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Sample records for activity change predict

  1. Stock price change rate prediction by utilizing social network activities.

    Science.gov (United States)

    Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

    2014-01-01

    Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

  2. Stock Price Change Rate Prediction by Utilizing Social Network Activities

    Directory of Open Access Journals (Sweden)

    Shangkun Deng

    2014-01-01

    Full Text Available Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL and genetic algorithm (GA. MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

  3. Neural activity predicts attitude change in cognitive dissonance.

    Science.gov (United States)

    van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

    2009-11-01

    When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

  4. Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

    Directory of Open Access Journals (Sweden)

    Dandan Zhao

    2018-03-01

    Full Text Available Wetlands in the mid- and high-latitudes are particularly vulnerable to environmental changes and have declined dramatically in recent decades. Climate change and human activities are arguably the most important factors driving wetland distribution changes which will have important implications for wetland ecological functions and services. We analyzed the importance of driving variables for wetland distribution and investigated the relative importance of climatic factors and human activity factors in driving historical wetland distribution changes. We predicted wetland distribution changes under climate change and human activities over the 21st century using the Random Forest model in a mid- and high-latitude region of Northeast China. Climate change scenarios included three Representative Concentration Pathways (RCPs based on five general circulation models (GCMs downloaded from the Coupled Model Intercomparison Project, Phase 5 (CMIP5. The three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5 predicted radiative forcing to peak at 2.6, 4.5, and 8.5 W/m2 by the 2100s, respectively. Our results showed that the variables with high importance scores were agricultural population proportion, warmness index, distance to water body, coldness index, and annual mean precipitation; climatic variables were given higher importance scores than human activity variables on average. Average predicted wetland area among three emission scenarios were 340,000 ha, 123,000 ha, and 113,000 ha for the 2040s, 2070s, and 2100s, respectively. Average change percent in predicted wetland area among three periods was greatest under the RCP 8.5 emission scenario followed by RCP 4.5 and RCP 2.6 emission scenarios, which were 78%, 64%, and 55%, respectively. Losses in predicted wetland distribution were generally around agricultural lands and expanded continually from the north to the whole region over time, while the gains were mostly associated with grasslands and water in the

  5. Factors predicting changes in physical activity through adolescence: the Young-HUNT Study, Norway.

    Science.gov (United States)

    Rangul, Vegar; Holmen, Turid Lingaas; Bauman, Adrian; Bratberg, Grete H; Kurtze, Nanna; Midthjell, Kristian

    2011-06-01

    The purpose of this prospective population-based study was to analyze predictors of changes in physical activity (PA) levels from early to late adolescence. Data presented are from 2,348 adolescents and their parents who participated in the Nord-Trøndelag Health study (HUNT 2, 1995-1997) and at follow-up in Young-HUNT 2, 2000-2001 Participants completed a self-reported questionnaire and participated in a clinical examination that included measurements of height and weight. Four patterns of PA emerged in the study: active or inactive at both time points (active maintainers, 13%; inactive maintainers, 59%), inactive and became active (adopters, 12%), active and became inactive (relapsers, 16%). Being overweight, dissatisfied with life, and not actively participating in sports at baseline were significant predictors of change regarding PA among boys at follow-up. For girls, smoking, drinking, low maternal education, and physical inactivity predicted relapsers and inactive maintainers. Higher levels of education and more physically active parents at baseline seemed to protect against decreased PA during follow-up for both genders. Predictors of change in, or maintaining PA status during adolescence differed by gender. These results suggest that PA-promoting interventions should be tailored by gender and focus on encouraging activity for inactive adolescents and maintenance of PA in those already active. Copyright © 2011 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  6. Predicting Persuasion-Induced Behavior Change from the Brain

    Science.gov (United States)

    Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

    2011-01-01

    Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

  7. Situational Motivation and Perceived Intensity: Their Interaction in Predicting Changes in Positive Affect from Physical Activity

    Directory of Open Access Journals (Sweden)

    Eva Guérin

    2012-01-01

    Full Text Available There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE] in predicting changes in positive affect following an acute bout of preferred physical activity, namely, running. Fourty-one female runners engaged in a 30-minute self-paced treadmill run in a laboratory context. Situational motivation for running, pre- and post-running positive affect, and RPE were assessed via validated self-report questionnaires. Hierarchical regression analyses revealed a significant interaction effect between RPE and introjection (P<.05 but not between RPE and identified regulation or intrinsic motivation. At low levels of introjection, the influence of RPE on the change in positive affect was considerable, with higher RPE ratings being associated with greater increases in positive affect. The implications of the findings in light of SDT principles as well as the potential contingencies between the regulations and RPE in predicting positive affect among women are discussed.

  8. "Engage" therapy: Prediction of change of late-life major depression.

    Science.gov (United States)

    Alexopoulos, George S; O'Neil, Robert; Banerjee, Samprit; Raue, Patrick J; Victoria, Lindsay W; Bress, Jennifer N; Pollari, Cristina; Arean, Patricia A

    2017-10-15

    Engage grew out of the need for streamlined psychotherapies that can be accurately used by community therapists in late-life depression. Engage was based on the view that dysfunction of reward networks is the principal mechanism mediating depressive symptoms. Accordingly, Engage uses "reward exposure" (exposure to meaningful activities) and assumes that repeated activation of reward networks will normalize these systems. This study examined whether change in a behavioral activation scale, an index of reward system function, predicts change in depressive symptomatology. The participants (N = 48) were older adults with major depression treated with 9 weekly sessions of Engage and assessed 27 weeks after treatment. Depression was assessed with the 24-item Hamilton Depression Rating Scale (HAM-D) and behavioral activation with the four subscales of Behavioral Activation for Depression Scale (activation, avoidance/rumination, work impairment, social impairment) at baseline, 6 weeks (mid-treatment), 9 weeks (end of treatment), and 36 weeks. Change only in the Activation subscale during successive periods of assessment predicted depression severity (HAM-D) at the end of each period (F 1, 47 = 21.05, psocial support. Change in behavioral activation predicts improvement of depressive symptoms and signs in depressed older adults treated with Engage. Copyright © 2017. Published by Elsevier B.V.

  9. LSD-induced entropic brain activity predicts subsequent personality change.

    Science.gov (United States)

    Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

    2016-09-01

    Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  10. Situational motivation and perceived intensity: their interaction in predicting changes in positive affect from physical activity.

    Science.gov (United States)

    Guérin, Eva; Fortier, Michelle S

    2012-01-01

    There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE)] in predicting changes in positive affect following an acute bout of preferred physical activity, namely, running. Fourty-one female runners engaged in a 30-minute self-paced treadmill run in a laboratory context. Situational motivation for running, pre- and post-running positive affect, and RPE were assessed via validated self-report questionnaires. Hierarchical regression analyses revealed a significant interaction effect between RPE and introjection (P positive affect was considerable, with higher RPE ratings being associated with greater increases in positive affect. The implications of the findings in light of SDT principles as well as the potential contingencies between the regulations and RPE in predicting positive affect among women are discussed.

  11. Prediction of Physical Activity Level Using Processes of Change From the Transtheoretical Model: Experiential, Behavioral, or an Interaction Effect?

    Science.gov (United States)

    Romain, Ahmed Jérôme; Horwath, Caroline; Bernard, Paquito

    2018-01-01

    The purpose of the present study was to compare prediction of physical activity (PA) by experiential or behavioral processes of change (POCs) or an interaction between both types of processes. A cross-sectional study. This study was conducted using an online questionnaire. A total of 394 participants (244 women, 150 men), with a mean age of 35.12 ± 12.04 years and a mean body mass index of 22.97 ± 4.25 kg/m 2 were included. Participants completed the Processes of Change, Stages of Change questionnaires, and the International Physical Activity Questionnaire to evaluate self-reported PA level (total, vigorous, and moderate PA). Hierarchical multiple regression models were used to test the prediction of PA level. For both total PA (β = .261; P behavioral POCs were a significant predictor. Regarding moderate PA, only the interaction between experiential and behavioral POCs was a significant predictor (β = .123; P = .017). Our results provide confirmation that behavioral processes are most prominent in PA behavior. Nevertheless, it is of interest to note that the interaction between experiential and behavioral POCs was the only element predicting moderate PA level. Experiential processes were not associated with PA level.

  12. Empirical analysis of change metrics for software fault prediction

    NARCIS (Netherlands)

    Choudhary, Garvit Rajesh; Kumar, Sandeep; Kumar, Kuldeep; Mishra, Alok; Catal, Cagatay

    2018-01-01

    A quality assurance activity, known as software fault prediction, can reduce development costs and improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are

  13. Differential Patterns of Amygdala and Ventral Striatum Activation Predict Gender-Specific Changes in Sexual Risk Behavior

    Science.gov (United States)

    Sansosti, Alexandra A.; Bowman, Hilary C.; Hariri, Ahmad R.

    2015-01-01

    Although the initiation of sexual behavior is common among adolescents and young adults, some individuals express this behavior in a manner that significantly increases their risk for negative outcomes including sexually transmitted infections. Based on accumulating evidence, we have hypothesized that increased sexual risk behavior reflects, in part, an imbalance between neural circuits mediating approach and avoidance in particular as manifest by relatively increased ventral striatum (VS) activity and relatively decreased amygdala activity. Here, we test our hypothesis using data from seventy 18- to 22-year-old university students participating in the Duke Neurogenetics Study. We found a significant three-way interaction between amygdala activation, VS activation, and gender predicting changes in the number of sexual partners over time. Although relatively increased VS activation predicted greater increases in sexual partners for both men and women, the effect in men was contingent on the presence of relatively decreased amygdala activation and the effect in women was contingent on the presence of relatively increased amygdala activation. These findings suggest unique gender differences in how complex interactions between neural circuit function contributing to approach and avoidance may be expressed as sexual risk behavior in young adults. As such, our findings have the potential to inform the development of novel, gender-specific strategies that may be more effective at curtailing sexual risk behavior. PMID:26063921

  14. Neural response to pictorial health warning labels can predict smoking behavioral change.

    Science.gov (United States)

    Riddle, Philip J; Newman-Norlund, Roger D; Baer, Jessica; Thrasher, James F

    2016-11-01

    In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). © The Author (2016). Published by Oxford University Press.

  15. Features and prospects of juridical predicting of entrepreneurial activity

    Directory of Open Access Journals (Sweden)

    Natalya V. Rubtsova

    2017-03-01

    Full Text Available Objective to identify characteristics and prospects of predicting the business activity. Methods historical sociological logical systematicstructural formallegal comparativelegal legal modeling method. Results in article suggests the legal definition of prediction of business activity as a scientific and practical study aimed at the determination of the future state and prospects of development of business activity consisting of the evaluation of legal regulation and analysis of the prospectsof further socioeconomic development which aims to select the optimal solution for the further development of entrepreneurship through legal regulators. The work proves the necessity of achieving a balanced legal regulation of social relations by changing the legislation in the field of business agreements investment and innovation. Scientific novelty the article for the first time formulates the concept characteristics and features of legal prediction of business activity substantiates the impact of predicting on the development of legal regulation of social relations. Practical significance the main provisions and conclusions of the article can be used in research and teaching while considering the issues of predicting both the socioeconomic processes in general and business activity in particular.

  16. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  17. Catchment coevolution: A useful framework for improving predictions of hydrological change?

    Science.gov (United States)

    Troch, Peter A.

    2017-04-01

    The notion that landscape features have co-evolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this presentation we present a general framework of catchment coevolution that could improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space-for-time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole-system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that implements the concepts of catchment coevolution for building

  18. Predicting persuasion-induced behavior change from the brain.

    Science.gov (United States)

    Falk, Emily B; Berkman, Elliot T; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D

    2010-06-23

    Although persuasive messages often alter people's self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance.

  19. Human medial frontal cortex activity predicts learning from errors.

    Science.gov (United States)

    Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B

    2008-08-01

    Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."

  20. Can tail damage outbreaks in the pig be predicted by behavioural change?

    DEFF Research Database (Denmark)

    Larsen, Mona Lilian Vestbjerg; Andersen, Heidi Mai-Lis; Pedersen, Lene Juul

    2016-01-01

    preventive methods. One strategy is the surveillance of the pigs' behaviour for known preceding indicators of tail damage, which makes it possible to predict a tail damage outbreak and prevent it in proper time. This review discusses the existing literature on behavioural changes observed prior to a tail...... damage outbreak. Behaviours found to change prior to an outbreak include increased activity level, increased performance of enrichment object manipulation, and a changed proportion of tail posture with more tails between the legs. Monitoring these types of behaviours is also discussed for the purpose......, starting with the description of the temporal development of the predictive behaviour in relation to tail damage outbreaks...

  1. Climate Change as a Predictable Surprise

    International Nuclear Information System (INIS)

    Bazerman, M.H.

    2006-01-01

    In this article, I analyze climate change as a 'predictable surprise', an event that leads an organization or nation to react with surprise, despite the fact that the information necessary to anticipate the event and its consequences was available (Bazerman and Watkins, 2004). I then assess the cognitive, organizational, and political reasons why society fails to implement wise strategies to prevent predictable surprises generally and climate change specifically. Finally, I conclude with an outline of a set of response strategies to overcome barriers to change

  2. Which psychological, social and physical environmental characteristics predict changes in physical activity and sedentary behaviors during early retirement? A longitudinal study

    Directory of Open Access Journals (Sweden)

    Delfien Van Dyck

    2017-05-01

    Full Text Available Background In the context of healthy ageing, it is necessary to identify opportunities to implement health interventions in order to develop an active lifestyle with sufficient physical activity and limited sedentary time in middle-aged and older adults. The transition to retirement is such an opportunity, as individuals tend to establish new routines at the start of retirement. Before health interventions can be developed, the psychological, social and physical environmental determinants of physical activity and sedentary behaviors during early retirement should be identified, ideally with longitudinal studies. The aim of this paper was first to examine whether psychological, social and physical environmental factors at the start of retirement predict longitudinal changes in physical activity and sedentary behaviors during the first years of retirement. Second, moderating effects of gender and educational levels were examined. Methods This longitudinal study was conducted in Flanders, Belgium. In total, 180 recently retired (>1 month, <2 years at baseline adults completed a postal questionnaire twice (in 2012–2013 and two years later in 2014–2015. The validated questionnaire assessed socio-demographic information, physical activity, sedentary behaviors, and psychological, social and physical environmental characteristics. Multiple moderated hierarchic regression analyses were conducted in SPSS 22.0. Results Higher perceived residential density (p < 0.001 and lower aesthetics (p = 0.08 predicted an increase in active transportation (adjusted R2 = 0.18. Higher baseline self-efficacy was associated with an increase in leisure-time physical activity (p = 0.001, adjusted R2 = 0.13. A more positive perception of old age (p = 0.04 and perceiving less street connectivity (p = 0.001 were associated with an increase in screen time (adjusted R2 = 0.06. Finally, higher baseline levels of modeling from friends (p = 0.06 and lower

  3. Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions.

    Science.gov (United States)

    Fox, Naomi J; Marion, Glenn; Davidson, Ross S; White, Piran C L; Hutchings, Michael R

    2012-03-06

    Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

  4. Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions

    Directory of Open Access Journals (Sweden)

    Ross S. Davidson

    2012-03-01

    Full Text Available Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

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

    Directory of Open Access Journals (Sweden)

    Brenda eHanna-Pladdy

    2012-07-01

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

  6. Are abrupt climate changes predictable?

    Science.gov (United States)

    Ditlevsen, Peter

    2013-04-01

    It is taken for granted that the limited predictability in the initial value problem, the weather prediction, and the predictability of the statistics are two distinct problems. Lorenz (1975) dubbed this predictability of the first and the second kind respectively. Predictability of the first kind in a chaotic dynamical system is limited due to the well-known critical dependence on initial conditions. Predictability of the second kind is possible in an ergodic system, where either the dynamics is known and the phase space attractor can be characterized by simulation or the system can be observed for such long times that the statistics can be obtained from temporal averaging, assuming that the attractor does not change in time. For the climate system the distinction between predictability of the first and the second kind is fuzzy. This difficulty in distinction between predictability of the first and of the second kind is related to the lack of scale separation between fast and slow components of the climate system. The non-linear nature of the problem furthermore opens the possibility of multiple attractors, or multiple quasi-steady states. As the ice-core records show, the climate has been jumping between different quasi-stationary climates, stadials and interstadials through the Dansgaard-Oechger events. Such a jump happens very fast when a critical tipping point has been reached. The question is: Can such a tipping point be predicted? This is a new kind of predictability: the third kind. If the tipping point is reached through a bifurcation, where the stability of the system is governed by some control parameter, changing in a predictable way to a critical value, the tipping is predictable. If the sudden jump occurs because internal chaotic fluctuations, noise, push the system across a barrier, the tipping is as unpredictable as the triggering noise. In order to hint at an answer to this question, a careful analysis of the high temporal resolution NGRIP isotope

  7. Predicting space climate change

    Science.gov (United States)

    Balcerak, Ernie

    2011-10-01

    Galactic cosmic rays and solar energetic particles can be hazardous to humans in space, damage spacecraft and satellites, pose threats to aircraft electronics, and expose aircrew and passengers to radiation. A new study shows that these threats are likely to increase in coming years as the Sun approaches the end of the period of high solar activity known as “grand solar maximum,” which has persisted through the past several decades. High solar activity can help protect the Earth by repelling incoming galactic cosmic rays. Understanding the past record can help scientists predict future conditions. Barnard et al. analyzed a 9300-year record of galactic cosmic ray and solar activity based on cosmogenic isotopes in ice cores as well as on neutron monitor data. They used this to predict future variations in galactic cosmic ray flux, near-Earth interplanetary magnetic field, sunspot number, and probability of large solar energetic particle events. The researchers found that the risk of space weather radiation events will likely increase noticeably over the next century compared with recent decades and that lower solar activity will lead to increased galactic cosmic ray levels. (Geophysical Research Letters, doi:10.1029/2011GL048489, 2011)

  8. Are Some Semantic Changes Predictable?

    DEFF Research Database (Denmark)

    Schousboe, Steen

    2010-01-01

      Historical linguistics is traditionally concerned with phonology and syntax. With the exception of grammaticalization - the development of auxiliary verbs, the syntactic rather than localistic use of prepositions, etc. - semantic change has usually not been described as a result of regular...... developments, but only as specific meaning changes in individual words. This paper will suggest some regularities in semantic change, regularities which, like sound laws, have predictive power and can be tested against recorded languages....

  9. Neural activity to a partner's facial expression predicts self-regulation after conflict

    Science.gov (United States)

    Hooker, Christine I.; Gyurak, Anett; Verosky, Sara; Miyakawa, Asako; Ayduk, Özlem

    2009-01-01

    Introduction Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to the regulation of emotional experience in response to lab-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk-factor for mood and behavior problems after an interpersonal stressor. However, it remains unclear whether LPFC activity to a lab-based affective challenge predicts self-regulation in real-life. Method We investigated whether LPFC activity to a lab-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During an fMRI scan, healthy, adult participants in committed, dating relationships (N = 27) viewed positive, negative, and neutral facial expressions of their partners. In an online daily-diary, participants reported conflict occurrence, level of negative mood, rumination, and substance-use. Results LPFC activity in response to the lab-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to the change in mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted the change in mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance-use. Conclusions Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. PMID:20004365

  10. Cognitive biases to healthy and unhealthy food words predict change in BMI.

    Science.gov (United States)

    Calitri, Raff; Pothos, Emmanuel M; Tapper, Katy; Brunstrom, Jeffrey M; Rogers, Peter J

    2010-12-01

    The current study explored the predictive value of cognitive biases to food cues (assessed by emotional Stroop and dot probe tasks) on weight change over a 1-year period. This was a longitudinal study with undergraduate students (N = 102) living in shared student accommodation. After controlling for the effects of variables associated with weight (e.g., physical activity, stress, restrained eating, external eating, and emotional eating), no effects of cognitive bias were found with the dot probe. However, for the emotional Stroop, cognitive bias to unhealthy foods predicted an increase in BMI whereas cognitive bias to healthy foods was associated with a decrease in BMI. Results parallel findings in substance abuse research; cognitive biases appear to predict behavior change. Accordingly, future research should consider strategies for attentional retraining, encouraging individuals to reorient attention away from unhealthy eating cues.

  11. Predicting coastal morphological changes with empirical orthogonal functionmethod

    Directory of Open Access Journals (Sweden)

    Fernando Alvarez

    2016-01-01

    Full Text Available In order to improve the accuracy of prediction when using the empirical orthogonal function (EOF method, this paper describes a novel approach for two-dimensional (2D EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component.

  12. "You've Changed": Low Self-Concept Clarity Predicts Lack of Support for Partner Change.

    Science.gov (United States)

    Emery, Lydia F; Gardner, Wendi L; Finkel, Eli J; Carswell, Kathleen L

    2018-03-01

    People often pursue self-change, and having a romantic partner who supports these changes increases relationship satisfaction. However, most existing research focuses only on the experience of the person who is changing. What predicts whether people support their partner's change? People with low self-concept clarity resist self-change, so we hypothesized that they would be unsupportive of their partner's changes. People with low self-concept clarity did not support their partner's change (Study 1a), because they thought they would have to change, too (Study 1b). Low self-concept clarity predicted failing to support a partner's change, but not vice versa (Studies 2 and 3), and only for larger changes (Study 3). Not supporting a partner's change predicted decreases in relationship quality for both members of the couple (Studies 2 and 3). This research underscores the role of partners in self-change, suggesting that failing to support a partner's change may stem from self-concept confusion.

  13. Resting alpha activity predicts learning ability in alpha neurofeedback

    Directory of Open Access Journals (Sweden)

    Wenya eNan

    2014-07-01

    Full Text Available Individuals differ in their ability to learn how to regulate the alpha activity by neurofeedback. This study aimed to investigate whether the resting alpha activity is related to the learning ability of alpha enhancement in neurofeedback and could be used as a predictor. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback in order to learn how to enhance activity in the alpha frequency band. The learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback.

  14. Global vegetation change predicted by the modified Budyko model

    Energy Technology Data Exchange (ETDEWEB)

    Monserud, R.A.; Tchebakova, N.M.; Leemans, R. (US Department of Agriculture, Moscow, ID (United States). Intermountain Research Station, Forest Service)

    1993-09-01

    A modified Budyko global vegetation model is used to predict changes in global vegetation patterns resulting from climate change (CO[sub 2] doubling). Vegetation patterns are predicted using a model based on a dryness index and potential evaporation determined by solving radiation balance equations. Climate change scenarios are derived from predictions from four General Circulation Models (GCM's) of the atmosphere (GFDL, GISS, OSU, and UKMO). All four GCM scenarios show similar trends in vegetation shifts and in areas that remain stable, although the UKMO scenario predicts greater warming than the others. Climate change maps produced by all four GCM scenarios show good agreement with the current climate vegetation map for the globe as a whole, although over half of the vegetation classes show only poor to fair agreement. The most stable areas are Desert and Ice/Polar Desert. Because most of the predicted warming is concentrated in the Boreal and Temperate zones, vegetation there is predicted to undergo the greatest change. Most vegetation classes in the Subtropics and Tropics are predicted to expand. Any shift in the Tropics favouring either Forest over Savanna, or vice versa, will be determined by the magnitude of the increased precipitation accompanying global warming. Although the model predicts equilibrium conditions to which many plant species cannot adjust (through migration or microevolution) in the 50-100 y needed for CO[sub 2] doubling, it is not clear if projected global warming will result in drastic or benign vegetation change. 72 refs., 3 figs., 3 tabs.

  15. Making predictions in a changing world-inference, uncertainty, and learning.

    Science.gov (United States)

    O'Reilly, Jill X

    2013-01-01

    To function effectively, brains need to make predictions about their environment based on past experience, i.e., they need to learn about their environment. The algorithms by which learning occurs are of interest to neuroscientists, both in their own right (because they exist in the brain) and as a tool to model participants' incomplete knowledge of task parameters and hence, to better understand their behavior. This review focusses on a particular challenge for learning algorithms-how to match the rate at which they learn to the rate of change in the environment, so that they use as much observed data as possible whilst disregarding irrelevant, old observations. To do this algorithms must evaluate whether the environment is changing. We discuss the concepts of likelihood, priors and transition functions, and how these relate to change detection. We review expected and estimation uncertainty, and how these relate to change detection and learning rate. Finally, we consider the neural correlates of uncertainty and learning. We argue that the neural correlates of uncertainty bear a resemblance to neural systems that are active when agents actively explore their environments, suggesting that the mechanisms by which the rate of learning is set may be subject to top down control (in circumstances when agents actively seek new information) as well as bottom up control (by observations that imply change in the environment).

  16. Predicting the unpredictable: Critical analysis and practical implications of predictive anticipatory activity

    Directory of Open Access Journals (Sweden)

    Julia eMossbridge

    2014-03-01

    Full Text Available A recent meta-analysis of experiments from seven independent laboratories (n=26 published since 1978 indicates that the human body can apparently detect randomly delivered stimuli occurring 1-10 seconds in the future (Mossbridge, Tressoldi, & Utts, 2012. The key observation in these studies is that human physiology appears to be able to distinguish between unpredictable dichotomous future stimuli, such as emotional vs. neutral images or sound vs. silence. This phenomenon has been called presentiment (as in feeling the future. In this paper we call it predictive anticipatory activity or PAA. The phenomenon is predictive because it can distinguish between upcoming stimuli; it is anticipatory because the physiological changes occur before a future event; and it is an activity because it involves changes in the cardiopulmonary, skin, and/or nervous systems. PAA is an unconscious phenomenon that seems to be a time-reversed reflection of the usual physiological response to a stimulus. It appears to resemble precognition (consciously knowing something is going to happen before it does, but PAA specifically refers to unconscious physiological reactions as opposed to conscious premonitions. Though it is possible that PAA underlies the conscious experience of precognition, experiments testing this idea have not produced clear results. The first part of this paper reviews the evidence for PAA and examines the two most difficult challenges for obtaining valid evidence for it: expectation bias and multiple analyses. The second part speculates on possible mechanisms and the theoretical implications of PAA for understanding physiology and consciousness. The third part examines potential practical applications.

  17. Hemostatic system changes predictive value in patients with ischemic brain disorders

    Directory of Open Access Journals (Sweden)

    Raičević Ranko

    2002-01-01

    Full Text Available The aim of this research was to determine the importance of tracking the dynamics of changes of the hemostatic system factors (aggregation of thrombocytes, D-dimer, PAI-1, antithrombin III, protein C and protein S, factor VII and factor VIII, fibrin degradation products, euglobulin test and the activated partial thromboplastin time – aPTPV in relation to the level of the severity of ischemic brain disorders (IBD and the level of neurological and functional deficiency in the beginning of IBD manifestation from 7 to 10 days, 19 to 21 day, and after 3 to 6 months. The research results confirmed significant predictive value of changes of hemostatic system with the predomination of procoagulant factors, together with the insufficiency of fibrinolysis. Concerning the IBD severity and it's outcome, the significant predictive value was shown in the higher levels of PAI-1 and the lower level of antithrombin III, and borderline significant value was shown in the accelerated aggregation of thrombocytes and the increased concentration of D-dimer. It could be concluded that the tracking of the dynamics of changes in parameters of hemostatic system proved to be an easily accessible method with the significant predictive value regarding the development of more severe. IBD cases and the outcome of the disease itself.

  18. Prediction of body lipid change in pregnancy and lactation.

    Science.gov (United States)

    Friggens, N C; Ingvartsen, K L; Emmans, G C

    2004-04-01

    A simple method to predict the genetically driven pattern of body lipid change through pregnancy and lactation in dairy cattle is proposed. The rationale and evidence for genetically driven body lipid change have their basis in evolutionary considerations and in the homeorhetic changes in lipid metabolism through the reproductive cycle. The inputs required to predict body lipid change are body lipid mass at calving (kg) and the date of conception (days in milk). Body lipid mass can be derived from body condition score and live weight. A key assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between calving and a genetically determined time in lactation (T') at which a particular level of body lipid (L') is sought. A second assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between T' and the next calving. The resulting model was evaluated using 2 sets of data. The first was from Holstein cows with 3 different levels of body fatness at calving. The second was from Jersey cows in first, second, and third parity. The model was found to reproduce the observed patterns of change in body lipid reserves through lactation in both data sets. The average error of prediction was low, less than the variation normally associated with the recording of condition score, and was similar for the 2 data sets. When the model was applied using the initially suggested parameter values derived from the literature the average error of prediction was 0.185 units of condition score (+/- 0.086 SD). After minor adjustments to the parameter values, the average error of prediction was 0.118 units of condition score (+/- 0.070 SD). The assumptions on which the model is based were sufficient to predict the changes in body lipid of both Holstein and Jersey cows under different nutritional conditions and parities. Thus, the model presented here shows that it is possible to predict genetically driven curves of body

  19. CERAPP: Collaborative estrogen receptor activity prediction project

    DEFF Research Database (Denmark)

    Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra

    2016-01-01

    ). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. oBjectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project...... States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical......: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. conclusion: This project demonstrated...

