERIC Educational Resources Information Center

Herold, Edward S.

1981-01-01

This paper determined factors predictive of contraceptive embarrassment, and the relationship of contraceptive embarrassment to contraceptive use among young unmarried females. The most important predictors found were parental attitude to premarital intercourse and sexual guilt. The embarrassment scale had significant correlations with…

Multiphase Modelling of Bacteria Removal in a CSO Stream

EPA Science Inventory

Indicator bacteria are an important determinant of water quality in many water resources management situations. They are also one of the more complex phenomena to model and predict. Sources abound, the populations are dynamic and influenced by many factors, and mobility through...

Annotation: Understanding the Development of Psychopathy

ERIC Educational Resources Information Center

Viding, Essi

2004-01-01

Background: Psychopaths are not only antisocial, but also have a callous and unemotional personality profile. This article selectively reviews evidence that psychopathic personality traits are an important factor in understanding and predicting the development of persistent antisocial conduct. Cognitive neuroscience research and more tentative…

What to do on spring break? The role of predicted, on-line, and remembered experience in future choice.

PubMed

Wirtz, Derrick; Kruger, Justin; Napa Scollon, Christie; Diener, Ed

2003-09-01

When individuals choose future activities on the basis of their past experiences, what guides those choices? The present study compared students' predicted, on-line, and remembered spring-break experiences, as well as the influence of these factors on students' desire to take a similar vacation in the future. Predicted and remembered experiences were both more positive-and, paradoxically, more negative-than on-line experiences. Of key importance, path analyses revealed that remembered experience, but neither on-line nor anticipated experience, directly predicted the desire to repeat the experience. These results suggest that although on-line measures may be superior to retrospective measures for approximating objective experience, retrospective measures may be superior for predicting choice.

Predictors of Performance on the MMSE and the DRS-2 Among American Indian Elders

PubMed Central

Jervis, Lori L.; Fickenscher, Alexandra; Beals, Janette; Cullum, C. Munro; Novins, Douglas K.; Manson, Spero M.; Arciniegas, David B.

2015-01-01

Little is known about factors that predict older American Indians’ performance on cognitive tests. This study examined 137 American Indian elders’ performance on the MMSE and the Dementia Rating Scale—Second Edition (DRS-2). Multivariate regression identified younger age, more education, not receiving Supplemental Security Income, and frequent receipt of needed health care as predictors of better performance on the MMSE. Better performance on the DRS-2 was predicted by more education, boarding school attendance, not receiving Supplemental Security Income, and frequent receipt of needed health care. This study points to the importance of economic and educational factors on cognitive test performance among American Indian elders. PMID:21037127

Predictors of performance on the MMSE and the DRS-2 among American Indian elders.

PubMed

Jervis, Lori L; Fickenscher, Alexandra; Beals, Janette; Cullum, C Munro; Novins, Douglas K; Manson, Spero M; Arciniegas, David B

2010-01-01

Little is known about factors that predict older American Indians' performance on cognitive tests. This study examined 137 American Indian elders' performance on the MMSE and the Dementia Rating Scale-Second Edition (DRS-2). Multivariate regression identified younger age, more education, not receiving Supplemental Security Income, and frequent receipt of needed health care as predictors of better performance on the MMSE. Better performance on the DRS-2 was predicted by more education, boarding school attendance, not receiving Supplemental Security Income, and frequent receipt of needed health care. This study points to the importance of economic and educational factors on cognitive test performance among American Indian elders.

Size versus electronic factors in transition metal carbide and TCP phase stability

NASA Astrophysics Data System (ADS)

Pettifor, D. G.; Seiser, B.; Margine, E. R.; Kolmogorov, A. N.; Drautz, R.

2013-09-01

The contributions of atomic size and electronic factors to the structural stability of transition metal carbides and topologically close-packed (TCP) phases are investigated. The hard-sphere model that has been used by Cottrell to rationalize the occurrence of the octahedral and trigonal local coordination polyhedra within the transition metal carbides is shown to have limitations in TiC since density functional theory (DFT) predicts that the second most metastable phase closest to the B1 (NaCl) ground state takes the B? (BN) structure type with 5-atom local coordination polyhedra with very short Ti-C bond lengths. The importance of electronic factors in the TCP phases is demonstrated by DFT predictions that the A15, ? and ? phases are stabilized between groups VI and VII of the elemental transition metals, whereas the ? and Laves phases are destabilized. The origin of this difference is related to the bimodal shape parameter of the electronic density of states by using the bond-order potential expansion of the structural energy within a canonical tight-binding model. The importance of the size factor in the TCP phases is illustrated by the DFT heats of formation for the binary systems Mo-Re, Mo-Ru, Nb-Re and Nb-Ru which show that the ? and Laves phases become more and more stable compared to A15, ? and ? as the size factor increases from Mo-Re through to Nb-Ru.

Effects of forest fragmentation on nocturnal Asian birds: A case study from Xishuangbanna, China

PubMed Central

DAYANANDA, Salindra K.; GOODALE, Eben; LEE, Myung-bok; LIU, Jia-Jia; MAMMIDES, Christos; PASION, Bonifacio O.; QUAN, Rui-Chang; SLIK, J. W. Ferry; SREEKAR, Rachakonda; TOMLINSON, Kyle W.; YASUDA, Mika

2016-01-01

Owls have the potential to be keystone species for conservation in fragmented landscapes, as the absence of these predators could profoundly change community structure. Yet few studies have examined how whole communities of owls respond to fragmentation, especially in the tropics. When evaluating the effect of factors related to fragmentation, such as fragment area and distance to the edge, on these birds, it is also important in heterogeneous landscapes to ask how ‘location factors’ such as the topography, vegetation and soil of the fragment predict their persistence. In Xishuangbanna, southwest China, we established 43 transects (200 m×60 m) within 20 forest fragments to sample nocturnal birds, both visually and aurally. We used a multimodel inference approach to identify the factors that influence owl species richness, and generalized linear mixed models to predict the occurrence probabilities of each species. We found that fragmentation factors dominated location factors, with larger fragments having more species, and four of eight species were significantly more likely to occur in large fragments. Given the potential importance of these birds on regulating small mammal and other animal populations, and thus indirectly affecting seed dispersal, we suggest further protection of large fragments and programs to increase their connectivity to the remaining smaller fragments. PMID:27265653

Prediction of earthquake-triggered landslide event sizes

NASA Astrophysics Data System (ADS)

Braun, Anika; Havenith, Hans-Balder; Schlögel, Romy

2016-04-01

Seismically induced landslides are a major environmental effect of earthquakes, which may significantly contribute to related losses. Moreover, in paleoseismology landslide event sizes are an important proxy for the estimation of the intensity and magnitude of past earthquakes and thus allowing us to improve seismic hazard assessment over longer terms. Not only earthquake intensity, but also factors such as the fault characteristics, topography, climatic conditions and the geological environment have a major impact on the intensity and spatial distribution of earthquake induced landslides. We present here a review of factors contributing to earthquake triggered slope failures based on an "event-by-event" classification approach. The objective of this analysis is to enable the short-term prediction of earthquake triggered landslide event sizes in terms of numbers and size of the affected area right after an earthquake event occurred. Five main factors, 'Intensity', 'Fault', 'Topographic energy', 'Climatic conditions' and 'Surface geology' were used to establish a relationship to the number and spatial extend of landslides triggered by an earthquake. The relative weight of these factors was extracted from published data for numerous past earthquakes; topographic inputs were checked in Google Earth and through geographic information systems. Based on well-documented recent earthquakes (e.g. Haiti 2010, Wenchuan 2008) and on older events for which reliable extensive information was available (e.g. Northridge 1994, Loma Prieta 1989, Guatemala 1976, Peru 1970) the combination and relative weight of the factors was calibrated. The calibrated factor combination was then applied to more than 20 earthquake events for which landslide distribution characteristics could be cross-checked. One of our main findings is that the 'Fault' factor, which is based on characteristics of the fault, the surface rupture and its location with respect to mountain areas, has the most important contribution for the prediction of the number (and concentration) of induced landslides. This, for instance, partly explains why the Wenchuan 2008 earthquake triggered far more landslides than the Nepal 2015 earthquake. Moreover, according to our prediction the most severe earthquake-triggered landslide event would have been the Assam 1950 earthquake (India), followed by the 2008 Wenchuan earthquake. Regarding the overall performance of our prediction method it can be seen that the number of landslides is overestimated for a series of earthquakes, while the size of the affected area is often underestimated. Especially for older events the incompleteness of the published catalogues can partly explain the overestimation of the landslide numbers. The underestimation of the affected area however is real and must be attributed to particular remote effects of earthquakes.

Prediction Model for Predicting Powdery Mildew using ANN for Medicinal Plant— Picrorhiza kurrooa

NASA Astrophysics Data System (ADS)

Shivling, V. D.; Ghanshyam, C.; Kumar, Rakesh; Kumar, Sanjay; Sharma, Radhika; Kumar, Dinesh; Sharma, Atul; Sharma, Sudhir Kumar

2017-02-01

Plant disease fore casting system is an important system as it can be used for prediction of disease, further it can be used as an alert system to warn the farmers in advance so as to protect their crop from being getting infected. Fore casting system will predict the risk of infection for crop by using the environmental factors that favor in germination of disease. In this study an artificial neural network based system for predicting the risk of powdery mildew in Picrorhiza kurrooa was developed. For development, Levenberg-Marquardt backpropagation algorithm was used having a single hidden layer of ten nodes. Temperature and duration of wetness are the major environmental factors that favor infection. Experimental data was used as a training set and some percentage of data was used for testing and validation. The performance of the system was measured in the form of the coefficient of correlation (R), coefficient of determination (R2), mean square error and root mean square error. For simulating the network an inter face was developed. Using this interface the network was simulated by putting temperature and wetness duration so as to predict the level of risk at that particular value of the input data.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Young adults' internet addiction: Prediction by the interaction of parental marital conflict and respiratory sinus arrhythmia.

PubMed

Zhang, Hui; Spinrad, Tracy L; Eisenberg, Nancy; Luo, Yun; Wang, Zhenhong

2017-10-01

The aim of the current study was to address the potential moderating roles of respiratory sinus arrhythmia (RSA; baseline and suppression) and participant sex in the relation between parents' marital conflict and young adults' internet addiction. Participants included 105 (65 men) Chinese young adults who reported on their internet addiction and their parents' marital conflict. Marital conflict interacted with RSA suppression to predict internet addiction. Specifically, high RSA suppression was associated with low internet addiction, regardless of parental marital conflict; however, for participants with low RSA suppression, a positive relation between marital conflict and internet addiction was found. Internet addiction also was predicted by a significant three-way interaction among baseline RSA, marital conflict, and participant sex. Specifically, for men, marital conflict positively predicted internet addiction under conditions of low (but not high) baseline RSA. For women, marital conflict positively predicted internet addiction under conditions of high (but not low) baseline RSA. Findings highlight the importance of simultaneous consideration of physiological factors, in conjunction with family factors, in the prediction of young adults' internet addiction. Copyright © 2017 Elsevier B.V. All rights reserved.

Long-term prediction of fish growth under varying ambient temperature using a multiscale dynamic model

PubMed Central

2009-01-01

Background Feed composition has a large impact on the growth of animals, particularly marine fish. We have developed a quantitative dynamic model that can predict the growth and body composition of marine fish for a given feed composition over a timespan of several months. The model takes into consideration the effects of environmental factors, particularly temperature, on growth, and it incorporates detailed kinetics describing the main metabolic processes (protein, lipid, and central metabolism) known to play major roles in growth and body composition. Results For validation, we compared our model's predictions with the results of several experimental studies. We showed that the model gives reliable predictions of growth, nutrient utilization (including amino acid retention), and body composition over a timespan of several months, longer than most of the previously developed predictive models. Conclusion We demonstrate that, despite the difficulties involved, multiscale models in biology can yield reasonable and useful results. The model predictions are reliable over several timescales and in the presence of strong temperature fluctuations, which are crucial factors for modeling marine organism growth. The model provides important improvements over existing models. PMID:19903354

Major Histocompatibility Complex, demographic, and environmental predictors of antibody presence in a free-ranging mammal.

PubMed

Ruiz-López, María José; Monello, Ryan J; Schuttler, Stephanie G; Lance, Stacey L; Gompper, Matthew E; Eggert, Lori S

2014-12-01

Major Histocompatibility Complex (MHC) variability plays a key role in pathogen resistance, but its relative importance compared to environmental and demographic factors that also influence resistance is unknown. We analyzed the MHC II DRB exon 2 for 165 raccoons (Procyon lotor) in Missouri (USA). For each animal we also determined the presence of immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to two highly virulent pathogens, canine distemper virus (CDV) and parvovirus. We investigated the role of MHC polymorphism and other demographic and environmental factors previously associated with predicting seroconversion. In addition, using an experimental approach, we studied the relative importance of resource availability and contact rates. We found important associations between IgG antibody presence and several MHC alleles and supertypes but not between IgM antibody presence and MHC. No effect of individual MHC diversity was found. For CDV, supertype S8, one allele within S8 (Prlo-DRB(∗)222), and a second allele (Prlo-DRB(∗)204) were positively associated with being IgG+, while supertype S4 and one allele within the supertype (Prlo-DRB(∗)210) were negatively associated with being IgG+. Age, year, and increased food availability were also positively associated with being IgG+, but allele Prlo-DRB(∗)222 was a stronger predictor. For parvovirus, only one MHC allele was negatively associated with being IgG+ and age and site were stronger predictors of seroconversion. Our results show that negative-frequency dependent selection is likely acting on the raccoon MHC and that while the role of MHC in relation to other factors depends on the pathogen of interest, it may be one of the most important factors predicting successful immune response. Copyright © 2014 Elsevier B.V. All rights reserved.

[Drivers of human-caused fire occurrence and its variation trend under climate change in the Great Xing'an Mountains, Northeast China].

PubMed

Li, Shun; Wu, Zhi Wei; Liang, Yu; He, Hong Shi

2017-01-01

The Great Xing'an Mountains are an important boreal forest region in China with high frequency of fire occurrences. With climate change, this region may have a substantial change in fire frequency. Building the relationship between spatial pattern of human-caused fire occurrence and its influencing factors, and predicting the spatial patterns of human-caused fires under climate change scenarios are important for fire management and carbon balance in boreal forests. We employed a spatial point pattern model to explore the relationship between the spatial pattern of human-caused fire occurrence and its influencing factors based on a database of historical fire records (1967-2006) in the Great Xing'an Mountains. The fire occurrence time was used as dependent variable. Nine abiotic (annual temperature and precipitation, elevation, aspect, and slope), biotic (vegetation type), and human factors (distance to the nearest road, road density, and distance to the nearest settlement) were selected as explanatory variables. We substituted the climate scenario data (RCP 2.6 and RCP 8.5) for the current climate data to predict the future spatial patterns of human-caused fire occurrence in 2050. Our results showed that the point pattern progress (PPP) model was an effective tool to predict the future relationship between fire occurrence and its spatial covariates. The climatic variables might significantly affect human-caused fire occurrence, while vegetation type, elevation and human variables were important predictors of human-caused fire occurrence. The human-caused fire occurrence probability was expected to increase in the south of the area, and the north and the area along the main roads would also become areas with high human-caused fire occurrence. The human-caused fire occurrence would increase by 72.2% under the RCP 2.6 scenario and by 166.7% under the RCP 8.5 scenario in 2050. Under climate change scenarios, the spatial patterns of human-caused fires were mainly influenced by the climate and human factors.

Modeling the influence of local environmental factors on malaria transmission in Benin and its implications for cohort study.

PubMed

Cottrell, Gilles; Kouwaye, Bienvenue; Pierrat, Charlotte; le Port, Agnès; Bouraïma, Aziz; Fonton, Noël; Hounkonnou, Mahouton Norbert; Massougbodji, Achille; Corbel, Vincent; Garcia, André

2012-01-01

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission--even at a very local scale--is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors.As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages.This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics.

Modeling the Influence of Local Environmental Factors on Malaria Transmission in Benin and Its Implications for Cohort Study

PubMed Central

Pierrat, Charlotte; le Port, Agnès; Bouraïma, Aziz; Fonton, Noël; Hounkonnou, Mahouton Norbert; Massougbodji, Achille; Corbel, Vincent; Garcia, André

2012-01-01

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission—even at a very local scale—is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors. As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages. This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics. PMID:22238582

The Work Ability of Hong Kong Construction Workers in Relation to Individual and Work-Related Factors.