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

    Science.gov (United States)

    Hanna-Pladdy, Brenda; Gajewski, Byron

    2012-01-01

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

  1. Relationship between efficiency and predictability in stock price change

    Science.gov (United States)

    Eom, Cheoljun; Oh, Gabjin; Jung, Woo-Sung

    2008-09-01

    In this study, we evaluate the relationship between efficiency and predictability in the stock market. The efficiency, which is the issue addressed by the weak-form efficient market hypothesis, is calculated using the Hurst exponent and the approximate entropy (ApEn). The predictability corresponds to the hit-rate; this is the rate of consistency between the direction of the actual price change and that of the predicted price change, as calculated via the nearest neighbor prediction method. We determine that the Hurst exponent and the ApEn value are negatively correlated. However, predictability is positively correlated with the Hurst exponent.

  2. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory.

    Directory of Open Access Journals (Sweden)

    Luis-Miguel Chevin

    2010-04-01

    Full Text Available Many species are experiencing sustained environmental change mainly due to human activities. The unusual rate and extent of anthropogenic alterations of the environment may exceed the capacity of developmental, genetic, and demographic mechanisms that populations have evolved to deal with environmental change. To begin to understand the limits to population persistence, we present a simple evolutionary model for the critical rate of environmental change beyond which a population must decline and go extinct. We use this model to highlight the major determinants of extinction risk in a changing environment, and identify research needs for improved predictions based on projected changes in environmental variables. Two key parameters relating the environment to population biology have not yet received sufficient attention. Phenotypic plasticity, the direct influence of environment on the development of individual phenotypes, is increasingly considered an important component of phenotypic change in the wild and should be incorporated in models of population persistence. Environmental sensitivity of selection, the change in the optimum phenotype with the environment, still crucially needs empirical assessment. We use environmental tolerance curves and other examples of ecological and evolutionary responses to climate change to illustrate how these mechanistic approaches can be developed for predictive purposes.

  3. Can tail damage outbreaks in the pig be predicted by behavioural change?

    Science.gov (United States)

    Larsen, Mona Lilian Vestbjerg; Andersen, Heidi Mai-Lis; Pedersen, Lene Juul

    2016-03-01

    Tail biting, resulting in outbreaks of tail damage in pigs, is a multifactorial welfare and economic problem which is usually partly prevented through tail docking. According to European Union legislation, tail docking is not allowed on a routine basis; thus there is a need for alternative preventive methods. One strategy is the surveillance of the pigs' behaviour for known preceding indicators of tail damage, which makes it possible to predict a tail damage outbreak and prevent it in proper time. This review discusses the existing literature on behavioural changes observed prior to a tail damage outbreak. Behaviours found to change prior to an outbreak include increased activity level, increased performance of enrichment object manipulation, and a changed proportion of tail posture with more tails between the legs. Monitoring these types of behaviours is also discussed for the purpose of developing an automatic warning system for tail damage outbreaks, with activity level showing promising results for being monitored automatically. Encouraging results have been found so far for the development of an automatic warning system; however, there is a need for further investigation and development, starting with the description of the temporal development of the predictive behaviour in relation to tail damage outbreaks. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Linear filters as a method of real-time prediction of geomagnetic activity

    International Nuclear Information System (INIS)

    McPherron, R.L.; Baker, D.N.; Bargatze, L.F.

    1985-01-01

    Important factors controlling geomagnetic activity include the solar wind velocity, the strength of the interplanetary magnetic field (IMF), and the field orientation. Because these quantities change so much in transit through the solar wind, real-time monitoring immediately upstream of the earth provides the best input for any technique of real-time prediction. One such technique is linear prediction filtering which utilizes past histories of the input and output of a linear system to create a time-invariant filter characterizing the system. Problems of nonlinearity or temporal changes of the system can be handled by appropriate choice of input parameters and piecewise approximation in various ranges of the input. We have created prediction filters for all the standard magnetic indices and tested their efficiency. The filters show that the initial response of the magnetosphere to a southward turning of the IMF peaks in 20 minutes and then again in 55 minutes. After a northward turning, auroral zone indices and the midlatitude ASYM index return to background within 2 hours, while Dst decays exponentially with a time constant of about 8 hours. This paper describes a simple, real-time system utilizing these filters which could predict a substantial fraction of the variation in magnetic activity indices 20 to 50 minutes in advance

  5. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    International Nuclear Information System (INIS)

    Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J.-P.

    2014-01-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility

  6. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    Energy Technology Data Exchange (ETDEWEB)

    Morton, M.J., E-mail: michael.morton@astrazeneca.com [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Armstrong, D.; Abi Gerges, N. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Bridgland-Taylor, M. [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Pollard, C.E.; Bowes, J.; Valentin, J.-P. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom)

    2014-09-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.

  7. Predicting physical activity in adolescents: the role of compensatory health beliefs within the Health Action Process Approach.

    Science.gov (United States)

    Berli, Corina; Loretini, Philipp; Radtke, Theda; Hornung, Rainer; Scholz, Urte

    2014-01-01

    Compensatory health beliefs (CHBs), defined as beliefs that healthy behaviours can compensate for unhealthy behaviours, may be one possible factor hindering people in adopting a healthier lifestyle. This study examined the contribution of CHBs to the prediction of adolescents' physical activity within the theoretical framework of the Health Action Process Approach (HAPA). The study followed a prospective survey design with assessments at baseline (T1) and two weeks later (T2). Questionnaire data on physical activity, HAPA variables and CHBs were obtained twice from 430 adolescents of four different Swiss schools. Multilevel modelling was applied. CHBs added significantly to the prediction of intentions and change in intentions, in that higher CHBs were associated with lower intentions to be physically active at T2 and a reduction in intentions from T1 to T2. No effect of CHBs emerged for the prediction of self-reported levels of physical activity at T2 and change in physical activity from T1 to T2. Findings emphasise the relevance of examining CHBs in the context of an established health behaviour change model and suggest that CHBs are of particular importance in the process of intention formation.

  8. Caregiver Confidence: Does It Predict Changes in Disability among Elderly Home Care Recipients?

    Science.gov (United States)

    Li, Lydia W.; McLaughlin, Sara J.

    2012-01-01

    Purpose of the study: The primary aim of this investigation was to determine whether caregiver confidence in their care recipients' functional capabilities predicts changes in the performance of activities of daily living (ADL) among elderly home care recipients. A secondary aim was to explore how caregiver confidence and care recipient functional…

  9. Changes in Pilot Behavior with Predictive System Status Information

    Science.gov (United States)

    Trujillo, Anna C.

    1998-01-01

    Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, changes in pilot behavior associated with using this predictive information have not been ascertained. The study described here quantified these changes using three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and three initial time intervals until a parameter alert range was reached (ITIs) (1 minute, 5 minutes, and 15 minutes). With predictive information, subjects accomplished most of their tasks before an alert occurred. Subjects organized the time they did their tasks by locus-of-control with no predictive information and for the 1-minute ITI, and by aviatenavigate-communicate for the time for a parameter to reach an alert range and the 15-minute conditions. Overall, predictive information and the longer ITIs moved subjects to performing tasks before the alert actually occurred and had them more mission oriented as indicated by their tasks grouping of aviate-navigate-communicate.

  10. Prediction of Human Activity by Discovering Temporal Sequence Patterns.

    Science.gov (United States)

    Li, Kang; Fu, Yun

    2014-08-01

    Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

  11. Finite element predictions of active buckling control of stiffened panels

    Science.gov (United States)

    Thompson, Danniella M.; Griffin, O. H., Jr.

    1993-04-01

    Materials systems and structures that can respond 'intelligently' to their environment are currently being proposed and investigated. A series of finite element analyses was performed to investigate the potential for active buckling control of two different stiffened panels by embedded shape memory alloy (SMA) rods. Changes in the predicted buckling load increased with the magnitude of the actuation level for a given structural concept. Increasing the number of actuators for a given concept yielded greater predicted increases in buckling load. Considerable control authority was generated with a small number of actuators, with greater authority demonstrated for those structural concepts where the activated SMA rods could develop greater forces and moments on the structure. Relatively simple and inexpensive analyses were performed with standard finite elements to determine such information, indicating the viability of these types of models for design purposes.

  12. Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

    Science.gov (United States)

    Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

    2014-01-01

    Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

  13. Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure.

    Science.gov (United States)

    Dhurandhar, E J; Kaiser, K A; Dawson, J A; Alcorn, A S; Keating, K D; Allison, D B

    2015-08-01

    Public health and clinical interventions for obesity in free-living adults may be diminished by individual compensation for the intervention. Approaches to predict weight outcomes do not account for all mechanisms of compensation, so they are not well suited to predict outcomes in free-living adults. Our objective was to quantify the range of compensation in energy intake or expenditure observed in human randomized controlled trials (RCTs). We searched multiple databases (PubMed, CINAHL, SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for RCTs evaluating the effect of dietary and/or physical activity interventions on body weight/composition. subjects per treatment arm ≥5; ≥1 week intervention; a reported outcome of body weight/body composition; the intervention was either a prescribed amount of over- or underfeeding and/or supervised or monitored physical activity was prescribed; ≥80% compliance; and an objective method was used to verify compliance with the intervention (for example, observation and electronic monitoring). Data were independently extracted and analyzed by multiple reviewers with consensus reached by discussion. We compared observed weight change with predicted weight change using two models that predict weight change accounting only for metabolic compensation. Twenty-eight studies met inclusion criteria. Overfeeding studies indicate 96% less weight gain than expected if no compensation occurred. Dietary restriction and exercise studies may result in up to 12-44% and 55-64% less weight loss than expected, respectively, under an assumption of no behavioral compensation. Compensation is substantial even in high-compliance conditions, resulting in far less weight change than would be expected. The simple algorithm we report allows for more realistic predictions of intervention effects in free-living populations by accounting for the significant compensation that occurs.

  14. Predicting mountain lion activity using radiocollars equipped with mercury tip-sensors

    Science.gov (United States)

    Janis, Michael W.; Clark, Joseph D.; Johnson, Craig

    1999-01-01

    Radiotelemetry collars with tip-sensors have long been used to monitor wildlife activity. However, comparatively few researchers have tested the reliability of the technique on the species being studied. To evaluate the efficacy of using tip-sensors to assess mountain lion (Puma concolor) activity, we radiocollared 2 hand-reared mountain lions and simultaneously recorded their behavior and the associated telemetry signal characteristics. We noted both the number of pulse-rate changes and the percentage of time the transmitter emitted a fast pulse rate (i.e., head up) within sampling intervals ranging from 1-5 minutes. Based on 27 hours of observations, we were able to correctly distinguish between active and inactive behaviors >93% of the time using a logistic regression model. We present several models to predict activity of mountain lions; the selection of which to us would depend on study objectives and logistics. Our results indicate that field protocols that use only pulse-rate changes to indicate activity can lead to significant classification errors.

  15. Decline in physical activity during adolescence is not associated with changes in mental health

    Directory of Open Access Journals (Sweden)

    Martin L. Van Dijk

    2016-04-01

    Full Text Available Abstract Background The majority of studies investigating associations between physical activity and mental health in adolescents have been cross-sectional in design. Potential associations between physical activity and mental health may be better examined longitudinally as physical activity levels tend to decrease in adolescence. Few studies have investigated these associations longitudinally in adolescents and none by measuring physical activity objectively. Methods A total of 158 Dutch adolescents (mean age 13.6 years, 38.6 % boys, grades 7 and 9 at baseline participated in this longitudinal study. Physical activity, depressive symptoms and self-esteem were measured at baseline and at the 1-year follow-up. Physical activity was objectively measured with an ActivPAL3™ accelerometer during one full week. Depressive symptoms were measured with the Center for Epidemiologic Studies Depression Scale (CES-D and self-esteem was assessed with the Rosenberg Self-Esteem Scale (RSE. Results were analysed using structural equation modelling. Results Physical activity levels decreased 15.3 % over a 1-year period (p < .001, with significantly (p = .001 greater decreases during grade 7 (-20.7 % than during grade 9 (-5.0 %. Overall, depressive symptoms decreased (-12.1 %, p < .001 over a 1-year period, while self-esteem did not change significantly (+2.9 %, p = .066. Higher levels of depressive symptoms at baseline predicted a greater decline in depressive symptoms (β = -.51, p < .001 and higher levels of self-esteem at baseline predicted a smaller increase in self-esteem (β = -.48, p < .001. The decline in physical activity did not appear to predict any change in depressive symptoms and self-esteem. Conclusion The decline in physical activity over a 1-year period during adolescence is not associated with a change in mental health. Further studies in adolescents aiming to investigate whether a change in physical

  16. Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

    Directory of Open Access Journals (Sweden)

    Boris Cheval

    Full Text Available Understanding the determinants of non-exercise activity thermogenesis (NEAT is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91 completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA and sedentary behaviors (IASB. Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a positively predicted by IAPA, (b negatively predicted by IASB, and (c was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

  17. Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts.

    Directory of Open Access Journals (Sweden)

    J Lucas McKay

    Full Text Available Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3 across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2× compared to individual muscle control. Our results are consistent with the idea that hierarchical, task

  18. Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data.

    Directory of Open Access Journals (Sweden)

    Evi Berchtold

    Full Text Available Several methods predict activity changes of transcription factors (TFs from a given regulatory network and measured expression data. But available gene regulatory networks are incomplete and contain many condition-dependent regulations that are not relevant for the specific expression measurement. It is not known which combination of active TFs is needed to cause a change in the expression of a target gene. A method to systematically evaluate the inferred activity changes is missing. We present such an evaluation strategy that indicates for how many target genes the observed expression changes can be explained by a given set of active TFs. To overcome the problem that the exact combination of active TFs needed to activate a gene is typically not known, we assume a gene to be explained if there exists any combination for which the predicted active TFs can possibly explain the observed change of the gene. We introduce the i-score (inconsistency score, which quantifies how many genes could not be explained by the set of activity changes of TFs. We observe that, even for these minimal requirements, published methods yield many unexplained target genes, i.e. large i-scores. This holds for all methods and all expression datasets we evaluated. We provide new optimization methods to calculate the best possible (minimal i-score given the network and measured expression data. The evaluation of this optimized i-score on a large data compendium yields many unexplained target genes for almost every case. This indicates that currently available regulatory networks are still far from being complete. Both the presented Act-SAT and Act-A* methods produce optimal sets of TF activity changes, which can be used to investigate the difficult interplay of expression and network data. A web server and a command line tool to calculate our i-score and to find the active TFs associated with the minimal i-score is available from https://services.bio.ifi.lmu.de/i-score.

  19. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

    Data.gov (United States)

    U.S. Environmental Protection Agency — Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational...

  20. Behind the stage of deliberate self-persuasion: When changes in valence of associations to an attitude object predict attitude change.

    Science.gov (United States)

    Lu, Tong; Lord, Charles G; Yoke, Kristin

    2015-12-01

    Modern theory and research on evaluative processes, combined with a comprehensive review of deliberate self-persuasion (Maio & Thomas, 2007, Pers. Soc. Psychol. Bull., 11, 46), suggest two types of strategies people can use to construct new, more desired attitudes. Epistemic strategies change the perceived valence of associations activated by the attitude object. Teleologic strategies, in contrast, keep undesired associations from being activated in the first place, thus obviating the need to change their perceived valence. Change in perceived valence of associations therefore might predict attitude change better when people pursue epistemic than teleologic strategies for deliberate self-persuasion. This hypothesis gained convergent support from three studies in which use of epistemic versus teleologic strategies was measured as an individual difference (Study 1) and manipulated (studies 2 and 3). The results of these studies supported the theoretical distinction between the two strategies and suggested further research directions. © 2015 The British Psychological Society.

  1. Who will increase their physical activity? Predictors of change in objectively measured physical activity over 12 months in the ProActive cohort

    Directory of Open Access Journals (Sweden)

    Sutton Stephen

    2010-04-01

    Full Text Available Abstract Background The aim was to identify predictors of change in objectively measured physical activity over 12 months in the ProActive cohort to improve understanding of factors influencing change in physical activity. Methods ProActive is a physical activity promotion trial that took place in Eastern England (1999-2004. 365 offspring of people with type 2 diabetes underwent measurement of physical activity energy expenditure (PAEE using heart rate monitoring, fitness, and anthropometric and biochemical status at baseline and 1 year (n = 321. Linear regression was used to quantify the associations between baseline demographic, clinical, psychosocial and behavioural variables and change in PAEE over 12 months. This study is registered as ISRCTN61323766. Results ProActive participants significantly increased their PAEE by 0.6 kj/min (SD 4.2, p = 0.006 over one year, the equivalent of around 20 minutes brisk walking/day. Male sex and higher fitness at baseline predicted increase in PAEE. No significant associations were found for any other variables. Very few baseline demographic, clinical, psychosocial and behavioural predictors were associated with change in objectively measured physical activity. Conclusions Traditional baseline determinants of self-reported physical activity targeted by behavioural interventions may be relatively weak predictors of change in objectively measured physical activity. Further research is needed to improve our understanding of factors influencing change in physical activity to inform the development and targeting of interventions.

  2. Application of General Regression Neural Network to the Prediction of LOD Change

    Science.gov (United States)

    Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao

    2012-01-01

    Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.

  3. Regime shifts limit the predictability of land-system change

    DEFF Research Database (Denmark)

    Müller, Daniel; Sun, Zhanli; Vongvisouk, Thoumthone

    2014-01-01

    Payment schemes for ecosystem services such as Reducing Emissions from Deforestation and forest Degradation (REDD) rely on the prediction of ‘business-as-usual’ scenarios to ensure that emission reductions from carbon credits are additional. However, land systems often undergo periods of nonlinear...... and abrupt change that invalidate predictions calibrated on past trends. Rapid land-system change can occur when critical thresholds in broad-scale underlying drivers such as commodity prices and climate conditions are crossed or when sudden events such as political change or natural disasters punctuate long...

  4. GABAA receptors in visual and auditory cortex and neural activity changes during basic visual stimulation

    Directory of Open Access Journals (Sweden)

    Pengmin eQin

    2012-12-01

    Full Text Available Recent imaging studies have demonstrated that levels of resting GABA in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABAA receptors, in the changes in brain activity between the eyes closed (EC and eyes open (EO state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: An EO and EC block design, allowing the modelling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [18F]Flumazenil PET measure GABAA receptor binding potentials. It was demonstrated that the local-to-global ratio of GABAA receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABAA receptor binding potential in the visual cortex also predicts the change of functional connectivity between visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABAA receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity.

  5. Climate-Induced Boreal Forest Change: Predictions versus Current Observations

    Science.gov (United States)

    Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

    2007-01-01

    For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

  6. Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke

    Directory of Open Access Journals (Sweden)

    Firdaus Fabrice Hannanu

    2017-01-01

    In subacute stroke, fMRI brain activity related to passive movement measured in a sensorimotor network defined by activity during voluntary movement predicted motor recovery better than baseline motor-FMS alone. Furthermore, fMRI sensorimotor network activity measures considered alone allowed excellent clinical recovery prediction and may provide reliable biomarkers for assessing new therapies in clinical trial contexts. Our findings suggest that neural reorganization related to motor recovery from moderate to severe stroke results from balanced changes in ipsilesional MI (BA4a and a set of phylogenetically more archaic sensorimotor regions in the ventral sensorimotor trend, in which OP1 and OP4 processes may complement the ipsilesional dorsal motor cortex in achieving compensatory sensorimotor recovery.

  7. Markov Model Predicts Changes in STH Prevalence during Control Activities Even with a Reduced Amount of Baseline Information.

    Directory of Open Access Journals (Sweden)

    Antonio Montresor

    2016-04-01

    Full Text Available Estimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models.We analysed data from field studies conducted with different combination of three parameters: (i the frequency of drug administration; (ii the drug distributed; and (iii the target treatment population (entire population or school-aged children only. This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1. We also formulated three equations (one for each STH parasite to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2 to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on changes in

  8. Contextual remapping in visual search after predictable target-location changes.

    Science.gov (United States)

    Conci, Markus; Sun, Luning; Müller, Hermann J

    2011-07-01

    Invariant spatial context can facilitate visual search. For instance, detection of a target is faster if it is presented within a repeatedly encountered, as compared to a novel, layout of nontargets, demonstrating a role of contextual learning for attentional guidance ('contextual cueing'). Here, we investigated how context-based learning adapts to target location (and identity) changes. Three experiments were performed in which, in an initial learning phase, observers learned to associate a given context with a given target location. A subsequent test phase then introduced identity and/or location changes to the target. The results showed that contextual cueing could not compensate for target changes that were not 'predictable' (i.e. learnable). However, for predictable changes, contextual cueing remained effective even immediately after the change. These findings demonstrate that contextual cueing is adaptive to predictable target location changes. Under these conditions, learned contextual associations can be effectively 'remapped' to accommodate new task requirements.

  9. Decreased dopamine activity predicts relapse in methamphetamine abusers

    Energy Technology Data Exchange (ETDEWEB)

    Wang G. J.; Wang, G.-J.; Smith, L.; Volkow, N.D.; Telang, F.; Logan, J.; Tomasi, D.; Wong, C.T.; Hoffman, W.; Jayne, M.; Alia-Klein, N.; Thanos, P.; Fowler, J.S.

    2011-01-20

    Studies in methamphetamine (METH) abusers showed that the decreases in brain dopamine (DA) function might recover with protracted detoxification. However, the extent to which striatal DA function in METH predicts recovery has not been evaluated. Here we assessed whether striatal DA activity in METH abusers is associated with clinical outcomes. Brain DA D2 receptor (D2R) availability was measured with positron emission tomography and [{sup 11}C]raclopride in 16 METH abusers, both after placebo and after challenge with 60 mg oral methylphenidate (MPH) (to measure DA release) to assess whether it predicted clinical outcomes. For this purpose, METH abusers were tested within 6 months of last METH use and then followed up for 9 months of abstinence. In parallel, 15 healthy controls were tested. METH abusers had lower D2R availability in caudate than in controls. Both METH abusers and controls showed decreased striatal D2R availability after MPH and these decreases were smaller in METH than in controls in left putamen. The six METH abusers who relapsed during the follow-up period had lower D2R availability in dorsal striatum than in controls, and had no D2R changes after MPH challenge. The 10 METH abusers who completed detoxification did not differ from controls neither in striatal D2R availability nor in MPH-induced striatal DA changes. These results provide preliminary evidence that low striatal DA function in METH abusers is associated with a greater likelihood of relapse during treatment. Detection of the extent of DA dysfunction may be helpful in predicting therapeutic outcomes.

  10. Decreased dopamine activity predicts relapse in methamphetamine abusers

    International Nuclear Information System (INIS)

    Wang, G.J.; Smith, L.; Volkow, N.D.; Telang, F.; Logan, J.; Tomasi, D.; Wong, C.T.; Hoffman, W.; Jayne, M.; Alia-Klein, N.; Thanos, P.; Fowler, J.S.

    2011-01-01

    Studies in methamphetamine (METH) abusers showed that the decreases in brain dopamine (DA) function might recover with protracted detoxification. However, the extent to which striatal DA function in METH predicts recovery has not been evaluated. Here we assessed whether striatal DA activity in METH abusers is associated with clinical outcomes. Brain DA D2 receptor (D2R) availability was measured with positron emission tomography and ( 11 C)raclopride in 16 METH abusers, both after placebo and after challenge with 60 mg oral methylphenidate (MPH) (to measure DA release) to assess whether it predicted clinical outcomes. For this purpose, METH abusers were tested within 6 months of last METH use and then followed up for 9 months of abstinence. In parallel, 15 healthy controls were tested. METH abusers had lower D2R availability in caudate than in controls. Both METH abusers and controls showed decreased striatal D2R availability after MPH and these decreases were smaller in METH than in controls in left putamen. The six METH abusers who relapsed during the follow-up period had lower D2R availability in dorsal striatum than in controls, and had no D2R changes after MPH challenge. The 10 METH abusers who completed detoxification did not differ from controls neither in striatal D2R availability nor in MPH-induced striatal DA changes. These results provide preliminary evidence that low striatal DA function in METH abusers is associated with a greater likelihood of relapse during treatment. Detection of the extent of DA dysfunction may be helpful in predicting therapeutic outcomes.

  11. Predicting Immediate Belief Change and Adherence to Argument Claims.

    Science.gov (United States)

    Hample, Dale

    1978-01-01

    Discusses the probative potential of evidence in argument, and evaluates the importance of evidence in predicting belief change. Predicts adherence to argument claims and confirms the traditionally recognized importance of evidence to persuasion. (JMF)

  12. Spontaneous brain activity predicts learning ability of foreign sounds.

    Science.gov (United States)

    Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

    2013-05-29

    Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

  13. Predicting Mood Changes in Bipolar Disorder through Heartbeat Nonlinear Dynamics.

    Science.gov (United States)

    Valenza, Gaetano; Nardelli, Mimma; Lanata', Antonio; Gentili, Claudio; Bertschy, Gilles; Kosel, Markus; Scilingo, Enzo Pasquale

    2016-04-20

    Bipolar Disorder (BD) is characterized by an alternation of mood states from depression to (hypo)mania. Mixed states, i.e., a combination of depression and mania symptoms at the same time, can also be present. The diagnosis of this disorder in the current clinical practice is based only on subjective interviews and questionnaires, while no reliable objective psychophysiological markers are available. Furthermore, there are no biological markers predicting BD outcomes, or providing information about the future clinical course of the phenomenon. To overcome this limitation, here we propose a methodology predicting mood changes in BD using heartbeat nonlinear dynamics exclusively, derived from the ECG. Mood changes are here intended as transitioning between two mental states: euthymic state (EUT), i.e., the good affective balance, and non-euthymic (non-EUT) states. Heart Rate Variability (HRV) series from 14 bipolar spectrum patients (age: 33.439.76, age range: 23-54; 6 females) involved in the European project PSYCHE, undergoing whole night ECG monitoring were analyzed. Data were gathered from a wearable system comprised of a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire ECGs. Each patient was monitored twice a week, for 14 weeks, being able to perform normal (unstructured) activities. From each acquisition, the longest artifact-free segment of heartbeat dynamics was selected for further analyses. Sub-segments of 5 minutes of this segment were used to estimate trends of HRV linear and nonlinear dynamics. Considering data from a current observation at day t0, and past observations at days (t1, t2,...,), personalized prediction accuracies in forecasting a mood state (EUT/non-EUT) at day t+1 were 69% on average, reaching values as high as 83.3%. This approach opens to the possibility of predicting mood states in bipolar patients through heartbeat nonlinear dynamics exclusively.

  14. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

    Science.gov (United States)

    Laubach, Mark; Wessberg, Johan; Nicolelis, Miguel A. L.

    2000-06-01

    When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

  15. Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments.

    Science.gov (United States)

    Gorter, Florien A; Aarts, Mark G M; Zwaan, Bas J; de Visser, J Arjan G M

    2018-01-01

    The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change. Copyright © 2018 by the Genetics Society of America.

  16. A mathematical model for predicting lane changes using the steering wheel angle.

    Science.gov (United States)

    Schmidt, Kim; Beggiato, Matthias; Hoffmann, Karl Heinz; Krems, Josef F

    2014-06-01

    Positive safety effects of advanced driver assistance systems can only become effective if drivers accept and use these systems. Early detection of driver's intention would allow for selective system activation and therefore reduce false alarms. This driving simulator study aims at exploring early predictors of lane changes. In total, 3111 lane changes of 51 participants on a simulated highway track were analyzed. Results show that drivers stopped their engagement in a secondary task about 7s before crossing the lane, which indicates a first planning phase of the maneuver. Subsequently, drivers start moving toward the lane, marking a mean steering wheel angle of 2.5°. Steering wheel angle as a directly measurable vehicle parameter appears as a promising early predictor of a lane change. A mathematical model of the steering wheel angle is presented, which is supposed to contribute for predicting lane change maneuvers. The mathematical model will be part of a further predictor of lane changes. This predictor can be a new advanced driver assistance system able to recognize a driver's intention. With this knowledge, other systems can be activated or deactivated so drivers get no annoying and exhausting alarm signals. This is one way how we can increase the acceptance of assistance systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Predicting postoperative haemoglobin changes after burn surgery

    African Journals Online (AJOL)

    Burn surgery is associated with significant peri-operative haemoglobin. (Hb) changes. ... operative factors predictive of an Hb <7 g/dL on the first day after surgery, which were ..... clinical judgement, taking into consideration the risk associated.

  18. GABA(A) receptors in visual and auditory cortex and neural activity changes during basic visual stimulation.

    Science.gov (United States)

    Qin, Pengmin; Duncan, Niall W; Wiebking, Christine; Gravel, Paul; Lyttelton, Oliver; Hayes, Dave J; Verhaeghe, Jeroen; Kostikov, Alexey; Schirrmacher, Ralf; Reader, Andrew J; Northoff, Georg

    2012-01-01

    Recent imaging studies have demonstrated that levels of resting γ-aminobutyric acid (GABA) in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABA(A) receptors, in the changes in brain activity between the eyes closed (EC) and eyes open (EO) state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: an EO and EC block design, allowing the modeling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [(18)F]Flumazenil PET to measure GABA(A) receptor binding potentials. It was demonstrated that the local-to-global ratio of GABA(A) receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABA(A) receptor binding potential in the visual cortex also predicted the change in functional connectivity between the visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABA(A) receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity.

  19. Predicting active school travel: The role of planned behavior and habit strength

    Science.gov (United States)

    2012-01-01

    Background Despite strong support for predictive validity of the theory of planned behavior (TPB) substantial variance in both intention and behavior is unaccounted for by the model’s predictors. The present study tested the extent to which habit strength augments the predictive validity of the TPB in relation to a currently under-researched behavior that has important health implications, namely children’s active school travel. Method Participants (N = 126 children aged 8–9 years; 59 % males) were sampled from five elementary schools in the west of Scotland and completed questionnaire measures of all TPB constructs in relation to walking to school and both walking and car/bus use habit. Over the subsequent week, commuting steps on school journeys were measured objectively using an accelerometer. Hierarchical multiple regressions were used to test the predictive utility of the TPB and habit strength in relation to both intention and subsequent behavior. Results The TPB accounted for 41 % and 10 % of the variance in intention and objectively measured behavior, respectively. Together, walking habit and car/bus habit significantly increased the proportion of explained variance in both intention and behavior by 6 %. Perceived behavioral control and both walking and car/bus habit independently predicted intention. Intention and car/bus habit independently predicted behavior. Conclusions The TPB significantly predicts children’s active school travel. However, habit strength augments the predictive validity of the model. The results indicate that school travel is controlled by both intentional and habitual processes. In practice, interventions could usefully decrease the habitual use of motorized transport for travel to school and increase children’s intention to walk (via increases in perceived behavioral control and walking habit, and decreases in car/bus habit). Further research is needed to identify effective strategies for changing these

  20. Activity, exposure rate and spectrum prediction with Java programming

    International Nuclear Information System (INIS)

    Sahin, D.; Uenlue, K.

    2009-01-01

    In order to envision the radiation exposure during Neutron Activation Analysis (NAA) experiments, a software called Activity Predictor is developed using Java TM programming language. The Activity Predictor calculates activities, exposure rates and gamma spectra of activated samples for NAA experiments performed at Radiation Science and Engineering Center (RSEC), Penn State Breazeale Reactor (PSBR). The calculation procedure for predictions involves both analytical and Monte Carlo methods. The Activity Predictor software is validated with a series of activation experiments. It has been found that Activity Predictor software calculates the activities and exposure rates precisely. The software also predicts gamma spectrum for each measurement. The predicted spectra agreed partially with measured spectra. The error in net photo peak areas varied from 4.8 to 51.29%, which is considered to be due to simplistic modeling, statistical fluctuations and unknown contaminants in the samples. (author)

  1. Supporting change processes in design: Complexity, prediction and reliability

    Energy Technology Data Exchange (ETDEWEB)

    Eckert, Claudia M. [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: cme26@cam.ac.uk; Keller, Rene [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: rk313@cam.ac.uk; Earl, Chris [Open University, Department of Design and Innovation, Walton Hall, Milton Keynes MK7 6AA (United Kingdom)]. E-mail: C.F.Earl@open.ac.uk; Clarkson, P. John [Engineering Design Centre, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ (United Kingdom)]. E-mail: pjc10@cam.ac.uk

    2006-12-15

    Change to existing products is fundamental to design processes. New products are often designed through change or modification to existing products. Specific parts or subsystems are changed to similar ones whilst others are directly reused. Design by modification applies particularly to safety critical products where the reuse of existing working parts and subsystems can reduce cost and risk. However change is rarely a matter of just reusing or modifying parts. Changing one part can propagate through the entire design leading to costly rework or jeopardising the integrity of the whole product. This paper characterises product change based on studies in the aerospace and automotive industry and introduces tools to aid designers in understanding the potential effects of change. Two ways of supporting designers are described: probabilistic prediction of the effects of change and visualisation of change propagation through product connectivities. Change propagation has uncertainties which are amplified by the choices designers make in practice as they implement change. Change prediction and visualisation is discussed with reference to complexity in three areas of product development: the structural backcloth of connectivities in the existing product (and its processes), the descriptions of the product used in design and the actions taken to carry out changes.

  2. Personality traits and individual differences predict threat-induced changes in postural control.

    Science.gov (United States)

    Zaback, Martin; Cleworth, Taylor W; Carpenter, Mark G; Adkin, Allan L

    2015-04-01

    This study explored whether specific personality traits and individual differences could predict changes in postural control when presented with a height-induced postural threat. Eighty-two healthy young adults completed questionnaires to assess trait anxiety, trait movement reinvestment (conscious motor processing, movement self-consciousness), physical risk-taking, and previous experience with height-related activities. Tests of static (quiet standing) and anticipatory (rise to toes) postural control were completed under low and high postural threat conditions. Personality traits and individual differences significantly predicted height-induced changes in static, but not anticipatory postural control. Individuals less prone to taking physical risks were more likely to lean further away from the platform edge and sway at higher frequencies and smaller amplitudes. Individuals more prone to conscious motor processing were more likely to lean further away from the platform edge and sway at larger amplitudes. Individuals more self-conscious about their movement appearance were more likely to sway at smaller amplitudes. Evidence is also provided that relationships between physical risk-taking and changes in static postural control are mediated through changes in fear of falling and physiological arousal. Results from this study may have indirect implications for balance assessment and treatment; however, further work exploring these factors in patient populations is necessary. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Predicting vulnerabilities of North American shorebirds to climate change.

    Directory of Open Access Journals (Sweden)

    Hector Galbraith

    Full Text Available Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at-risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners-in-Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90% taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower-risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change.

  4. Effects of the activation of self-esteem and perceived temporal distance on the preparation for an examination(2): Temporal changes in performance prediction

    OpenAIRE

    藤島, 喜嗣; Yoshitsugu, FUJISHIMA; 昭和女子大学大学院生活機構研究科

    2012-01-01

    Self-esteem is a global representation of the self that varies in its level of activation. Self-esteem should have an influence on future prediction depending on its activation level. According to the construal level theory, temporal distance moderates the influence of the activated self-esteem. Undergraduates (n=89) participated in a panel survey on predictions about their examination performance, in which their level of self-esteem activation was manipulated. Contrary to the hypothesis, the...

  5. Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

    Science.gov (United States)

    Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

    2014-08-01

    Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

    Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

  7. Lifestyle Behaviors Predict Negative and Positive Changes in Self-reported Health: The Role of Immigration to the United States for Koreans.

    Science.gov (United States)

    Baron-Epel, Orna; Hofstetter, C Richard; Irvin, Veronica L; Kang, Sunny; Hovell, Melbourne F

    2015-10-01

    Studies of changes in health following immigration are inconsistent, and few are based on longitudinal designs to test associations based on change. This study identified factors that predicted changes in self-reported health (SRH) among California residents of Korean descent. A sample of California residents of Korean descent were interviewed and followed-up 2 or 3 times by telephone during 2001-2009. The questionnaires dealt with SRH, lifestyle behaviors (smoking, physical activity, and fast food consumption), and socioeconomic measures. Statistical analysis included random-intercepts longitudinal regression models predicting change in SRH. A similar percentage of respondents reported improved and deteriorating SRH (30.3% and 29.1%, respectively). Smoking, consumption of fast foods, age, percentage of life spent in the United States, and being female were predictors of deteriorating SRH, whereas physical activity, education, and living with a partner were predictive of improvement in SRH. The effect of immigration on SRH is influenced by socioeconomic factors and lifestyle practices. Results support promotion of healthy lifestyle practices among immigrants. © 2015 APJPH.

  8. Fire behavior potential in central Saskatchewan under predicted climate change : summary document

    International Nuclear Information System (INIS)

    Parisien, M.; Hirsch, K.; Todd, B.; Flannigan, M.; Kafka, V.; Flynn, N.

    2005-01-01

    This study assesses fire danger and fire behaviour potential in central Saskatchewan using simulated climate scenarios produced by the Canadian Regional Climate Model (CRCM), including scenario analysis of base, double and triple level carbon dioxide in the atmosphere and uses available forest fuels to develop an absolute measure of fire behaviour. For each of these climate scenarios, the CRCM-generated weather was used as input variables into the Canadian Forest Fire Behavior Prediction (FBP) System. Fire behavior potential was quantified using head fire intensity, a measure of the fire's energy output because it can be related to fire behavior characteristics, suppression effectiveness, and fire effects. The report discusses the implications of fire behavior potential changes for fire and forest management. Preliminary results suggest a large increase in area burned in the study area by the end of the twenty-first century. Some of the possible fire management activities for long-term prediction include: pre-positioning of resources, preparedness planning, prioritization of fire and forest management activities and fire threat evaluation. 16 refs., 1 tab, 7 figs

  9. Predicting impacts of climate change on Fasciola hepatica risk.

    Science.gov (United States)

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  10. Predicting impacts of climate change on Fasciola hepatica risk.

    Directory of Open Access Journals (Sweden)

    Naomi J Fox

    2011-01-01

    Full Text Available Fasciola hepatica (liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  11. Structure prediction and activity analysis of human heme oxygenase-1 and its mutant.

    Science.gov (United States)

    Xia, Zhen-Wei; Zhou, Wen-Pu; Cui, Wen-Jun; Zhang, Xue-Hong; Shen, Qing-Xiang; Li, Yun-Zhu; Yu, Shan-Chang

    2004-08-15

    To predict wild human heme oxygenase-1 (whHO-1) and hHO-1 His25Ala mutant (delta hHO-1) structures, to clone and express them and analyze their activities. Swiss-PdbViewer and Antheprot 5.0 were used for the prediction of structure diversity and physical-chemical changes between wild and mutant hHO-1. hHO-1 His25Ala mutant cDNA was constructed by site-directed mutagenesis in two plasmids of E. coli DH5alpha. Expression products were purified by ammonium sulphate precipitation and Q-Sepharose Fast Flow column chromatography, and their activities were measured. rHO-1 had the structure of a helical fold with the heme sandwiched between heme-heme oxygenase-1 helices. Bond angle, dihedral angle and chemical bond in the active pocket changed after Ala25 was replaced by His25, but Ala25 was still contacting the surface and the electrostatic potential of the active pocket was negative. The mutated enzyme kept binding activity to heme. Two vectors pBHO-1 and pBHO-1(M) were constructed and expressed. Ammonium sulphate precipitation and column chromatography yielded 3.6-fold and 30-fold higher purities of whHO-1, respectively. The activity of delta hHO-1 was reduced 91.21% after mutation compared with whHO-1. Proximal His25 ligand is crucial for normal hHO-1 catalytic activity. delta hHO-1 is deactivated by mutation but keeps the same binding site as whHO-1. delta hHO-1 might be a potential inhibitor of whHO-1 for preventing neonatal hyperbilirubinemia.

  12. Lateral prefrontal cortex activity during cognitive control of emotion predicts response to social stress in schizophrenia

    Directory of Open Access Journals (Sweden)

    Laura M. Tully, PhD

    2014-01-01

    Full Text Available LPFC dysfunction is a well-established neural impairment in schizophrenia and is associated with worse symptoms. However, how LPFC activation influences symptoms is unclear. Previous findings in healthy individuals demonstrate that lateral prefrontal cortex (LPFC activation during cognitive control of emotional information predicts mood and behavior in response to interpersonal conflict, thus impairments in these processes may contribute to symptom exacerbation in schizophrenia. We investigated whether schizophrenia participants show LPFC deficits during cognitive control of emotional information, and whether these LPFC deficits prospectively predict changes in mood and symptoms following real-world interpersonal conflict. During fMRI, 23 individuals with schizophrenia or schizoaffective disorder and 24 healthy controls completed the Multi-Source Interference Task superimposed on neutral and negative pictures. Afterwards, schizophrenia participants completed a 21-day online daily-diary in which they rated the extent to which they experienced mood and schizophrenia-spectrum symptoms, as well as the occurrence and response to interpersonal conflict. Schizophrenia participants had lower dorsal LPFC activity (BA9 during cognitive control of task-irrelevant negative emotional information. Within schizophrenia participants, DLPFC activity during cognitive control of emotional information predicted changes in positive and negative mood on days following highly distressing interpersonal conflicts. Results have implications for understanding the specific role of LPFC in response to social stress in schizophrenia, and suggest that treatments targeting LPFC-mediated cognitive control of emotion could promote adaptive response to social stress in schizophrenia.

  13. Changes in Predictive Task Switching with Age and with Cognitive Load.

    Science.gov (United States)

    Levy-Tzedek, Shelly

    2017-01-01

    Predictive control of movement is more efficient than feedback-based control, and is an important skill in everyday life. We tested whether the ability to predictively control movements of the upper arm is affected by age and by cognitive load. A total of 63 participants were tested in two experiments. In both experiments participants were seated, and controlled a cursor on a computer screen by flexing and extending their dominant arm. In Experiment 1, 20 young adults and 20 older adults were asked to continuously change the frequency of their horizontal arm movements, with the goal of inducing an abrupt switch between discrete movements (at low frequencies) and rhythmic movements (at high frequencies). We tested whether that change was performed based on a feed-forward (predictive) or on a feedback (reactive) control. In Experiment 2, 23 young adults performed the same task, while being exposed to a cognitive load half of the time via a serial subtraction task. We found that both aging and cognitive load diminished, on average, the ability of participants to predictively control their movements. Five older adults and one young adult under a cognitive load were not able to perform the switch between rhythmic and discrete movement (or vice versa). In Experiment 1, 40% of the older participants were able to predictively control their movements, compared with 70% in the young group. In Experiment 2, 48% of the participants were able to predictively control their movements with a cognitively loading task, compared with 70% in the no-load condition. The ability to predictively change a motor plan in anticipation of upcoming changes may be an important component in performing everyday functions, such as safe driving and avoiding falls.

  14. Predicting human activities in sequences of actions in RGB-D videos

    Science.gov (United States)

    Jardim, David; Nunes, Luís.; Dias, Miguel

    2017-03-01

    In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

  15. Hippocampus activation related to 'real-time' processing of visuospatial change.

    Science.gov (United States)

    Beudel, M; Leenders, K L; de Jong, B M

    2016-12-01

    The delay associated with cerebral processing time implies a lack of real-time representation of changes in the observed environment. To bridge this gap for motor actions in a dynamical environment, the brain uses predictions of the most plausible future reality based on previously provided information. To optimise these predictions, adjustments to actual experiences are necessary. This requires a perceptual memory buffer. In our study we gained more insight how the brain treats (real-time) information by comparing cerebral activations related to judging past-, present- and future locations of a moving ball, respectively. Eighteen healthy subjects made these estimations while fMRI data was obtained. All three conditions evoked bilateral dorsal-parietal and premotor activations, while judgment of the location of the ball at the moment of judgment showed increased bilateral posterior hippocampus activation relative to making both future and past judgments at the one-second time-sale. Since the condition of such 'real-time' judgments implied undistracted observation of the ball's actual movements, the associated hippocampal activation is consistent with the concept that the hippocampus participates in a top-down exerted sensory gating mechanism. In this way, it may play a role in novelty (saliency) detection. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Change in avian abundance predicted from regional forest inventory data

    Science.gov (United States)

    Twedt, Daniel J.; Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R.; Uihlein, William B.; Fitzgerald, Jane A.

    2010-01-01

    An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will

  17. Changing predictions, stable recognition: Children's representations of downward incline motion.

    Science.gov (United States)

    Hast, Michael; Howe, Christine

    2017-11-01

    Various studies to-date have demonstrated children hold ill-conceived expressed beliefs about the physical world such as that one ball will fall faster than another because it is heavier. At the same time, they also demonstrate accurate recognition of dynamic events. How these representations relate is still unresolved. This study examined 5- to 11-year-olds' (N = 130) predictions and recognition of motion down inclines. Predictions were typically in error, matching previous work, but children largely recognized correct events as correct and rejected incorrect ones. The results also demonstrate while predictions change with increasing age, recognition shows signs of stability. The findings provide further support for a hybrid model of object representations and argue in favour of stable core cognition existing alongside developmental changes. Statement of contribution What is already known on this subject? Children's predictions of physical events show limitations in accuracy Their recognition of such events suggests children may use different knowledge sources in their reasoning What the present study adds? Predictions fluctuate more strongly than recognition, suggesting stable core cognition But recognition also shows some fluctuation, arguing for a hybrid model of knowledge representation. © 2017 The British Psychological Society.

  18. Prediction of Land Use Change Based on Markov and GM(1,1 Models

    Directory of Open Access Journals (Sweden)

    SUN Yi-yang

    2016-05-01

    Full Text Available In order to explore the law of land use change in Laiwu City, Markov and GM(1,1 were respectively employed in the prediction of land use change in Laiwu from 2015 to 2050, after which the results were analyzed and discussed. The results showed that:(1The variational trends of all kinds of land use change predicted by the two models were consistent and the goodness of fit of the predictive value in corresponding years in the near future was high, illustrating that the predicted results in the near future were credible and the trend predicted in mid long term could be used as reference. (2The cultivated land would remanin almost no change from 2015 to 2020, and then gradually decreaseed in a small range from 2020 to 2050. The garden, the woodland, the grassland always reducing and the decreare range of the grassland was the largest. The urban village and industrial and mining land, the transportation land would be continuously increased and the range of urban village and industrial and mining land was the largest. The water and water conservancy facilities land and the other land would be always reduced in a very small range. It could be concluded that the results predicted by the two models in the near future were credible and could provide scientific basis for land use planning of Laiwu, while the method could provide reference for the prediction of land use change.

  19. Environmental change and hydrological responses in the interior of western Canada: Towards improved understanding, diagnosis, and prediction by the Changing Cold Regions Network

    Science.gov (United States)

    DeBeer, C. M.; Wheater, H. S.; Carey, S. K.; Pomeroy, J. W.; Stewart, R. E.

    2016-12-01

    The past several decades have been a period of rapid climatic and environmental change. In western Canada, as in other areas globally, warming and changes in precipitation have led to vast reductions in seasonal snowcover and freshwater ice cover, retreating glaciers, thawing permafrost, changing forest composition and structure, increasing northern shrub coverage, and earlier timing of river flows in spring. Yet streamflow volume has exhibited a variety of responses across the region and over different time scales, and patterns of change are not easily generalizable. Improved understanding, diagnosis, and prediction of the rapidly changing components of the Earth system are key to managing uncertain water futures, but this is challenging due to complex system behavior and sometimes compensatory responses. The Changing Cold Regions Network (CCRN) is a Canadian research network and GEWEX Regional Hydroclimate Project that is addressing these issues, with a geographic focus on the Saskatchewan and Mackenzie River basins. This paper will present examples of the changes that have been observed at a set of long-term and well-studied headwater research basins, and highlight how various processes confound hydrological responses here, pointing to the need for careful diagnosis. We will discuss some recent CCRN activities and progress toward improving conceptual understanding and developing scenarios of change for the 21st century, which can then be applied within process-based hydrological models for future prediction. Several priority research areas that will be a focus of continued work in CCRN will be recommended.

  20. Predicting Climate Change Impacts to the Canadian Boreal Forest

    Directory of Open Access Journals (Sweden)

    Trisalyn A. Nelson

    2014-03-01

    Full Text Available Climate change is expected to alter temperature, precipitation, and seasonality with potentially acute impacts on Canada’s boreal. In this research we predicted future spatial distributions of biodiversity in Canada’s boreal for 2020, 2050, and 2080 using indirect indicators derived from remote sensing and based on vegetation productivity. Vegetation productivity indices, representing annual amounts and variability of greenness, have been shown to relate to tree and wildlife richness in Canada’s boreal. Relationships between historical satellite-derived productivity and climate data were applied to modelled scenarios of future climate to predict and map potential future vegetation productivity for 592 regions across Canada. Results indicated that the pattern of vegetation productivity will become more homogenous, particularly west of Hudson Bay. We expect climate change to impact biodiversity along north/south gradients and by 2080 vegetation distributions will be dominated by processes of seasonality in the north and a combination of cumulative greenness and minimum cover in the south. The Hudson Plains, which host the world’s largest and most contiguous wetland, are predicted to experience less seasonality and more greenness. The spatial distribution of predicted trends in vegetation productivity was emphasized over absolute values, in order to support regional biodiversity assessments and conservation planning.

  1. A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

    Directory of Open Access Journals (Sweden)

    M Irfan Ashraf

    Full Text Available Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model. Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2 5-year(-1 and volume: 0.0008 m(3 5-year(-1. Model variability described by root mean squared error (RMSE in basal area prediction was 40.53 cm(2 5-year(-1 and 0.0393 m(3 5-year(-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence

  2. Prediction of Positions of Active Compounds Makes It Possible To Increase Activity in Fragment-Based Drug Development

    Directory of Open Access Journals (Sweden)

    Yoshifumi Fukunishi

    2011-05-01

    Full Text Available We have developed a computational method that predicts the positions of active compounds, making it possible to increase activity as a fragment evolution strategy. We refer to the positions of these compounds as the active position. When an active fragment compound is found, the following lead generation process is performed, primarily to increase activity. In the current method, to predict the location of the active position, hydrogen atoms are replaced by small side chains, generating virtual compounds. These virtual compounds are docked to a target protein, and the docking scores (affinities are examined. The hydrogen atom that gives the virtual compound with good affinity should correspond to the active position and it should be replaced to generate a lead compound. This method was found to work well, with the prediction of the active position being 2 times more efficient than random synthesis. In the current study, 15 examples of lead generation were examined. The probability of finding active positions among all hydrogen atoms was 26%, and the current method accurately predicted 60% of the active positions.

  3. Changes in retinal venular oxygen saturation predict activity of proliferative diabetic retinopathy 3 months after panretinal photocoagulation.

    Science.gov (United States)

    Torp, Thomas Lee; Kawasaki, Ryo; Wong, Tien Yin; Peto, Tunde; Grauslund, Jakob

    2018-03-01

    Proliferative diabetic retinopathy (PDR) is a severe blinding condition. We investigated whether retinal metabolism, measured by retinal oximetry, may predict PDR activity after panretinal laser photocoagulation (PRP). We performed a prospective, interventional, clinical study of patients with treatment-naive PDR. Wide-field fluorescein angiography (OPTOS, Optomap) and global and focal retinal oximetry (Oxymap T1) were performed at baseline (BL), and 3 months (3M) after PRP. Angiographic findings were used to divide patients according to progression or non-progression of PDR after PRP. We evaluated differences in global and focal retinal oxygen saturation between patients with and without progression of PDR after PRP treatment. We included 45 eyes of 37 patients (median age and duration of diabetes were 51.6 and 20 years). Eyes with progression of PDR developed a higher retinal venous oxygen saturation than eyes with non-progression at 3M (global: +5.9% (95% CI -1.5 to 12.9), focal: +5.4%, (95% CI -4.1 to 14.8)). Likewise, progression of PDR was associated with a lower arteriovenular (AV) oxygen difference between BL and 3M (global: -6.1%, (95% CI -13.4 to -1.4), focal: -4.5% (95% CI -12.1 to 3.2)). In a multiple logistic regression model, increment in global retinal venular oxygen saturation (OR 1.30 per 1%-point increment, p=0.017) and decrement in AV oxygen saturation difference (OR 0.72 per 1%-point increment, p=0.016) at 3M independently predicted progression of PDR. Development of higher retinal venular and lower AV global oxygen saturation independently predicts progression of PDR despite standard PRP and might be a potential non-invasive marker of angiogenic disease activity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  4. Prediction of permeability changes in an excavation response zone

    International Nuclear Information System (INIS)

    Kinoshita, Naoto; Ishii, Takashi; Kuroda, Hidetaka; Tada, Hiroyuki

    1992-01-01

    In geologic disposal of radioactive wastes, stress changes due to cavern excavation may expand the existing fractures and create possible bypasses for groundwater. This paper proposes a simple method for predicting permeability changes in the excavation response zones. Numerical analyses using this method predict that the response zones created by cavern excavation would differ greatly in thickness and permeability depending on the depth of the cavern site and the initial in-situ stress, that when the cavern site is deeper, response zones would expand more and permeability would increases more, and that if the ratio of horizontal to vertical in-situ stress is small, extensive permeable zones at the crown and the bottom would occur, whereas if the ratio is large, extensive permeable zones would occur in the side walls. (orig.)

  5. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

    Science.gov (United States)

    Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

    2017-07-01

    The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was

  6. Thermal and hydrologic responses to climate change predict marked alterations in boreal stream invertebrate assemblages.

    Science.gov (United States)

    Mustonen, Kaisa-Riikka; Mykrä, Heikki; Marttila, Hannu; Sarremejane, Romain; Veijalainen, Noora; Sippel, Kalle; Muotka, Timo; Hawkins, Charles P

    2018-06-01

    Air temperature at the northernmost latitudes is predicted to increase steeply and precipitation to become more variable by the end of the 21st century, resulting in altered thermal and hydrological regimes. We applied five climate scenarios to predict the future (2070-2100) benthic macroinvertebrate assemblages at 239 near-pristine sites across Finland (ca. 1200 km latitudinal span). We used a multitaxon distribution model with air temperature and modeled daily flow as predictors. As expected, projected air temperature increased the most in northernmost Finland. Predicted taxonomic richness also increased the most in northern Finland, congruent with the predicted northwards shift of many species' distributions. Compositional changes were predicted to be high even without changes in richness, suggesting that species replacement may be the main mechanism causing climate-induced changes in macroinvertebrate assemblages. Northern streams were predicted to lose much of the seasonality of their flow regimes, causing potentially marked changes in stream benthic assemblages. Sites with the highest loss of seasonality were predicted to support future assemblages that deviate most in compositional similarity from the present-day assemblages. Macroinvertebrate assemblages were also predicted to change more in headwaters than in larger streams, as headwaters were particularly sensitive to changes in flow patterns. Our results emphasize the importance of focusing protection and mitigation on headwater streams with high-flow seasonality because of their vulnerability to climate change. © 2018 John Wiley & Sons Ltd.

  7. An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

    Directory of Open Access Journals (Sweden)

    Guoxing Li

    2015-01-01

    Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

  8. The predicted influence of climate change on lesser prairie-chicken reproductive parameters

    Science.gov (United States)

    Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, D.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

    2013-01-01

    The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

  9. Ventromedial Prefrontal Cortex Activation Is Associated with Memory Formation for Predictable Rewards

    Science.gov (United States)

    Bialleck, Katharina A.; Schaal, Hans-Peter; Kranz, Thorsten A.; Fell, Juergen; Elger, Christian E.; Axmacher, Nikolai

    2011-01-01

    During reinforcement learning, dopamine release shifts from the moment of reward consumption to the time point when the reward can be predicted. Previous studies provide consistent evidence that reward-predicting cues enhance long-term memory (LTM) formation of these items via dopaminergic projections to the ventral striatum. However, it is less clear whether memory for items that do not precede a reward but are directly associated with reward consumption is also facilitated. Here, we investigated this question in an fMRI paradigm in which LTM for reward-predicting and neutral cues was compared to LTM for items presented during consumption of reliably predictable as compared to less predictable rewards. We observed activation of the ventral striatum and enhanced memory formation during reward anticipation. During processing of less predictable as compared to reliably predictable rewards, the ventral striatum was activated as well, but items associated with less predictable outcomes were remembered worse than items associated with reliably predictable outcomes. Processing of reliably predictable rewards activated the ventromedial prefrontal cortex (vmPFC), and vmPFC BOLD responses were associated with successful memory formation of these items. Taken together, these findings show that consumption of reliably predictable rewards facilitates LTM formation and is associated with activation of the vmPFC. PMID:21326612

  10. Changes in Leisure Activities and Dimensions of Depressive Symptoms in Later Life: A 12-Year Follow-Up.

    Science.gov (United States)

    Chao, Shiau-Fang

    2016-06-01

    Although leisure activities benefit the mental health of the elderly population, the effect of changes in leisure activities on dimensions of depressive symptoms remains unclear. This investigation examined the influences of changes in intellectual, social, and physical activities between waves on four dimensions of depressive symptoms at follow-up. Random effects modeling was utilized with data from a nationwide longitudinal study conducted in Taiwan. The study data comprised 6,942 observations from 2,660 older adults over a 12-year period. The results suggested that changes in physical activities contributed to depressive symptoms which reflected positive affect in the later wave. Increased social activities between waves predicted higher positive affect and lower interpersonal difficulties scores at follow-up. Increased intellectual activities between waves did not substantially affect any domain of depressive symptoms. In contrast, declines in intellectual activities between waves predicted higher scores in three depressive symptoms domains, including depressed mood, somatic symptoms, and interpersonal difficulties. Engagement in a varied range of activities benefits mental health among elders more than participation in any single type of activity among elders. Reducing physical activities can lower positive affect, while the adverse effect may be balanced by increasing social activities. Also, the impact of decreasing intellectual activities on the interpersonal difficulties domain of depressive symptoms may be offset by increasing social activities. © The Author 2014. 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.