PubMed

Ng, Jacky Y K; Chan, Alan H S

2018-05-14

The shortage in Hong Kong of construction workers is expected to worsen in future due to the aging population and increasing construction activity. Construction work is dangerous and to help reduce the premature loss of construction workers due to work-related disabilities, this study measured the work ability of 420 Hong Kong construction workers with a Work Ability Index (WAI) which can be used to predict present and future work performance. Given the importance of WAI, in this study the effects of individual and work-related factors on WAI were examined to develop and validate a WAI model to predict how individual and work-related factors affect work ability. The findings will be useful for formulating a pragmatic intervention program to improve the work ability of construction workers and keep them in the work force.

On the neutralization of acid rock drainage by carbonate and silicate minerals

NASA Astrophysics Data System (ADS)

Sherlock, E. J.; Lawrence, R. W.; Poulin, R.

1995-02-01

The net result of acid-generating and-neutralizing reactions within mining wastes is termed acid rock drainage (ARD). The oxidation of sulfide minerals is the major contributor to acid generation. Dissolution and alteration of various minerals can contribute to the neutralization of acid. Definitions of alkalinity, acidity, and buffer capacity are reviewed, and a detailed discussion of the dissolution and neutralizing capacity of carbonate and silicate minerals related to equilibium conditions, dissolution mechanism, and kinetics is provided. Factors that determine neutralization rate by carbonate and silicate minerals include: pH, PCO 2, equilibrium conditions, temperature, mineral composition and structure, redox conditions, and the presence of “foreign” ions. Similar factors affect sulfide oxidation. Comparison of rates shows sulfides react fastest, followed by carbonates and silicates. The differences in the reaction mechanisms and kinetics of neutralization have important implications in the prediction, control, and regulation of ARD. Current static and kinetic prediction methods upon which mine permitting, ARD control, and mine closure plans are based do not consider sample mineralogy or the kinetics of the acid-generating and-neutralizing reactions. Erroneous test interpretations and predictions can result. The importance of considering mineralogy for site-specific interpretation is highlighted. Uncertainty in prediction leads to difficulties for the mine operator in developing satisfactory and cost-effective control and remediation measures. Thus, the application of regulations and guidelines for waste management planning need to beflexible.

Positive surgical margins after robotic assisted radical prostatectomy: a multi-institutional study.

PubMed

Patel, Vipul R; Coelho, Rafael F; Rocco, Bernardo; Orvieto, Marcelo; Sivaraman, Ananthakrishnan; Palmer, Kenneth J; Kameh, Darien; Santoro, Luigi; Coughlin, Geoff D; Liss, Michael; Jeong, Wooju; Malcolm, John; Stern, Joshua M; Sharma, Saurabh; Zorn, Kevin C; Shikanov, Sergey; Shalhav, Arieh L; Zagaja, Gregory P; Ahlering, Thomas E; Rha, Koon H; Albala, David M; Fabrizio, Michael D; Lee, David I; Chauhan, Sanket

2011-08-01

Positive surgical margins are an independent predictive factor for biochemical recurrence after radical prostatectomy. We analyzed the incidence of and associative factors for positive surgical margins in a multi-institutional series of 8,418 robotic assisted radical prostatectomies. We analyzed the records of 8,418 patients who underwent robotic assisted radical prostatectomy at 7 institutions. Of the patients 323 had missing data on margin status. Positive surgical margins were categorized into 4 groups, including apex, bladder neck, posterolateral and multifocal. The records of 6,169 patients were available for multivariate analysis. The variables entered into the logistic regression models were age, body mass index, preoperative prostate specific antigen, biopsy Gleason score, prostate weight and pathological stage. A second model was built to identify predictive factors for positive surgical margins in the subset of patients with organ confined disease (pT2). The overall positive surgical margin rate was 15.7% (1,272 of 8,095 patients). The positive surgical margin rate for pT2 and pT3 disease was 9.45% and 37.2%, respectively. On multivariate analysis pathological stage (pT2 vs pT3 OR 4.588, p<0.001) and preoperative prostate specific antigen (4 or less vs greater than 10 ng/ml OR 2.918, p<0.001) were the most important independent predictive factors for positive surgical margins after robotic assisted radical prostatectomy. Increasing prostate weight was associated with a lower risk of positive surgical margins after robotic assisted radical prostatectomy (OR 0.984, p<0.001) and a higher body mass index was associated with a higher risk of positive surgical margins (OR 1.032, p<0.001). For organ confined disease preoperative prostate specific antigen was the most important factor that independently correlated with positive surgical margins (4 or less vs greater than 10 ng/ml OR 3.8, p<0.001). The prostatic apex followed by a posterolateral site was the most common location of positive surgical margins after robotic assisted radical prostatectomy. Factors that correlated with cancer aggressiveness, such as pathological stage and preoperative prostate specific antigen, were the most important factors independently associated with an increased risk of positive surgical margins after robotic assisted radical prostatectomy. Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Coping with a cleft II: Factors associated with psychosocial adjustment of adolescents with a cleft lip and palate and their parents.

PubMed

Berger, Zoe E; Dalton, Louise J

2011-01-01

TO explore the factors that predict psychosocial adjustment in young people with a cleft and their parents. DESIGN, PARTICIPANTS, SETTING: The study used a cross-sectional postal questionnaire design involving young people aged between 11 and 16 and their parents from two cleft services. Data are presented for 91 adolescents and their mothers. Participants completed measures of psychological adjustment, coping, social experiences, satisfaction with appearance, stressful life events, cleft-related factors, and demographic information. Psychosocial adjustment in adolescents was predicted by their social experiences and maternal well-being. Satisfaction with appearance, perceived speech problems, and the use of avoidant coping strategies were also important factors relating to their adjustment. For mothers, adjustment was predicted by use of coping strategies such as self-blame, venting, and acceptance, in addition to perceived problems with their child's hearing and the number of stressful life events experienced. The findings are discussed in relation to the concepts of adjustment, coping, satisfaction with appearance, and maternal mental health. Directions for future research are outlined, and a number of opportunities and challenges for cleft services regarding the provision of timely interventions for this age group and their families are discussed.

The influence of television and video game use on attention and school problems: a multivariate analysis with other risk factors controlled.

PubMed

Ferguson, Christopher J

2011-06-01

Research on youth mental health has increasingly indicated the importance of multivariate analyses of multiple risk factors for negative outcomes. Television and video game use have often been posited as potential contributors to attention problems, but previous studies have not always been well-controlled or used well-validated outcome measures. The current study examines the multivariate nature of risk factors for attention problems symptomatic of attention deficit hyperactivity disorder and poor school performance. A predominantly Hispanic population of 603 children (ages 10-14) and their parents/guardians responded to multiple behavioral measures. Outcome measures included parent and child reported attention problem behaviors on the Child Behavior Checklist (CBCL) as well as poor school performance as measured by grade point average (GPA). Results found that internal factors such as male gender, antisocial traits, family environment and anxiety best predicted attention problems. School performance was best predicted by family income. Television and video game use, whether total time spent using, or exposure to violent content specifically, did not predict attention problems or GPA. Television and video game use do not appear to be significant predictors of childhood attention problems. Intervention and prevention efforts may be better spent on other risk factors. Copyright © 2010 Elsevier Ltd. All rights reserved.

Cardiovascular risk

PubMed Central

Payne, Rupert A

2012-01-01

Cardiovascular disease is a major, growing, worldwide problem. It is important that individuals at risk of developing cardiovascular disease can be effectively identified and appropriately stratified according to risk. This review examines what we understand by the term risk, traditional and novel risk factors, clinical scoring systems, and the use of risk for informing prescribing decisions. Many different cardiovascular risk factors have been identified. Established, traditional factors such as ageing are powerful predictors of adverse outcome, and in the case of hypertension and dyslipidaemia are the major targets for therapeutic intervention. Numerous novel biomarkers have also been described, such as inflammatory and genetic markers. These have yet to be shown to be of value in improving risk prediction, but may represent potential therapeutic targets and facilitate more targeted use of existing therapies. Risk factors have been incorporated into several cardiovascular disease prediction algorithms, such as the Framingham equation, SCORE and QRISK. These have relatively poor predictive power, and uncertainties remain with regards to aspects such as choice of equation, different risk thresholds and the roles of relative risk, lifetime risk and reversible factors in identifying and treating at-risk individuals. Nonetheless, such scores provide objective and transparent means of quantifying risk and their integration into therapeutic guidelines enables equitable and cost-effective distribution of health service resources and improves the consistency and quality of clinical decision making. PMID:22348281

Autonomic Nervous System and Stress to Predict Secondary Ischemic Events after Transient Ischemic Attack or Minor Stroke: Possible Implications of Heart Rate Variability.

PubMed

Guan, Ling; Collet, Jean-Paul; Mazowita, Garey; Claydon, Victoria E

2018-01-01

Transient ischemic attack (TIA) and minor stroke have high risks of recurrence and deterioration into severe ischemic strokes. Risk stratification of TIA and minor stroke is essential for early effective treatment. Traditional tools have only moderate predictive value, likely due to their inclusion of the limited number of stroke risk factors. Our review follows Hans Selye's fundamental work on stress theory and the progressive shift of the autonomic nervous system (ANS) from adaptation to disease when stress becomes chronic. We will first show that traditional risk factors and acute triggers of ischemic stroke are chronic and acute stress factors or "stressors," respectively. Our first review shows solid evidence of the relationship between chronic stress and stroke occurrence. The stress response is tightly regulated by the ANS whose function can be assessed with heart rate variability (HRV). Our second review demonstrates that stress-related risk factors of ischemic stroke are correlated with ANS dysfunction and impaired HRV. Our conclusions support the idea that HRV parameters may represent the combined effects of all body stressors that are risk factors for ischemic stroke and, thus, may be of important predictive value for the risk of subsequent ischemic events after TIA or minor stroke.

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

PubMed

Razmara, Jafar; Zaboli, Mohammad Hassan; Hassankhani, Hadi

2016-11-01

Falls play a critical role in older people's life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

Autonomic Nervous System and Stress to Predict Secondary Ischemic Events after Transient Ischemic Attack or Minor Stroke: Possible Implications of Heart Rate Variability

PubMed Central

Guan, Ling; Collet, Jean-Paul; Mazowita, Garey; Claydon, Victoria E.

2018-01-01

Transient ischemic attack (TIA) and minor stroke have high risks of recurrence and deterioration into severe ischemic strokes. Risk stratification of TIA and minor stroke is essential for early effective treatment. Traditional tools have only moderate predictive value, likely due to their inclusion of the limited number of stroke risk factors. Our review follows Hans Selye’s fundamental work on stress theory and the progressive shift of the autonomic nervous system (ANS) from adaptation to disease when stress becomes chronic. We will first show that traditional risk factors and acute triggers of ischemic stroke are chronic and acute stress factors or “stressors,” respectively. Our first review shows solid evidence of the relationship between chronic stress and stroke occurrence. The stress response is tightly regulated by the ANS whose function can be assessed with heart rate variability (HRV). Our second review demonstrates that stress-related risk factors of ischemic stroke are correlated with ANS dysfunction and impaired HRV. Our conclusions support the idea that HRV parameters may represent the combined effects of all body stressors that are risk factors for ischemic stroke and, thus, may be of important predictive value for the risk of subsequent ischemic events after TIA or minor stroke. PMID:29556209

Predicting Great Lakes fish yields: tools and constraints

USGS Publications Warehouse

Lewis, C.A.; Schupp, D.H.; Taylor, W.W.; Collins, J.J.; Hatch, Richard W.

1987-01-01

Prediction of yield is a critical component of fisheries management. The development of sound yield prediction methodology and the application of the results of yield prediction are central to the evolution of strategies to achieve stated goals for Great Lakes fisheries and to the measurement of progress toward those goals. Despite general availability of species yield models, yield prediction for many Great Lakes fisheries has been poor due to the instability of the fish communities and the inadequacy of available data. A host of biological, institutional, and societal factors constrain both the development of sound predictions and their application to management. Improved predictive capability requires increased stability of Great Lakes fisheries through rehabilitation of well-integrated communities, improvement of data collection, data standardization and information-sharing mechanisms, and further development of the methodology for yield prediction. Most important is the creation of a better-informed public that will in turn establish the political will to do what is required.

Spatiotemporal patterns of evapotranspiration in response to multiple environmental factors simulated by the Community Land Model

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

Shi, Xiaoying; Mao, Jiafu; Thornton, Peter E

In this study, spatial and temporal patterns of evapotranspiration (ET) over the period of 1982-2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates derived from the FLUXNET network of eddy covariance towers using the model tree ensembles (MTE) approach. We find that climate trends and variability dominate predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, andmore » functions as the dominant factor controlling ET changes over North America, South America and Asia regions. Compared to the effect of climate change and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. For example, the aerosol deposition contribution is the third-most important factor for trends of ET over Europe, while it has the smallest impact on ET trend over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use and land cover change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.« less

Productive Vocabulary among Three Groups of Bilingual American Children: Comparison and Prediction

PubMed Central

Cote, Linda R.; Bornstein, Marc H.

2015-01-01

The importance of input factors for bilingual children’s vocabulary development was investigated. Forty-seven Argentine, 42 South Korean, 51 European American, 29 Latino immigrant, 26 Japanese immigrant, and 35 Korean immigrant mothers completed checklists of their 20-month-old children’s productive vocabularies. Bilingual children’s vocabulary sizes in each language separately were consistently smaller than their monolingual peers but only Latino bilingual children had smaller total vocabularies than monolingual children. Bilingual children’s vocabulary sizes were similar to each other. Maternal acculturation predicted the amount of input in each language, which then predicted children’s vocabulary size in each language. Maternal acculturation also predicted children’s English-language vocabulary size directly. PMID:25620820

Combined Screening for Early Detection of Pre-Eclampsia

PubMed Central

Park, Hee Jin; Shim, Sung Shin; Cha, Dong Hyun

2015-01-01

Although the precise pathophysiology of pre-eclampsia remains unknown, this condition continues to be a major cause of maternal and fetal mortality. Early prediction of pre-eclampsia would allow for timely initiation of preventive therapy. A combination of biophysical and biochemical markers are superior to other tests for early prediction of the development of pre-eclampsia. Apart from the use of parameters in first-trimester aneuploidy screening, cell-free fetal DNA quantification is emerging as a promising marker for prediction of pre-eclampsia. This article reviews the current research of the most important strategies for prediction of pre-eclampsia, including the use of maternal risk factors, mean maternal arterial pressure, ultrasound parameters, and biomarkers. PMID:26247944

A Framework for Integrating Multiple Biological Networks to Predict MicroRNA-Disease Associations.

PubMed

Peng, Wei; Lan, Wei; Yu, Zeng; Wang, Jianxin; Pan, Yi

2017-03-01

MicroRNAs have close relationship with human diseases. Therefore, identifying disease related MicroRNAs plays an important role in disease diagnosis, prognosis and therapy. However, designing an effective computational method which can make good use of various biological resources and correctly predict the associations between MicroRNA and disease is still a big challenge. Previous researchers have pointed out that there are complex relationships among microRNAs, diseases and environment factors. There are inter-relationships between microRNAs, diseases or environment factors based on their functional similarity or phenotype similarity or chemical structure similarity and so on. There are also intra-relationships between microRNAs and diseases, microRNAs and environment factors, diseases and environment factors. Moreover, functionally similar microRNAs tend to associate with common diseases and common environment factors. The diseases with similar phenotypes are likely caused by common microRNAs and common environment factors. In this work, we propose a framework namely ThrRWMDE which can integrate these complex relationships to predict microRNA-disease associations. In this framework, microRNA similarity network (MFN), disease similarity network (DSN) and environmental factor similarity network (ESN) are constructed according to certain biological properties. Then, an unbalanced three random walking algorithm is implemented on the three networks so as to obtain information from neighbors in corresponding networks. This algorithm not only can flexibly infer information from different levels of neighbors with respect to the topological and structural differences of the three networks, but also in the course of working the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment show that our method achieves better prediction performance than other state-of-the-art methods.

Prediction of Central Nervous System Side Effects Through Drug Permeability to Blood-Brain Barrier and Recommendation Algorithm.

PubMed

Fan, Jun; Yang, Jing; Jiang, Zhenran

2018-04-01

Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.

Quality of life among people with multiple sclerosis: Replication of a three-factor prediction model.