  11. Predicting forest insect flight activity: A Bayesian network approach.

    Directory of Open Access Journals (Sweden)

    Stephen M Pawson

    Full Text Available Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

  12. Predicting environmental restoration activities through static simulation

    International Nuclear Information System (INIS)

    Ross, T.L.; King, D.A.; Wilkins, M.L.; Forward, M.F.

    1994-12-01

    This paper discusses a static simulation model that predicts several performance measures of environmental restoration activities over different remedial strategies. Basic model operation consists of manipulating and processing waste streams via selecting and applying remedial technologies according to the strategy. Performance measure prediction is possible for contaminated soil, solid waste, surface water, groundwater, storage tank, and facility sites. Simulations are performed for the U.S. Department of Energy in support of its Programmatic Environmental Impact Statement

  13. Managing time in a changing world: Timing of avian annual cycle stages under climate change

    NARCIS (Netherlands)

    Tomotani, B.M.

    2017-01-01

    Animals need to time their seasonal activities such as breeding and migration to occur at the right time. They use cues from the environment to predict changes and organise their activities accordingly. What happens, then, when climate change interferes with this ability to make predictions? Climate

  14. Improving behavioral performance under full attention by adjusting response criteria to changes in stimulus predictability.

    Science.gov (United States)

    Katzner, Steffen; Treue, Stefan; Busse, Laura

    2012-09-04

    One of the key features of active perception is the ability to predict critical sensory events. Humans and animals can implicitly learn statistical regularities in the timing of events and use them to improve behavioral performance. Here, we used a signal detection approach to investigate whether such improvements in performance result from changes of perceptual sensitivity or rather from adjustments of a response criterion. In a regular sequence of briefly presented stimuli, human observers performed a noise-limited motion detection task by monitoring the stimulus stream for the appearance of a designated target direction. We manipulated target predictability through the hazard rate, which specifies the likelihood that a target is about to occur, given it has not occurred so far. Analyses of response accuracy revealed that improvements in performance could be accounted for by adjustments of the response criterion; a growing hazard rate was paralleled by an increasing tendency to report the presence of a target. In contrast, the hazard rate did not affect perceptual sensitivity. Consistent with previous research, we also found that reaction time decreases as the hazard rate grows. A simple rise-to-threshold model could well describe this decrease and attribute predictability effects to threshold adjustments rather than changes in information supply. We conclude that, even under conditions of full attention and constant perceptual sensitivity, behavioral performance can be optimized by dynamically adjusting the response criterion to meet ongoing changes in the likelihood of a target.

  15. Understanding, Predicting, and Preventing Life Changing and Life Threatening Health Changes among Aging Veterans and Civilians with Spinal Cord Injury

    Science.gov (United States)

    2017-10-01

    AWARD NUMBER: W81XWH-16-1-0629 TITLE: Understanding, Predicting, and Preventing Life -Changing and Life -Threatening Health Changes among Aging...Annual 3. DATES COVERED 30 Sep 2016 - 29 Sep 2017 4. TITLE AND SUBTITLE Understanding, Predicting, and Preventing Life -Changing and Life ... hope of preventing them. Our purpose is to better understand the how and why of the development of negative health spirals and how they may best be

  16. The use of specialisation indices to predict vulnerability of coral-feeding butterflyfishes to environmental change

    KAUST Repository

    Lawton, Rebecca J.

    2011-07-14

    In the absence of detailed assessments of extinction risk, ecological specialisation is often used as a proxy of vulnerability to environmental disturbances and extinction risk. Numerous indices can be used to estimate specialisation; however, the utility of these different indices to predict vulnerability to future environmental change is unknown. Here we compare the performance of specialisation indices using coral-feeding butterflyfishes as a model group. Our aims were to 1) quantify the dietary preferences of three butterflyfish species across habitats with differing levels of resource availability; 2) investigate how estimates of dietary specialisation vary with the use of different specialisation indices; 3) determine which specialisation indices best inform predictions of vulnerability to environmental change; and 4) assess the utility of resource selection functions to inform predictions of vulnerability to environmental change. The relative level of dietary specialisation estimated for all three species varied when different specialisation indices were used, indicating that the choice of index can have a considerable impact upon estimates of specialisation. Specialisation indices that do not consider resource abundance may fail to distinguish species that primarily use common resources from species that actively target resources disproportionately more than they are available. Resource selection functions provided the greatest insights into the potential response of species to changes in resource availability. Examination of resource selection functions, in addition to specialisation indices, indicated that Chaetodon trifascialis was the most specialised feeder, with highly conserved dietary preferences across all sites, suggesting that this species is highly vulnerable to the impacts of climate-induced coral loss on reefs. Our results indicate that vulnerability assessments based on some specialisation indices may be misleading and the best estimates of

  17. Woody plants and the prediction of climate-change impacts on bird diversity

    DEFF Research Database (Denmark)

    Kissling, W. Daniel; Field, R.; Korntheuer, H.

    2010-01-01

    Current methods of assessing climate-induced shifts of species distributions rarely account for species interactions and usually ignore potential differences in response times of interacting taxa to climate change. Here, we used species-richness data from 1005 breeding bird and 1417 woody plant...... species in Kenya and employed model-averaged coefficients from regression models and median climatic forecasts assembled across 15 climate-change scenarios to predict bird species richness under climate change. Forecasts assuming an instantaneous response of woody plants and birds to climate change...... suggested increases in future bird species richness across most of Kenya whereas forecasts assuming strongly lagged woody plant responses to climate change indicated a reversed trend, i.e. reduced bird species richness. Uncertainties in predictions of future bird species richness were geographically...

  18. Inter-decadal change in potential predictability of the East Asian summer monsoon

    Science.gov (United States)

    Li, Jiao; Ding, Ruiqiang; Wu, Zhiwei; Zhong, Quanjia; Li, Baosheng; Li, Jianping

    2018-05-01

    The significant inter-decadal change in potential predictability of the East Asian summer monsoon (EASM) has been investigated using the signal-to-noise ratio method. The relatively low potential predictability appears from the early 1950s through the late 1970s and during the early 2000s, whereas the potential predictability is relatively high from the early 1980s through the late 1990s. The inter-decadal change in potential predictability of the EASM can be attributed mainly to variations in the external signal of the EASM. The latter is mostly caused by the El Niño-Southern Oscillation (ENSO) inter-decadal variability. As a major external signal of the EASM, the ENSO inter-decadal variability experiences phase transitions from negative to positive phases in the late 1970s, and to negative phases in the late 1990s. Additionally, ENSO is generally strong (weak) during a positive (negative) phase of the ENSO inter-decadal variability. The strong ENSO is expected to have a greater influence on the EASM, and vice versa. As a result, the potential predictability of the EASM tends to be high (low) during a positive (negative) phase of the ENSO inter-decadal variability. Furthermore, a suite of Pacific Pacemaker experiments suggests that the ENSO inter-decadal variability may be a key pacemaker of the inter-decadal change in potential predictability of the EASM.

  19. Autonomous Motivation Predicts 7-Day Physical Activity in Hong Kong Students.

    Science.gov (United States)

    Ha, Amy S; Ng, Johan Y Y

    2015-07-01

    Autonomous motivation predicts positive health behaviors such as physical activity. However, few studies have examined the relation between motivational regulations and objectively measured physical activity and sedentary behaviors. Thus, we investigated whether different motivational regulations (autonomous motivation, controlled motivation, and amotivation) predicted 7-day physical activity, sedentary behaviors, and health-related quality of life (HRQoL) of students. A total of 115 students (mean age = 11.6 years, 55.7% female) self-reported their motivational regulations and health-related quality of life. Physical activity and sedentary behaviors were measured using accelerometers for seven days. Using multilevel modeling, we found that autonomous motivation predicted higher levels of moderate-to-vigorous physical activity, less sedentary behaviors, and better HRQoL. Controlled motivation and amotivation each only negatively predicted one facet of HRQoL. Results suggested that autonomous motivation could be an important predictor of physical activity behaviors in Hong Kong students. Promotion of this form of motivational regulation may also increase HRQoL. © 2015 The International Association of Applied Psychology.

  20. HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

    Directory of Open Access Journals (Sweden)

    S. J. Schymanski

    2011-02-01

    Full Text Available Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places to define parameter values and unobservable processes that are needed to fully characterize the structure and functioning of the landscape. Transfer in time has to deal with the likely temporal changes to both parameters and processes under future changed conditions. This remains an important obstacle to addressing some of the most urgent prediction questions in hydrology, such as prediction in ungauged basins and prediction under global change. In this paper, we propose a new approach to catchment hydrological modeling, based on universal principles that do not change in time and that remain valid across many places. The key to this framework, which we call behavioral modeling, is to assume that there are universal and time-invariant organizing principles that can be used to identify the most appropriate model structure (including parameter values and responses for a given ecosystem at a given moment in time. These organizing principles may be derived from fundamental physical or biological laws, or from empirical laws that have been demonstrated to be time-invariant and to hold at many places and scales. Much fundamental research remains to be undertaken to help discover these organizing principles on the basis of exploration of observed patterns of landscape structure and hydrological behavior and their interpretation as legacy effects of past co-evolution of climate, soils, topography, vegetation and humans. Our hope is that the new behavioral modeling framework will be a step forward towards a new vision for hydrology where models are capable of more confidently predicting the behavior of catchments beyond what has been observed or experienced before.

  1. Predictors of physical activity change among adults using observational designs.

    Science.gov (United States)

    Rhodes, Ryan E; Quinlan, Alison

    2015-03-01

    Regular physical activity (PA) is foundational to human health, yet most people are inactive. A sound understanding of the determinants of PA may be instructive for building interventions and/or identifying critical target groups to promote PA. Most research on PA correlates has been biased by cross-sectional or passive prospective designs that fail to examine within-person analysis of PA change. The purpose of this review was to collect and appraise the available literature on the predictors of PA change conceived broadly in terms of increases/decreases from baseline assessment as well as specifically in terms of adoption and maintenance. Eligible studies were from English, peer-reviewed published articles that examined predictors of natural change of PA over 3 months + using observational (non-experimental) data in adult samples. Searches were performed from June 2012 to January 2014 in eight databases. Sixty-seven independent data-sets, from 12 countries, primarily of medium quality/risk of bias, were identified with 26 correlates spanning demographic, behavioral, intra-individual, inter-individual, and environmental categories. Only intention and the onset of motherhood could reliably predict overall PA change. Among datasets configured to predict PA adoption, affective judgments and behavioral processes of change were the only reliable predictors, although both only have a small number of available studies. There were no reliable predictors of maintenance when compared to PA relapse. The results underscore the importance of individual-level motivation and behavioral regulation in PA change, but also denote critical social variables. These findings, however, are constrained by PA measurement bias and limited studies that employed time-varying covariation between predictor variables and PA.

  2. Do symptom-specific stages of change predict eating disorder treatment outcome?

    Science.gov (United States)

    Ackard, Diann M; Cronemeyer, Catherine L; Richter, Sara; Egan, Amber

    2015-03-01

    Interview methods to assess stages of change (SOC) in eating disorders (ED) indicate that SOC are positively correlated with symptom improvement over time. However, interviews require significant time and staff training and global measures of SOC do not capture varying levels of motivation across ED symptoms. This study used a self-report, ED symptom-specific SOC measure to determine prevalence of stages across symptoms and identify if SOC predict treatment outcome. Participants [N = 182; age 13-58 years; 92% Caucasian; 96% female; average BMI 21.7 (SD = 5.9); 50% ED not otherwise specified (EDNOS), 30.8% bulimia nervosa (BN), 19.2% anorexia nervosa (AN)] seeking ED treatment at a diverse-milieu multi-disciplinary facility in the United States completed stages of change, behavioral (ED symptom use and frequency) and psychological (ED concerns, anxiety, depression) measures at intake assessment and at 3, 6 and 12 months thereafter. Descriptive summaries were generated using ANOVA or Kruskal-Wallis (continuous) and χ (2) (categorical) tests. Repeated measures linear regression models with autoregressive correlation structure predicted treatment outcome. At intake assessment, 53.3% of AN, 34.0% of BN and 18.1% of EDNOS patients were in Preparation/Action. Readiness to change specific symptoms was highest for binge-eating (57.8%) and vomiting (56.5%). Frequency of fasting and restricting behaviors, and scores on all eating disorder and psychological measures improved over time regardless of SOC at intake assessment. Symptom-specific SOC did not predict reductions in ED symptom frequency. Overall SOC predicted neither improvement in Eating Disorder Examination Questionnaire (EDE-Q) scores nor reduction in depression or trait anxiety; however, higher overall SOC predicted lower state anxiety across follow-up. Readiness to change ED behaviors varies considerably. Most patients reduced eating disorder behaviors and increased psychological functioning regardless of stages

  3. Prediction of changes in memory performance by plasma homovanillic acid levels in clozapine-treated patients with schizophrenia.

    Science.gov (United States)

    Sumiyoshi, Tomiki; Roy, A; Kim, C-H; Jayathilake, K; Lee, M A; Sumiyoshi, C; Meltzer, H Y

    2004-12-01

    Cognitive dysfunction in schizophrenia has been demonstrated to be dependent, in part, on dopaminergic activity. Clozapine has been found to improve some domains of cognition, including verbal memory, in patients with schizophrenia. This study tested the hypothesis that plasma homovanillic acid (pHVA) levels, a peripheral measure of central dopaminergic activity, would predict the change in memory performance in patients with schizophrenia treated with clozapine. Twenty-seven male patients with schizophrenia received clozapine treatment for 6 weeks. Verbal list learning (VLL)-Delayed Recall (VLL-DR), a test of secondary verbal memory, was administered before and after clozapine treatment. Blood samples to measure pHVA levels were collected at baseline. Baseline pHVA levels were negatively correlated with change in performance on VLL-DR; the lower baseline pHVA level was associated with greater improvement in performance on VLL-DR during treatment with clozapine. Baseline pHVA levels in subjects who showed improvement in verbal memory during clozapine treatment ( n=13) were significantly lower than those in subjects whose memory performance did not improve ( n=14). The results of this study indicate that baseline pHVA levels predict the ability of clozapine to improve memory performance in patients with schizophrenia.

  4. Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

    Science.gov (United States)

    Hunter, H. E.; Amato, R. A.

    1972-01-01

    The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

  5. Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.

    Science.gov (United States)

    Bajaj, Sahil; Adhikari, Bhim Mani; Dhamala, Mukesh

    2013-01-01

    The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz) and slow-2 (0.198-0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not 'entirely' at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural

  6. Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

    Science.gov (United States)

    Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

    2013-12-01

    Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

  7. When activation changes, what else changes? the relationship between change in patient activation measure (PAM) and employees' health status and health behaviors.

    Science.gov (United States)

    Harvey, Lisa; Fowles, Jinnet Briggs; Xi, Min; Terry, Paul

    2012-08-01

    To test whether changes in the patient activation measure (PAM) are related to changes in health status and healthy behaviors. Data for this secondary analysis were taken from a group-randomized, controlled trial comparing a traditional health promotion program for employees with an activated consumer program and a control program. The study population included 320 employees (with and without chronic disease) from two U.S. companies: a large, integrated health care system and a national airline. Survey and biometric data were collected in Spring 2005 (baseline) and Spring 2007 (follow-up). Change in PAM was associated with changes in health behaviors at every level (1-4), especially at level 4. Changes related to overall risk score and many of its components: aerobic exercise, safety, cancer risk, stress and mental health. Other changes included frequency of eating breakfast and the likelihood of knowing about health plans and how they compare. Level 4 of patient activation is not an end-point. People are capable of continuing to make significant change within this level. Interventions should be designed to encourage movement from lower to higher levels of activation. Even people at the most activated level improve health behaviors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  8. Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

    Science.gov (United States)

    Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

    2017-07-01

    Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

  9. Nonlinear Predictive Sliding Mode Control for Active Suspension System

    Directory of Open Access Journals (Sweden)

    Dazhuang Wang

    2018-01-01

    Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

  10. Changing University Students’ Alternative Conceptions of Optics by Active Learning

    Directory of Open Access Journals (Sweden)

    Zalkida Hadžibegović

    2013-01-01

    Full Text Available Active learning is individual and group participation in effective activities such as in-class observing, writing, experimenting, discussion, solving problems, and talking about to-be-learned topics. Some instructors believe that active learning is impossible, or at least extremely difficult to achieve in large lecture sessions. Nevertheless, the truly impressive implementation results of theSCALE-UP learning environment suggest that such beliefs are false (Beichner et al., 2000. In this study, we present a design of an active learning environment with positive effect on students. The design is based on the following elements: (1 helping students to learn from interactive lecture experiment; (2 guiding students to use justified explanation and prediction after observing and exploring a phenomenon; (3 developing a conceptual question sequencedesigned for use in an interactive lecture with students answering questions in worksheets by writing and drawing; (4 evaluating students’ conceptual change and gains by questions related to light reflection, refraction, and image formation in an exam held a week after the active learning session. Data were collected from 95 science freshmen with different secondary school backgrounds. They participated in geometrical optics classes organized for collecting research results during and after only one active learning session.The results have showed that around 60% of the students changed their initial alternative conceptions of vision and of image formation. It was also found that a large group of university students is likely to be engaged in active learning, shifting from a passive role they usually play during teacher’s lectures.

  11. Predicting hydrological response to forest changes by simple statistical models: the selection of the best indicator of forest changes with a hydrological perspective

    Science.gov (United States)

    Ning, D.; Zhang, M.; Ren, S.; Hou, Y.; Yu, L.; Meng, Z.

    2017-01-01

    Forest plays an important role in hydrological cycle, and forest changes will inevitably affect runoff across multiple spatial scales. The selection of a suitable indicator for forest changes is essential for predicting forest-related hydrological response. This study used the Meijiang River, one of the headwaters of the Poyang Lake as an example to identify the best indicator of forest changes for predicting forest change-induced hydrological responses. Correlation analysis was conducted first to detect the relationships between monthly runoff and its predictive variables including antecedent monthly precipitation and indicators for forest changes (forest coverage, vegetation indices including EVI, NDVI, and NDWI), and by use of the identified predictive variables that were most correlated with monthly runoff, multiple linear regression models were then developed. The model with best performance identified in this study included two independent variables -antecedent monthly precipitation and NDWI. It indicates that NDWI is the best indicator of forest change in hydrological prediction while forest coverage, the most commonly used indicator of forest change is insignificantly related to monthly runoff. This highlights the use of vegetation index such as NDWI to indicate forest changes in hydrological studies. This study will provide us with an efficient way to quantify the hydrological impact of large-scale forest changes in the Meijiang River watershed, which is crucial for downstream water resource management and ecological protection in the Poyang Lake basin.

  12. Predicting Effects of Water Regime Changes on Waterbirds: Insights from Staging Swans

    NARCIS (Netherlands)

    Nolet, Bart A.; Gyimesi, Abel; Krimpen, Van Roderick R.D.; Boer, de Fred; Stillman, Richard A.; Green, Andy J.

    2016-01-01

    Predicting the environmental impact of a proposed development is notoriously difficult,
    especially when future conditions fall outside the current range of conditions. Individualbased
    approaches have been developed and applied to predict the impact of environmental
    changes on wintering

  13. Predicting effects of water regime changes on waterbirds : insights from staging swans

    NARCIS (Netherlands)

    Nolet, B.A.; Gyimesi, A.; van Krimpen, R.R.D.; de Boer, W.F.; Stillman, R.A.

    2016-01-01

    Predicting the environmental impact of a proposed development is notoriously difficult, especially when future conditions fall outside the current range of conditions. Individual-based approaches have been developed and applied to predict the impact of environmental changes on wintering and staging

  14. Online communication predicts Belgian adolescents' initiation of romantic and sexual activity.

    Science.gov (United States)

    Vandenbosch, Laura; Beyens, Ine; Vangeel, Laurens; Eggermont, Steven

    2016-04-01

    Online communication is associated with offline romantic and sexual activity among college students. Yet, it is unknown whether online communication is associated with the initiation of romantic and sexual activity among adolescents. This two-wave panel study investigated whether chatting, visiting dating websites, and visiting erotic contact websites predicted adolescents' initiation of romantic and sexual activity. We analyzed two-wave panel data from 1163 Belgian adolescents who participated in the MORES Study. We investigated the longitudinal impact of online communication on the initiation of romantic relationships and sexual intercourse using logistic regression analyses. The odds ratios of initiating a romantic relationship among romantically inexperienced adolescents who frequently used chat rooms, dating websites, or erotic contact websites were two to three times larger than those of non-users. Among sexually inexperienced adolescents who frequently used chat rooms, dating websites, or erotic contact websites, the odds ratios of initiating sexual intercourse were two to five times larger than that among non-users, even after a number of other relevant factors were introduced. This is the first study to demonstrate that online communication predicts the initiation of offline sexual and romantic activity as early as adolescence. Practitioners and parents need to consider the role of online communication in adolescents' developing sexuality. • Adolescents increasingly communicate online with peers. • Online communication predicts romantic and sexual activity among college students. What is New: • Online communication predicts adolescents' offline romantic activity over time. • Online communication predicts adolescents' offline sexual activity over time.

  15. Get Active Orlando: changing the built environment to increase physical activity.

    Science.gov (United States)

    McCreedy, Malisa; Leslie, Jill G

    2009-12-01

    Active Living by Design's Get Active Orlando partnership (GAO) focused on downtown Orlando's Community Redevelopment Area, including the Parramore Heritage District, home to many low-income and ethnically diverse residents, including many seniors. The area had undergone substantial development, and GAO aimed to incorporate active living considerations into the city's changing landscape. Get Active Orlando conducted a baseline survey of all streets, sidewalks, and bicycle lanes in the project area and identified a sequence of plans and policies in which to incorporate changes identified in the assessment. To create more immediate opportunities for active living, the partnership initiated a senior walking program, a bicycle refurbishment and giveaway program, and community bicycle-riding events, and led a social-marketing campaign that emphasized simple lifestyle changes. Get Active Orlando influenced adoption of public policies supporting active living in Orlando, including the Downtown Transportation Plan, Streetscape Guidelines, Design Standards Review Checklist, and growth management policies. Establishment of the Mayor's Advisory Council on Active Living is testament to the heightened significance of active living in Orlando. Initial assessment data served as a strong platform for policy change. Creating connections across disciplines including land-use planning, transportation, public health, and economic development allowed GAO to secure substantial policy change to influence design of the built environment. Engaging community members, including youth, as leaders was an important factor in program success. The physical environment in Orlando's Community Redevelopment Area is beginning to change as a reflection of a new policy framework designed to support active living.

  16. Early changes in socioeconomic status do not predict changes in body mass in the first decade of life.

    Science.gov (United States)

    Starkey, Leighann; Revenson, Tracey A

    2015-04-01

    Many studies link childhood socioeconomic status (SES) to body mass index (BMI), but few account for the impact of socioeconomic mobility throughout the lifespan. This study aims to investigate the impact of socioeconomic mobility on changes in BMI in childhood. Analyses tested whether [1] socioeconomic status influences BMI, [2] changes in socioeconomic status impact changes in BMI, and [3] timing of socioeconomic status mobility impacts BMI. Secondary data spanning birth to age 9 were analyzed. SES and BMI were investigated with gender, birth weight, maternal race/ethnicity, and maternal nativity as covariates. Autoregressive structural equation modeling and latent growth modeling were used. Socioeconomic status in the first year of life predicted body mass index. Child covariates were consistently associated with body mass index. Rate of change in socioeconomic status did not predict change in body mass index. The findings suggest that early socioeconomic status may most influence body mass in later childhood.

  17. Prediction of adolescents doing physical activity after completing secondary education.

    Science.gov (United States)

    Moreno-Murcia, Juan Antonio; Huéscar, Elisa; Cervelló, Eduardo

    2012-03-01

    The purpose of this study, based on the self-determination theory (Ryan & Deci, 2000) was to test the prediction power of student's responsibility, psychological mediators, intrinsic motivation and the importance attached to physical education in the intention to continue to practice some form of physical activity and/or sport, and the possible relationships that exist between these variables. We used a sample of 482 adolescent students in physical education classes, with a mean age of 14.3 years, which were measured for responsibility, psychological mediators, sports motivation, the importance of physical education and intention to be physically active. We completed an analysis of structural equations modelling. The results showed that the responsibility positively predicted psychological mediators, and this predicted intrinsic motivation, which positively predicted the importance students attach to physical education, and this, finally, positively predicted the intention of the student to continue doing sport. Results are discussed in relation to the promotion of student's responsibility towards a greater commitment to the practice of physical exercise.

  18. Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

    Science.gov (United States)

    Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

    2015-12-01

    Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Association of parents' and children's physical activity and sedentary time in Year 4 (8-9) and change between Year 1 (5-6) and Year 4: a longitudinal study.

    Science.gov (United States)

    Jago, Russell; Solomon-Moore, Emma; Macdonald-Wallis, Corrie; Thompson, Janice L; Lawlor, Deborah A; Sebire, Simon J

    2017-08-17

    Parents could be important influences on child physical activity and parents are often encouraged to be more active with their child. This paper examined the association between parent and child physical activity and sedentary time in a UK cohort of children assessed when the children were in Year 1 (5-6 years old) and in Year 4 (8-9 years old). One thousand two hundred twenty three children and parents provided data in Year 4 and of these 685 participated in Year 1. Children and parents wore an accelerometer for five days including a weekend. Mean minutes of sedentary time and moderate-to-vigorous intensity physical activity (MVPA) were derived. Multiple imputation was used to impute all missing data and create complete datasets. Linear regression models examined whether parent MVPA and sedentary time at Year 4 and at Year 1 predicted child MVPA and sedentary time at Year 4. Change in parent MVPA and sedentary time was used to predict change in child MVPA and sedentary time between Year 1 and Year 4. Imputed data showed that at Year 4, female parent sedentary time was associated with child sedentary time (0.13, 95% CI = 0.00 to 0.27 mins/day), with a similar association for male parents (0.15, 95% CI = -0.02 to 0.32 mins/day). Female parent and child MVPA at Year 4 were associated (0.16, 95% CI = 0.08 to 0.23 mins/day) with a smaller association for male parents (0.08, 95% CI = -0.01 to 0.17 mins/day). There was little evidence that either male or female parent MVPA at Year 1 predicted child MVPA at Year 4 with similar associations for sedentary time. There was little evidence that change in parent MVPA or sedentary time predicted change in child MVPA or sedentary time respectively. Parents who were more physically active when their child was 8-9 years old had a child who was more active, but the magnitude of association was generally small. There was little evidence that parental activity from three years earlier predicted child activity at age 8-9, or

  20. Responsiveness of a simple tool for assessing change in behavioral intention after continuing professional development activities.