PubMed

Bishop, Malachy; Rumrill, Phillip D; Roessler, Richard T

2015-01-01

This article presents a replication of Rumrill, Roessler, and Fitzgerald's 2004 analysis of a three-factor model of the impact of multiple sclerosis (MS) on quality of life (QOL). The three factors in the original model included illness-related, employment-related, and psychosocial adjustment factors. To test hypothesized relationships between QOL and illness-related, employment-related, and psychosocial variables using data from a survey of the employment concerns of Americans with MS (N = 1,839). An ex post facto, multiple correlational design was employed incorporating correlational and multiple regression analyses. QOL was positively related to educational level, employment status, job satisfaction, and job-match, and negatively related to number of symptoms, severity of symptoms, and perceived stress level. The three-factor model explained approximately 37 percent of the variance in QOL scores. The results of this replication confirm the continuing value of the three-factor model for predicting the QOL of adults with MS, and demonstrate the importance of medical, mental health, and vocational rehabilitation interventions and services in promoting QOL.

Biological and behavioral factors modify urinary arsenic metabolic profiles in a U.S. population.

PubMed

Hudgens, Edward E; Drobna, Zuzana; He, Bin; Le, X C; Styblo, Miroslav; Rogers, John; Thomas, David J

2016-05-26

Because some adverse health effects associated with chronic arsenic exposure may be mediated by methylated arsenicals, interindividual variation in capacity to convert inorganic arsenic into mono- and di-methylated metabolites may be an important determinant of risk associated with exposure to this metalloid. Hence, identifying biological and behavioral factors that modify an individual's capacity to methylate inorganic arsenic could provide insights into critical dose-response relations underlying adverse health effects. A total of 904 older adults (≥45 years old) in Churchill County, Nevada, who chronically used home tap water supplies containing up to 1850 μg of arsenic per liter provided urine and toenail samples for determination of total and speciated arsenic levels. Effects of biological factors (gender, age, body mass index) and behavioral factors (smoking, recent fish or shellfish consumption) on patterns of arsenicals in urine were evaluated with bivariate analyses and multivariate regression models. Relative contributions of inorganic, mono-, and di-methylated arsenic to total speciated arsenic in urine were unchanged over the range of concentrations of arsenic in home tap water supplies used by study participants. Gender predicted both absolute and relative amounts of arsenicals in urine. Age predicted levels of inorganic arsenic in urine and body mass index predicted relative levels of mono- and di-methylated arsenic in urine. Smoking predicted both absolute and relative levels of arsenicals in urine. Multivariate regression models were developed for both absolute and relative levels of arsenicals in urine. Concentration of arsenic in home tap water and estimated water consumption were strongly predictive of levels of arsenicals in urine as were smoking, body mass index, and gender. Relative contributions of arsenicals to urinary arsenic were not consistently predicted by concentrations of arsenic in drinking water supplies but were more consistently predicted by gender, body mass index, age, and smoking. These findings suggest that analyses of dose-response relations in arsenic-exposed populations should account for biological and behavioral factors that modify levels of inorganic and methylated arsenicals in urine. Evidence of significant effects of these factors on arsenic metabolism may also support mode of action studies in appropriate experimental models.

Predicting compliance with an information-based residential outdoor water conservation program

NASA Astrophysics Data System (ADS)

Landon, Adam C.; Kyle, Gerard T.; Kaiser, Ronald A.

2016-05-01

Residential water conservation initiatives often involve some form of education or persuasion intended to change the attitudes and behaviors of residential consumers. However, the ability of these instruments to change attitudes toward conservation and their efficacy in affecting water use remains poorly understood. In this investigation the authors examine consumer attitudes toward complying with a persuasive water conservation program, the extent to which those attitudes predict compliance, and the influence of environmental contextual factors on outdoor water use. Results indicate that the persuasive program was successful in developing positive attitudes toward compliance, and that those attitudes predict water use. However, attitudinal variables explain a relatively small proportion of the variance in objectively measured water use behavior. Recommendations for policy are made stressing the importance of understanding both the effects of attitudes and environmental contextual factors in behavior change initiatives in the municipal water sector.

Can personality close the intention-behavior gap for healthy eating? An examination with the HEXACO personality traits.

PubMed

Monds, Lauren A; MacCann, Carolyn; Mullan, Barbara A; Wong, Cara; Todd, Jemma; Roberts, Richard D

2016-10-01

The aim of this study was to investigate the predictive and moderating effects of HEXACO personality factors, in addition to theory of planned behavior (TPB) variables, on fruit and vegetable consumption. American college students (N = 1036) from 24 institutions were administered the TPB, HEXACO and a self-reported fruit and vegetable consumption measure. The TPB predicted 11-17% of variance in fruit and vegetable consumption, with greater variance accounted for in healthy weight compared to overweight individuals. Personality did not significantly improve the prediction of behavior above TPB constructs; however, conscientiousness was a significant incremental predictor of intention in both healthy weight and overweight/obese groups. While support was found for the TPB as an important predictor of fruit and vegetable consumption in students, little support was found for personality factors. Such findings have implications for interventions designed to target students at risk of chronic disease.

Taxonomies of Higher Educational Institutions Predicted from Organizational Climate.

ERIC Educational Resources Information Center

Lysons, Art

1990-01-01

Application of the Perceived Climate Questionnaire involving senior-level staff from Australian institutions used climate factors as the basis for testing hypothesized taxonomies of the institutions. Results reinforce the relevance of contemporary management theories and demonstrate the importance of leadership styles in organizational…

Is bad intent negligible? Linking victim justice sensitivity, hostile attribution bias, and aggression.

PubMed

Bondü, Rebecca

2018-05-03

The hostile attribution bias (HAB) is a well-established risk factor for aggression. It is considered part of the suspicious mindset that may cause highly victim-justice sensitive individuals to behave uncooperatively. Thus, links of victim justice sensitivity (JS) with negative behavior, such as aggression, may be better explained by HAB. The present study tested this hypothesis in N = 279 German adolescents who rated their JS, HAB, and physical, relational, verbal, reactive, and proactive aggression. Victim JS predicted physical, relational, verbal, reactive, and proactive aggression when HAB was controlled. HAB only predicted physical and proactive aggression. There were no moderator effects. Injustice seems an important reason for aggression irrespective of whether or not it is intentionally caused, particularly among those high in victim JS. Thus, victim JS should be considered as a potential important risk factor for aggression and receive more attention by research on aggression and preventive efforts. © 2018 Wiley Periodicals, Inc.

Emotional suppression and breast cancer: validation research on the Spanish Adaptation of the Courtauld Emotional Control Scale (CECS).

PubMed

Durá, Estrella; Andreu, Yolanda; Galdón, Maria José; Ibáñez, Elena; Pérez, Sandra; Ferrando, Maite; Murgui, Sergio; Martínez, Paula

2010-05-01

Emotional suppression has played an important role in the research on psychosocial factors related to cancer. It has been argued to be an important psychological factor predicting worse psychosocial adjustment in people with cancer and it may mediate health outcomes. The reference instrument in the research on emotional suppression is the Courtauld Emotional Control Scale (CECS). The present study analysed construct validity of a new Spanish adaptation of the CECS in a sample of 175 breast cancer patients. The results confirmed the proposal by Watson and Greer claiming that the CECS is composed of three subscales that measure different dimensions, but not independent, from emotional control. The present Spanish version of the CECS showed high internal consistency in each subseale as well as the total score. According to Derogatis (BSI-18) criteria, emotional suppression predicts clinically significant distress. In short, our results support the reliability, validity and utility of this Spanish adaptation of the CECS in clinical and research settings.

Environmental Predictors of US County Mortality Patterns on a National Basis.

PubMed

Chan, Melissa P L; Weinhold, Robert S; Thomas, Reuben; Gohlke, Julia M; Portier, Christopher J

2015-01-01

A growing body of evidence has found that mortality rates are positively correlated with social inequalities, air pollution, elevated ambient temperature, availability of medical care and other factors. This study develops a model to predict the mortality rates for different diseases by county across the US. The model is applied to predict changes in mortality caused by changing environmental factors. A total of 3,110 counties in the US, excluding Alaska and Hawaii, were studied. A subset of 519 counties from the 3,110 counties was chosen by using systematic random sampling and these samples were used to validate the model. Step-wise and linear regression analyses were used to estimate the ability of environmental pollutants, socio-economic factors and other factors to explain variations in county-specific mortality rates for cardiovascular diseases, cancers, chronic obstructive pulmonary disease (COPD), all causes combined and lifespan across five population density groups. The estimated models fit adequately for all mortality outcomes for all population density groups and, adequately predicted risks for the 519 validation counties. This study suggests that, at local county levels, average ozone (0.07 ppm) is the most important environmental predictor of mortality. The analysis also illustrates the complex inter-relationships of multiple factors that influence mortality and lifespan, and suggests the need for a better understanding of the pathways through which these factors, mortality, and lifespan are related at the community level.

Environmental Predictors of US County Mortality Patterns on a National Basis

PubMed Central

Thomas, Reuben; Gohlke, Julia M.; Portier, Christopher J.

2015-01-01

A growing body of evidence has found that mortality rates are positively correlated with social inequalities, air pollution, elevated ambient temperature, availability of medical care and other factors. This study develops a model to predict the mortality rates for different diseases by county across the US. The model is applied to predict changes in mortality caused by changing environmental factors. A total of 3,110 counties in the US, excluding Alaska and Hawaii, were studied. A subset of 519 counties from the 3,110 counties was chosen by using systematic random sampling and these samples were used to validate the model. Step-wise and linear regression analyses were used to estimate the ability of environmental pollutants, socio-economic factors and other factors to explain variations in county-specific mortality rates for cardiovascular diseases, cancers, chronic obstructive pulmonary disease (COPD), all causes combined and lifespan across five population density groups. The estimated models fit adequately for all mortality outcomes for all population density groups and, adequately predicted risks for the 519 validation counties. This study suggests that, at local county levels, average ozone (0.07 ppm) is the most important environmental predictor of mortality. The analysis also illustrates the complex inter-relationships of multiple factors that influence mortality and lifespan, and suggests the need for a better understanding of the pathways through which these factors, mortality, and lifespan are related at the community level. PMID:26629706

Clinical trial: factors associated with freedom from relapse of heartburn in patients with healed reflux oesophagitis--results from the maintenance phase of the EXPO study.

PubMed

Labenz, J; Armstrong, D; Zetterstrand, S; Eklund, S; Leodolter, A

2009-06-01

Ability to predict freedom from heartburn relapse during maintenance therapy for healed reflux oesophagitis may facilitate optimal treatment choices for individual patients. To determine factors predicting freedom from heartburn relapse during maintenance proton pump inhibitor therapy in patients with healed reflux oesophagitis. This post-hoc analysis used data from the maintenance phase of the EXPO study (AstraZeneca study code: SH-NEG-0008); 2766 patients with healed reflux oesophagitis and resolved heartburn received once-daily esomeprazole 20 mg or pantoprazole 20 mg for 6 months. Multiple logistic regression analysis determined factors associated with freedom from heartburn relapse. Heartburn relapse rates were lower with esomeprazole than pantoprazole in all subgroups analysed. Esomeprazole treatment was the factor most strongly associated with freedom from heartburn relapse (odds ratio 2.08; P < 0.0001). Other factors significantly associated with freedom from heartburn relapse were Helicobacter pylori infection, greater age, non-obesity, absence of epigastric pain at baseline, pre-treatment nonsevere heartburn and GERD symptom duration < or =5 years. Several factors predict freedom from heartburn relapse during maintenance proton pump inhibitor therapy for healed reflux oesophagitis, the strongest being choice of proton pump inhibitor. These findings outline the importance of optimizing acid control and identifying predictors of relapse for effective long-term symptom management in reflux oesophagitis patients.

Personality factors and posttraumatic stress: associations in civilians one year after air attacks.

PubMed

Lecic-Tosevski, Dusica; Gavrilovic, Jelena; Knezevic, Goran; Priebe, Stefan

2003-12-01

There is an ongoing debate on which risk factors for developing posttraumatic stress symptoms are more important--personality traits reflecting vulnerability, previous stressful experiences or characteristics of the traumatic event. In this study, posttraumatic stress symptoms and their relationship with personality traits, previous stressful experiences and exposure to stressful events during air attacks in Yugoslavia were investigated. The Millon Clinical Multiaxial Inventory (MCMI; Millon, 1983), Impact of Events Scale (IES; Horowitz, Wilner, & Alvarez, 1979), Life Stressor Checklist Revised (LSCL-R; Wolfe & Kimerling, 1997), and List of Stressors were administered to a homogeneous group of medical students 1 year after the attacks. In multiple regression analyses, compulsive and passive-aggressive personality traits and a higher level of exposure to stressors during air attacks independently predicted the degree of intrusion symptoms. Avoidance symptoms were predicted by avoidant personality traits and a higher exposure to stressors both previously in life and during the attacks. In the next step, we tested in analyses of variance whether personality traits, previous stressful experiences, and stressful events during attacks as independent variables interact in predicting intrusion and avoidance symptoms. For this, students were clustered into three groups depending on their predominant personality traits. In addition to direct predictive effects, there were significant interaction effects in predicting both intrusion and avoidance. The findings suggest that each of the tested factors, i.e., personality traits, previous stressful experiences, and exposure to traumatic events may have an independent and direct influence on developing posttraumatic stress. However, the effect of these factors cannot just be added up. Rather, the factors interact in their impact on posttraumatic stress symptoms. Bigger samples and longitudinal designs will be required to understand precisely how different personality traits influence response to stressful events.

ACUTE PANCREATITIS GRAVITY PREDICTIVE FACTORS: WHICH AND WHEN TO USE THEM?

PubMed Central

FERREIRA, Alexandre de Figueiredo; BARTELEGA, Janaina Alves; URBANO, Hugo Corrêa de Andrade; de SOUZA, Iure Kalinine Ferraz

2015-01-01

Introduction: Acute pancreatitis has as its main causes lithiasic biliary disease and alcohol abuse. Most of the time, the disease shows a self-limiting course, with a rapid recovery, only with supportive treatment. However, in a significant percentage of cases, it runs with important local and systemic complications associated with high mortality rates. Aim: To present the current state of the use of these prognostic factors (predictive scores) of gravity, as the time of application, complexity and specificity. Method: A non-systematic literature review through 28 papers, with emphasis on 13 articles published in indexed journals between 2008 and 2013 using Lilacs, Medline, Pubmed. Results: Several clinical, laboratory analysis, molecular and image variables can predict the development of severe acute pancreatitis. Some of them by themselves can be determinant to the progression of the disease to a more severe form, such as obesity, hematocrit, age and smoking. Hematocrit with a value lower than 44% and serum urea lower than 20 mg/dl, both at admission, appear as risk factors for pancreatic necrosis. But the PCR differentiates mild cases of serious ones in the first 24 h. Multifactorial scores measured on admission and during the first 48 h of hospitalization have been used in intensive care units, being the most ones used: Ranson, Apache II, Glasgow, Iget and Saps II. Conclusion: Acute pancreatitis is a disease in which several prognostic factors are employed being useful in predicting mortality and on the development of the severe form. It is suggested that the association of a multifactorial score, especially the Saps II associated with Iget, may increase the prognosis accuracy. However, the professional's preferences, the experience on the service as well as the available tools, are factors that have determined the choice of the most suitable predictive score. PMID:26537149

Biomechanical and lifestyle risk factors for medial tibia stress syndrome in army recruits: a prospective study.

PubMed

Sharma, Jagannath; Golby, Jim; Greeves, Julie; Spears, Iain R

2011-03-01

Medial tibial stress syndrome (MTSS) is a common injury in active populations and has been suggested to be a result of both biomechanical and lifestyle factors. The main aim of this study was to determine prospectively whether gait biomechanics and lifestyle factors can be used as a predictor of MTSS development. British infantry male recruits (n=468) were selected for the study. Plantar pressure variables, lifestyle factors comprising smoking habit and aerobic fitness as measured by a 1.5 mile timed-run were collected on the first day of training. Injury data were collected during the 26 week training period and incidence rate was 7.9% (n=37). A logistic regression model for membership of the MTSS and non-MTSS groups was developed. An imbalance in foot pressure with greater pressure on the medial side than on the lateral side was the primary risk factor. Low aerobic fitness, as deduced from a 1.5 mile timed-run and smoking habit were also important, but were additive risk factors for MTSS. In conclusion, "poor" biomechanics were the strongest predictors of MTSS development but lifestyle factors were also important. The logistic regression model combining all three risk factors was capable of predicting 96.9% of the non-injured group and 67.5% of the MTSS group with an overall accuracy of 87.7%. While the model has yet to be validated against an external sample and limitations exist with regards to the quality of the data collected, it is nonetheless suggested that the combined analysis of biomechanical and lifestyle factors has the potential to improve the prediction of MTSS. Copyright © 2010 Elsevier B.V. All rights reserved.

Boredom proneness and emotion regulation predict emotional eating.

PubMed

Crockett, Amanda C; Myhre, Samantha K; Rokke, Paul D

2015-05-01

Emotional eating is considered a risk factor for eating disorders and an important contributor to obesity and its associated health problems. It has been suggested that boredom may be an important contributor to overeating, but has received relatively little attention. A sample of 552 college students was surveyed. Linear regression analyses found that proneness to boredom and difficulties in emotion regulation simultaneously predicted inappropriate eating behavior, including eating in response to boredom, other negative emotions, and external cues. The unique contributions of these variables to emotional eating were discussed. These findings help to further identify which individuals could be at risk for emotional eating and potentially for unhealthy weight gain. © The Author(s) 2015.

The importance of social exchange to nurses and nurse assistants: impact on retention factors.

PubMed

Trybou, Jeroen; De Pourcq, Kaat; Paeshuyse, Michel; Gemmel, Paul

2014-07-01

The purpose of this study was to test the norm of reciprocity by examining relationships between perceived organisational support (POS), the quality of leader-member exchange (LMX) and psychological contract breach (PCB) and important nurse retention factors identified in the literature. A major cause of turnover among nurses is related to unsatisfying workplaces. Previous research, mainly outside the nursing setting, found that social exchange affects employees' work-related attitudes. A cross-sectional survey was conducted on 217 nurses and nursing assistants to test and refine a model linking POS, LMX, PCB with job satisfaction, trust and turnover intentions. Hierarchical multiple linear regression revealed that POS, PCB and LMX explained significant variance in all three retention factors: job satisfaction (adjusted R² = 0.502), trust (adjusted R² = 0.462) and turnover intentions (adjusted R² = 0.196). POS and PCB predicted most strongly job satisfaction (P < 0.001) and trust (P < 0.001 and P < 0.01, respectively). LMX predicted most strongly intention to leave (P < 0.01). In our study, POS, the quality of LMX and PCB were strongly related to job satisfaction, trust and turnover intentions. Nursing managers and leaders should recognize the importance of social exchange within their organisation to build trust, satisfy and retain scarce nurses and nursing assistants. © 2013 John Wiley & Sons Ltd.

Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence.

PubMed

Akter, Rokeya; Hu, Wenbiao; Naish, Suchithra; Banu, Shahera; Tong, Shilu

2017-06-01

To assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission. Following PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer-reviewed, freely available and full-text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review. Twenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors. Due to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems. © 2017 John Wiley & Sons Ltd.

Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score.

PubMed

Cuppen, Bvj; Fritsch-Stork, Rde; Eekhout, I; de Jager, W; Marijnissen, A C; Bijlsma, Jwj; Custers, M; van Laar, J M; Lafeber, Fpjg; Welsing, Pmj

2018-01-01

In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value. In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI). For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11). No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.

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

PubMed

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

2016-01-01

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

Relapse prediction in Graves´ disease: Towards mathematical modeling of clinical, immune and genetic markers.

PubMed

Langenstein, Christoph; Schork, Diana; Badenhoop, Klaus; Herrmann, Eva

2016-12-01

Graves' disease (GD) is an important and prevalent thyroid autoimmune disorder. Standard therapy for GD consists of antithyroid drugs (ATD) with treatment periods of around 12 months but relapse is frequent. Since predictors for relapse are difficult to identify the individual decision making for optimal treatment is often arbitrary. After reviewing the literature on this topic we summarize important factors involved in GD and with respect to their potential for relapse prediction from markers before and after treatment. This information was used to design a mathematical model integrating thyroid hormone parameters, thyroid size, antibody titers and a complex algorithm encompassing genetic predisposition, environmental exposures and current immune activity in order to arrive at a prognostic index for relapse risk after treatment. In the search for a tool to analyze and predict relapse in GD mathematical modeling is a promising approach. In analogy to mathematical modeling approaches in other diseases such as viral infections, we developed a differential equation model on the basis of published clinical trials in patients with GD. Although our model needs further evaluation to be applicable in a clinical context, it provides a perspective for an important contribution to a final statistical prediction model.

Interaction between Digestive Strategy and Niche Specialization Predicts Speciation Rates across Herbivorous Mammals.

PubMed

Tran, Lucy A P

2016-04-01

Biotic and abiotic factors often are treated as mutually exclusive drivers of diversification processes. In this framework, ecological specialists are expected to have higher speciation rates than generalists if abiotic factors are the primary controls on species diversity but lower rates if biotic interactions are more important. Speciation rate is therefore predicted to positively correlate with ecological specialization in the purely abiotic model but negatively correlate in the biotic model. In this study, I show that the positive relationship between ecological specialization and speciation expected from the purely abiotic model is recovered only when a species-specific trait, digestive strategy, is modeled in the terrestrial, herbivorous mammals (Mammalia). This result suggests a more nuanced model in which the response of specialized lineages to abiotic factors is dependent on a biological trait. I also demonstrate that the effect of digestive strategy on the ecological specialization-speciation rate relationship is not due to a difference in either the degree of ecological specialization or the speciation rate between foregut- and hindgut-fermenting mammals. Together, these findings suggest that a biological trait, alongside historical abiotic events, played an important role in shaping mammal speciation at long temporal and large geographic scales.

Manganese availability is negatively associated with carbon storage in northern coniferous forest humus layers.

PubMed

Stendahl, Johan; Berg, Björn; Lindahl, Björn D

2017-11-14

Carbon sequestration below ground depends on organic matter input and decomposition, but regulatory bottlenecks remain unclear. The relative importance of plant production, climate and edaphic factors has to be elucidated to better predict carbon storage in forests. In Swedish forest soil inventory data from across the entire boreal latitudinal range (n = 2378), the concentration of exchangeable manganese was singled out as the strongest predictor (R 2  = 0.26) of carbon storage in the extensive organic horizon (mor layer), which accounts for one third of the total below ground carbon. In comparison, established ecosystem models applied on the same data have failed to predict carbon stocks (R 2  < 0.05), and in our study manganese availability overshadowed both litter production and climatic factors. We also identified exchangeable potassium as an additional strong predictor, however strongly correlated with manganese. The negative correlation between manganese and carbon highlights the importance of Mn-peroxidases in oxidative decomposition of recalcitrant organic matter. The results support the idea that the fungus-driven decomposition could be a critical factor regulating humus carbon accumulation in boreal forests, as Mn-peroxidases are specifically produced by basidiomycetes.

Identification of Transcription Factors ZmMYB111 and ZmMYB148 Involved in Phenylpropanoid Metabolism.

PubMed

Zhang, Junjie; Zhang, Shuangshuang; Li, Hui; Du, Hai; Huang, Huanhuan; Li, Yangping; Hu, Yufeng; Liu, Hanmei; Liu, Yinghong; Yu, Guowu; Huang, Yubi

2016-01-01

Maize is the leading crop worldwide in terms of both planting area and total yields, but environmental stresses cause significant losses in productivity. Phenylpropanoid compounds play an important role in plant stress resistance; however, the mechanism of their synthesis is not fully understood, especially in regard to the expression and regulation of key genes. Phenylalanine ammonia-lyase (PAL) is the first key enzyme involved in phenylpropanoid metabolism, and it has a significant effect on the synthesis of important phenylpropanoid compounds. According to the results of sequence alignments and functional prediction, we selected two conserved R2R3-MYB transcription factors as candidate genes for the regulation of phenylpropanoid metabolism. The two candidate R2R3-MYB genes, which we named ZmMYB111 and ZmMYB148, were cloned, and then their structural characteristics and phylogenetic placement were predicted and analyzed. In addition, a series of evaluations were performed, including expression profiles, subcellular localization, transcription activation, protein-DNA interaction, and transient expression in maize endosperm. Our results indicated that both ZmMYB111 and ZmMYB148 are indeed R2R3-MYB transcription factors and that they may play a regulatory role in PAL gene expression.

Discriminative value of FRAX for fracture prediction in a cohort of Chinese postmenopausal women.

PubMed

Cheung, E Y N; Bow, C H; Cheung, C L; Soong, C; Yeung, S; Loong, C; Kung, A

2012-03-01

We followed 2,266 postmenopausal Chinese women for 4.5 years to determine which model best predicts osteoporotic fracture. A model that contains ethnic-specific risk factors, some of which reflect frailty, performed as well as or better than the well-established FRAX model. Clinical risk assessment, with or without T-score, can predict fractures in Chinese postmenopausal women although it is unknown which combination of clinical risk factors is most effective. This prospective study sought to compare the accuracy for fracture prediction using various models including FRAX, our ethnic-specific clinical risk factors (CRF) and other simple models. This study is part of the Hong Kong Osteoporosis Study. A total of 2,266 treatment naïve postmenopausal women underwent clinical risk factor and bone mineral density assessment. Subjects were followed up for outcome of major osteoporotic fracture and receiver operating characteristic (ROC) curves for different models were compared. The percentage of subjects in different quartiles of risk according to various models who actually fractured was also compared. The mean age at baseline was 62.1 ± 8.5 years and mean follow-up time was 4.5 ± 2.8 years. A total of 106 new major osteoporotic fractures were reported, of which 21 were hip fractures. Ethnic-specific CRF with T-score performed better than FRAX with T-score (based on both Chinese normative and National Health and Nutrition Examination Survey (NHANES) databases) in terms of AUC comparison for prediction of major osteoporotic fracture. The two models were similar in hip fracture prediction. The ethnic-specific CRF model had a 10% higher sensitivity than FRAX at a specificity of 0.8 or above. CRF related to frailty and differences in lifestyle between populations are likely to be important in fracture prediction. Further work is required to determine which and how CRF can be applied to develop a fracture prediction model in our population.

Systematic literature review of the risk factors, comorbidities, and consequences of hypogonadism in men.

PubMed

Zarotsky, V; Huang, M-Y; Carman, W; Morgentaler, A; Singhal, P K; Coffin, D; Jones, T H

2014-11-01

The objective of this review was to summarize the literature on the risk factors, comorbidities, and consequences of male hypogonadism, which is defined as a syndrome complex that includes biochemical confirmation of low testosterone (T) and the consistent symptoms and signs associated with low T. A systematic literature search was performed in PubMed/MEDLINE, EMBASE, Cochrane Library for articles published in the last 10 years on risk factors, comorbidities, and consequences of male hypogonadism. Of the 53 relevant studies identified, nine examined potential risk factors, 14 examined potential comorbidities, and 30 examined potential consequences of male hypogonadism. Based on studies conducted in Asia, Australia, Europe, and North & South America, the important factors that predicted and correlated with hypogonadism were advanced age, obesity, a diagnosis of metabolic syndrome (MetS), and a poor general health status. Diabetes mellitus was correlated with hypogonadism in most studies, but was not established as a risk factor. Although diseases, such as coronary heart disease, hypertension, stroke, and peripheral arterial disease did not predict hypogonadism, they did correlate with incident low T. The data reviewed on potential consequences suggest that low T levels may be linked to earlier all-cause and cardiovascular related mortality among men. This literature review suggests that men with certain factors, such as advanced age, obesity, MetS, and poor general health, are more likely to have and develop hypogonadism. Low levels of T may have important long-term negative health consequences. © 2014 American Society of Andrology and European Academy of Andrology.

Recognition of predictors for mid-long term runoff prediction based on lasso

NASA Astrophysics Data System (ADS)

Xie, S.; Huang, Y.

2017-12-01

Reliable and accuracy mid-long term runoff prediction is of great importance in integrated management of reservoir. And many methods are proposed to model runoff time series. Almost all forecast lead times (LT) of these models are 1 month, and the predictors are previous runoff with different time lags. However, runoff prediction with increased LT, which is more beneficial, is not popular in current researches. It is because the connection between previous runoff and current runoff will be weakened with the increase of LT. So 74 atmospheric circulation factors (ACFs) together with pre-runoff are used as alternative predictors for mid-long term runoff prediction of Longyangxia reservoir in this study. Because pre-runoff and 74 ACFs with different time lags are so many and most of these factors are useless, lasso, which means `least absolutely shrinkage and selection operator', is used to recognize predictors. And the result demonstrates that 74 ACFs are beneficial for runoff prediction in both validation and test sets when LT is greater than 6. And there are 6 factors other than pre-runoff, most of which are with big time lag, are selected as predictors frequently. In order to verify the effect of 74 ACFs, 74 stochastic time series generated from normalized 74 ACFs are used as input of model. The result shows that these 74 stochastic time series are useless, which confirm the effect of 74 ACFs on mid-long term runoff prediction.

Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model

NASA Astrophysics Data System (ADS)

Li, Fawen; Wei, Wan; Zhao, Yong; Qiao, Jiale

2017-01-01

There is a close relationship between groundwater level in a shallow aquifer and the surface ecological environment; hence, it is important to accurately simulate and predict the groundwater level in eco-environmental construction projects. The multiple linear regression (MLR) model is one of the most useful methods to predict groundwater level (depth); however, the predicted values by this model only reflect the mean distribution of the observations and cannot effectively fit the extreme distribution data (outliers). The study reported here builds a prediction model of groundwater-depth dynamics in a shallow aquifer using the quantile regression (QR) method on the basis of the observed data of groundwater depth and related factors. The proposed approach was applied to five sites in Tianjin city, north China, and the groundwater depth was calculated in different quantiles, from which the optimal quantile was screened out according to the box plot method and compared to the values predicted by the MLR model. The results showed that the related factors in the five sites did not follow the standard normal distribution and that there were outliers in the precipitation and last-month (initial state) groundwater-depth factors because the basic assumptions of the MLR model could not be achieved, thereby causing errors. Nevertheless, these conditions had no effect on the QR model, as it could more effectively describe the distribution of original data and had a higher precision in fitting the outliers.

Predictors of insubordinate aggression among captive female rhesus macaques.

PubMed

Seil, Shannon K; Hannibal, Darcy L; Beisner, Brianne A; McCowan, Brenda

2017-11-01

Cercopithicine primates tend to have nepotistic hierarchies characterized by predictable, kinship-based dominance. Although aggression is typically directed down the hierarchy, insubordinate aggression does occur. Insubordination is important to understand because it can precipitate social upheaval and undermine group stability; however, the factors underlying it are not well understood. We test whether key social and demographic variables predict insubordination among captive female rhesus macaques. To identify factors influencing insubordination, multivariate analyses of 10,821 dyadic conflicts among rhesus macaque females were conducted, using data from six captive groups. A segmented regression analysis was used to identify dyads with insubordination. Negative binomial regression analyses and an information theoretic approach were used to assess predictors of insubordination among dyads. In the best models, weight difference (w = 1.0; IRR = 0.930), age (dominant: w = 1.0, IRR = 0.681; subordinate: w = 1.0, IRR = 1.069), the subordinate's total number of allies (w = 0.727, IRR = 1.060) or non-kin allies (w = 0.273, IRR = 1.165), the interaction of the dominant's kin allies and weight difference (w = 0.938, IRR = 1.046), violation of youngest ascendancy (w = 1.0; IRR = 2.727), and the subordinate's maternal support (w = 1.0; IRR = 2.928), are important predictors of insubordination. These results show that both intrinsic and social factors influence insubordinate behavior. This adds to evidence of the importance of intrinsic factors and flexibility in a social structure thought to be rigid and predetermined by external factors. Further, because insubordination can precipitate social overthrow, determining predictors of insubordination will shed light on mechanisms underlying stability in nepotistic societies. © 2017 Wiley Periodicals, Inc.

[The survival prediction model of advanced gallbladder cancer based on Bayesian network: a multi-institutional study].