    Science.gov (United States)

    Légaré, France; Freitas, Adriana; Turcotte, Stéphane; Borduas, Francine; Jacques, André; Luconi, Francesca; Godin, Gaston; Boucher, Andrée; Sargeant, Joan; Labrecque, Michel

    2017-01-01

    Continuing professional development (CPD) activities are one way that new knowledge can be translated into changes in practice. However, few tools are available for evaluating the extent to which these activities change health professionals' behavior. We developed a questionnaire called CPD-Reaction for assessing the impact of CPD activities on health professionals' clinical behavioral intentions. We evaluated its responsiveness to change in behavioral intention and verified its acceptability among stakeholders. We enrolled 376 health professionals who completed CPD-Reaction before and immediately after attending a CPD activity. We contacted them three months later and asked them to self-report on any behavior change. We compared the mean rankings on each CPD-Reaction construct before and immediately after CPD activities. To estimate its predictive validity, we compared the median behavioral intention score (post-activity) of health professionals reporting a behavior change three months later with the median behavioral intention score of physicians who reported no change. We explored stakeholders' views on CPD-Reaction in semi-structured interviews. Participants were mostly family physicians (62.2%), with an average of 19 years of clinical practice. Post-activity, we observed an increase in intention-related scores for all constructs (P behavior change. We observed no statistically significant difference in intention between health professionals who later reported a behavior change and those who reported no change (P = 0.30). Overall, CPD stakeholders found the CPD-Reaction questionnaire of interest and suggested potential solutions to perceived barriers to its implementation. The CPD-Reaction questionnaire seems responsive to change in behavioral intention. Although CPD stakeholders found it interesting, future implementation will require addressing barriers they identified.

  1. Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model

    Science.gov (United States)

    Liu, Q. B.; Wang, Q. J.; Lei, M. F.

    2015-09-01

    It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.

  2. Using the Change Manager Model for the Hippocampal System to Predict Connectivity and Neurophysiological Parameters in the Perirhinal Cortex

    Science.gov (United States)

    Coward, L. Andrew; Gedeon, Tamas D.

    2016-01-01

    Theoretical arguments demonstrate that practical considerations, including the needs to limit physiological resources and to learn without interference with prior learning, severely constrain the anatomical architecture of the brain. These arguments identify the hippocampal system as the change manager for the cortex, with the role of selecting the most appropriate locations for cortical receptive field changes at each point in time and driving those changes. This role results in the hippocampal system recording the identities of groups of cortical receptive fields that changed at the same time. These types of records can also be used to reactivate the receptive fields active during individual unique past events, providing mechanisms for episodic memory retrieval. Our theoretical arguments identify the perirhinal cortex as one important focal point both for driving changes and for recording and retrieving episodic memories. The retrieval of episodic memories must not drive unnecessary receptive field changes, and this consideration places strong constraints on neuron properties and connectivity within and between the perirhinal cortex and regular cortex. Hence the model predicts a number of such properties and connectivity. Experimental test of these falsifiable predictions would clarify how change is managed in the cortex and how episodic memories are retrieved. PMID:26819594

  3. Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers

    Directory of Open Access Journals (Sweden)

    Al-Amin Danladi Bello

    2017-07-01

    Full Text Available Predicting the impact of climate change and human activities on river systems is imperative for effective management of aquatic ecosystems. Unique information can be derived that is critical to the survival of aquatic species under dynamic environmental conditions. Therefore, the response of a tropical river system under climate and land-use changes from the aspects of water temperature and dissolved oxygen concentration were evaluated. Nine designed projected climate change scenarios and three future land-use scenarios were integrated into the Hydrological Simulation Program FORTRAN (HSPF model to determine the impact of climate change and land-use on water temperature and dissolved oxygen (DO concentration using basin-wide simulation of river system in Malaysia. The model performance coefficients showed a good correlation between simulated and observed streamflow, water temperature, and DO concentration in a monthly time step simulation. The Nash–Sutcliffe Efficiency for streamflow was 0.88 for the calibration period and 0.82 for validation period. For water temperature and DO concentration, data from three stations were calibrated and the Nash–Sutcliffe Efficiency for both water temperature and DO ranged from 0.53 to 0.70. The output of the calibrated model under climate change scenarios show that increased rainfall and air temperature do not affects DO concentration and water temperature as much as the condition of a decrease in rainfall and increase in air temperature. The regression model on changes in streamflow, DO concentration, and water temperature under the climate change scenarios illustrates that scenarios that produce high to moderate streamflow, produce small predicted change in water temperatures and DO concentrations compared with the scenarios that produced a low streamflow. It was observed that climate change slightly affects the relationship between water temperatures and DO concentrations in the tropical rivers that we

  4. National Scale Prediction of Soil Carbon Sequestration under Scenarios of Climate Change

    Science.gov (United States)

    Izaurralde, R. C.; Thomson, A. M.; Potter, S. R.; Atwood, J. D.; Williams, J. R.

    2006-12-01

    Carbon sequestration in agricultural soils is gaining momentum as a tool to mitigate the rate of increase of atmospheric CO2. Researchers from the Pacific Northwest National Laboratory, Texas A&M University, and USDA-NRCS used the EPIC model to develop national-scale predictions of soil carbon sequestration with adoption of no till (NT) under scenarios of climate change. In its current form, the EPIC model simulates soil C changes resulting from heterotrophic respiration and wind / water erosion. Representative modeling units were created to capture the climate, soil, and management variability at the 8-digit hydrologic unit (USGS classification) watershed scale. The soils selected represented at least 70% of the variability within each watershed. This resulted in 7,540 representative modeling units for 1,412 watersheds. Each watershed was assigned a major crop system: corn, soybean, spring wheat, winter wheat, cotton, hay, alfalfa, corn-soybean rotation or wheat-fallow rotation based on information from the National Resource Inventory. Each representative farm was simulated with conventional tillage and no tillage, and with and without irrigation. Climate change scenarios for two future periods (2015-2045 and 2045-2075) were selected from GCM model runs using the IPCC SRES scenarios of A2 and B2 from the UK Hadley Center (HadCM3) and US DOE PCM (PCM) models. Changes in mean and standard deviation of monthly temperature and precipitation were extracted from gridded files and applied to baseline climate (1960-1990) for each of the 1,412 modeled watersheds. Modeled crop yields were validated against historical USDA NASS county yields (1960-1990). The HadCM3 model predicted the most severe changes in climate parameters. Overall, there would be little difference between the A2 and B2 scenarios. Carbon offsets were calculated as the difference in soil C change between conventional and no till. Overall, C offsets during the first 30-y period (513 Tg C) are predicted to

  5. Saliency predicts change detection in pictures of natural scenes.

    Science.gov (United States)

    Wright, Michael J

    2005-01-01

    It has been proposed that the visual system encodes the salience of objects in the visual field in an explicit two-dimensional map that guides visual selective attention. Experiments were conducted to determine whether salience measurements applied to regions of pictures of outdoor scenes could predict the detection of changes in those regions. To obtain a quantitative measure of change detection, observers located changes in pairs of colour pictures presented across an interstimulus interval (ISI). Salience measurements were then obtained from different observers for image change regions using three independent methods, and all were positively correlated with change detection. Factor analysis extracted a single saliency factor that accounted for 62% of the variance contained in the four measures. Finally, estimates of the magnitude of the image change in each picture pair were obtained, using nine separate visual filters representing low-level vision features (luminance, colour, spatial frequency, orientation, edge density). None of the feature outputs was significantly associated with change detection or saliency. On the other hand it was shown that high-level (structural) properties of the changed region were related to saliency and to change detection: objects were more salient than shadows and more detectable when changed.

  6. [Effects of sampling plot number on tree species distribution prediction under climate change].

    Science.gov (United States)

    Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

    2013-05-01

    Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

  7. Epigenetic Changes during Hepatic Stellate Cell Activation.

    Directory of Open Access Journals (Sweden)

    Silke Götze

    Full Text Available Hepatic stellate cells (HSC, which can participate in liver regeneration and fibrogenesis, have recently been identified as liver-resident mesenchymal stem cells. During their activation HSC adopt a myofibroblast-like phenotype accompanied by profound changes in the gene expression profile. DNA methylation changes at single genes have been reported during HSC activation and may participate in the regulation of this process, but comprehensive DNA methylation analyses are still missing. The aim of the present study was to elucidate the role of DNA methylation during in vitro activation of HSC.The analysis of DNA methylation changes by antibody-based assays revealed a strong decrease in the global DNA methylation level during culture-induced activation of HSC. To identify genes which may be regulated by DNA methylation, we performed a genome-wide Methyl-MiniSeq EpiQuest sequencing comparing quiescent and early culture-activated HSC. Approximately 400 differentially methylated regions with a methylation change of at least 20% were identified, showing either hypo- or hypermethylation during activation. Further analysis of selected genes for DNA methylation and expression were performed revealing a good correlation between DNA methylation changes and gene expression. Furthermore, global DNA demethylation during HSC activation was investigated by 5-bromo-2-deoxyuridine assay and L-mimosine treatment showing that demethylation was independent of DNA synthesis and thereby excluding a passive DNA demethylation mechanism.In summary, in vitro activation of HSC initiated strong DNA methylation changes, which were associated with gene regulation. These results indicate that epigenetic mechanisms are important for the control of early HSC activation. Furthermore, the data show that global DNA demethylation during activation is based on an active DNA demethylation mechanism.

  8. Predicting future changes in Muskegon River Watershed game fish distributions under future land cover alteration and climate change scenarios

    Science.gov (United States)

    Steen, Paul J.; Wiley, Michael J.; Schaeffer, Jeffrey S.

    2010-01-01

    Future alterations in land cover and climate are likely to cause substantial changes in the ranges of fish species. Predictive distribution models are an important tool for assessing the probability that these changes will cause increases or decreases in or the extirpation of species. Classification tree models that predict the probability of game fish presence were applied to the streams of the Muskegon River watershed, Michigan. The models were used to study three potential future scenarios: (1) land cover change only, (2) land cover change and a 3°C increase in air temperature by 2100, and (3) land cover change and a 5°C increase in air temperature by 2100. The analysis indicated that the expected change in air temperature and subsequent change in water temperatures would result in the decline of coldwater fish in the Muskegon watershed by the end of the 21st century while cool- and warmwater species would significantly increase their ranges. The greatest decline detected was a 90% reduction in the probability that brook trout Salvelinus fontinalis would occur in Bigelow Creek. The greatest increase was a 276% increase in the probability that northern pike Esox lucius would occur in the Middle Branch River. Changes in land cover are expected to cause large changes in a few fish species, such as walleye Sander vitreus and Chinook salmon Oncorhynchus tshawytscha, but not to drive major changes in species composition. Managers can alter stream environmental conditions to maximize the probability that species will reside in particular stream reaches through application of the classification tree models. Such models represent a good way to predict future changes, as they give quantitative estimates of the n-dimensional niches for particular species.

  9. ABRUPT LONGITUDINAL MAGNETIC FIELD CHANGES IN FLARING ACTIVE REGIONS

    International Nuclear Information System (INIS)

    Petrie, G. J. D.; Sudol, J. J.

    2010-01-01

    We characterize the changes in the longitudinal photospheric magnetic field during 38 X-class and 39 M-class flares within 65 0 of disk center using 1 minute GONG magnetograms. In all 77 cases, we identify at least one site in the flaring active region where clear, permanent, stepwise field changes occurred. The median duration of the field changes was about 15 minutes and was approximately equal for X-class and for M-class flares. The absolute values of the field changes ranged from the detection limit of ∼10 G to as high as ∼450 G in two exceptional cases. The median value was 69 G. Field changes were significantly stronger for X-class than for M-class flares and for limb flares than for disk-center flares. Longitudinal field changes less than 100 G tended to decrease longitudinal field strengths, both close to disk center and close to the limb, while field changes greater than 100 G showed no such pattern. Likewise, longitudinal flux strengths tended to decrease during flares. Flux changes, particularly net flux changes near disk center, correlated better than local field changes with GOES peak X-ray flux. The strongest longitudinal field and flux changes occurred in flares observed close to the limb. We estimate the change of Lorentz force associated with each flare and find that this is large enough in some cases to power seismic waves. We find that longitudinal field decreases would likely outnumber increases at all parts of the solar disk within 65 0 of disk center, as in our observations, if photospheric field tilts increase during flares as predicted by Hudson et al.

  10. Validation of Quantitative Structure-Activity Relationship (QSAR Model for Photosensitizer Activity Prediction

    Directory of Open Access Journals (Sweden)

    Sharifuddin M. Zain

    2011-11-01

    Full Text Available Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA method. Based on the method, r2 value, r2 (CV value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.

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

    Science.gov (United States)

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

    2017-06-14

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

  12. Predicting short-term weight loss using four leading health behavior change theories

    Directory of Open Access Journals (Sweden)

    Barata José T

    2007-04-01

    Full Text Available Abstract Background This study was conceived to analyze how exercise and weight management psychosocial variables, derived from several health behavior change theories, predict weight change in a short-term intervention. The theories under analysis were the Social Cognitive Theory, the Transtheoretical Model, the Theory of Planned Behavior, and Self-Determination Theory. Methods Subjects were 142 overweight and obese women (BMI = 30.2 ± 3.7 kg/m2; age = 38.3 ± 5.8y, participating in a 16-week University-based weight control program. Body weight and a comprehensive psychometric battery were assessed at baseline and at program's end. Results Weight decreased significantly (-3.6 ± 3.4%, p Conclusion The present models were able to predict 20–30% of variance in short-term weight loss and changes in weight management self-efficacy accounted for a large share of the predictive power. As expected from previous studies, exercise variables were only moderately associated with short-term outcomes; they are expected to play a larger explanatory role in longer-term results.

  13. Modeling and Predicting the Daily Equatorial Plasma Bubble Activity Using the Tiegcm

    Science.gov (United States)

    Carter, B. A.; Retterer, J. M.; Yizengaw, E.; Wiens, K. C.; Wing, S.; Groves, K. M.; Caton, R. G.; Bridgwood, C.; Francis, M. J.; Terkildsen, M. B.; Norman, R.; Zhang, K.

    2014-12-01

    Describing and understanding the daily variability of Equatorial Plasma Bubble (EPB) occurrence has remained a significant challenge over recent decades. In this study we use the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM), which is driven by solar (F10.7) and geomagnetic (Kp) activity indices, to study daily variations of the linear Rayleigh-Taylor (R-T) instability growth rate in relation to the measured scintillation strength at five longitudinally distributed stations. For locations characterized by generally favorable conditions for EPB growth (i.e., within the scintillation season for that location) we find that the TIEGCM is capable of identifying days when EPB development, determined from the calculated R-T growth rate, is suppressed as a result of geomagnetic activity. Both observed and modeled upward plasma drift indicate that the pre-reversal enhancement scales linearly with Kp from several hours prior, from which it is concluded that even small Kp changes cause significant variations in daily EPB growth. This control of Kp variations on EPB growth prompted an investigation into the use of predicted Kp values from the Wing Kp model over a 2-month equinoctial campaign in 2014. It is found that both the 1-hr and 4-hr predicted Kp values can be reliably used as inputs into the TIEGCM to forecast the EPB growth conditions during scintillation season, when daily EPB variability is governed by the suppression of EPBs on days with increased, but not necessarily high, geomagnetic activity.

  14. THE PROCESS OF CHANGE - PREDICTION OF SPORT ACHIEVEMENTS HISTORICAL TENDENCY

    Directory of Open Access Journals (Sweden)

    Izenedin Mehmeti

    2015-05-01

    Full Text Available The aim of this paper is to summarize the different standpoints and different approaches in regard to the sport performance preparation and achievement prediction. Sports researchers are concerned more directly with learning about scientific sports prediction. Their involvement in the sport sciences focuses on understanding how sports organized and how changes in that organization might influence sports experiences for both athletes and coaches. The goal of these scholars is often to improve sport experiences and performance prediction for current participants and make sport participation more attractive and accessible for those who do not currently play sports, prospective athletes. They also may want to help athletes improve their performance, help coaches work effectively with athletes and win more games. Sports researchers intention is also to assist and help sport organizations grow and operate more efficiently and profitably, and improve sport achievement prediction.

  15. Performance of a process-based hydrodynamic model in predicting shoreline change

    Science.gov (United States)

    Safak, I.; Warner, J. C.; List, J. H.

    2012-12-01

    Shoreline change is controlled by a complex combination of processes that include waves, currents, sediment characteristics and availability, geologic framework, human interventions, and sea level rise. A comprehensive data set of shoreline position (14 shorelines between 1978-2002) along the continuous and relatively non-interrupted North Carolina Coast from Oregon Inlet to Cape Hatteras (65 km) reveals a spatial pattern of alternating erosion and accretion, with an erosional average shoreline change rate of -1.6 m/yr and up to -8 m/yr in some locations. This data set gives a unique opportunity to study long-term shoreline change in an area hit by frequent storm events while relatively uninfluenced by human interventions and the effects of tidal inlets. Accurate predictions of long-term shoreline change may require a model that accurately resolves surf zone processes and sediment transport patterns. Conventional methods for predicting shoreline change such as one-line models and regression of shoreline positions have been designed for computational efficiency. These methods, however, not only have several underlying restrictions (validity for small angle of wave approach, assuming bottom contours and shoreline to be parallel, depth of closure, etc.) but also their empirical estimates of sediment transport rates in the surf zone have been shown to vary greatly from the calculations of process-based hydrodynamic models. We focus on hind-casting long-term shoreline change using components of the process-based, three-dimensional coupled-ocean-atmosphere-wave-sediment transport modeling system (COAWST). COAWST is forced with historical predictions of atmospheric and oceanographic data from public-domain global models. Through a method of coupled concurrent grid-refinement approach in COAWST, the finest grid with resolution of O(10 m) that covers the surf zone along the section of interest is forced at its spatial boundaries with waves and currents computed on the grids

  16. Self-reported Physical Activity Predicts Pain Inhibitory and Facilitatory Function

    Science.gov (United States)

    Naugle, Kelly M.; Riley, Joseph L.

    2013-01-01

    Considerable evidence suggests regular physical activity can reduce chronic pain symptoms. Dysfunction of endogenous facilitatory and inhibitory systems has been implicated in multiple chronic pain conditions. However, few studies have investigated the relationship between levels of physical activity and descending pain modulatory function. Purpose This study’s purpose was to determine whether self-reported levels of physical activity in healthy adults predicted 1) pain sensitivity to heat and cold stimuli, 2) pain facilitatory function as tested by temporal summation of pain (TS), and 3) pain inhibitory function as tested by conditioned pain modulation (CPM) and offset analgesia. Methods Forty-eight healthy adults (age range 18–76) completed the International Physical Activity Questionnaire (IPAQ) and the following pain tests: heat pain thresholds (HPT), heat pain suprathresholds, cold pressor pain (CPP), temporal summation of heat pain, conditioned pain modulation, and offset analgesia. The IPAQ measured levels of walking, moderate, vigorous and total physical activity over the past seven days. Hierarchical linear regressions were conducted to determine the relationship between each pain test and self-reported levels of physical activity, while controlling for age, sex and psychological variables. Results Self-reported total and vigorous physical activity predicted TS and CPM (p’s pain and greater CPM. The IPAQ measures did not predict any of the other pain measures. Conclusion Thus, these results suggest that healthy older and younger adults who self-report greater levels of vigorous and total physical activity exhibit enhanced descending pain modulatory function. Improved descending pain modulation may be a mechanism through which exercise reduces or prevents chronic pain symptoms. PMID:23899890

  17. Horticultural activity predicts later localized limb status in a contemporary pre-industrial population.

    Science.gov (United States)

    Stieglitz, Jonathan; Trumble, Benjamin C; Kaplan, Hillard; Gurven, Michael

    2017-07-01

    Modern humans may have gracile skeletons due to low physical activity levels and mechanical loading. Tests using pre-historic skeletons are limited by the inability to assess behavior directly, while modern industrialized societies possess few socio-ecological features typical of human evolutionary history. Among Tsimane forager-horticulturalists, we test whether greater activity levels and, thus, increased loading earlier in life are associated with greater later-life bone status and diminished age-related bone loss. We used quantitative ultrasonography to assess radial and tibial status among adults aged 20+ years (mean ± SD age = 49 ± 15; 52% female). We conducted systematic behavioral observations to assess earlier-life activity patterns (mean time lag between behavioural observation and ultrasound = 12 years). For a subset of participants, physical activity was again measured later in life, via accelerometry, to determine whether earlier-life time use is associated with later-life activity levels. Anthropometric and demographic data were collected during medical exams. Structural decline with age is reduced for the tibia (female: -0.25 SDs/decade; male: 0.05 SDs/decade) versus radius (female: -0.56 SDs/decade; male: -0.20 SDs/decade), which is expected if greater loading mitigates bone loss. Time allocation to horticulture, but not hunting, positively predicts later-life radial status (β Horticulture  = 0.48, p = 0.01), whereas tibial status is not significantly predicted by subsistence or sedentary leisure participation. Patterns of activity- and age-related change in bone status indicate localized osteogenic responses to loading, and are generally consistent with the logic of bone functional adaptation. Nonmechanical factors related to subsistence lifestyle moderate the association between activity patterns and bone structure. © 2017 Wiley Periodicals, Inc.

  18. Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

    Science.gov (United States)

    Wang, Qijie

    2015-08-01

    The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

  19. Change in active travel and changes in recreational and total physical activity in adults: longitudinal findings from the iConnect study

    Science.gov (United States)

    2013-01-01

    Background To better understand the health benefits of promoting active travel, it is important to understand the relationship between a change in active travel and changes in recreational and total physical activity. Methods These analyses, carried out in April 2012, use longitudinal data from 1628 adult respondents (mean age 54 years; 47% male) in the UK-based iConnect study. Travel and recreational physical activity were measured using detailed seven-day recall instruments. Adjusted linear regression models were fitted with change in active travel defined as ‘decreased’ (15 min/week) as the primary exposure variable and changes in (a) recreational and (b) total physical activity (min/week) as the primary outcome variables. Results Active travel increased in 32% (n=529), was maintained in 33% (n=534) and decreased in 35% (n=565) of respondents. Recreational physical activity decreased in all groups but this decrease was not greater in those whose active travel increased. Conversely, changes in active travel were associated with commensurate changes in total physical activity. Compared with those whose active travel remained unchanged, total physical activity decreased by 176.9 min/week in those whose active travel had decreased (adjusted regression coefficient −154.9, 95% CI −195.3 to −114.5) and was 112.2 min/week greater among those whose active travel had increased (adjusted regression coefficient 135.1, 95% CI 94.3 to 175.9). Conclusion An increase in active travel was associated with a commensurate increase in total physical activity and not a decrease in recreational physical activity. PMID:23445724

  20. Structural integrity of frontostriatal connections predicts longitudinal changes in self-esteem.

    Science.gov (United States)

    Chavez, Robert S; Heatherton, Todd F

    2017-06-01

    Diverse neurological and psychiatric conditions are marked by a diminished sense of positive self-regard, and reductions in self-esteem are associated with risk for these disorders. Recent evidence has shown that the connectivity of frontostriatal circuitry reflects individual differences in self-esteem. However, it remains an open question as to whether the integrity of these connections can predict self-esteem changes over larger timescales. Using diffusion magnetic resonance imaging and probabilistic tractography, we demonstrate that the integrity of white matter pathways linking the medial prefrontal cortex to the ventral striatum predicts changes in self-esteem 8 months after initial scanning in a sample of 30 young adults. Individuals with greater integrity of this pathway during the scanning session at Time 1 showed increased levels of self-esteem at follow-up, whereas individuals with lower integrity showed stifled or decreased levels of self-esteem. These results provide evidence that frontostriatal white matter integrity predicts the trajectory of self-esteem development in early adulthood, which may contribute to blunted levels of positive self-regard seen in multiple psychiatric conditions, including depression and anxiety.

  1. Predicting the genetic consequences of future climate change: The power of coupling spatial demography, the coalescent, and historical landscape changes.

    Science.gov (United States)

    Brown, Jason L; Weber, Jennifer J; Alvarado-Serrano, Diego F; Hickerson, Michael J; Franks, Steven J; Carnaval, Ana C

    2016-01-01

    Climate change is a widely accepted threat to biodiversity. Species distribution models (SDMs) are used to forecast whether and how species distributions may track these changes. Yet, SDMs generally fail to account for genetic and demographic processes, limiting population-level inferences. We still do not understand how predicted environmental shifts will impact the spatial distribution of genetic diversity within taxa. We propose a novel method that predicts spatially explicit genetic and demographic landscapes of populations under future climatic conditions. We use carefully parameterized SDMs as estimates of the spatial distribution of suitable habitats and landscape dispersal permeability under present-day, past, and future conditions. We use empirical genetic data and approximate Bayesian computation to estimate unknown demographic parameters. Finally, we employ these parameters to simulate realistic and complex models of responses to future environmental shifts. We contrast parameterized models under current and future landscapes to quantify the expected magnitude of change. We implement this framework on neutral genetic data available from Penstemon deustus. Our results predict that future climate change will result in geographically widespread declines in genetic diversity in this species. The extent of reduction will heavily depend on the continuity of population networks and deme sizes. To our knowledge, this is the first study to provide spatially explicit predictions of within-species genetic diversity using climatic, demographic, and genetic data. Our approach accounts for climatic, geographic, and biological complexity. This framework is promising for understanding evolutionary consequences of climate change, and guiding conservation planning. © 2016 Botanical Society of America.

  2. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  3. Are climate-related changes to the character of global-mean precipitation predictable?

    International Nuclear Information System (INIS)

    Stephens, Graeme L; Hu, Yongxiang

    2010-01-01

    The physical basis for the change in global-mean precipitation projected to occur with the warming associated with increased greenhouse gases is discussed. The expected increases to column water vapor W control the rate of increase of global precipitation accumulation through its affect on the planet's energy balance. The key role played by changes to downward longwave radiation controlled by this changing water vapor is emphasized. The basic properties of molecular absorption by water vapor dictate that the fractional rate of increase of global-mean precipitation must be significantly less that the fractional rate of increase in water vapor and it is further argued that this reduced rate of precipitation increase implies that the timescale for water re-cycling is increased in the global mean. This further implies less frequent precipitation over a fixed period of time, and the intensity of these less frequent precipitating events must subsequently increase in the mean to realize the increased global accumulation. These changes to the character of global-mean precipitation, predictable consequences of equally predictable changes to W, apply only to the global-mean state and not to the regional or local scale changes in precipitation.

  4. Healthy work revisited: do changes in time strain predict well-being?

    Science.gov (United States)

    Moen, Phyllis; Kelly, Erin L; Lam, Jack

    2013-04-01

    Building on Karasek and Theorell (R. Karasek & T. Theorell, 1990, Healthy work: Stress, productivity, and the reconstruction of working life, New York, NY: Basic Books), we theorized and tested the relationship between time strain (work-time demands and control) and seven self-reported health outcomes. We drew on survey data from 550 employees fielded before and 6 months after the implementation of an organizational intervention, the results only work environment (ROWE) in a white-collar organization. Cross-sectional (wave 1) models showed psychological time demands and time control measures were related to health outcomes in expected directions. The ROWE intervention did not predict changes in psychological time demands by wave 2, but did predict increased time control (a sense of time adequacy and schedule control). Statistical models revealed increases in psychological time demands and time adequacy predicted changes in positive (energy, mastery, psychological well-being, self-assessed health) and negative (emotional exhaustion, somatic symptoms, psychological distress) outcomes in expected directions, net of job and home demands and covariates. This study demonstrates the value of including time strain in investigations of the health effects of job conditions. Results encourage longitudinal models of change in psychological time demands as well as time control, along with the development and testing of interventions aimed at reducing time strain in different populations of workers.

  5. Impact of Different Active-Speech-Ratios on PESQ’s Predictions in Case of Independent and Dependent Losses (in Presence of Receiver-Side Comfort-Noise

    Directory of Open Access Journals (Sweden)

    P. Pocta

    2010-04-01

    Full Text Available This paper deals with the investigation of PESQ’s behavior under independent and dependent loss conditions from an Active-Speech-Ratio perspective in presence of receiver-side comfort-noise. This reference signal characteristic is defined very broadly by ITU-T Recommendation P.862.3. That is the reason to investigate an impact of this characteristic on speech quality prediction more in-depth. We assess the variability of PESQ’s predictions with respect to Active-Speech-Ratios and loss conditions, as well as their accuracy, by comparing the predictions with subjective assessments. Our results show that an increase in amount of speech in the reference signal (expressed by the Active-Speech-Ratio characteristic may result in an increase of the reference signal sensitivity to packet loss change. Interestingly, we have found two additional effects in this investigated case. The use of higher Active-Speech-Ratios may lead to negative shifting effect in MOS domain and also PESQ’s predictions accuracy declining. Predictions accuracy could be improved by higher packet losses.

  6. Sexual selection predicts advancement of avian spring migration in response to climate change

    DEFF Research Database (Denmark)

    Spottiswoode, Claire N; Tøttrup, Anders P; Coppack, Timothy

    2006-01-01

    Global warming has led to earlier spring arrival of migratory birds, but the extent of this advancement varies greatly among species, and it remains uncertain to what degree these changes are phenotypically plastic responses or microevolutionary adaptations to changing environmental conditions. We...... suggest that sexual selection could help to understand this variation, since early spring arrival of males is favoured by female choice. Climate change could weaken the strength of natural selection opposing sexual selection for early migration, which would predict greatest advancement in species...... in the timing of first-arriving individuals, suggesting that selection has not only acted on protandrous males. These results suggest that sexual selection may have an impact on the responses of organisms to climate change, and knowledge of a species' mating system might help to inform attempts at predicting...

  7. The importance of biotic factors in predicting global change effects on decomposition of temperate forest leaf litter.

    Science.gov (United States)

    Rouifed, Soraya; Handa, I Tanya; David, Jean-François; Hättenschwiler, Stephan

    2010-05-01

    Increasing atmospheric CO(2) and temperature are predicted to alter litter decomposition via changes in litter chemistry and environmental conditions. The extent to which these predictions are influenced by biotic factors such as litter species composition or decomposer activity, and in particular how these different factors interact, is not well understood. In a 5-week laboratory experiment we compared the decomposition of leaf litter from four temperate tree species (Fagus sylvatica, Quercus petraea, Carpinus betulus and Tilia platyphyllos) in response to four interacting factors: elevated CO(2)-induced changes in litter quality, a 3 degrees C warmer environment during decomposition, changes in litter species composition, and presence/absence of a litter-feeding millipede (Glomeris marginata). Elevated CO(2) and temperature had much weaker effects on decomposition than litter species composition and the presence of Glomeris. Mass loss of elevated CO(2)-grown leaf litter was reduced in Fagus and increased in Fagus/Tilia mixtures, but was not affected in any other leaf litter treatment. Warming increased litter mass loss in Carpinus and Tilia, but not in the other two litter species and in none of the mixtures. The CO(2)- and temperature-related differences in decomposition disappeared completely when Glomeris was present. Overall, fauna activity stimulated litter mass loss, but to different degrees depending on litter species composition, with a particularly strong effect on Fagus/Tilia mixtures (+58%). Higher fauna-driven mass loss was not followed by higher C mineralization over the relatively short experimental period. Apart from a strong interaction between litter species composition and fauna, the tested factors had little or no interactive effects on decomposition. We conclude that if global change were to result in substantial shifts in plant community composition and macrofauna abundance in forest ecosystems, these interacting biotic factors could have

  8. Ideologically motivated activism: How activist groups influence corporate social change activities

    NARCIS (Netherlands)

    den Hond, F.; de Bakker, F.G.A.; Hickman, G. R.

    2010-01-01

    Using insights from the social movement literature and institutional change theory, we explore how activism influences corporate social change activities. As the responsibility for addressing a variety of social issues is transferred from the state to the private sector, activist groups increasingly

  9. Analysis Of The Method Of Predictive Control Applicable To Active Magnetic Suspension Systems Of Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Kurnyta-Mazurek Paulina

    2015-12-01

    Full Text Available Conventional controllers are usually synthesized on the basis of already known parameters associated with the model developed for the object to be controlled. However, sometimes it proves extremely difficult or even infeasible to find out these parameters, in particular when they subject to changes during the exploitation lifetime. If so, much more sophisticated control methods have to be applied, e.g. the method of predictive control. Thus, the paper deals with application of the predictive control approach to follow-up tracking of an active magnetic suspension where the mathematical and simulation models for such a control system are disclosed with preliminary results from simulation investigations of the control system in question.

  10. Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yinfeng Wu

    2009-10-01

    Full Text Available In wireless sensor networks (WSNs, there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node‟s neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

  11. Cross-layer active predictive congestion control protocol for wireless sensor networks.

    Science.gov (United States)

    Wan, Jiangwen; Xu, Xiaofeng; Feng, Renjian; Wu, Yinfeng

    2009-01-01

    In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

  12. Predicting activities after stroke : what is clinically relevant?

    NARCIS (Netherlands)

    Kwakkel, G.; Kollen, B. J.

    Knowledge about factors that determine the final outcome after stroke is important for early stroke management, rehabilitation goals, and discharge planning. This narrative review provides an overview of current knowledge about the prediction of activities after stroke. We reviewed the pattern of

  13. 'Red Flag' Predictions

    DEFF Research Database (Denmark)

    Hallin, Carina Antonia; Andersen, Torben Juul; Tveterås, Sigbjørn

    -generation prediction markets and outline its unique features as a third-generation prediction market. It is argued that frontline employees gain deep insights when they execute operational activities on an ongoing basis in the organization. The experiential learning from close interaction with internal and external......This conceptual article introduces a new way to predict firm performance based on aggregation of sensing among frontline employees about changes in operational capabilities to update strategic action plans and generate innovations. We frame the approach in the context of first- and second...

  14. A linear regression model for predicting PNW estuarine temperatures in a changing climate

    Science.gov (United States)

    Pacific Northwest coastal regions, estuaries, and associated ecosystems are vulnerable to the potential effects of climate change, especially to changes in nearshore water temperature. While predictive climate models simulate future air temperatures, no such projections exist for...

  15. Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

    Science.gov (United States)

    Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

    2017-09-01

    Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

  16. Predicting ecological responses in a changing ocean: the effects of future climate uncertainty.

    Science.gov (United States)

    Freer, Jennifer J; Partridge, Julian C; Tarling, Geraint A; Collins, Martin A; Genner, Martin J

    2018-01-01

    Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica . Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.

  17. Predictive rhythmic tapping to isochronous and tempo changing metronomes in the nonhuman primate.

    Science.gov (United States)

    Gámez, Jorge; Yc, Karyna; Ayala, Yaneri A; Dotov, Dobromir; Prado, Luis; Merchant, Hugo

    2018-04-30

    Beat entrainment is the ability to entrain one's movements to a perceived periodic stimulus, such as a metronome or a pulse in music. Humans have a capacity to predictively respond to a periodic pulse and to dynamically adjust their movement timing to match the varying music tempos. Previous studies have shown that monkeys share some of the human capabilities for rhythmic entrainment, such as tapping regularly at the period of isochronous stimuli. However, it is still unknown whether monkeys can predictively entrain to dynamic tempo changes like humans. To address this question, we trained monkeys in three tapping tasks and compared their rhythmic entrainment abilities with those of humans. We found that, when immediate feedback about the timing of each movement is provided, monkeys can predictively entrain to an isochronous beat, generating tapping movements in anticipation of the metronome pulse. This ability also generalized to a novel untrained tempo. Notably, macaques can modify their tapping tempo by predicting the beat changes of accelerating and decelerating visual metronomes in a manner similar to humans. Our findings support the notion that nonhuman primates share with humans the ability of temporal anticipation during tapping to isochronous and smoothly changing sequences of stimuli. © 2018 New York Academy of Sciences.

  18. Stages of Change or Changes of Stage? Predicting Transitions in Transtheoretical Model Stages in Relation to Healthy Food Choice

    Science.gov (United States)

    Armitage, Christopher J.; Sheeran, Paschal; Conner, Mark; Arden, Madelynne A.

    2004-01-01

    Relatively little research has examined factors that account for transitions between transtheoretical model (TTM) stages of change. The present study (N=787) used sociodemographic, TTM, and theory of planned behavior (TPB) variables, as well as theory-driven interventions to predict changes in stage. Longitudinal analyses revealed that…

  19. prediction of the impacts of climate changes on the stream flow

    African Journals Online (AJOL)

    HOD

    Soil and Water Assessment Tool, (SWAT) model was used to predict the impacts of Climate Change on Ajali River watershed ... Climate is the synthesis of atmospheric conditions characteristic of a .... generator available in the SWAT model.

  20. Within-person changes in salivary testosterone and physical characteristics of puberty predict boys' daily affect.

    Science.gov (United States)

    Klipker, Kathrin; Wrzus, Cornelia; Rauers, Antje; Boker, Steven M; Riediger, Michaela

    2017-09-01

    Recent investigations highlighted the role of within-person pubertal changes for adolescents' behavior. Yet, little is known about effects on adolescents' daily affect, particularly regarding the hormonal changes underlying physical changes during puberty. In a study with 148 boys aged 10 to 20years, we tested whether within-person physical and hormonal changes over eight months predicted everyday affect fluctuations, measured with experience sampling. As expected, greater within-person changes in testosterone (but not in dehydroepiandrosterone) were associated with higher affect fluctuations in daily life. Additionally, greater physical changes predicted higher affect fluctuations for individuals in the beginning of puberty. The findings demonstrate the relevance of physical and hormonal changes in boys' affective (in)stability. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Objectively measured active travel and uses of activity-friendly neighborhood resources: Does change in use relate to change in physical activity and BMI?

    Directory of Open Access Journals (Sweden)

    Barbara B. Brown

    2017-12-01

    Full Text Available Few studies examine how objectively measured use of local physical activity resources contributes to objectively-measured healthy physical activity and weight changes over time. We utilized objective measures to test whether changes in active travel and uses of three physical activity (PA resources–parks, recreation centers, and transit– related to changes in PA and BMI. Adults (n=536 in Salt Lake City, UT, wore accelerometer and GPS units in 2012 and 2013, before and after neighborhood rail completion. Regression outcomes included accelerometer counts per minute (cpm, MVPA (moderate-to-vigorous activity minutes/10h accelerometer wear and measured BMI; key predictors were changes in active travel and PA resource uses (former and new uses. Significant results (all p<0.05 showed that increased active travel related to increased total PA (59.86cpm and 8.50 MVPA; decreased active travel related to decreased MVPA (−3.01 MVPA. Poorer outcomes were seen after discontinuing use of parks (−36.29cpm, −5.73 MVPA, and +0.44 BMI points, recreation centers (−6.18 MVPA, and transit (−48.14cpm, −5.43 MVPA, and +0.66 BMI. Healthier outcomes were seen after commencing use of parks (29.83cpm, 5.25 MVPA, recreation centers (44.63cpm and transit (38.44cpm, 4.17 MVPA, and −0.56 BMI. Transit and park/recreational center uses were unrelated, although park users were more likely to be recreation center users. Active travel and use of three neighborhood PA resources relate to healthy activity and could be fostered by policy and design. Keywords: Recreation center, Accelerometry, Active transport, Built environment, Parks, Global positioning system

  2. Cross-modal prediction changes the timing of conscious access during the motion-induced blindness.

    Science.gov (United States)

    Chang, Acer Y C; Kanai, Ryota; Seth, Anil K

    2015-01-01

    Despite accumulating evidence that perceptual predictions influence perceptual content, the relations between these predictions and conscious contents remain unclear, especially for cross-modal predictions. We examined whether predictions of visual events by auditory cues can facilitate conscious access to the visual stimuli. We trained participants to learn associations between auditory cues and colour changes. We then asked whether congruency between auditory cues and target colours would speed access to consciousness. We did this by rendering a visual target subjectively invisible using motion-induced blindness and then gradually changing its colour while presenting congruent or incongruent auditory cues. Results showed that the visual target gained access to consciousness faster in congruent than in incongruent trials; control experiments excluded potentially confounding effects of attention and motor response. The expectation effect was gradually established over blocks suggesting a role for extensive training. Overall, our findings show that predictions learned through cross-modal training can facilitate conscious access to visual stimuli. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change.

    Directory of Open Access Journals (Sweden)

    Giovanni Rapacciuolo

    Full Text Available Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records

  4. Anthropogenic Changes in Mid-latitude Storm and Blocking Activities from Observations and Climate Models

    Science.gov (United States)

    Li, D.

    2017-12-01

    Fingerprints of anthropogenic climate change can be most readily detected in the high latitudes of Northern Hemisphere, where temperature has been rising faster than the rest of the globe and sea ice cover has shrunk dramatically over recent decades. Reducing the meridional temperature gradient, this amplified warming over the high latitudes influences weather in the middle latitudes by modulating the jet stream, storms, and atmospheric blocking activities. Whether observational records have revealed significant changes in mid-latitude storms and blocking activities, however, has remained a subject of much debate. Buried deep in strong year-to-year variations, the long-term dynamic responses of the atmosphere are more difficult to identify, compared with its thermodynamic responses. Variabilities of decadal and longer timescales further obscure any trends diagnosed from satellite observations, which are often shorter than 40 years. Here, new metrics reflecting storm and blocking activities are developed using surface air temperature and pressure records, and their variations and long-term trends are examined. This approach gives an inkling of the changes in storm and blocking activities since the Industrial Revolution in regions with abundant long-term observational records, e.g. Europe and North America. The relationship between Atlantic Multi-decadal Oscillation and variations in storm and blocking activities across the Atlantic is also scrutinized. The connection between observed centennial trends and anthropogenic forcings is investigated using a hierarchy of numerical tools, from highly idealized to fully coupled atmosphere-ocean models. Pre-industrial control simulations and a set of large ensemble simulations forced by increased CO2 are analyzed to evaluate the range of natural variabilities, which paves the way to singling out significant anthropogenic changes from observational records, as well as predicting future changes in mid-latitude storm and

  5. Changing currents: a strategy for understanding and predicting the changing ocean circulation.

    Science.gov (United States)

    Bryden, Harry L; Robinson, Carol; Griffiths, Gwyn

    2012-12-13

    Within the context of UK marine science, we project a strategy for ocean circulation research over the next 20 years. We recommend a focus on three types of research: (i) sustained observations of the varying and evolving ocean circulation, (ii) careful analysis and interpretation of the observed climate changes for comparison with climate model projections, and (iii) the design and execution of focused field experiments to understand ocean processes that are not resolved in coupled climate models so as to be able to embed these processes realistically in the models. Within UK-sustained observations, we emphasize smart, cost-effective design of the observational network to extract maximum information from limited field resources. We encourage the incorporation of new sensors and new energy sources within the operational environment of UK-sustained observational programmes to bridge the gap that normally separates laboratory prototype from operational instrument. For interpreting the climate-change records obtained through a variety of national and international sustained observational programmes, creative and dedicated UK scientists should lead efforts to extract the meaningful signals and patterns of climate change and to interpret them so as to project future changes. For the process studies, individual scientists will need to work together in team environments to combine observational and process modelling results into effective improvements in the coupled climate models that will lead to more accurate climate predictions.

  6. Seasonal prediction of lightning activity in North Western Venezuela: Large-scale versus local drivers

    Science.gov (United States)

    Muñoz, Á. G.; Díaz-Lobatón, J.; Chourio, X.; Stock, M. J.

    2016-05-01

    The Lake Maracaibo Basin in North Western Venezuela has the highest annual lightning rate of any place in the world (~ 200 fl km- 2 yr- 1), whose electrical discharges occasionally impact human and animal lives (e.g., cattle) and frequently affect economic activities like oil and natural gas exploitation. Lightning activity is so common in this region that it has a proper name: Catatumbo Lightning (plural). Although short-term lightning forecasts are now common in different parts of the world, to the best of the authors' knowledge, seasonal prediction of lightning activity is still non-existent. This research discusses the relative role of both large-scale and local climate drivers as modulators of lightning activity in the region, and presents a formal predictability study at seasonal scale. Analysis of the Catatumbo Lightning Regional Mode, defined in terms of the second Empirical Orthogonal Function of monthly Lightning Imaging Sensor (LIS-TRMM) and Optical Transient Detector (OTD) satellite data for North Western South America, permits the identification of potential predictors at seasonal scale via a Canonical Correlation Analysis. Lightning activity in North Western Venezuela responds to well defined sea-surface temperature patterns (e.g., El Niño-Southern Oscillation, Atlantic Meridional Mode) and changes in the low-level meridional wind field that are associated with the Inter-Tropical Convergence Zone migrations, the Caribbean Low Level Jet and tropical cyclone activity, but it is also linked to local drivers like convection triggered by the topographic configuration and the effect of the Maracaibo Basin Nocturnal Low Level Jet. The analysis indicates that at seasonal scale the relative contribution of the large-scale drivers is more important than the local (basin-wide) ones, due to the synoptic control imposed by the former. Furthermore, meridional CAPE transport at 925 mb is identified as the best potential predictor for lightning activity in the Lake

  7. Life history and spatial traits predict extinction risk due to climate change

    Science.gov (United States)

    Pearson, Richard G.; Stanton, Jessica C.; Shoemaker, Kevin T.; Aiello-Lammens, Matthew E.; Ersts, Peter J.; Horning, Ned; Fordham, Damien A.; Raxworthy, Christopher J.; Ryu, Hae Yeong; McNees, Jason; Akçakaya, H. Reşit

    2014-03-01

    There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change based on the expectation that established assessments such as the IUCN Red List need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

  8. Improving predictive capabilities of environmental change with GLOBE data

    Science.gov (United States)

    Robin, Jessica Hill

    This dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (>35°) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (>60°) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March--mid May) when vegetation is greening up (NDVI 0.60). Results from the second study showed that a climate threshold of 153 +/- 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the interannual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.

  9. Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination

    Science.gov (United States)

    Ren, Zhoupeng; Wang, Duoquan; Ma, Aimin; Hwang, Jimee; Bennett, Adam; Sturrock, Hugh J. W.; Fan, Junfu; Zhang, Wenjie; Yang, Dian; Feng, Xinyu; Xia, Zhigui; Zhou, Xiao-Nong; Wang, Jinfeng

    2016-02-01

    Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

  10. Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination.

    Science.gov (United States)

    Ren, Zhoupeng; Wang, Duoquan; Ma, Aimin; Hwang, Jimee; Bennett, Adam; Sturrock, Hugh J W; Fan, Junfu; Zhang, Wenjie; Yang, Dian; Feng, Xinyu; Xia, Zhigui; Zhou, Xiao-Nong; Wang, Jinfeng

    2016-02-12

    Projecting the distribution of malaria vectors under climate change is essential for planning integrated vector control activities for sustaining elimination and preventing reintroduction of malaria. In China, however, little knowledge exists on the possible effects of climate change on malaria vectors. Here we assess the potential impact of climate change on four dominant malaria vectors (An. dirus, An. minimus, An. lesteri and An. sinensis) using species distribution models for two future decades: the 2030 s and the 2050 s. Simulation-based estimates suggest that the environmentally suitable area (ESA) for An. dirus and An. minimus would increase by an average of 49% and 16%, respectively, under all three scenarios for the 2030 s, but decrease by 11% and 16%, respectively in the 2050 s. By contrast, an increase of 36% and 11%, respectively, in ESA of An. lesteri and An. sinensis, was estimated under medium stabilizing (RCP4.5) and very heavy (RCP8.5) emission scenarios. in the 2050 s. In total, we predict a substantial net increase in the population exposed to the four dominant malaria vectors in the decades of the 2030 s and 2050 s, considering land use changes and urbanization simultaneously. Strategies to achieve and sustain malaria elimination in China will need to account for these potential changes in vector distributions and receptivity.

  11. Can phenological models predict tree phenology accurately under climate change conditions?

    Science.gov (United States)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  12. Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

    Science.gov (United States)

    Drzewiecki, Wojciech

    2016-12-01

    In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

  13. An Application to the Prediction of LOD Change Based on General Regression Neural Network

    Science.gov (United States)

    Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

    2011-07-01

    Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

  14. Social Networking Site Use Predicts Changes in Young Adults’ Psychological Adjustment

    Science.gov (United States)

    Szwedo, David E.; Mikami, Amori Yee; Allen, Joseph P.

    2012-01-01

    This study examined youths’ friendships and posted pictures on social networking sites as predictors of changes in their adjustment over time. Observational, self-report, and peer report data were obtained from a community sample of 89 young adults interviewed at age 21 and again at age 22. Findings were consistent with a leveling effect for online friendships, predicting decreases in internalizing symptoms for youth with lower initial levels of social acceptance, but increases in symptoms for youth with higher initial levels over the following year. Across the entire sample, deviant behavior in posted photos predicted increases in young adults’ problematic alcohol use over time. The importance of considering the interplay between online and offline social factors for predicting adjustment is discussed. PMID:23109797

  15. Prediction signatures in the brain: Semantic pre-activation during language comprehension

    Directory of Open Access Journals (Sweden)

    Burkhard Maess

    2016-11-01

    Full Text Available There is broad agreement that context-based predictions facilitate lexical-semantic processing. A robust index of semantic prediction during language comprehension is an evoked response, known as the N400, whose amplitude is modulated as a function of semantic context. However, the underlying neural mechanisms that utilize relations of the prior context and the embedded word within it are largely unknown. We measured magnetoencephalography (MEG data while participants were listening to simple German sentences in which the verbs were either highly predictive for the occurrence of a particular noun (i.e., provided context or not. The identical set of nouns was presented in both conditions. Hence, differences for the evoked responses of the nouns can only be due to differences in the earlier context. We observed a reduction of the N400 response for highly predicted nouns. Interestingly, the opposite pattern was observed for the preceding verbs: Highly predictive (that is more informative verbs yielded stronger neural magnitude compared to less predictive verbs. A negative correlation between the N400 effect of the verb and that of the noun was found in a distributed brain network, indicating an integral relation between the predictive power of the verb and the processing of the subsequent noun. This network consisted of left hemispheric superior and middle temporal areas and a subcortical area; the parahippocampus. Enhanced activity for highly predictive relative to less predictive verbs, likely reflects establishing semantic features associated with the expected nouns, that is a pre-activation of the expected nouns.

  16. Sediment bacterial community structures and their predicted functions implied the impacts from natural processes and anthropogenic activities in coastal area.

    Science.gov (United States)

    Su, Zhiguo; Dai, Tianjiao; Tang, Yushi; Tao, Yile; Huang, Bei; Mu, Qinglin; Wen, Donghui

    2018-06-01

    Coastal ecosystem structures and functions are changing under natural and anthropogenic influences. In this study, surface sediment samples were collected from disturbed zone (DZ), near estuary zone (NEZ), and far estuary zone (FEZ) of Hangzhou Bay, one of the most seriously polluted bays in China. The bacterial community structures and predicted functions varied significantly in different zones. Firmicutes were found most abundantly in DZ, highlighting the impacts of anthropogenic activities. Sediment total phosphorus was most influential on the bacterial community structures. Predicted by PICRUSt analysis, DZ significantly exceeded FEZ and NEZ in the subcategory of Xenobiotics Biodegradation and Metabolism; and DZ enriched all the nitrate reduction related genes, except nrfA gene. Seawater salinity and inorganic nitrogen, respectively as the representative natural and anthropogenic factor, performed exact-oppositely in nitrogen metabolism functions. The changes of bacterial community compositions and predicted functions provide a new insight into human-induced pollution impacts on coastal ecosystem. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Systematic Survey of Serine Hydrolase Activity in Mycobacterium tuberculosis Defines Changes Associated with Persistence

    Energy Technology Data Exchange (ETDEWEB)

    Ortega, Corrie; Anderson, Lindsey N.; Frando, Andrew; Sadler, Natalie C.; Brown, Robert W.; Smith, Richard D.; Wright, Aaron T.; Grundner, Christoph

    2016-02-01

    The transition between replication and non-replication underlies much of Mycobacterium tuberculosis (Mtb) pathogenicity, as non- or slowly replicating Mtb are responsible for persistence and poor treatment outcomes. Therapeutic targeting of non-replicating, persistent populations is a priority for tuberculosis treatment, but only few drug targets in non-replicating Mtb are currently known. Here, we directly measure the activity of the highly diverse and druggable serine hydrolases (SHs) during active replication and non-replication by activity-based proteomics. We predict serine hydrolase activity for 78 proteins, including 27 proteins with previously unknown function, and identify 37 SHs that remain active even in the absence of replication, providing a set of candidate persistence targets. Non-replication was associated with large shifts in the activity of the majority of SHs. These activity changes were largely independent of SH abundance, indicating extensive post-translational regulation. By probing a large cross-section of druggable Mtb enzyme space during replication and non-replication, we identify new SHs and suggest new persistence targets.

  18. Prediction of DVH parameter changes due to setup errors for breast cancer treatment based on 2D portal dosimetry

    International Nuclear Information System (INIS)

    Nijsten, S. M. J. J. G.; Elmpt, W. J. C. van; Mijnheer, B. J.; Minken, A. W. H.; Persoon, L. C. G. G.; Lambin, P.; Dekker, A. L. A. J.

    2009-01-01

    Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V 90 and V 95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our

  19. A predictive framework to understand forest responses to global change.

    Science.gov (United States)

    McMahon, Sean M; Dietze, Michael C; Hersh, Michelle H; Moran, Emily V; Clark, James S

    2009-04-01

    Forests are one of Earth's critical biomes. They have been shown to respond strongly to many of the drivers that are predicted to change natural systems over this century, including climate, introduced species, and other anthropogenic influences. Predicting how different tree species might respond to this complex of forces remains a daunting challenge for forest ecologists. Yet shifts in species composition and abundance can radically influence hydrological and atmospheric systems, plant and animal ranges, and human populations, making this challenge an important one to address. Forest ecologists have gathered a great deal of data over the past decades and are now using novel quantitative and computational tools to translate those data into predictions about the fate of forests. Here, after a brief review of the threats to forests over the next century, one of the more promising approaches to making ecological predictions is described: using hierarchical Bayesian methods to model forest demography and simulating future forests from those models. This approach captures complex processes, such as seed dispersal and mortality, and incorporates uncertainty due to unknown mechanisms, data problems, and parameter uncertainty. After describing the approach, an example by simulating drought for a southeastern forest is offered. Finally, there is a discussion of how this approach and others need to be cast within a framework of prediction that strives to answer the important questions posed to environmental scientists, but does so with a respect for the challenges inherent in predicting the future of a complex biological system.

  20. Integration of climate change in flood prediction: application to the Somme river (France)

    Science.gov (United States)

    Pinault, J.-L.; Amraoui, N.; Noyer, M.-L.

    2003-04-01

    Exceptional floods that have occurred for the last two years in western and central Europe were very unlikely. The concomitance of such rare events shows that they might be imputable to climate change. The statistical analysis of long rainfall series confirms that both the cumulated annual height and the temporal variability have increased for the last decade. This paper is devoted to the analysis of climate change impact on flood prediction applied to the Somme river. The exceptional pluviometry that occurred from October 2000 to April 2001, about the double of the mean value, entailed catastrophic flood between the high Somme and Abbeville. The flow reached a peak at the beginning of May 2001, involving damages in numerous habitations and communication routes, and economical activity of the region had been flood-bound for more than 2 months. The flood caught unaware the population and caused deep traumas in France since it was the first time such a sudden event was recognized as resulting from groundwater discharge. Mechanisms of flood generation were studied tightly in order to predict the behavior of the Somme catchment and other urbanized basins when the pluviometry is exceptional in winter or in spring, which occurs more and more frequently in the northern part of Europe. The contribution of groundwater in surface water flow was calculated by inverse modeling from piezometers that are representative of aquifers in valleys. They were found on the slopes and near the edge of plateaus in order to characterize the drainage processes of the watertable to the surface water network. For flood prediction, a stochastic process is used, consisting in the generation of both rainfall and PET time series. The precipitation generator uses Markov chain Monte Carlo and simulated annealing from the Hastings -- Metropolis algorithm. Coupling of rainfall and PET generators with transfer enables a new evaluation of the probability of occurrence of floods, taking into account

  1. On the importance of paleoclimate modelling for improving predictions of future climate change

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2009-12-01

    Full Text Available We use an ensemble of runs from the MIROC3.2 AGCM with slab-ocean to explore the extent to which mid-Holocene simulations are relevant to predictions of future climate change. The results are compared with similar analyses for the Last Glacial Maximum (LGM and pre-industrial control climate. We suggest that the paleoclimate epochs can provide some independent validation of the models that is also relevant for future predictions. Considering the paleoclimate epochs, we find that the stronger global forcing and hence larger climate change at the LGM makes this likely to be the more powerful one for estimating the large-scale changes that are anticipated due to anthropogenic forcing. The phenomena in the mid-Holocene simulations which are most strongly correlated with future changes (i.e., the mid to high northern latitude land temperature and monsoon precipitation do, however, coincide with areas where the LGM results are not correlated with future changes, and these are also areas where the paleodata indicate significant climate changes have occurred. Thus, these regions and phenomena for the mid-Holocene may be useful for model improvement and validation.

  2. Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis

    Energy Technology Data Exchange (ETDEWEB)

    Volkova, Svitlana; Bell, Eric B.

    2016-06-17

    Social networks are dynamically changing over time e.g., some accounts are being created and some are being deleted or become private. This ephemerality at both an account level and content level results from a combination of privacy concerns, spam, and deceptive behaviors. In this study we analyze a large dataset of 180,340 accounts active during the Russian-Ukrainian crisis to discover a series of predictive features for the removal or shutdown of a suspicious account. We find that unlike previously reported profile and net- work features, lexical features form the basis for highly accurate prediction of the deletion of an account.