PubMed

Tang, Z H; Geng, Z M; Chen, C; Si, S B; Cai, Z Q; Song, T Q; Gong, P; Jiang, L; Qiu, Y H; He, Y; Zhai, W L; Li, S P; Zhang, Y C; Yang, Y

2018-05-01

Objective: To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery. Methods: The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test. Results: A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8(>23.77 months), the importance ranking showed that NMLN(0.366 6), margin(0.350 1), T stage(0.319 2) and pathological grade(0.258 9) were the top 4 prognosis factors influencing the postoperative MST.These four factors were taken as observation variables to get the probability of patients in different survival periods.Basing on these results, a survival prediction score system including NMLN, margin, T stage and pathological grade was designed, the median survival time(month) of 4-9 points were 66.8, 42.4, 26.0, 9.0, 7.5 and 2.3, respectively, there was a statistically significant difference in the different points( P <0.01). Conclusions: The survival prediction model of GBC based on Bayesian network has high accuracy.NMLN, margin, T staging and pathological grade are the top 4 risk factors affecting the survival of patients with advanced GBC who underwent curative resection.The survival prediction score system based on these four factors could be used to predict the survival and to guide the decision making of patients with advanced GBC.

"Yes, we can!": Perceptions of collective efficacy sources in volleyball.

PubMed

Fransen, Katrien; Vanbeselaere, Norbert; Exadaktylos, Vasileios; Vande Broek, Gert; De Cuyper, Bert; Berckmans, Daniel; Ceux, Tanja; De Backer, Maarten; Boen, Filip

2012-01-01

Collective efficacy can be defined as a group's shared confidence that they will successfully achieve their goal. We examined which behaviours and events are perceived as sources of collective efficacy beliefs in a volleyball context. In study 1, volleyball coaches from the highest volleyball leagues (n = 33) in Belgium indicated the most important sources of collective efficacy. This list was then adapted based on the literature and on feedback given by an expert focus group, resulting in a 40-item questionnaire. In Study 2, coaches and players from all levels of volleyball in Belgium (n = 2365) rated each of these sources on their predictive value for collective efficacy. A principal component analysis revealed that the 40 sources could be divided into eight internally consistent factors. Positive supportive communication (e.g., enthusiasm after making a point) was identified as the factor most predictive for positive collective efficacy beliefs. The factor referring to the negative emotional reactions of players (e.g., discouraging body language) was the most predictive for negative collective efficacy beliefs. These findings offer a starting point for the design of continuous measurements of collective efficacy through observation.

Computational Prediction and Validation of BAHD1 as a Novel Molecule for Ulcerative Colitis

NASA Astrophysics Data System (ADS)

Zhu, Huatuo; Wan, Xingyong; Li, Jing; Han, Lu; Bo, Xiaochen; Chen, Wenguo; Lu, Chao; Shen, Zhe; Xu, Chenfu; Chen, Lihua; Yu, Chaohui; Xu, Guoqiang

2015-07-01

Ulcerative colitis (UC) is a common inflammatory bowel disease (IBD) producing intestinal inflammation and tissue damage. The precise aetiology of UC remains unknown. In this study, we applied a rank-based expression profile comparative algorithm, gene set enrichment analysis (GSEA), to evaluate the expression profiles of UC patients and small interfering RNA (siRNA)-perturbed cells to predict proteins that might be essential in UC from publicly available expression profiles. We used quantitative PCR (qPCR) to characterize the expression levels of those genes predicted to be the most important for UC in dextran sodium sulphate (DSS)-induced colitic mice. We found that bromo-adjacent homology domain (BAHD1), a novel heterochromatinization factor in vertebrates, was the most downregulated gene. We further validated a potential role of BAHD1 as a regulatory factor for inflammation through the TNF signalling pathway in vitro. Our findings indicate that computational approaches leveraging public gene expression data can be used to infer potential genes or proteins for diseases, and BAHD1 might act as an indispensable factor in regulating the cellular inflammatory response in UC.

Risk terrain modeling predicts child maltreatment.

PubMed

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Experimental evaluation of a mathematical model for predicting transfer efficiency of a high volume-low pressure air spray gun.

PubMed

Tan, Y M; Flynn, M R

2000-10-01

The transfer efficiency of a spray-painting gun is defined as the amount of coating applied to the workpiece divided by the amount sprayed. Characterizing this transfer process allows for accurate estimation of the overspray generation rate, which is important for determining a spray painter's exposure to airborne contaminants. This study presents an experimental evaluation of a mathematical model for predicting the transfer efficiency of a high volume-low pressure spray gun. The effects of gun-to-surface distance and nozzle pressure on the agreement between the transfer efficiency measurement and prediction were examined. Wind tunnel studies and non-volatile vacuum pump oil in place of commercial paint were used to determine transfer efficiency at nine gun-to-surface distances and four nozzle pressure levels. The mathematical model successfully predicts transfer efficiency within the uncertainty limits. The least squares regression between measured and predicted transfer efficiency has a slope of 0.83 and an intercept of 0.12 (R2 = 0.98). Two correction factors were determined to improve the mathematical model. At higher nozzle pressure settings, 6.5 psig and 5.5 psig, the correction factor is a function of both gun-to-surface distance and nozzle pressure level. At lower nozzle pressures, 4 psig and 2.75 psig, gun-to-surface distance slightly influences the correction factor, while nozzle pressure has no discernible effect.

Cowpeas in Northern Ghana and the Factors that Predict Caregivers’ Intention to Give Them to Schoolchildren

PubMed Central

Abizari, Abdul-Razak; Pilime, Nerisa; Armar-Klemesu, Margaret; Brouwer, Inge D.

2013-01-01

Background Cowpeas are important staple legumes among the rural poor in northern Ghana. Our objectives were to assess the iron and zinc content of cowpea landraces and identify factors that predict the intention of mothers/caregivers to give cowpeas to their schoolchildren. Methods and Findings We performed biochemical analysis on 14 landraces of cowpeas and assessed the opinion of 120 caregiver-child pairs on constructs based on the combined model of the Theory of Planned Behaviour and Health Belief Model. We used correlations and multiple regressions to measure simple associations between constructs and identify predictive constructs. Cowpea landraces contained iron and zinc in the range of 4.9–8.2 mg/100 g d.w and 2.7–4.1 mg/100 g d.w respectively. The landraces also contained high amounts of phytate (477–1110 mg/100 g d.w) and polyphenol (327–1055 mg/100 g d.w). Intention of mothers was strongly associated (rs = 0.72, P<0.001) with and predicted (β = 0.63, P<0.001) behaviour. The constructs, barriers (β = –0.42, P = 0.001) and attitudes towards behaviour (β = 0.25, P<0.028), significantly predicted intention albeit the predictive ability of the model was weak. Conclusions We conclude that some cowpea landraces from northern Ghana have appreciable amounts of iron and zinc but probably with poor bioavailability. Attitudes towards giving cowpeas and perception of barriers are important predictors of caregivers’ intention to give cowpeas to their schoolchildren. Finally our results suggest that increasing knowledge on nutritional benefits of cowpeas may increase health values caregivers hold for their children in support of giving cowpeas to schoolchildren. PMID:23951289

Prediction of treatment refractoriness in ulcerative colitis and Crohn's disease--do we have reliable markers?

PubMed

Gelbmann, C M

2000-05-01

Treatment refractoriness is a severe problem in the management of patients with ulcerative colitis and Crohn's disease. Despite some promising new therapeutic approaches, corticosteroids are still the preferential primary treatment for moderate to severe Crohn's disease and of severe ulcerative colitis. However, clinical response to corticosteroids varies, and many patients are resistant to such treatment. Since corticosteroids have frequent and even severe side effects, and toxicity increases with chronic steroid intake, factors predictive of response to such treatment would be very helpful for decisions on further management of these patients. At least in severe attacks of ulcerative colitis, the consensus seems to be that a high frequency of bowel movements as well as a high C-reactive protein and low serum albumin recorded after a few days of intensive medical treatment are important signs for early prediction of treatment failure in the majority of the patients. In Crohn's disease thus far, data on predictive factors are conflicting. No reliable marker with sufficient predictive value for treatment refractoriness could be identified. This might be due to the tremendous heterogeneity of Crohn's disease with many clinical phenotypes, which requires subgroup analysis with sufficient numbers of patients. Corticosteroids as well as other immunomodulating and immunosuppressive medications interfere with the immune system, which plays a central role in the mediation of intestinal inflammation. Treatment refractoriness might have its origin in specific immunological peculiarities eventually reflected in abnormal immunological, biochemical, and clinical parameters. Further exploration of those parameters to predict treatment refractoriness in patients with ulcerative colitis or Crohn's disease is of great clinical importance for safe and efficient management of patients.

Can changes in psychosocial factors and residency explain the decrease in physical activity during the transition from high school to college or university?

PubMed

Van Dyck, Delfien; De Bourdeaudhuij, Ilse; Deliens, Tom; Deforche, Benedicte

2015-04-01

When students make the transition from high school to college or university, their physical activity (PA) levels decrease strongly. Consequently, it is of crucial importance to identify the determinants of this decline in PA. The study aims were to (1) examine changes in psychosocial factors in students during the transition from high school to college/university, (2) examine if changes in psychosocial factors and residency can predict changes in PA, and (3) investigate the moderating effects of residency on the relationship between changes in psychosocial factors and changes in PA. Between March 2008 and October 2010, 291 Flemish students participated in a longitudinal study, with baseline measurements during the final year of high school and follow-up measurements at the start of second year of college/university. At both time points, participants completed a questionnaire assessing demographics, active transportation, leisure-time sports, psychosocial variables, and residency. Repeated measures MANOVA analyses and multiple moderated hierarchic regression analyses were conducted. Modeling, self-efficacy, competition-related benefits, and health-related, external and social barriers decreased, while health-related benefits and time-related barriers increased from baseline to follow-up. Decreases in modeling and time-related barriers were associated with a decrease in active transportation (adjusted R(2) = 3.2%); residency, decreases in self-efficacy, competition-related benefits, and increases in health- and time-related barriers predicted a decrease in leisure-time sports (adjusted R(2) = 29.3%). Residency only moderated two associations between psychosocial factors and changes in PA. Residency and changes in psychosocial factors were mainly important to explain the decrease in leisure-time sports. Other factors such as distance to college/university are likely more important to explain the decrease in active transportation; these are worth exploring in future studies. Because few interactions were found, similar interventions, focusing on self-efficacy, time management, and increasing perceived benefits may be effective to increase leisure-time sports in all students.

Folate intake in a Swedish adult population: Food sources and predictive factors.

PubMed

Monteagudo, Celia; Scander, Henrik; Nilsen, Bente; Yngve, Agneta

2017-01-01

Introduction : Folate plays an important role in cell metabolism, but international studies show that intake is currently below recommendations, especially among women. The study objective was to identify folate food sources by food group, gender, and age group, and to identify factors influencing folate intake, based on food consumption data for Swedish adults in the 2010-11 Riksmaten study. M ethods : The sample included a representative Swedish population aged 18-80 years ( n  = 1657; 56.3% female). Food and nutrient intakes were estimated from self-reported food records during 4 consecutive days. Food consumption was categorized into 26 food groups. Stepwise regression was used to analyze food groups as folate sources for participants. Factors predicting the highest folate intake (third tertile) were determined by logistic regression analysis. Results : Vegetables and pulses represented the most important folate source for all age groups and both genders, especially in women aged 45-64 years (49.7% of total folate intake). The next folate source in importance was dairy products for the youngest group (18-30 years), bread for men, and fruit and berries for women. The likelihood of being in the highest tertile of folate intake (odds ratio = 1.69, 95% confidence interval 1.354-2.104) was higher for men. Influencing factors for folate intake in the highest tertile were low body mass index and high educational level in the men, and high educational level, vegetarian diet, organic product consumption, non-smoking, and alcohol consumption within recommendations in the women. Conclusion : This study describes the folate intake per food group of Swedish adults according to the 2010-11 Riksmaten survey, identifying vegetables and pulses as the most important source. Data obtained on factors related to folate consumption may be useful for the development of specific nutrition education programs to increase the intake of this vitamin in high-risk groups.

Folate intake in a Swedish adult population: Food sources and predictive factors

PubMed Central

Monteagudo, Celia; Scander, Henrik; Nilsen, Bente; Yngve, Agneta

2017-01-01

ABSTRACT Introduction: Folate plays an important role in cell metabolism, but international studies show that intake is currently below recommendations, especially among women. The study objective was to identify folate food sources by food group, gender, and age group, and to identify factors influencing folate intake, based on food consumption data for Swedish adults in the 2010–11 Riksmaten study. Methods: The sample included a representative Swedish population aged 18–80 years (n = 1657; 56.3% female). Food and nutrient intakes were estimated from self-reported food records during 4 consecutive days. Food consumption was categorized into 26 food groups. Stepwise regression was used to analyze food groups as folate sources for participants. Factors predicting the highest folate intake (third tertile) were determined by logistic regression analysis. Results: Vegetables and pulses represented the most important folate source for all age groups and both genders, especially in women aged 45–64 years (49.7% of total folate intake). The next folate source in importance was dairy products for the youngest group (18–30 years), bread for men, and fruit and berries for women. The likelihood of being in the highest tertile of folate intake (odds ratio = 1.69, 95% confidence interval 1.354–2.104) was higher for men. Influencing factors for folate intake in the highest tertile were low body mass index and high educational level in the men, and high educational level, vegetarian diet, organic product consumption, non-smoking, and alcohol consumption within recommendations in the women. Conclusion: This study describes the folate intake per food group of Swedish adults according to the 2010–11 Riksmaten survey, identifying vegetables and pulses as the most important source. Data obtained on factors related to folate consumption may be useful for the development of specific nutrition education programs to increase the intake of this vitamin in high-risk groups. PMID:28659736

Possession attachment predicts cell phone use while driving.

PubMed

Weller, Joshua A; Shackleford, Crystal; Dieckmann, Nathan; Slovic, Paul

2013-04-01

Distracted driving has become an important public health concern. However, little is known about the predictors of this health-risking behavior. One overlooked risk factor for distracted driving is the perceived attachment that one feels toward his or her phone. Prior research has suggested that individuals develop bonds toward objects, and qualitative research suggests that the bond between young drivers and their phones can be strong. It follows that individuals who perceive a strong attachment to their phone would be more likely to use it, even when driving. In a nationally representative sample of young drivers (17-28 years), participants (n = 1,006) completed a survey about driving behaviors and phone use. Risk perception surrounding cell phone use while driving and perceived attachment to one's phone were assessed by administering factor-analytically derived scales that were created as part of a larger project. Attachment toward one's phone predicted the proportion of trips in which a participant reported using their cell phone while driving, beyond that accounted for by risk perception and overall phone use. Further, attachment predicted self-reported distracted driving behaviors, such as the use of social media while driving. Attachment to one's phone may be an important but overlooked risk factor for the engagement of potentially health-risking driving behaviors. Understanding that phone attachment may adversely affect driving behaviors has the potential to inform prevention and intervention efforts designed to reduce distracted driving behaviors, especially in young drivers. 2013 APA, all rights reserved

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Proceedings of the International Congress/Actes du 6eme Congres International (6th) Held in Nice, France on 5-9 July 1993: Noise as a Public Health Problem. Volume 3

DTIC Science & Technology

1993-07-09

other risk factors , suggests that previous studies may have overestimated NIHL due to insufficient control of noise-free subjects. ISO 7029 does not...contain factors for hearing protection which are based on very limited data. Recent studies in the U.S. and in France have provided new insights into...noise susceptibility, there are certainly cochlear factors which are of some importance. 133 PREDICTIONS OF NIHL BASED ON ANIMAL STUDIES ... Cochlear

Population-level genetic variation and climate change in a biodiversity hotspot.