  3. A rapid method of predicting radiocaesium concentrations in sheep from activity levels in faeces

    International Nuclear Information System (INIS)

    McGee, E.J.; Synnott, H.J.; Colgan, P.A.; Keatinge, M.J.

    1994-01-01

    The use of faecal samples taken from sheep flocks as a means of predicting radiocaesium concentrations in live animals was studied. Radiocaesium levels in 1726 sheep from 29 flocks were measured using in vivo techniques and a single faecal sample taken from each flock was also analysed. A highly significant relationship was found to exist between mean flock activity and activity in the corresponding faecal samples. Least-square regression yielded a simple model for predicting mean flock radiocaesium concentrations based on activity levels in faecal samples. A similar analysis of flock maxima and activity levels in faeces provides an alternative model for predicting the expected within-flock maximum radiocaesium concentration. (Author)

  4. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils.

    Science.gov (United States)

    Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M

    2015-01-01

    Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

  5. Chaos and the (un)predictability of evolution in a changing environment.

    Science.gov (United States)

    Rego-Costa, Artur; Débarre, Florence; Chevin, Luis-Miguel

    2018-02-01

    Among the factors that may reduce the predictability of evolution, chaos, characterized by a strong dependence on initial conditions, has received much less attention than randomness due to genetic drift or environmental stochasticity. It was recently shown that chaos in phenotypic evolution arises commonly under frequency-dependent selection caused by competitive interactions mediated by many traits. This result has been used to argue that chaos should often make evolutionary dynamics unpredictable. However, populations also evolve largely in response to external changing environments, and such environmental forcing is likely to influence the outcome of evolution in systems prone to chaos. We investigate how a changing environment causing oscillations of an optimal phenotype interacts with the internal dynamics of an eco-evolutionary system that would be chaotic in a constant environment. We show that strong environmental forcing can improve the predictability of evolution by reducing the probability of chaos arising, and by dampening the magnitude of chaotic oscillations. In contrast, weak forcing can increase the probability of chaos, but it also causes evolutionary trajectories to track the environment more closely. Overall, our results indicate that, although chaos may occur in evolution, it does not necessarily undermine its predictability. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

  6. Individual differences in decision making and reward processing predict changes in cannabis use: a prospective functional magnetic resonance imaging study.

    Science.gov (United States)

    Cousijn, Janna; Wiers, Reinout W; Ridderinkhof, K Richard; van den Brink, Wim; Veltman, Dick J; Porrino, Linda J; Goudriaan, Anna E

    2013-11-01

    Decision-making deficits are thought to play an important role in the development and persistence of substance use disorders. Individual differences in decision-making abilities and their underlying neurocircuitry may, therefore, constitute an important predictor for the course of substance use and the development of substance use disorders. Here, we investigate the predictive value of decision making and neural mechanisms underlying decision making for future cannabis use and problem severity in a sample of heavy cannabis users. Brain activity during a monetary decision-making task (Iowa gambling task) was compared between 32 heavy cannabis users and 41 matched non-using controls using functional magnetic resonance imaging. In addition, within the group of heavy cannabis users, associations were examined between task-related brain activations, cannabis use and cannabis use-related problems at baseline, and change in cannabis use and problem severity after a 6-month follow-up. Despite normal task performance, heavy cannabis users compared with controls showed higher activation during wins in core areas associated with decision making. Moreover, within the group of heavy cannabis users, win-related activity and activity anticipating loss outcomes in areas generally involved in executive functions predicted change in cannabis use after 6 months. These findings are consistent with previous studies and point to abnormal processing of motivational information in heavy cannabis users. A new finding is that individuals who are biased toward immediate rewards have a higher probability of increasing drug use, highlighting the importance of the relative balance between motivational processes and regulatory executive processes in the development of substance use disorders. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

  7. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates.

    Science.gov (United States)

    Glazier, Douglas S; Hirst, Andrew G; Atkinson, David

    2015-03-07

    Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  8. Neural activity to a partner's facial expression predicts self-regulation after conflict.

    Science.gov (United States)

    Hooker, Christine I; Gyurak, Anett; Verosky, Sara C; Miyakawa, Asako; Ayduk, Ozlem

    2010-03-01

    Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to emotion regulation in response to laboratory-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk factor for mood and behavior problems after an interpersonal conflict. However, it remains unclear whether LPFC activity to a laboratory-based affective challenge predicts self-regulation in real life. We investigated whether LPFC activity to a laboratory-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During a functional magnetic resonance imaging scan, healthy, adult participants in committed relationships (n = 27) viewed positive, negative, and neutral facial expressions of their partners. In a three-week online daily diary, participants reported conflict occurrence, level of negative mood, rumination, and substance use. LPFC activity in response to the laboratory-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance use. Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  9. Short-term changes in arterial inflammation predict long-term changes in atherosclerosis progression

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, Philip [Massachusetts General Hospital and Harvard Medical School, Cardiology Division and Cardiac MR PET CT Program, Boston, MA (United States); McMaster University, Population Health Research Institute, Department of Medicine, and Department of Radiology, Hamilton, ON (Canada); Ishai, Amorina; Tawakol, Ahmed [Massachusetts General Hospital and Harvard Medical School, Cardiology Division and Cardiac MR PET CT Program, Boston, MA (United States); Mani, Venkatesh [Icahn School of Medicine at Mount Sinai School of Medicine, Translational and Molecular Imaging Institute and Department of Radiology, New York, NY (United States); Kallend, David [The Medicines Company, Parsippany, NJ (United States); Rudd, James H.F. [University of Cambridge, Division of Cardiovascular Medicine, Cambridge (United Kingdom); Fayad, Zahi A. [Icahn School of Medicine at Mount Sinai School of Medicine, Translational and Molecular Imaging Institute and Department of Radiology, New York, NY (United States); Icahn School of Medicine at Mount Sinai School of Medicine, Hess CSM Building Floor TMII, Rm S1-104, Translational and Molecular Imaging Institute and Department of Radiology, New York, NY (United States)

    2017-01-15

    It remains unclear whether changes in arterial wall inflammation are associated with subsequent changes in the rate of structural progression of atherosclerosis. In this sub-study of the dal-PLAQUE clinical trial, multi-modal imaging was performed using 18-fludeoxyglucose (FDG) positron emission tomography (PET, at 0 and 6 months) and magnetic resonance imaging (MRI, at 0 and 24 months). The primary objective was to determine whether increasing FDG uptake at 6 months predicted atherosclerosis progression on MRI at 2 years. Arterial inflammation was measured by the carotid FDG target-to-background ratio (TBR), and atherosclerotic plaque progression was defined as the percentage change in carotid mean wall area (MWA) and mean wall thickness (MWT) on MRI between baseline and 24 months. A total of 42 participants were included in this sub-study. The mean age of the population was 62.5 years, and 12 (28.6 %) were women. In participants with (vs. without) any increase in arterial inflammation over 6 months, the long-term changes in both MWT (% change MWT: 17.49 % vs. 1.74 %, p = 0.038) and MWA (% change MWA: 25.50 % vs. 3.59 %, p = 0.027) were significantly greater. Results remained significant after adjusting for clinical and biochemical covariates. Individuals with no increase in arterial inflammation over 6 months had no significant structural progression of atherosclerosis over 24 months as measured by MWT (p = 0.616) or MWA (p = 0.373). Short-term changes in arterial inflammation are associated with long-term structural atherosclerosis progression. These data support the concept that therapies that reduce arterial inflammation may attenuate or halt progression of atherosclerosis. (orig.)

  10. Short-term changes in arterial inflammation predict long-term changes in atherosclerosis progression

    International Nuclear Information System (INIS)

    Joseph, Philip; Ishai, Amorina; Tawakol, Ahmed; Mani, Venkatesh; Kallend, David; Rudd, James H.F.; Fayad, Zahi A.

    2017-01-01

    It remains unclear whether changes in arterial wall inflammation are associated with subsequent changes in the rate of structural progression of atherosclerosis. In this sub-study of the dal-PLAQUE clinical trial, multi-modal imaging was performed using 18-fludeoxyglucose (FDG) positron emission tomography (PET, at 0 and 6 months) and magnetic resonance imaging (MRI, at 0 and 24 months). The primary objective was to determine whether increasing FDG uptake at 6 months predicted atherosclerosis progression on MRI at 2 years. Arterial inflammation was measured by the carotid FDG target-to-background ratio (TBR), and atherosclerotic plaque progression was defined as the percentage change in carotid mean wall area (MWA) and mean wall thickness (MWT) on MRI between baseline and 24 months. A total of 42 participants were included in this sub-study. The mean age of the population was 62.5 years, and 12 (28.6 %) were women. In participants with (vs. without) any increase in arterial inflammation over 6 months, the long-term changes in both MWT (% change MWT: 17.49 % vs. 1.74 %, p = 0.038) and MWA (% change MWA: 25.50 % vs. 3.59 %, p = 0.027) were significantly greater. Results remained significant after adjusting for clinical and biochemical covariates. Individuals with no increase in arterial inflammation over 6 months had no significant structural progression of atherosclerosis over 24 months as measured by MWT (p = 0.616) or MWA (p = 0.373). Short-term changes in arterial inflammation are associated with long-term structural atherosclerosis progression. These data support the concept that therapies that reduce arterial inflammation may attenuate or halt progression of atherosclerosis. (orig.)

  11. Active diagnosis of hybrid systems - A model predictive approach

    DEFF Research Database (Denmark)

    Tabatabaeipour, Seyed Mojtaba; Ravn, Anders P.; Izadi-Zamanabadi, Roozbeh

    2009-01-01

    A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and fault...... can be used as a test signal for sanity check at the commissioning or for detection of faults hidden by regulatory actions of the controller. The method is tested on the two tank benchmark example. ©2009 IEEE....

  12. Predicting the effects of climate change on marine communities and the consequences for fisheries

    DEFF Research Database (Denmark)

    Jennings, Simon; Brander, Keith

    2010-01-01

    for the community under the same climate scenario. The main weakness of the community approach is that the methods predict abundance and production by size-class rather than taxonomic group, and society would be particularly concerned if climate driven changes had a strong effect on the relative production...... of fishable and non-fishable species in the community. The main strength of the community approach is that it provides widely applicable ‘null’ models for assessing the biological effects of climate change and a baseline for model comparisons.......Climate effects on the structure and function of marine communities have received scant attention. The few existing approaches for predicting climate effects suggest that community responses might be predicted from the responses of component populations. These approaches require a very complex...

  13. Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change.

    Science.gov (United States)

    Supple, Megan Ann; Bragg, Jason G; Broadhurst, Linda M; Nicotra, Adrienne B; Byrne, Margaret; Andrew, Rose L; Widdup, Abigail; Aitken, Nicola C; Borevitz, Justin O

    2018-04-24

    As species face rapid environmental change, we can build resilient populations through restoration projects that incorporate predicted future climates into seed sourcing decisions. Eucalyptus melliodora is a foundation species of a critically endangered community in Australia that is a target for restoration. We examined genomic and phenotypic variation to make empirical based recommendations for seed sourcing. We examined isolation by distance and isolation by environment, determining high levels of gene flow extending for 500 km and correlations with climate and soil variables. Growth experiments revealed extensive phenotypic variation both within and among sampling sites, but no site-specific differentiation in phenotypic plasticity. Model predictions suggest that seed can be sourced broadly across the landscape, providing ample diversity for adaptation to environmental change. Application of our landscape genomic model to E. melliodora restoration projects can identify genomic variation suitable for predicted future climates, thereby increasing the long term probability of successful restoration. © 2018, Supple et al.

  14. Predicting CD4 count changes among patients on antiretroviral treatment: Application of data mining techniques.

    Science.gov (United States)

    Kebede, Mihiretu; Zegeye, Desalegn Tigabu; Zeleke, Berihun Megabiaw

    2017-12-01

    To monitor the progress of therapy and disease progression, periodic CD4 counts are required throughout the course of HIV/AIDS care and support. The demand for CD4 count measurement is increasing as ART programs expand over the last decade. This study aimed to predict CD4 count changes and to identify the predictors of CD4 count changes among patients on ART. A cross-sectional study was conducted at the University of Gondar Hospital from 3,104 adult patients on ART with CD4 counts measured at least twice (baseline and most recent). Data were retrieved from the HIV care clinic electronic database and patients` charts. Descriptive data were analyzed by SPSS version 20. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was followed to undertake the study. WEKA version 3.8 was used to conduct a predictive data mining. Before building the predictive data mining models, information gain values and correlation-based Feature Selection methods were used for attribute selection. Variables were ranked according to their relevance based on their information gain values. J48, Neural Network, and Random Forest algorithms were experimented to assess model accuracies. The median duration of ART was 191.5 weeks. The mean CD4 count change was 243 (SD 191.14) cells per microliter. Overall, 2427 (78.2%) patients had their CD4 counts increased by at least 100 cells per microliter, while 4% had a decline from the baseline CD4 value. Baseline variables including age, educational status, CD8 count, ART regimen, and hemoglobin levels predicted CD4 count changes with predictive accuracies of J48, Neural Network, and Random Forest being 87.1%, 83.5%, and 99.8%, respectively. Random Forest algorithm had a superior performance accuracy level than both J48 and Artificial Neural Network. The precision, sensitivity and recall values of Random Forest were also more than 99%. Nearly accurate prediction results were obtained using Random Forest algorithm. This algorithm could be

  15. Color vision predicts processing modes of goal activation during action cascading.

    Science.gov (United States)

    Jongkees, Bryant J; Steenbergen, Laura; Colzato, Lorenza S

    2017-09-01

    One of the most important functions of cognitive control is action cascading: the ability to cope with multiple response options when confronted with various task goals. A recent study implicates a key role for dopamine (DA) in this process, suggesting higher D1 efficiency shifts the action cascading strategy toward a more serial processing mode, whereas higher D2 efficiency promotes a shift in the opposite direction by inducing a more parallel processing mode (Stock, Arning, Epplen, & Beste, 2014). Given that DA is found in high concentration in the retina and modulation of retinal DA release displays characteristics of D2-receptors (Peters, Schweibold, Przuntek, & Müller, 2000), color vision discrimination might serve as an index of D2 efficiency. We used color discrimination, assessed with the Lanthony Desaturated Panel D-15 test, to predict individual differences (N = 85) in a stop-change paradigm that provides a well-established measure of action cascading. In this task it is possible to calculate an individual slope value for each participant that estimates the degree of overlap in task goal activation. When the stopping process of a previous task goal has not finished at the time the change process toward a new task goal is initiated (parallel processing), the slope value becomes steeper. In case of less overlap (more serial processing), the slope value becomes flatter. As expected, participants showing better color vision were more prone to activate goals in a parallel manner as indicated by a steeper slope. Our findings suggest that color vision might represent a predictor of D2 efficiency and the predisposed processing mode of goal activation during action cascading. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Prediction of Active Site and Distal Residues in E. coli DNA Polymerase III alpha Polymerase Activity.

    Science.gov (United States)

    Parasuram, Ramya; Coulther, Timothy A; Hollander, Judith M; Keston-Smith, Elise; Ondrechen, Mary Jo; Beuning, Penny J

    2018-02-20

    The process of DNA replication is carried out with high efficiency and accuracy by DNA polymerases. The replicative polymerase in E. coli is DNA Pol III, which is a complex of 10 different subunits that coordinates simultaneous replication on the leading and lagging strands. The 1160-residue Pol III alpha subunit is responsible for the polymerase activity and copies DNA accurately, making one error per 10 5 nucleotide incorporations. The goal of this research is to determine the residues that contribute to the activity of the polymerase subunit. Homology modeling and the computational methods of THEMATICS and POOL were used to predict functionally important amino acid residues through their computed chemical properties. Site-directed mutagenesis and biochemical assays were used to validate these predictions. Primer extension, steady-state single-nucleotide incorporation kinetics, and thermal denaturation assays were performed to understand the contribution of these residues to the function of the polymerase. This work shows that the top 15 residues predicted by POOL, a set that includes the three previously known catalytic aspartate residues, seven remote residues, plus five previously unexplored first-layer residues, are important for function. Six previously unidentified residues, R362, D405, K553, Y686, E688, and H760, are each essential to Pol III activity; three additional residues, Y340, R390, and K758, play important roles in activity.

  17. Predicting flow at work: investigating the activities and job characteristics that predict flow states at work.

    Science.gov (United States)

    Nielsen, Karina; Cleal, Bryan

    2010-04-01

    Flow (a state of consciousness where people become totally immersed in an activity and enjoy it intensely) has been identified as a desirable state with positive effects for employee well-being and innovation at work. Flow has been studied using both questionnaires and Experience Sampling Method (ESM). In this study, we used a newly developed 9-item flow scale in an ESM study combined with a questionnaire to examine the predictors of flow at two levels: the activities (brainstorming, planning, problem solving and evaluation) associated with transient flow states and the more stable job characteristics (role clarity, influence and cognitive demands). Participants were 58 line managers from two companies in Denmark; a private accountancy firm and a public elder care organization. We found that line managers in elder care experienced flow more often than accountancy line managers, and activities such as planning, problem solving, and evaluation predicted transient flow states. The more stable job characteristics included in this study were not, however, found to predict flow at work. Copyright 2010 APA, all rights reserved.

  18. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

    Science.gov (United States)

    Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

    2017-08-03

    Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

  19. Perceived stress and anhedonia predict short-and long-term weight change, respectively, in healthy adults.

    Science.gov (United States)

    Ibrahim, Mostafa; Thearle, Marie S; Krakoff, Jonathan; Gluck, Marci E

    2016-04-01

    Perceived stress; emotional eating; anhedonia; depression and dietary restraint, hunger, and disinhibition have been studied as risk factors for obesity. However, the majority of studies have been cross-sectional and the directionality of these relationships remains unclear. In this longitudinal study, we assess their impact on future weight change. Psychological predictors of weight change in short- (6month) and long-term (>1year) periods were studied in 65 lean and obese individuals in two cohorts. Subjects participated in studies of food intake and metabolism that did not include any type of medication or weight loss interventions. They completed psychological questionnaires at baseline and weight change was monitored at follow-up visits. At six months, perceived stress predicted weight gain (r(2)=0.23, P=0.02). There was a significant interaction (r(2)=.38, P=0.009) between perceived stress and positive emotional eating, such that higher scores in both predicted greater weight gain, while those with low stress but high emotional eating scores lost weight. For long-term, higher anhedonia scores predicted weight gain (r(2)=0.24, P=0.04). Depression moderated these effects such that higher scores in both predicted weight gain but higher depression and lower anhedonia scores predicted weight loss. There are different behavioral determinants for short- and long-term weight change. Targeting perceived stress may help with short-term weight loss while depression and anhedonia may be better targets for long-term weight regulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow.

    Science.gov (United States)

    van Strien, Maarten J; Keller, Daniela; Holderegger, Rolf; Ghazoul, Jaboury; Kienast, Felix; Bolliger, Janine

    2014-03-01

    For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (landscape composition as well as some measures of habitat configuration. Additionally, a complete sampling of all populations in our study area allowed incorporating measures of population topology. These measures together with the landscape metrics formed the predictor variables in linear models with gene flow as response variable (F(ST) and mean pairwise assignment probability). With a modified leave-one-out cross-validation approach, we selected the model with the highest predictive accuracy. With this model, we predicted gene flow under several landscape-change scenarios, which simulated construction, rezoning or restoration projects, and the establishment of a new population. For some landscape-change scenarios, significant increase or decrease in gene flow was predicted, while for others little change was forecast. Furthermore, we found that the measures of population topology strongly increase model fit in landscape genetic analysis. This study demonstrates the use of predictive landscape-genetic models in conservation and landscape planning.

  1. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    DEFF Research Database (Denmark)

    Hudson, Lawrence N; Newbold, Tim; Contu, Sara

    2017-01-01

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity ...

  2. Changes in event-related potential functional networks predict traumatic brain injury in piglets.

    Science.gov (United States)

    Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

    2018-06-01

    Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. Predicting the activity coefficients of free-solvent for concentrated globular protein solutions using independently determined physical parameters.

    Directory of Open Access Journals (Sweden)

    Devin W McBride

    Full Text Available The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations.

  4. Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

    Science.gov (United States)

    Pegors, Teresa K; Tompson, Steven; O'Donnell, Matthew Brook; Falk, Emily B

    2017-08-15

    Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

    DEFF Research Database (Denmark)

    Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey

    2016-01-01

    Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil...... functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global...

  6. Burrowing behavior of a deposit feeding bivalve predicts change in intertidal ecosystem state

    Directory of Open Access Journals (Sweden)

    Tanya Joan Compton

    2016-03-01

    Full Text Available Behavior has a predictive power that is often underutilized as a tool for signaling ecological change. The burrowing behavior of the deposit feeding bivalve Macoma balthica reflects a typical food-safety trade-off. The choice to live close to the sediment surface comes at a risk of predation and is a decision made when predation danger, food intake rates or future fitness prospects are low. In parts of the Dutch Wadden Sea, Macoma populations declined by 90% in the late 1990s, in parallel with large-scale mechanical cockle-dredging activities. During this decline, the burrowing depth of Macoma became shallow and was correlated with the population decline in the following year, indicating that it forecasted population change. Recently, there has been a series of large recruitment events in Macoma. According to the food-safety trade-off, we expected that Macoma should now live deeper, and have a higher body condition in association with this change in depth of living. Indeed, we observed that Macoma now lives deeper and that living depth in a given year forecasted population growth to the next year, especially in individuals larger than 14 mm. As living depth and body condition were strongly correlated in individuals larger than 14 mm, larger Macoma could be living deeper to protect their reproductive assets. Our results confirmed that burrowing depth signals impending population change and, together with body condition, can provide an early warning signal of ecological change. We suggest that population recovery is being driven by improved intertidal habitat quality in the Dutch Wadden Sea, rather than by the proposed climate-change related effects. This shift in ecosystem state is suggested to include the recovery of diatom habitat in the top layer of the sediment after cockle-dredging ended.

  7. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    NARCIS (Netherlands)