PubMed

Schierenbeck, Kristina A

2017-01-01

Estimated future climate scenarios can be used to predict where hotspots of endemism may occur over the next century, but life history, ecological and genetic traits will be important in informing the varying responses within myriad taxa. Essential to predicting the consequences of climate change to individual species will be an understanding of the factors that drive genetic structure within and among populations. Here, I review the factors that influence the genetic structure of plant species in California, but are applicable elsewhere; existing levels of genetic variation, life history and ecological characteristics will affect the ability of an individual taxon to persist in the presence of anthropogenic change. Persistence in the face of climate change is likely determined by life history characteristics: dispersal ability, generation time, reproductive ability, degree of habitat specialization, plant-insect interactions, existing genetic diversity and availability of habitat or migration corridors. Existing levels of genetic diversity in plant populations vary based on a number of evolutionary scenarios that include endemism, expansion since the last glacial maximum, breeding system and current range sizes. A number of well-documented examples are provided from the California Floristic Province. Some predictions can be made for the responses of plant taxa to rapid environmental changes based on geographic position, evolutionary history, existing genetic variation, and ecological amplitude. The prediction of how species will respond to climate change will require a synthesis drawing from population genetics, geography, palaeontology and ecology. The important integration of the historical factors that have shaped the distribution and existing genetic structure of California's plant taxa will enable us to predict and prioritize the conservation of species and areas most likely to be impacted by rapid climate change, human disturbance and invasive species. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Factors affecting seasonal habitat use, and predicted range of two tropical deer in Indonesian rainforest

NASA Astrophysics Data System (ADS)

Rahman, Dede Aulia; Gonzalez, Georges; Haryono, Mohammad; Muhtarom, Aom; Firdaus, Asep Yayus; Aulagnier, Stéphane

2017-07-01

There is an urgent recognized need for conservation of tropical forest deer. In order to identify some environmental factors affecting conservation, we analyzed the seasonal habitat use of two Indonesian deer species, Axis kuhlii in Bawean Island and Muntiacus muntjak in south-western Java Island, in response to several physical, climatic, biological, and anthropogenic variables. Camera trapping was performed in different habitat types during both wet and dry season to record these elusive species. The highest number of photographs was recorded in secondary forest and during the dry season for both Bawean deer and red muntjac. In models, anthropogenic and climatic variables were the main predictors of habitat use. Distances to cultivated area and to settlement were the most important for A. kuhlii in the dry season. Distances to cultivated area and annual rainfall were significant for M. muntjak in both seasons. Then we modelled their predictive range using Maximum entropy modelling (Maxent). We concluded that forest landscape is the fundamental scale for deer management, and that secondary forests are potentially important landscape elements for deer conservation. Important areas for conservation were identified accounting of habitat transformation in both study areas.

An Examination of the Relationship between Motor Coordination and Executive Functions in Adolescents

ERIC Educational Resources Information Center

Rigoli, Daniela; Piek, Jan P.; Kane, Robert; Oosterlaan, Jaap

2012-01-01

Aim: Research suggests important links between motor coordination and executive functions. The current study examined whether motor coordination predicts working memory, inhibition, and switching performance, extending previous research by accounting for attention-deficit-hyperactivity disorder (ADHD) symptomatology and other confounding factors,…

Future Time Orientation Predicts Academic Engagement among First-Year University Students

ERIC Educational Resources Information Center

Horstmanshof, Louise; Zimitat, Craig

2007-01-01

Background: Enhancing student engagement is considered an important strategy for improving retention. Students' Time Perspective is an under-researched factor that may significantly influence student engagement. Aims: This study examines interrelationships between elements of student engagement and relationship with Time Perspective. We propose…

Patch occupancy of stream fauna across a land cover gradient in the southern Appalachians, USA

Treesearch

John R. Frisch; James T. Peterson; Kristen K. Cecala; John C. Maerz; C. Rhett Jackson; Ted Gragson; Catherine M. Pringle

2016-01-01

We modeled patch occupancy to examine factors that best predicted the prevalence of four functionally important focal stream consumers (Tallaperla spp., Cambarus spp., Pleurocera proxima, and Cottus bairdi) among 37 reaches within the Little Tennessee River basin of the...

The Prevalence of Insomnia on School Principals and Superintendents in Missouri

ERIC Educational Resources Information Center

Williams, Regina Johnson

2017-01-01

Chronic insomnia can lead to depression, anxiety, cognitive difficulties, workplace absenteeism, underperformance, and high employee turnover as well as medical issues such as Alzheimer's, hypertension, myocardial infarction, obesity, and diabetes. Researchers have argued that healthful sleep is the most important factor in predicting longevity…

Pore-scale simulations to determine the applied hydrodynamic torque and colloid immobilization

USDA-ARS?s Scientific Manuscript database

The importance of adhesive and diffusion forces on colloid retention is well established, and theory has been developed in the literature to predict these factors. Conversely, the role of hydrodynamic forces and torques on colloid retention has received considerably less attention. Recent research ...

Main predictors of periphyton species richness depend on adherence strategy and cell size

PubMed Central

Siqueira, Tadeu; Landeiro, Victor Lemes; Rodrigues, Liliana; Bonecker, Claudia Costa; Rodrigues, Luzia Cleide; Santana, Natália Fernanda; Thomaz, Sidinei Magela; Bini, Luis Mauricio

2017-01-01

Periphytic algae are important components of aquatic ecosystems. However, the factors driving periphyton species richness variation remain largely unexplored. Here, we used data from a subtropical floodplain (Upper Paraná River floodplain, Brazil) to quantify the influence of environmental variables (total suspended matter, temperature, conductivity, nutrient concentrations, hydrology, phytoplankton biomass, phytoplankton species richness, aquatic macrophyte species richness and zooplankton density) on overall periphytic algal species richness and on the richness of different algal groups defined by morphological traits (cell size and adherence strategy). We expected that the coefficients of determination of the models estimated for different trait-based groups would be higher than the model coefficient of determination of the entire algal community. We also expected that the relative importance of explanatory variables in predicting species richness would differ among algal groups. The coefficient of determination for the model used to predict overall periphytic algal species richness was higher than the ones obtained for models used to predict the species richness of the different groups. Thus, our first prediction was not supported. Species richness of aquatic macrophytes was the main predictor of periphyton species richness of the entire community and a significant predictor of the species richness of small mobile, large mobile and small-loosely attached algae. Abiotic variables, phytoplankton species richness, chlorophyll-a concentration, and hydrology were also significant predictors, depending on the group. These results suggest that habitat heterogeneity (as proxied by aquatic macrophytes richness) is important for maintaining periphyton species richness in floodplain environments. However, other factors played a role, suggesting that the analysis of species richness of different trait-based groups unveils relationships that were not detectable when the entire community was analysed together. PMID:28742122

Analysis of fluid film lubrication in artificial hip joint replacements with surfaces of high elastic modulus.

PubMed

Jin, Z M; Dowson, D; Fisher, J

1997-01-01

Lubrication mechanisms and contact mechanics have been analysed for total hip joint replacements made from hard bearing surfaces such as metal-on-metal and ceramic-on-ceramic. A similar analysis for ultra-high molecular weight polyethylene (UHMWPE) against a hard bearing surface has also been carried out and used as a reference. The most important factor influencing the predicted lubrication film thickness has been found to be the radial clearance between the ball and the socket. Full fluid film lubrication may be achieved in these hard/hard bearings provided that the surface finish of the bearing surface and the radial clearance are chosen correctly and maintained. Furthermore, there is a close relation between the predicted contact half width and the predicted lubrication film thickness. Therefore, it is important to analyse the contact mechanics in artificial hip joint replacements. Practical considerations of manufacturing these bearing surfaces have also been discussed.

Prediction of the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors.

PubMed

Takahara, Mitsuyoshi; Katakami, Naoto; Kaneto, Hideaki; Noguchi, Midori; Shimomura, Iichiro

2014-01-01

The aim of the current study was to develop a predictive model of insulin resistance using general health checkup data in Japanese employees with one or more metabolic risk factors. We used a database of 846 Japanese employees with one or more metabolic risk factors who underwent general health checkup and a 75-g oral glucose tolerance test (OGTT). Logistic regression models were developed to predict existing insulin resistance evaluated using the Matsuda index. The predictive performance of these models was assessed using the C statistic. The C statistics of body mass index (BMI), waist circumference and their combined use were 0.743, 0.732 and 0.749, with no significant differences. The multivariate backward selection model, in which BMI, the levels of plasma glucose, high-density lipoprotein (HDL) cholesterol, log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment remained, had a C statistic of 0.816, with a significant difference compared to the combined use of BMI and waist circumference (p＜0.01). The C statistic was not significantly reduced when the levels of log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment were simultaneously excluded from the multivariate model (p=0.14). On the other hand, further exclusion of any of the remaining three variables significantly reduced the C statistic (all p＜0.01). When predicting the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors, it is important to take into consideration the BMI and fasting plasma glucose and HDL cholesterol levels.

Evaluation of easily measured risk factors in the prediction of osteoporotic fractures

PubMed Central

Bensen, Robert; Adachi, Jonathan D; Papaioannou, Alexandra; Ioannidis, George; Olszynski, Wojciech P; Sebaldt, Rolf J; Murray, Timothy M; Josse, Robert G; Brown, Jacques P; Hanley, David A; Petrie, Annie; Puglia, Mark; Goldsmith, Charlie H; Bensen, W

2005-01-01

Background Fracture represents the single most important clinical event in patients with osteoporosis, yet remains under-predicted. As few premonitory symptoms for fracture exist, it is of critical importance that physicians effectively and efficiently identify individuals at increased fracture risk. Methods Of 3426 postmenopausal women in CANDOO, 40, 158, 99, and 64 women developed a new hip, vertebral, wrist or rib fracture, respectively. Seven easily measured risk factors predictive of fracture in research trials were examined in clinical practice including: age (<65, 65–69, 70–74, 75–79, 80+ years), rising from a chair with arms (yes, no), weight (< 57, ≥ 57kg), maternal history of hip facture (yes, no), prior fracture after age 50 (yes, no), hip T-score (>-1, -1 to >-2.5, ≤-2.5), and current smoking status (yes, no). Multivariable logistic regression analysis was conducted. Results The inability to rise from a chair without the use of arms (3.58; 95% CI: 1.17, 10.93) was the most significant risk factor for new hip fracture. Notable risk factors for predicting new vertebral fractures were: low body weight (1.57; 95% CI: 1.04, 2.37), current smoking (1.95; 95% CI: 1.20, 3.18) and age between 75–79 years (1.96; 95% CI: 1.10, 3.51). New wrist fractures were significantly identified by low body weight (1.71, 95% CI: 1.01, 2.90) and prior fracture after 50 years (1.96; 95% CI: 1.19, 3.22). Predictors of new rib fractures include a maternal history of a hip facture (2.89; 95% CI: 1.04, 8.08) and a prior fracture after 50 years (2.16; 95% CI: 1.20, 3.87). Conclusion This study has shown that there exists a variety of predictors of future fracture, besides BMD, that can be easily assessed by a physician. The significance of each variable depends on the site of incident fracture. Of greatest interest is that an inability to rise from a chair is perhaps the most readily identifiable significant risk factor for hip fracture and can be easily incorporated into routine clinical practice. PMID:16143046

7Be(p,gamma)8B S-factor from Ab Initio Wave Functions

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

Navratil, P; Bertulani, C A; Caurier, E

2006-10-12

There has been a significant progress in ab initio approaches to the structure of light nuclei. Starting from realistic two- and three-nucleon interactions the ab initio no-core shell model (NCSM) predicts low-lying levels in p-shell nuclei. It is a challenging task to extend ab initio methods to describe nuclear reactions. We present here a brief overview of the first steps taken toward nuclear reaction applications. In particular, we discuss our calculation of the {sup 7}Be(p,{gamma}){sup 8}B S-factor. We also present our first results of the {sup 3}He({alpha},{gamma}){sup 7}Be S-factor and of the S-factor of the mirror reaction {sup 3}H({alpha},{gamma}){sup 7}Li.more » The {sup 7}Be(p,{gamma}){sup 8}B and {sup 3}He({alpha},{gamma}){sup 7}Be reactions correspond to the most important uncertainties in solar model predictions of neutrino fluxes.« less

Geographic profiling to assess the risk of rare plant poaching in natural areas

USGS Publications Warehouse

Young, J.A.; Van Manen, F.T.; Thatcher, C.A.

2011-01-01

We demonstrate the use of an expert-assisted spatial model to examine geographic factors influencing the poaching risk of a rare plant (American ginseng, Panax quinquefolius L.) in Shenandoah National Park, Virginia, USA. Following principles of the analytic hierarchy process (AHP), we identified a hierarchy of 11 geographic factors deemed important to poaching risk and requested law enforcement personnel of the National Park Service to rank those factors in a series of pair-wise comparisons. We used those comparisons to determine statistical weightings of each factor and combined them into a spatial model predicting poaching risk. We tested the model using 69 locations of previous poaching incidents recorded by law enforcement personnel. These locations occurred more frequently in areas predicted by the model to have a higher risk of poaching than random locations. The results of our study can be used to evaluate resource protection strategies and to target law enforcement activities. ?? Springer Science+Business Media, LLC (outside the USA) 2011.

Early adolescent symptoms of social phobia prospectively predict alcohol use.

PubMed

Dahne, Jennifer; Banducci, Anne N; Kurdziel, Gretchen; MacPherson, Laura

2014-11-01

The current study examined whether social phobia (SP) symptoms in early adolescence prospectively predicted alcohol use through middle adolescence in a community sample of youth. Data from an ongoing longitudinal study (N = 277) of mechanisms of HIV-related risk behaviors in youth were used to assess the extent to which SP symptoms in early adolescence (mean [SD] age = 11.00 years [0.81]) would predict alcohol use across five annual assessment waves. Adolescents completed measures of SP symptoms, depressive symptoms, and alcohol use at each wave. Higher SP symptoms at baseline predicted higher average odds of alcohol consumption during subsequent waves but did not significantly predict an increase in the odds of alcohol use as a function of time. Within a lagged model, SP symptoms measured at a prior assessment point (1 year earlier) predicted greater odds of drinking alcohol at the following assessment point. Importantly, alcohol use did not significantly predict SP symptoms over time. These results suggest that early SP symptoms are an important risk factor for increased odds of subsequent alcohol use. The present findings highlight that elevated SP symptoms place adolescents at risk for early alcohol use. Early interventions targeting SP symptoms may be crucial for the prevention of problematic alcohol use in early to mid-adolescence. Implications for prevention and treatment approaches are discussed.

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

PubMed Central

Jiang, Mingkai; Felzer, Benjamin S.; Sahagian, Dork

2016-01-01

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment. PMID:27425819

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble.

PubMed

Jiang, Mingkai; Felzer, Benjamin S; Sahagian, Dork

2016-07-18

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950-2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040-2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.

Understanding work contextual factors: a short-cut to evidence-based practice?

PubMed

Wallin, Lars; Ewald, Uwe; Wikblad, Karin; Scott-Findlay, Shannon; Arnetz, Bengt B

2006-01-01

It has become increasingly clear that workplace contextual factors make an important contribution to provider and patient outcomes. The potential for health care professionals of using research in practice is also linked to such factors, although the exact factors or mechanisms for enhancing this potential are not understood. From a perspective of implementing evidence-based nursing practice, the authors of this article report on a study examining contextual factors. The objective of this study was to identify predictors of organizational improvement by measuring staff perceptions of work contextual factors. The Quality Work Competence questionnaire was used in a repeated measurement survey with a 1-year break between the two periods of data collection. The sample consisted of 134 employees from four neonatal units in Sweden. Over the study period significant changes occurred among staff perceptions, both within and between units, on various factors. Changes in staff perceptions on skills development and participatory management were the major predictors of enhanced potential of overall organizational improvement. Perceived improvement in skills development and performance feedback predicted improvement in leadership. Change in commitment was predicted by perceived decreases in work tempo and work-related exhaustion. These findings indicate the potential for organizational improvement by developing a learning and supportive professional environment as well as by involving staff in decision-making at the unit level. Such initiatives are also likely to be of importance for enhanced use of research in practice and evidence-based nursing. On the other hand, high levels of work tempo and burnout appear to have negative consequences on staff commitment for improving care and the work environment. A better understanding of workplace contextual factors is necessary for improving the organizational potential of getting research into practice and should be considered in future implementation projects.

What predictors matter: Risk factors for late adolescent outcomes.

PubMed

Wall-Wieler, Elizabeth; Roos, Leslie L; Chateau, Dan G; Rosella, Laura C

2016-06-27

A life course approach and linked Manitoba data from birth to age 18 were used to facilitate comparisons of two important outcomes: high school graduation and Attention-Deficit/Hyperactivity Disorder (ADHD). With a common set of variables, we sought to answer the following questions: Do the measures predicting high school graduation differ from those that predict ADHD? Which factors are most important? How well do the models fit each outcome? Administrative data from the Population Health Research Data Repository at the Manitoba Centre for Health Policy were used to conduct one of the strongest observational designs: multilevel modelling of large population (n = 62,739) and sibling (n = 29,444) samples. Variables included are neighbourhood characteristics, measures of family stability, and mental and physical health conditions in childhood and adolescence. The adverse childhood experiences important for each outcome differ. While family instability and economic adversity more strongly affect failing to graduate from high school, adverse health events in childhood and early adolescence have a greater effect on late adolescent ADHD. The variables included in the model provided excellent accuracy and discrimination. These results offer insights on the role of several family and social variables and can serve as the basis for reliable, valid prediction tools that can identify high-risk individuals. Applying such a tool at the population level would provide insight into the future burden of these outcomes in an entire region or nation and further quantify the burden of risk in the population.

Utility function under decision theory: A construction arbitration application

NASA Astrophysics Data System (ADS)

Alozn, Ahmad E.; Galadari, Abdulla

2017-08-01

While a wide range of dispute resolution mechanisms exist, practitioners favor legally binding ones such as litigation and arbitration. Since initiating a litigation or arbitration case against a business partner may dissolve the business relationship between them, predicting the arbitrator's decision becomes valuable to the arbitrating parties. This paper proposes a construction-specific utility framework for the arbitrating party through decision theory, and based on expected utility theory. The proposed framework preserves the industry practicality and most importantly, considers direct short-term factors and indirect long-term factors as well. It is suggested that the arbitrating parties' utility functions could be then used to identify equilibrium points among them when interact via game theory principles, which would serve the purpose of predicting the arbitration outcome.

Maternity leave, women's employment, and marital incompatibility.

PubMed

Hyde, J S; Essex, M J; Clark, R; Klein, M H

2001-09-01

This research investigated the relationship between the length of women's maternity leave and marital incompatibility, in the context of other variables including the woman's employment, her dissatisfaction with the division of household labor, and her sense of role overload. Length of leave, work hours, and family salience were associated with several forms of dissatisfaction, which in turn predicted role overload. Role overload predicted increased marital incompatibility for experienced mothers but did not for first-time mothers, for whom discrepancies between preferred and actual child care were more important. Length of maternity leave showed significant interactions with other variables, supporting the hypothesis that a short leave is a risk factor that, when combined with another risk factor, contributes to personal and marital distress.

Independent Factors Affecting Postoperative Complication Rates After Custom-Made Porous Hydroxyapatite Cranioplasty: A Single-Center Review of 109 Cases.

PubMed

Still, Megan; Kane, Abdoulaye; Roux, Alexandre; Zanello, Marc; Dezamis, Edouard; Parraga, Eduardo; Sauvageon, Xavier; Meder, Jean-François; Pallud, Johan

2018-06-01

Cranioplasties are an important neurosurgical procedure not only for improved cosmesis but also for improved functional recovery after craniectomy with a large cranial defect. The aim of this study was to identify predictive factors of postcranioplasty complications using custom-made porous hydroxyapatite cranioplasty. Retrospective review was performed of all patients who underwent a reconstructive cranioplasty using custom-made hydroxyapatite at our institution between February 2008 and September 2017. Postoperative complications considered included bacterial infection, seizures, hydrocephalus requiring ventricular shunt placement, and cranioplasty-to-bone shift. Variables associated at P < 0.1 level in unadjusted analysis were entered into backward stepwise logistic regression models. Of 109 patients included, 15 (13.8%) experienced postoperative infection, with craniectomy performed at an outside institution (adjusted odds ratio [OR] 10.37 [95% confidence interval [CI], 2.03-75.27], P = 0.012) and a previous infection at the surgical site (adjusted OR 6.15 [95%CI, 1.90-19.92], P = 0.003) identified as independent predictors. Six patients (5.5%) experienced postoperative seizures, with stroke (ischemic and hemorrhagic) as a reason for craniectomy (adjusted OR 11.68 [95% CI, 2.56-24.13], P < 0.001) and the presence of seizures in the month before cranioplasty (adjusted OR 9.39 [95% CI, 2.04-127.67], P = 0.002) identified as independent predictors. Four patients (3.7%) experienced postcranioplasty hydrocephalus necessitating shunt placement, and 5 patients (4.6%) experienced cranioplasty-to-bone shift ≥5 mm, but no significant predictive factors were identified for either complication. This study identified possible predictive factors for postcranioplasty complications to help identify at-risk patients, guide prophylactic care, and improve morbidity of this important surgical procedure. Copyright © 2018 Elsevier Inc. All rights reserved.

Factors predicting meat and meat products consumption among middle-aged and elderly people: evidence from a consumer survey in Switzerland.

PubMed

Schmid, Alexandra; Gille, Doreen; Piccinali, Patrizia; Bütikofer, Ueli; Chollet, Magali; Altintzoglou, Themistoklis; Honkanen, Pirjo; Walther, Barbara; Stoffers, Helena

2017-01-01

Background : An adequate diet contributes to health and wellbeing in older age. This is nowadays more important than ever since in industrialised countries the elderly population is growing continually. However, information regarding the consumption behaviour of older persons in Switzerland is limited. Objective : The objective of this investigation was to explore how middle-aged and elderly Swiss view animal products in relation to diet and health, and what factors predict consumption frequency. Design : A representative consumer survey among 632 people over the age of 50 years, living in the German-, French- and Italian-speaking regions of Switzerland was conducted. Results : This paper presents the results related to meat and meat products consumption. Most participants consumed meat and meat products regularly. The majority of participants with low meat intake indicated that eating small amounts would be enough. Respondents judged fresh meat (except pork) to be healthier than meat products, and poultry to be the healthiest meat. Overall meat consumption frequency was predicted by language region, gender, household size, and BMI. Furthermore, participants' opinion about healthiness, taste and safety of meat but not their adherence to the Swiss food pyramid was found to be correlated to the consumption frequency of individual types of meat. Conclusion : Several factors have an impact on consumption frequency of meat and meat products in the middle-aged and elderly Swiss population and the importance varies according to the individual types of meat and meat products. The results show that the traditional food pyramid is not one of these factors for which reason new tools must be explored to support elderly people in regard to a healthy dietary behaviour.

Factors predicting meat and meat products consumption among middle-aged and elderly people: evidence from a consumer survey in Switzerland

PubMed Central

Schmid, Alexandra; Gille, Doreen; Piccinali, Patrizia; Bütikofer, Ueli; Chollet, Magali; Altintzoglou, Themistoklis; Honkanen, Pirjo; Walther, Barbara; Stoffers, Helena

2017-01-01

ABSTRACT Background: An adequate diet contributes to health and wellbeing in older age. This is nowadays more important than ever since in industrialised countries the elderly population is growing continually. However, information regarding the consumption behaviour of older persons in Switzerland is limited. Objective: The objective of this investigation was to explore how middle-aged and elderly Swiss view animal products in relation to diet and health, and what factors predict consumption frequency. Design: A representative consumer survey among 632 people over the age of 50 years, living in the German-, French- and Italian-speaking regions of Switzerland was conducted. Results: This paper presents the results related to meat and meat products consumption. Most participants consumed meat and meat products regularly. The majority of participants with low meat intake indicated that eating small amounts would be enough. Respondents judged fresh meat (except pork) to be healthier than meat products, and poultry to be the healthiest meat. Overall meat consumption frequency was predicted by language region, gender, household size, and BMI. Furthermore, participants’ opinion about healthiness, taste and safety of meat but not their adherence to the Swiss food pyramid was found to be correlated to the consumption frequency of individual types of meat. Conclusion: Several factors have an impact on consumption frequency of meat and meat products in the middle-aged and elderly Swiss population and the importance varies according to the individual types of meat and meat products. The results show that the traditional food pyramid is not one of these factors for which reason new tools must be explored to support elderly people in regard to a healthy dietary behaviour. PMID:28469547

The Work Ability of Hong Kong Construction Workers in Relation to Individual and Work-Related Factors

PubMed Central

Ng, Jacky Y. K.

2018-01-01

The shortage in Hong Kong of construction workers is expected to worsen in future due to the aging population and increasing construction activity. Construction work is dangerous and to help reduce the premature loss of construction workers due to work-related disabilities, this study measured the work ability of 420 Hong Kong construction workers with a Work Ability Index (WAI) which can be used to predict present and future work performance. Given the importance of WAI, in this study the effects of individual and work-related factors on WAI were examined to develop and validate a WAI model to predict how individual and work-related factors affect work ability. The findings will be useful for formulating a pragmatic intervention program to improve the work ability of construction workers and keep them in the work force. PMID:29758018

The Neighborhood Context of Hate Crime: A Comparison of Violent and Property Offenses Using Rare Events Modeling.

PubMed

Benier, Kathryn

2017-08-01

Many studies into the antecedents of hate crime in the neighborhood combine offense categories, meaning that it is unclear whether or not there are distinct contextual factors associated with violent and property hate offenses. This study uses rare events modeling to examine the household and neighborhood factors associated with violent and property offenses. Using the Australian Community Capacity Study, the study focuses on the neighborhood characteristics influencing self-reported violent and property hate crime for 4,396 residents in Brisbane. Findings demonstrate important differences between the offense types. Violence is predicted by household renting and non-English language, whereas property offenses are predicted by household non-English language, neighborhood median income, and change in non-English-speaking residents. In both offense types, neighborhood place attachment acts as a protective factor. These findings highlight the theoretical implications of combining distinct hate crime types for methodological reasons.

Factors Associated With Success in an Occupational Rehabilitation Program for Work-Related Musculoskeletal Disorders

PubMed Central

Hardison, Mark E.

2017-01-01

Work-related musculoskeletal disorders are a significant burden; however, no consensus has been reached on how to maximize occupational rehabilitation programs for people with these disorders, and the impact of simulating work tasks as a mode of intervention has not been well examined. In this retrospective cohort study, the authors used logistic regression to identify client and program factors predicting success for 95 clients in a general occupational rehabilitation program and 71 clients in a comprehensive occupational rehabilitation program. The final predictive model for general rehabilitation included gender, number of sessions completed, and performance of work simulation activities. Maximum hours per session was the only significant predictor of success in the comprehensive rehabilitation program. This study identifies new factors associated with success in occupational rehabilitation, specifically highlighting the importance of intensity (i.e., session length and number of sessions) of therapy and occupation-based activities for this population. PMID:28027046

Emotional Awareness: A Transdiagnostic Risk Factor for Internalizing Symptoms in Children and Adolescents?

PubMed Central

Kranzler, Amy; Young, Jami F.; Hankin, Benjamin L.; Abela, John R. Z.; Elias, Maurice J.; Selby, Edward A.

2015-01-01

The current study used longitudinal data to examine the role of emotional awareness as a transdiagnostic risk factor for internalizing symptoms. Participants were 204 youth, ages 7 to 16, who completed assessments every three months for a year. Results from hierarchical mixed effects modeling indicated that low emotional awareness predicted both depressive and anxiety symptoms for up to one year follow-up. In addition, emotional awareness predicted which youth went on to experience subsequent increases in depressive and anxiety symptoms over the course of the year. Emotional awareness also mediated both the cross-sectional and the longitudinal associations between anxiety and depressive symptoms. These findings suggest that emotional awareness may constitute a transdiagnostic risk factor for the development and/or maintenance of symptoms of depression and anxiety, which has important implications for youth treatment and prevention programs. PMID:25658297

THE ROLE OF PERSONALITY CHARACTERISTICS OF ATHLETES IN COACH-ATHLETE RELATIONSHIPS.

PubMed

Hülya Aşçı, F; Kelecek, Selen; AltintaŞ, Atahan

2015-10-01

This study investigated the relationship between athletes' personality characteristics and the quality of the coach-athlete relationship. 84 female (M age = 20.6 yr., SD = 2.8) and 129 male (M age = 22.0 yr., SD = 3.3) elite youth athletes competing at least for 7 yr. participated in this study. The Five-Factor Personality Inventory (short version) and the Quality of Relationships Inventory were administered to all participants. Stepwise multiple regression analysis assessed which of the five personality factors predicted scores for the different subscales of the Quality of Relationships Inventory (Depth, Support, and Conflict). Results indicated that depth of relationship was not predicted by personality factors. On the other hand, neuroticism and extraversion were significant predictors of support dimension of relationship. Analysis indicated that conscientiousness was the strongest predictor of conflict. In conclusion, athletes' personality characteristics may be important in determining the quality of the coach-athlete relationship.

Individual and community risk factors and sexually transmitted diseases among arrested youths: a two level analysis.

PubMed

Dembo, Richard; Belenko, Steven; Childs, Kristina; Wareham, Jennifer; Schmeidler, James

2009-08-01

High rates of infection for chlamydia and gonorrhea have been noted among youths involved in the juvenile justice system. Although both individual and community-level factors have been found to be associated with sexually transmitted disease (STD) risk, their relative importance has not been tested in this population. A two-level logistic regression analysis was completed to assess the influence of individual-level and community-level predictors on STD test results among arrested youths processed at a centralized intake facility. Results from weighted two level logistic regression analyses (n = 1,368) indicated individual-level factors of gender (being female), age, race (being African American), and criminal history predicted the youths' positive STD status. For the community-level predictors, concentrated disadvantage significantly and positively predicted the youths' STD status. Implications of these findings for future research and public health policy are discussed.

Determinants of job satisfaction among radiation therapy faculty.

PubMed

Swafford, Larry G; Legg, Jeffrey S

2009-01-01

Job satisfaction is one of the most significant predictors of employee retention in a variety of occupational settings, including health care and education. A national survey of radiation therapy educators (n = 90) has indicated that respondents are not satisfied with their jobs based on data collected using the Minnesota Satisfaction Questionnaire (MSQ). To predict the factors associated with job satisfaction or dissatisfaction, the authors used a nine-item questionnaire derived from the MSQ. Educators were grouped according to their job satisfaction scores, and multiple discriminant analysis was used to determine which factors were predictive of satisfaction among groups of educators. Statistical results indicate that ability utilization, institutional support, compensation, personnel, and job characteristics were key determinants of job satisfaction among radiation therapy educators. These results may better inform faculty and administration of important factors that can promote job satisfaction and retain faculty in radiation therapy education programs.

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place-based approach.

PubMed

Schwalm, Donelle; Epps, Clinton W; Rodhouse, Thomas J; Monahan, William B; Castillo, Jessica A; Ray, Chris; Jeffress, Mackenzie R

2016-04-01

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species' niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species' niches, resulting in predictions that are generally limited to climate-occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place-based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence-absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981-2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local-scale differences in the realized niche of the American pika. This variation resulted in diverse and - in some cases - highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place-based approach to species distribution modeling that includes fine-scale factors when assessing current and future climate impacts on species' distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas. © 2015 John Wiley & Sons Ltd.

Predicting the spread of aquatic invaders: insight from 200 years of invasion by zebra mussels.

PubMed

Karatayev, Alexander Y; Burlakova, Lyubov E; Mastitsky, Sergey E; Padilla, Dianna K

2015-03-01

Understanding factors controlling the introduction and spread of species is crucial to improving the management of both natural populations and introduced species. The zebra mussel, Dreissena polymorpha, is considered the most aggressive freshwater invader in the Northern Hemisphere, and is a convenient model system for invasion biology, offering one of the best aquatic examples for examining the invasion process. We used data on 553 of the 1040 glacial lakes in the Republic of Belarus that were examined for the presence of zebra mussels. We used these data to build, test, and construct modified models to predict the spread of this invader, including selection of important parameters that could limit the spread of this invader. In spite of 200 years of continuous invasion, by 1996, zebra mussels were found in only 16.8% of all lakes studied. Of those lakes without zebra mussels in 1996, 66% were predicted to be susceptible to invasion by zebra mussels in the future, and 33% were predicted to be immune to successful invasion due to their water chemistry. Eighty lakes free of zebra mussels in 1996 were reexamined from 1997 to 2008. Of these, zebra mussels successfully invaded an additional 31 lakes, all of which were classified initially as suitable for zebra mussels; none of the lakes previously classified as unsuitable were invaded. We used the Random Forests classification algorithm with 16 environmental variables to determine the most important factors that differed between invaded lakes and those lakes suitable for invasion that have not yet been invaded. Distance to the nearest infested lakes was found to be the most important variable, followed by the lake area, color, average depth, and concentration of chloride, magnesium, and bicarbonate. This study provides a useful approach for predicting the spread of an invader across a landscape with variable habitat suitability that can be applied to a variety of species and systems.

Rural health workers and their work environment: the role of inter-personal factors on job satisfaction of nurses in rural Papua New Guinea

PubMed Central

2012-01-01

Background Job satisfaction is an important focal attitude towards work. Understanding factors that relate to job satisfaction allows interventions to be developed to enhance work performance. Most research on job satisfaction among nurses has been conducted in acute care settings in industrialized countries. Factors that relate to rural nurses are different. This study examined inter-personal, intra-personal and extra-personal factors that influence job satisfaction among rural primary care nurses in a Low and Middle Income country (LMIC), Papua New Guinea. Methods Data was collected using self administered questionnaire from rural nurses attending a training program from 15 of the 20 provinces. Results of a total of 344 nurses were available for analysis. A measure of overall job satisfaction and measures for facets of job satisfaction was developed in the study based on literature and a qualitative study. Multi-variate analysis was used to test prediction models. Results There was significant difference in the level of job satisfaction by age and years in the profession. Higher levels of overall job satisfaction and intrinsic satisfaction were seen in nurses employed by Church facilities compared to government facilities (P <0.01). Ownership of facility, work climate, supervisory support and community support predicted 35% (R2 =0.35) of the variation in job satisfaction. The factors contributing most were work climate (17%) and supervisory support (10%). None of these factors were predictive of an intention to leave. Conclusions This study provides empirical evidence that inter-personal relationships: work climate and supportive supervision are the most important influences of job satisfaction for rural nurses in a LMIC. These findings highlight that the provision of a conducive environment requires attention to human relations aspects. For PNG this is very important as this critical cadre provide the frontline of primary health care for more than 70% of the population of the country. Many LMIC are focusing on rural health, with most of the attention given to aspects of workforce numbers and distribution. Much less attention is given to improving the aspects of the working environment that enhances intrinsic satisfaction and work climate for rural health workers who are currently in place if they are to be satisfied in their job and productive. PMID:22691270

Rural health workers and their work environment: the role of inter-personal factors on job satisfaction of nurses in rural Papua New Guinea.

PubMed

Jayasuriya, Rohan; Whittaker, Maxine; Halim, Grace; Matineau, Tim

2012-06-12

Job satisfaction is an important focal attitude towards work. Understanding factors that relate to job satisfaction allows interventions to be developed to enhance work performance. Most research on job satisfaction among nurses has been conducted in acute care settings in industrialized countries. Factors that relate to rural nurses are different. This study examined inter-personal, intra-personal and extra-personal factors that influence job satisfaction among rural primary care nurses in a Low and Middle Income country (LMIC), Papua New Guinea. Data was collected using self administered questionnaire from rural nurses attending a training program from 15 of the 20 provinces. Results of a total of 344 nurses were available for analysis. A measure of overall job satisfaction and measures for facets of job satisfaction was developed in the study based on literature and a qualitative study. Multi-variate analysis was used to test prediction models. There was significant difference in the level of job satisfaction by age and years in the profession. Higher levels of overall job satisfaction and intrinsic satisfaction were seen in nurses employed by Church facilities compared to government facilities (P <0.01). Ownership of facility, work climate, supervisory support and community support predicted 35% (R2 =0.35) of the variation in job satisfaction. The factors contributing most were work climate (17%) and supervisory support (10%). None of these factors were predictive of an intention to leave. This study provides empirical evidence that inter-personal relationships: work climate and supportive supervision are the most important influences of job satisfaction for rural nurses in a LMIC. These findings highlight that the provision of a conducive environment requires attention to human relations aspects. For PNG this is very important as this critical cadre provide the frontline of primary health care for more than 70% of the population of the country. Many LMIC are focusing on rural health, with most of the attention given to aspects of workforce numbers and distribution. Much less attention is given to improving the aspects of the working environment that enhances intrinsic satisfaction and work climate for rural health workers who are currently in place if they are to be satisfied in their job and productive.

Internalizing and externalizing problems in adolescence: general and dimension-specific effects of familial loadings and preadolescent temperament traits.

PubMed

Ormel, J; Oldehinkel, A J; Ferdinand, R F; Hartman, C A; De Winter, A F; Veenstra, R; Vollebergh, W; Minderaa, R B; Buitelaar, J K; Verhulst, F C

2005-12-01

We investigated the links between familial loading, preadolescent temperament, and internalizing and externalizing problems in adolescence, hereby distinguishing effects on maladjustment in general versus dimension-specific effects on either internalizing or externalizing problems. In a population-based sample of 2230 preadolescents (10-11 years) familial loading (parental lifetime psychopathology) and offspring temperament were assessed at baseline by parent report, and offspring psychopathology at 2.5-years follow-up by self-report, teacher report and parent report. We used purified measures of temperament and psychopathology and partialled out shared variance between internalizing and externalizing problems. Familial loading of internalizing psychopathology predicted offspring internalizing but not externalizing problems, whereas familial loading of externalizing psychopathology predicted offspring externalizing but not internalizing problems. Both familial loadings were associated with Frustration, low Effortful Control, and Fear. Frustration acted as a general risk factor predicting severity of maladjustment; low Effortful Control and Fear acted as dimension-specific risk factors that predicted a particular type of psychopathology; whereas Shyness, High-Intensity Pleasure, and Affiliation acted as direction markers that steered the conditional probability of internalizing versus externalizing problems, in the event of maladjustment. Temperament traits mediated one-third of the association between familial loading and psychopathology. Findings were robust across different composite measures of psychopathology, and applied to girls as well as boys. With regard to familial loading and temperament, it is important to distinguish general risk factors (Frustration) from dimension-specific risk factors (familial loadings, Effortful Control, Fear), and direction markers that act as pathoplastic factors (Shyness, High-Intensity Pleasure, Affiliation) from both types of risk factors. About one-third of familial loading effects on psychopathology in early adolescence are mediated by temperament.

Predicting groundwater recharge for varying land cover and climate conditions - a global meta-study

NASA Astrophysics Data System (ADS)

Mohan, Chinchu; Western, Andrew W.; Wei, Yongping; Saft, Margarita

2018-05-01

Groundwater recharge is one of the important factors determining the groundwater development potential of an area. Even though recharge plays a key role in controlling groundwater system dynamics, much uncertainty remains regarding the relationships between groundwater recharge and its governing factors at a large scale. Therefore, this study aims to identify the most influential factors of groundwater recharge, and to develop an empirical model to estimate diffuse rainfall recharge at a global scale. Recharge estimates reported in the literature from various parts of the world (715 sites) were compiled and used in model building and testing exercises. Unlike conventional recharge estimates from water balance, this study used a multimodel inference approach and information theory to explain the relationship between groundwater recharge and influential factors, and to predict groundwater recharge at 0.5° resolution. The results show that meteorological factors (precipitation and potential evapotranspiration) and vegetation factors (land use and land cover) had the most predictive power for recharge. According to the model, long-term global average annual recharge (1981-2014) was 134 mm yr-1 with a prediction error ranging from -8 to 10 mm yr-1 for 97.2 % of cases. The recharge estimates presented in this study are unique and more reliable than the existing global groundwater recharge estimates because of the extensive validation carried out using both independent local estimates collated from the literature and national statistics from the Food and Agriculture Organization (FAO). In a water-scarce future driven by increased anthropogenic development, the results from this study will aid in making informed decisions about groundwater potential at a large scale.

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Predictive variables for mortality after acute ischemic stroke.

PubMed

Carter, Angela M; Catto, Andrew J; Mansfield, Michael W; Bamford, John M; Grant, Peter J

2007-06-01

Stroke is a major healthcare issue worldwide with an incidence comparable to coronary events, highlighting the importance of understanding risk factors for stroke and subsequent mortality. In the present study, we determined long-term (all-cause) mortality in 545 patients with ischemic stroke compared with a cohort of 330 age-matched healthy control subjects followed up for a median of 7.4 years. We assessed the effect of selected demographic, clinical, biochemical, hematologic, and hemostatic factors on mortality in patients with ischemic stroke. Stroke subtype was classified according to the Oxfordshire Community Stroke Project criteria. Patients who died 30 days or less after the acute event (n=32) were excluded from analyses because this outcome is considered to be directly attributable to the acute event. Patients with ischemic stroke were at more than 3-fold increased risk of death compared with the age-matched control cohort. In multivariate analyses, age, stroke subtype, atrial fibrillation, and previous stroke/transient ischemic attack were predictive of mortality in patients with ischemic stroke. Albumin and creatinine and the hemostatic factors von Willebrand factor and beta-thromboglobulin were also predictive of mortality in patients with ischemic stroke after accounting for demographic and clinical variables. The results indicate that subjects with acute ischemic stroke are at increased risk of all-cause mortality. Advancing age, large-vessel stroke, atrial fibrillation, and previous stroke/transient ischemic attack predict mortality; and analysis of albumin, creatinine, von Willebrand factor, and beta-thromboglobulin will aid in the identification of patients at increased risk of death after stroke.

Predictors of Physical Inactivity in Men and Women With Type 2 Diabetes From the Detection of Ischemia in Asymptomatic Diabetics (DIAD) Study

PubMed Central

McCarthy, Margaret M.; Davey, Janice; Wackers, Frans J. Th.; Chyun, Deborah A.

2014-01-01

Purpose The purpose of this secondary analysis was to determine changes in physical inactivity from baseline to 5 years and to identify factors associated with and predictive of physical inactivity among individuals with type 2 diabetes enrolled in the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study. Methods DIAD was a prospective randomized screening trial that assessed the prevalence of silent ischemia in asymptomatic patients with type 2 diabetes. Subjects were recruited from diabetes and primary care practices at 14 centers throughout the United States and Canada. This is a secondary data analysis of the physical activity data (type and hours/week) collected. No intervention was conducted. Results In all subjects, physical inactivity rose from 24% at baseline to 33% at 5 years (S = 28.93; P < .0001). This change was significant in both men (S = 11.44; P < .0001), increasing from 23% to 31%, and women (S = 18.05; P < .0001), increasing from 25% to 36%. Gender differences were noted in several factors associated with baseline physical inactivity as well as in factors predictive of physical inactivity at 5 years. Important factors associated at both time points included lower level of education, current employment, presence of peripheral and autonomic neuropathy, and indicators of overweight/ obesity. Baseline physical inactivity was strongly predictive of physical inactivity at 5 years (odds ratio, 3.27; 95% confidence interval, 2.36-4.54; P < .0001). Conclusions Gender-related differences were noted in factors associated with and predictive of physical inactivity. PMID:24942531

Predictors of physical inactivity in men and women with type 2 diabetes from the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study.

PubMed

McCarthy, Margaret M; Davey, Janice; Wackers, Frans J Th; Chyun, Deborah A

2014-01-01

The purpose of this secondary analysis was to determine changes in physical inactivity from baseline to 5 years and to identify factors associated with and predictive of physical inactivity among individuals with type 2 diabetes enrolled in the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study. DIAD was a prospective randomized screening trial that assessed the prevalence of silent ischemia in asymptomatic patients with type 2 diabetes. Subjects were recruited from diabetes and primary care practices at 14 centers throughout the United States and Canada. This is a secondary data analysis of the physical activity data (type and hours/week) collected. No intervention was conducted. In all subjects, physical inactivity rose from 24% at baseline to 33% at 5 years (S = 28.93; P < .0001). This change was significant in both men (S = 11.44; P < .0001), increasing from 23% to 31%, and women (S = 18.05; P < .0001), increasing from 25% to 36%. Gender differences were noted in several factors associated with baseline physical inactivity as well as in factors predictive of physical inactivity at 5 years. Important factors associated at both time points included lower level of education, current employment, presence of peripheral and autonomic neuropathy, and indicators of overweight/obesity. Baseline physical inactivity was strongly predictive of physical inactivity at 5 years (odds ratio, 3.27; 95% confidence interval, 2.36-4.54; P < .0001). Gender-related differences were noted in factors associated with and predictive of physical inactivity. © 2014 The Author(s).

Fueling the Flames of the Green-Eyed Monster: The Role of Ruminative Thought in Reaction to Romantic Jealousy.

ERIC Educational Resources Information Center

Carson, Christine L.; Cupach, William R.

2000-01-01

Examines factors predicted to influence individuals' responses to romantic jealousy. Details a study in which undergraduate students completed scales measuring relationship-specific linking, relationship-specific rumination, possessiveness, trust, and communicative responses to jealousy. Suggests that jealous rumination is an important cognitive…

Student Perceptions of Online Course Quality: A Comparison by Academic Discipline

ERIC Educational Resources Information Center

Wilcox, Brian Riley

2013-01-01

The recent rapid proliferation of distance education necessitates the need for strong levels of academic accountability. An important factor found to influence and predict student success is students' perceptions of their online courses. Understanding how learners perceive their online learning environment is paramount to effective course design…

Predictors of Persistence in Online Graduate Nursing Students

ERIC Educational Resources Information Center

Cauble, Denise

2015-01-01

Persistence is an important measure of success for individual students and institutions of higher learning. The purpose of this study was to explore personal and academic factors that influence persistence in online graduate nursing students. A predictive correlational study design was used. Data were extracted from existing student records in two…

Effects of temperature and moisture on Mormon cricket reproduction with implications for responses to climate change

USDA-ARS?s Scientific Manuscript database

During the last decade, populations of flightless Mormon crickets Anabrus simplex (Orthoptera: Tettigoniidae) increased suddenly over vast areas of the western United States, suggesting that climate is an important factor driving outbreaks. Moreover summer temperatures are predicted to increase and...

The Role of Working Memory in Metaphor Production and Comprehension

ERIC Educational Resources Information Center

Chiappe, Dan L.; Chiappe, Penny

2007-01-01

The following tested Kintsch's [Kintsch, W. (2000). "Metaphor comprehension: a computational theory." "Psychonomic Bulletin & Review," 7, 257-266 and Kintsch, W. (2001). "Predication." "Cognitive Science," 25, 173-202] Predication Model, which predicts that working memory capacity is an important factor in metaphor processing. In support of his…

Differences and Predictors of Family Reunification among Subgroups of Runaway Youths Using Shelter Services.

ERIC Educational Resources Information Center

Thompson, Sanna J.; Safyer, Andrew W.; Pollio, David E.

2001-01-01

Article discusses two questions: (1) What are the differences among runaway-homeless, throwaway, and independent youth? (2) What youth demographics, personal characteristics, and family factors predict youth's reunification? Among runaway-homeless youths, family characteristics were most important for reunification; among throwaway youths, problem…

Factors Predicting Physician Assistant Faculty Intent to Leave

ERIC Educational Resources Information Center

Coniglio, David Martin

2013-01-01

An increasing demand for education of physician assistants (PAs) has resulted in rapid growth in the number of PA educational programs. Faculty for these programs may be recruited from existing programs. Understanding faculty turnover intention is important to guide faculty development and to improve faculty retention. The purpose of this research…

Examining Predictors of Group Leader Self-Efficacy for Preservice School Counselors

ERIC Educational Resources Information Center

Springer, Sarah I.

2016-01-01

Group counseling is an important treatment modality used to support clients in a variety of therapeutic settings. This article highlights the results of an exploratory study that examined site supervisory factors that predicted group leader self-efficacy for preservice school counselors. Results of multiple regression analyses suggest meaningful…

Productive Vocabulary among Three Groups of Bilingual American Children: Comparison and Prediction

ERIC Educational Resources Information Center

Cote, Linda R.; Bornstein, Marc H.

2014-01-01

The importance of input factors for bilingual children's vocabulary development was investigated. Forty-seven Argentine, 42 South Korean, 51 European American, 29 Latino immigrant, 26 Japanese immigrant, and 35 Korean immigrant mothers completed checklists of their 20-month-old children's productive vocabularies. Bilingual children's vocabulary…

Exploring Students' Reflective Thinking Practice, Deep Processing Strategies, Effort, and Achievement Goal Orientations

ERIC Educational Resources Information Center

Phan, Huy Phuong

2009-01-01

Recent research indicates that study processing strategies, effort, reflective thinking practice, and achievement goals are important factors contributing to the prediction of students' academic success. Very few studies have combined these theoretical orientations within one conceptual model. This study tested a conceptual model that included, in…

Factors Affecting Temporal Variability of Arsenic in Groundwater Used for Drinking Water Supply in the United States

EPA Science Inventory

The occurrence of arsenic in groundwater is a recognized environmental hazard with worldwide importance and much effort has been focused on surveying and predicting where arsenic occurs. Temporal variability is one aspect of this environmental hazard that has until recently recei...

Estimating plant available water for general crop simulations in ALMANAC/APEX/EPIC/SWAT

USDA-ARS?s Scientific Manuscript database

Process-based simulation models ALMANAC/APEX/EPIC/SWAT contain generalized plant growth subroutines to predict biomass and crop yield. Environmental constraints typically restrict plant growth and yield. Water stress is often an important limiting factor; it is calculated as the sum of water use f...