    Hudson, Lawrence N; Newbold, Tim; Contu, Sara; Hill, Samantha L L; Lysenko, Igor; De Palma, Adriana; Phillips, Helen R P; Alhusseini, Tamera I; Bedford, Felicity E; Bennett, Dominic J; Booth, Hollie; Burton, Victoria J; Chng, Charlotte W T; Choimes, Argyrios; Correia, David L P; Day, Julie; Echeverría-Londoño, Susy; Emerson, Susan R; Gao, Di; Garon, Morgan; Harrison, Michelle L K; Ingram, Daniel J; Jung, Martin; Kemp, Victoria; Kirkpatrick, Lucinda; Martin, Callum D; Pan, Yuan; Pask-Hale, Gwilym D; Pynegar, Edwin L; Robinson, Alexandra N; Sanchez-Ortiz, Katia; Senior, Rebecca A; Simmons, Benno I; White, Hannah J; Zhang, Hanbin; Aben, Job; Abrahamczyk, Stefan; Adum, Gilbert B; Aguilar-Barquero, Virginia; Aizen, Marcelo A; Albertos, Belén; Alcala, E L; Del Mar Alguacil, Maria; Alignier, Audrey; Ancrenaz, Marc; Andersen, Alan N; Arbeláez-Cortés, Enrique; Armbrecht, Inge; Arroyo-Rodríguez, Víctor; Aumann, Tom; Axmacher, Jan C; Azhar, Badrul; Azpiroz, Adrián B; Baeten, Lander; Bakayoko, Adama; Báldi, András; Banks, John E; Baral, Sharad K; Barlow, Jos; Barratt, Barbara I P; Barrico, Lurdes; Bartolommei, Paola; Barton, Diane M; Basset, Yves; Batáry, Péter; Bates, Adam J; Baur, Bruno; Bayne, Erin M; Beja, Pedro; Benedick, Suzan; Berg, Åke; Bernard, Henry; Berry, Nicholas J; Bhatt, Dinesh; Bicknell, Jake E; Bihn, Jochen H; Blake, Robin J; Bobo, Kadiri S; Bóçon, Roberto; Boekhout, Teun; Böhning-Gaese, Katrin; Bonham, Kevin J; Borges, Paulo A V; Borges, Sérgio H; Boutin, Céline; Bouyer, Jérémy; Bragagnolo, Cibele; Brandt, Jodi S; Brearley, Francis Q; Brito, Isabel; Bros, Vicenç; Brunet, Jörg; Buczkowski, Grzegorz; Buddle, Christopher M; Bugter, Rob; Buscardo, Erika; Buse, Jörn; Cabra-García, Jimmy; Cáceres, Nilton C; Cagle, Nicolette L; Calviño-Cancela, María; Cameron, Sydney A; Cancello, Eliana M; Caparrós, Rut; Cardoso, Pedro; Carpenter, Dan; Carrijo, Tiago F; Carvalho, Anelena L; Cassano, Camila R; Castro, Helena; Castro-Luna, Alejandro A; Rolando, Cerda B; Cerezo, Alexis; Chapman, Kim Alan; Chauvat, Matthieu; Christensen, Morten; Clarke, Francis M; Cleary, Daniel F R; Colombo, Giorgio; Connop, Stuart P; Craig, Michael D; Cruz-López, Leopoldo; Cunningham, Saul A; D'Aniello, Biagio; D'Cruze, Neil; da Silva, Pedro Giovâni; Dallimer, Martin; Danquah, Emmanuel; Darvill, Ben; Dauber, Jens; Davis, Adrian L V; Dawson, Jeff; de Sassi, Claudio; de Thoisy, Benoit; Deheuvels, Olivier; Dejean, Alain; Devineau, Jean-Louis; Diekötter, Tim; Dolia, Jignasu V; Domínguez, Erwin; Dominguez-Haydar, Yamileth; Dorn, Silvia; Draper, Isabel; Dreber, Niels; Dumont, Bertrand; Dures, Simon G; Dynesius, Mats; Edenius, Lars; Eggleton, Paul; Eigenbrod, Felix; Elek, Zoltán; Entling, Martin H; Esler, Karen J; de Lima, Ricardo F; Faruk, Aisyah; Farwig, Nina; Fayle, Tom M; Felicioli, Antonio; Felton, Annika M; Fensham, Roderick J; Fernandez, Ignacio C; Ferreira, Catarina C; Ficetola, Gentile F; Fiera, Cristina; Filgueiras, Bruno K C; Fırıncıoğlu, Hüseyin K; Flaspohler, David; Floren, Andreas; Fonte, Steven J; Fournier, Anne; Fowler, Robert E; Franzén, Markus; Fraser, Lauchlan H; Fredriksson, Gabriella M; Freire, Geraldo B; Frizzo, Tiago L M; Fukuda, Daisuke; Furlani, Dario; Gaigher, René; Ganzhorn, Jörg U; García, Karla P; Garcia-R, Juan C; Garden, Jenni G; Garilleti, Ricardo; Ge, Bao-Ming; Gendreau-Berthiaume, Benoit; Gerard, Philippa J; Gheler-Costa, Carla; Gilbert, Benjamin; Giordani, Paolo; Giordano, Simonetta; Golodets, Carly; Gomes, Laurens G L; Gould, Rachelle K; Goulson, Dave; Gove, Aaron D; Granjon, Laurent; Grass, Ingo; Gray, Claudia L; Grogan, James; Gu, Weibin; Guardiola, Moisès; Gunawardene, Nihara R; Gutierrez, Alvaro G; Gutiérrez-Lamus, Doris L; Haarmeyer, Daniela H; Hanley, Mick E; Hanson, Thor; Hashim, Nor R; Hassan, Shombe N; Hatfield, Richard G; Hawes, Joseph E; Hayward, Matt W; Hébert, Christian; Helden, Alvin J; Henden, John-André; Henschel, Philipp; Hernández, Lionel; Herrera, James P; Herrmann, Farina; Herzog, Felix; Higuera-Diaz, Diego; Hilje, Branko; Höfer, Hubert; Hoffmann, Anke; Horgan, Finbarr G; Hornung, Elisabeth; Horváth, Roland; Hylander, Kristoffer; Isaacs-Cubides, Paola; Ishida, Hiroaki; Ishitani, Masahiro; Jacobs, Carmen T; Jaramillo, Víctor J; Jauker, Birgit; Hernández, F Jiménez; Johnson, McKenzie F; Jolli, Virat; Jonsell, Mats; Juliani, S Nur; Jung, Thomas S; Kapoor, Vena; Kappes, Heike; Kati, Vassiliki; Katovai, Eric; Kellner, Klaus; Kessler, Michael; Kirby, Kathryn R; Kittle, Andrew M; Knight, Mairi E; Knop, Eva; Kohler, Florian; Koivula, Matti; Kolb, Annette; Kone, Mouhamadou; Kőrösi, Ádám; Krauss, Jochen; Kumar, Ajith; Kumar, Raman; Kurz, David J; Kutt, Alex S; Lachat, Thibault; Lantschner, Victoria; Lara, Francisco; Lasky, Jesse R; Latta, Steven C; Laurance, William F; Lavelle, Patrick; Le Féon, Violette; LeBuhn, Gretchen; Légaré, Jean-Philippe; Lehouck, Valérie; Lencinas, María V; Lentini, Pia E; Letcher, Susan G; Li, Qi; Litchwark, Simon A; Littlewood, Nick A; Liu, Yunhui; Lo-Man-Hung, Nancy; López-Quintero, Carlos A; Louhaichi, Mounir; Lövei, Gabor L; Lucas-Borja, Manuel Esteban; Luja, Victor H; Luskin, Matthew S; MacSwiney G, M Cristina; Maeto, Kaoru; Magura, Tibor; Mallari, Neil Aldrin; Malone, Louise A; Malonza, Patrick K; Malumbres-Olarte, Jagoba; Mandujano, Salvador; Måren, Inger E; Marin-Spiotta, Erika; Marsh, Charles J; Marshall, E J P; Martínez, Eliana; Martínez Pastur, Guillermo; Moreno Mateos, David; Mayfield, Margaret M; Mazimpaka, Vicente; McCarthy, Jennifer L; McCarthy, Kyle P; McFrederick, Quinn S; McNamara, Sean; Medina, Nagore G; Medina, Rafael; Mena, Jose L; Mico, Estefania; Mikusinski, Grzegorz; Milder, Jeffrey C; Miller, James R; Miranda-Esquivel, Daniel R; Moir, Melinda L; Morales, Carolina L; Muchane, Mary N; Muchane, Muchai; Mudri-Stojnic, Sonja; Munira, A Nur; Muoñz-Alonso, Antonio; Munyekenye, B F; Naidoo, Robin; Naithani, A; Nakagawa, Michiko; Nakamura, Akihiro; Nakashima, Yoshihiro; Naoe, Shoji; Nates-Parra, Guiomar; Navarrete Gutierrez, Dario A; Navarro-Iriarte, Luis; Ndang'ang'a, Paul K; Neuschulz, Eike L; Ngai, Jacqueline T; Nicolas, Violaine; Nilsson, Sven G; Noreika, Norbertas; Norfolk, Olivia; Noriega, Jorge Ari; Norton, David A; Nöske, Nicole M; Nowakowski, A Justin; Numa, Catherine; O'Dea, Niall; O'Farrell, Patrick J; Oduro, William; Oertli, Sabine; Ofori-Boateng, Caleb; Oke, Christopher Omamoke; Oostra, Vicencio; Osgathorpe, Lynne M; Otavo, Samuel Eduardo; Page, Navendu V; Paritsis, Juan; Parra-H, Alejandro; Parry, Luke; Pe'er, Guy; Pearman, Peter B; Pelegrin, Nicolás; Pélissier, Raphaël; Peres, Carlos A; Peri, Pablo L; Persson, Anna S; Petanidou, Theodora; Peters, Marcell K; Pethiyagoda, Rohan S; Phalan, Ben; Philips, T Keith; Pillsbury, Finn C; Pincheira-Ulbrich, Jimmy; Pineda, Eduardo; Pino, Joan; Pizarro-Araya, Jaime; Plumptre, A J; Poggio, Santiago L; Politi, Natalia; Pons, Pere; Poveda, Katja; Power, Eileen F; Presley, Steven J; Proença, Vânia; Quaranta, Marino; Quintero, Carolina; Rader, Romina; Ramesh, B R; Ramirez-Pinilla, Martha P; Ranganathan, Jai; Rasmussen, Claus; Redpath-Downing, Nicola A; Reid, J Leighton; Reis, Yana T; Rey Benayas, José M; Rey-Velasco, Juan Carlos; Reynolds, Chevonne; Ribeiro, Danilo Bandini; Richards, Miriam H; Richardson, Barbara A; Richardson, Michael J; Ríos, Rodrigo Macip; Robinson, Richard; Robles, Carolina A; Römbke, Jörg; Romero-Duque, Luz Piedad; Rös, Matthias; Rosselli, Loreta; Rossiter, Stephen J; Roth, Dana S; Roulston, T'ai H; Rousseau, Laurent; Rubio, André V; Ruel, Jean-Claude; Sadler, Jonathan P; Sáfián, Szabolcs; Saldaña-Vázquez, Romeo A; Sam, Katerina; Samnegård, Ulrika; Santana, Joana; Santos, Xavier; Savage, Jade; Schellhorn, Nancy A; Schilthuizen, Menno; Schmiedel, Ute; Schmitt, Christine B; Schon, Nicole L; Schüepp, Christof; Schumann, Katharina; Schweiger, Oliver; Scott, Dawn M; Scott, Kenneth A; Sedlock, Jodi L; Seefeldt, Steven S; Shahabuddin, Ghazala; Shannon, Graeme; Sheil, Douglas; Sheldon, Frederick H; Shochat, Eyal; Siebert, Stefan J; Silva, Fernando A B; Simonetti, Javier A; Slade, Eleanor M; Smith, Jo; Smith-Pardo, Allan H; Sodhi, Navjot S; Somarriba, Eduardo J; Sosa, Ramón A; Soto Quiroga, Grimaldo; St-Laurent, Martin-Hugues; Starzomski, Brian M; Stefanescu, Constanti; Steffan-Dewenter, Ingolf; Stouffer, Philip C; Stout, Jane C; Strauch, Ayron M; Struebig, Matthew J; Su, Zhimin; Suarez-Rubio, Marcela; Sugiura, Shinji; Summerville, Keith S; Sung, Yik-Hei; Sutrisno, Hari; Svenning, Jens-Christian; Teder, Tiit; Threlfall, Caragh G; Tiitsaar, Anu; Todd, Jacqui H; Tonietto, Rebecca K; Torre, Ignasi; Tóthmérész, Béla; Tscharntke, Teja; Turner, Edgar C; Tylianakis, Jason M; Uehara-Prado, Marcio; Urbina-Cardona, Nicolas; Vallan, Denis; Vanbergen, Adam J; Vasconcelos, Heraldo L; Vassilev, Kiril; Verboven, Hans A F; Verdasca, Maria João; Verdú, José R; Vergara, Carlos H; Vergara, Pablo M; Verhulst, Jort; Virgilio, Massimiliano; Vu, Lien Van; Waite, Edward M; Walker, Tony R; Wang, Hua-Feng; Wang, Yanping; Watling, James I; Weller, Britta; Wells, Konstans; Westphal, Catrin; Wiafe, Edward D; Williams, Christopher D; Willig, Michael R; Woinarski, John C Z; Wolf, Jan H D; Wolters, Volkmar; Woodcock, Ben A; Wu, Jihua; Wunderle, Joseph M; Yamaura, Yuichi; Yoshikura, Satoko; Yu, Douglas W; Zaitsev, Andrey S; Zeidler, Juliane; Zou, Fasheng; Collen, Ben; Ewers, Rob M; Mace, Georgina M; Purves, Drew W; Scharlemann, Jörn P W; Purvis, Andy

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of

  8. Six-month changes in spirituality and religiousness in alcoholics predict drinking outcomes at nine months.

    Science.gov (United States)

    Robinson, Elizabeth A R; Krentzman, Amy R; Webb, Jon R; Brower, Kirk J

    2011-07-01

    Although spiritual change is hypothesized to contribute to recovery from alcohol dependence, few studies have used prospective data to investigate this hypothesis. Prior studies have also been limited to treatment-seeking and Alcoholics Anonymous (AA) samples. This study included alcohol-dependent individuals, both in treatment and not, to investigate the effect of spiritual and religious (SR) change on subsequent drinking outcomes, independent of AA involvement. Alcoholics (N = 364) were recruited for a panel study from two abstinence-based treatment centers, a moderation drinking program, and untreated individuals from the local community. Quantitative measures of SR change between baseline and 6 months were used to predict 9-month drinking outcomes, controlling for baseline drinking and AA involvement. Significant 6-month changes in 8 of 12 SR measures were found, which included private SR practices, beliefs, daily spiritual experiences, three measures of forgiveness, negative religious coping, and purpose in life. Increases in private SR practices and forgiveness of self were the strongest predictors of improvements in drinking outcomes. Changes in daily spiritual experiences, purpose in life, a general measure of forgiveness, and negative religious coping also predicted favorable drinking outcomes. SR change predicted good drinking outcomes in alcoholics, even when controlling for AA involvement. SR variables, broadly defined, deserve attention in fostering change even among those who do not affiliate with AA or religious institutions. Last, future research should include SR variables, particularly various types of forgiveness, given the strong effects found for forgiveness of self.

  9. Predictive capabilities of the specific activity hypothesis for Cs and Zn in freshwater systems

    International Nuclear Information System (INIS)

    Seelye, J.G.

    1975-01-01

    Predictions of radioisotope concentrations in components of aquatic systems have been attempted using the specific activity concept, an approach that seems theoretically sound. A comprehensive examination of the specific activities of 134 Cs and 65 Zn in the components of a freshwater system, over a 10 month period, was conducted to evaluate the specific activity hypothesis under applied conditions. This study was designed to provide comparisons of predicted and observed specific activities and to test the equivalence of specific activities between all components of the system. One dose of radioisotopes was added to the system in this study and even after 10 months these radioisotopes were not distributed similarly to the stable isotopes. This suggests that the time necessary to reach a specific activity equilibrium might be a matter of years rather than months. More importantly, in natural systems, where the radioisotope addition is continuous a specific activity equilibrium may never be achieved. These things plus the non-conservative nature of the 134 Cs and 65 Zn predicted concentrations indicates that the use of the specific activity concept for predicting radioisotope concentrations of Cs and Zn in freshwater systems is questionable. A more rigorous approach must be used, considering isotope transfer rates between components and the complexity of the system. Problems with statistical comparisons of derived variables, such as specific activities, are discussed and were considered in interpreting the results of this study

  10. A cluster expansion model for predicting activation barrier of atomic processes

    International Nuclear Information System (INIS)

    Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit

    2013-01-01

    We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEB results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog

  11. Wave climate change, coastline response and hazard prediction in New South Wales, Australia

    International Nuclear Information System (INIS)

    Goodwin, Ian D.; Verdon, Danielle; Cowell, Peter

    2007-01-01

    Full text: Full text: Considerable research effort has been directed towards understanding and the gross prediction of shoreline response to sea level rise (eg. Cowell ef a/. 2003a, b). In contrast, synoptic prediction of changes in the planform configuration of shorelines in response to changes in wind and wave climates over many decades has been limited by the lack of geohistorical data on shoreline alignment evolution and long time series of wave climate. This paper presents new data sets on monthly mean wave direction variability based on: a. Waverider buoy data; b. a reconstruction of monthly mid-shelf wave direction, 1877 to 2002 AD from historical MSLP data (Goodwin 2005); and c. a multi-decadal reconstruction of wave direction, in association with the Interdecadal Pacific Oscillation and the Southern Annular Mode of climate variability, covering the past millennium. A model of coastline response to the wave climate variability is presented for northern and central New South Wales (NSW) for decadal to multi-decadal time scales, and is based on instrumental and geohistorical data. The sensitivity of the coastline position and alignment, and beach state to mean and extreme wave climate changes is demonstrated (e.g. Goodwin et al. 2006). State changes in geometric shoreline alignment rotation, sand volume (progradation/recession) for NSW and mean wave direction, are shown to be in agreement with the low-frequency change in Pacific-wide climate. Synoptic typing of climate patterns using Self Organised Mapping methods is used to downscale CSIRO GCM output for this century. The synoptic types are correlated to instrumental wave climate data and coastal behaviour. The shifts in downscaled synoptic types for 2030 and 2070 AD are then used as the basis for predicting mean wave climate changes, coastal behaviour and hazards along the NSW coastline. The associated coastal hazards relate to the definition of coastal land loss through rising sea levels and shoreline

  12. Development of METAL-ACTIVE SITE and ZINCCLUSTER tool to predict active site pockets.

    Science.gov (United States)

    Ajitha, M; Sundar, K; Arul Mugilan, S; Arumugam, S

    2018-03-01

    The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques. © 2017 Wiley Periodicals, Inc.

  13. Changes in psychiatric symptoms among persons with methamphetamine dependence predicts changes in severity of drug problems but not frequency of use.

    Science.gov (United States)

    Polcin, Douglas L; Korcha, Rachael; Bond, Jason; Galloway, Gantt; Nayak, Madhabika

    2016-01-01

    Few studies have examined how changes in psychiatric symptoms over time are associated with changes in drug use and severity of drug problems. No studies have examined these relationships among methamphetamine (MA)-dependent persons receiving motivational interviewing within the context of standard outpatient treatment. Two hundred seventeen individuals with MA dependence were randomly assigned to a standard single session of motivational interviewing (MI) or an intensive 9-session model of MI. Both groups received standard outpatient group treatment. The Addiction Severity Index (ASI) and timeline follow-back (TLFB) for MA use were administered at treatment entry and 2-, 4-, and 6-month follow-ups. Changes in ASI psychiatric severity between baseline and 2 months predicted changes in ASI drug severity during the same time period, but not changes on measures of MA use. Item analysis of the ASI drug scale showed that psychiatric severity predicted how troubled or bothered participants were by their drug us, how important they felt it was for them to get treatment, and the number of days they experienced drug problems. However, it did not predict the number days they used drugs in the past 30 days. These associations did not differ between study conditions, and they persisted when psychiatric severity and outcomes were compared across 4- and 6-month time periods. Results are among the first to track how changes in psychiatric severity over time are associated with changes in MA use and severity of drug problems. Treatment efforts targeting reduction of psychiatric symptoms among MA-dependent persons might be helpful in reducing the level of distress and problems associated with MA use but not how often it is used. There is a need for additional research describing the circumstances under which the experiences and perceptions of drug-related problems diverge from frequency of consumption.

  14. Contributions of climate change and human activities to runoff change in seven typical catchments across China.

    Science.gov (United States)

    Zhai, Ran; Tao, Fulu

    2017-12-15

    Climate change and human activities are two major factors affecting water resource change. It is important to understand the roles of the major factors in affecting runoff change in different basins for watershed management. Here, we investigated the trends in climate and runoff in seven typical catchments in seven basins across China from 1961 to 2014. Then we attributed the runoff change to climate change and human activities in each catchment and in three time periods (1980s, 1990s and 2000s), using the VIC model and long-term runoff observation data. During 1961-2014, temperature increased significantly, while the trends in precipitation were insignificant in most of the catchments and inconsistent among the catchments. The runoff in most of the catchments showed a decreasing trend except the Yingluoxia catchment in the northwestern China. The contributions of climate change and human activities to runoff change varied in different catchments and time periods. In the 1980s, climate change contributed more to runoff change than human activities, which was 84%, 59%, -66%, -50%, 59%, 94%, and -59% in the Nianzishan, Yingluoxia, Xiahui, Yangjiaping, Sanjiangkou, Xixian, and Changle catchment, respectively. After that, human activities had played a more essential role in runoff change. In the 1990s and 2000s, human activities contributed more to runoff change than in the 1980s. The contribution by human activities accounted for 84%, -68%, and 67% in the Yingluoxia, Xiahui, and Sanjiangkou catchment, respectively, in the 1990s; and -96%, -67%, -94%, and -142% in the Nianzishan, Yangjiaping, Xixian, and Changle catchment, respectively, in the 2000s. It is also noted that after 2000 human activities caused decrease in runoff in all catchments except the Yingluoxia. Our findings highlight that the effects of human activities, such as increase in water withdrawal, land use/cover change, operation of dams and reservoirs, should be well managed. Copyright © 2017 Elsevier

  15. Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction.

    Science.gov (United States)

    Iga, Katsumi; Kiriyama, Akiko

    2017-12-01

    Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF h ). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA i, CYP s) and victim-specific fractional CYP-isoform contributions to the clearance (vf m, CYP s)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

  16. Physical activity and 5-year changes in physical performance tests and bone mineral density in postmenopausal women: the Yokogoshi Study.

    Science.gov (United States)

    Kitamura, Kaori; Nakamura, Kazutoshi; Kobayashi, Ryosaku; Oshiki, Rieko; Saito, Toshiko; Oyama, Mari; Takahashi, Shunsuke; Nishiwaki, Tomoko; Iwasaki, Masanori; Yoshihara, Akihiro

    2011-09-01

    The effect of physical activity on musculoskeletal health in older adults is not completely understood. The aim of this study was to determine the relationship between physical activity and 5-year changes in physical performance tests and bone mineral density (BMD) in postmenopausal women. The design was a 5-year cohort study. Subjects were 507 women (55-74 years old) living in a rural community in Japan. Physical activity assessed included housework, farm work, and moderate leisure-time physical activity within the previous week. Measurements at baseline included handgrip strength, walking time (timed "Up & Go" test) and BMD of the femoral neck and vertebrae. Five-year changes in these measures (outcome variables) were compared among groups with different levels of physical activity by analysis of covariance. Women who did not do housework performed worse in changes in handgrip strength (difference=2.22 kg, P=0.0201) and worse in changes in the walking time (difference=0.54 s, P=0.0072) than those who did housework alone. Women who spent at least 9h per week (median=24) doing farm work performed better in changes in handgrip strength (difference=0.28 kg, P=0.0334), but worse in changes in the walking time (difference=0.66 s, Pwork. However, leisure-time activity was not associated with changes in any outcome variable, and none of the physical activities predicted BMD changes. Engaging in housework and farm work are determinants of physical function in postmenopausal women, which may help them maintain independence in daily living. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  17. Merlin : microsimulation system for predicting leisure activity-travel patterns

    NARCIS (Netherlands)

    Middelkoop, van M.; Borgers, A.W.J.; Timmermans, H.J.P.

    2004-01-01

    Development of a model of annual activity-travel patterns of leisure and vacation travel is reported. The simulation system, called Merlin, is a hybrid model system consisting of discrete choice models and rule-based models. It predicts the annual number of day trips and vacations, and the profile

  18. Health Impacts of Increased Physical Activity from Changes in Transportation Infrastructure: Quantitative Estimates for Three Communities

    Science.gov (United States)

    2015-01-01

    Recently, two quantitative tools have emerged for predicting the health impacts of projects that change population physical activity: the Health Economic Assessment Tool (HEAT) and Dynamic Modeling for Health Impact Assessment (DYNAMO-HIA). HEAT has been used to support health impact assessments of transportation infrastructure projects, but DYNAMO-HIA has not been previously employed for this purpose nor have the two tools been compared. To demonstrate the use of DYNAMO-HIA for supporting health impact assessments of transportation infrastructure projects, we employed the model in three communities (urban, suburban, and rural) in North Carolina. We also compared DYNAMO-HIA and HEAT predictions in the urban community. Using DYNAMO-HIA, we estimated benefit-cost ratios of 20.2 (95% C.I.: 8.7–30.6), 0.6 (0.3–0.9), and 4.7 (2.1–7.1) for the urban, suburban, and rural projects, respectively. For a 40-year time period, the HEAT predictions of deaths avoided by the urban infrastructure project were three times as high as DYNAMO-HIA's predictions due to HEAT's inability to account for changing population health characteristics over time. Quantitative health impact assessment coupled with economic valuation is a powerful tool for integrating health considerations into transportation decision-making. However, to avoid overestimating benefits, such quantitative HIAs should use dynamic, rather than static, approaches. PMID:26504832

  19. The Predictive Utility of Hypnotizability: The Change in Suggestibility Produced by Hypnosis

    Science.gov (United States)

    Milling, Leonard S.; Coursen, Elizabeth L.; Shores, Jessica S.; Waszkiewicz, Jolanta A.

    2010-01-01

    Objective: The predictive utility of hypnotizability, conceptualized as the change in suggestibility produced by a hypnotic induction, was investigated in the suggested reduction of experimental pain. Method: One hundred and seventy-three participants were assessed for nonhypnotic imaginative suggestibility. Thereafter, participants experienced…

  20. Changing relationships between land use and environmental characteristics and their consequences for spatially explicit land-use change prediction

    NARCIS (Netherlands)

    Bakker, M.; Veldkamp, A.

    2012-01-01

    Spatially explicit land-use change prediction is often based on environmental characteristics of land-use types, such as soil type and slope, as observed at one time instant. This approach presumes that relationships between land use and environment are constant over time. We argue that such

  1. The predictive skill of species distribution models for plankton in a changing climate

    DEFF Research Database (Denmark)

    Brun, Philipp Georg; Kiørboe, Thomas; Licandro, Priscilla

    2016-01-01

    Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real-world are rare. Here, we use data from the Continuous Plankton Recorder program, one...... null models, is essential to assess the robustness of projections of marine planktonic species under climate change...

  2. NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles.

    Directory of Open Access Journals (Sweden)

    Shunichi Kosugi

    2014-09-01

    Full Text Available The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS. Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper.

  3. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    Science.gov (United States)

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  4. The motivation to be sedentary predicts weight change when sedentary behaviors are reduced.

    Science.gov (United States)

    Epstein, Leonard H; Roemmich, James N; Cavanaugh, Meghan D; Paluch, Rocco A

    2011-02-22

    Obesity is correlated with a sedentary lifestyle, and the motivation to be active or sedentary is correlated with obesity. The present study tests the hypothesis that the motivation to be active or sedentary is correlated with weight change when children reduce their sedentary behavior. The motivation to be active or sedentary, changes in weight, and accelerometer assessed physical activity were collected for 55 families with overweight/obese children who participated in a nine-week field study to examine behavior and weight change as a function of reducing sedentary behavior. Children were studied in three 3-week phases, baseline, reduce targeted sedentary behaviors by 25% and reduce targeted sedentary behaviors by 50%. The targeted sedentary behaviors included television, video game playing, video watching, and computer use. The reinforcing value of sedentary behavior but not physical activity, was correlated with weight change, as losing weight was associated with lower reinforcing value of sedentary behaviors. Reducing sedentary behavior was not associated with a significant change in objectively measured physical activity, suggesting the main way in which reducing sedentary behavior influenced weight change is by complementary changes in energy intake. Estimated energy intake supported the hypothesis that reducing sedentary behaviors influences weight by reducing energy intake. These data show that the motivation to be sedentary limits the effects of reducing sedentary behavior on weight change in obese children. © 2011 Epstein et al; licensee BioMed Central Ltd.

  5. The motivation to be sedentary predicts weight change when sedentary behaviors are reduced

    Directory of Open Access Journals (Sweden)

    Paluch Rocco A

    2011-02-01

    Full Text Available Abstract Background Obesity is correlated with a sedentary lifestyle, and the motivation to be active or sedentary is correlated with obesity. The present study tests the hypothesis that the motivation to be active or sedentary is correlated with weight change when children reduce their sedentary behavior. Methods The motivation to be active or sedentary, changes in weight, and accelerometer assessed physical activity were collected for 55 families with overweight/obese children who participated in a nine-week field study to examine behavior and weight change as a function of reducing sedentary behavior. Children were studied in three 3-week phases, baseline, reduce targeted sedentary behaviors by 25% and reduce targeted sedentary behaviors by 50%. The targeted sedentary behaviors included television, video game playing, video watching, and computer use. Results The reinforcing value of sedentary behavior but not physical activity, was correlated with weight change, as losing weight was associated with lower reinforcing value of sedentary behaviors. Reducing sedentary behavior was not associated with a significant change in objectively measured physical activity, suggesting the main way in which reducing sedentary behavior influenced weight change is by complementary changes in energy intake. Estimated energy intake supported the hypothesis that reducing sedentary behaviors influences weight by reducing energy intake. Conclusions These data show that the motivation to be sedentary limits the effects of reducing sedentary behavior on weight change in obese children. Trial registration ClinicalTrials.gov: NCT00962247

  6. Anthropometry and physical activity level in the prediction of metabolic syndrome in children.

    Science.gov (United States)

    Andaki, Alynne Christian Ribeiro; Tinôco, Adelson Luiz Araújo; Mendes, Edmar Lacerda; Andaki Júnior, Roberto; Hills, Andrew P; Amorim, Paulo Roberto S

    2014-10-01

    To evaluate the effectiveness of anthropometric measures and physical activity level in the prediction of metabolic syndrome (MetS) in children. Cross-sectional study with children from public and private schools. Children underwent an anthropometric assessment, blood pressure measurement and biochemical evaluation of serum for determination of TAG, HDL-cholesterol and glucose. Physical activity level was calculated and number of steps per day obtained using a pedometer for seven consecutive days. Viçosa, south-eastern Brazil. Boys and girls (n 187), mean age 9·90 (SD 0·7) years. Conicity index, sum of four skinfolds, physical activity level and number of steps per day were accurate in predicting MetS in boys. Anthropometric indicators were accurate in predicting MetS for girls, specifically BMI, waist circumference measured at the narrowest point and at the level of the umbilicus, four skinfold thickness measures evaluated separately, the sum of subscapular and triceps skinfold thickness, the sum of four skinfolds and body fat percentage. The sum of four skinfolds was the most accurate method in predicting MetS in both genders.

  7. Predicting implementation from organizational readiness for change: a study protocol

    Directory of Open Access Journals (Sweden)

    Kelly P Adam

    2011-07-01

    Full Text Available Abstract Background There is widespread interest in measuring organizational readiness to implement evidence-based practices in clinical care. However, there are a number of challenges to validating organizational measures, including inferential bias arising from the halo effect and method bias - two threats to validity that, while well-documented by organizational scholars, are often ignored in health services research. We describe a protocol to comprehensively assess the psychometric properties of a previously developed survey, the Organizational Readiness to Change Assessment. Objectives Our objective is to conduct a comprehensive assessment of the psychometric properties of the Organizational Readiness to Change Assessment incorporating methods specifically to address threats from halo effect and method bias. Methods and Design We will conduct three sets of analyses using longitudinal, secondary data from four partner projects, each testing interventions to improve the implementation of an evidence-based clinical practice. Partner projects field the Organizational Readiness to Change Assessment at baseline (n = 208 respondents; 53 facilities, and prospectively assesses the degree to which the evidence-based practice is implemented. We will conduct predictive and concurrent validities using hierarchical linear modeling and multivariate regression, respectively. For predictive validity, the outcome is the change from baseline to follow-up in the use of the evidence-based practice. We will use intra-class correlations derived from hierarchical linear models to assess inter-rater reliability. Two partner projects will also field measures of job satisfaction for convergent and discriminant validity analyses, and will field Organizational Readiness to Change Assessment measures at follow-up for concurrent validity (n = 158 respondents; 33 facilities. Convergent and discriminant validities will test associations between organizational readiness and

  8. Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

    Science.gov (United States)

    Anderson, Andrew James; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Aguilar, Mario; Wang, Xixi; Doko, Donias; Raizada, Rajeev D S

    2017-09-01

    We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  9. Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change.

    Science.gov (United States)

    Mitchell, Duncan; Snelling, Edward P; Hetem, Robyn S; Maloney, Shane K; Strauss, Willem Maartin; Fuller, Andrea

    2018-02-26

    The accuracy of predictive models (also known as mechanistic or causal models) of animal responses to climate change depends on properly incorporating the principles of heat transfer and thermoregulation into those models. Regrettably, proper incorporation of these principles is not always evident. We have revisited the relevant principles of thermal physiology and analysed how they have been applied in predictive models of large mammals, which are particularly vulnerable, to climate change. We considered dry heat exchange, evaporative heat transfer, the thermoneutral zone and homeothermy, and we examined the roles of size and shape in the thermal physiology of large mammals. We report on the following misconceptions in influential predictive models: underestimation of the role of radiant heat transfer, misassignment of the role and misunderstanding of the sustainability of evaporative cooling, misinterpretation of the thermoneutral zone as a zone of thermal tolerance or as a zone of sustainable energetics, confusion of upper critical temperature and critical thermal maximum, overestimation of the metabolic energy cost of evaporative cooling, failure to appreciate that the current advantages of size and shape will become disadvantageous as climate change advances, misassumptions about skin temperature and, lastly, misconceptions about the relationship between body core temperature and its variability with body mass in large mammals. Not all misconceptions invalidate the models, but we believe that preventing inappropriate assumptions from propagating will improve model accuracy, especially as models progress beyond their current typically static format to include genetic and epigenetic adaptation that can result in phenotypic plasticity. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

  10. Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok, India

    Science.gov (United States)

    Mukhopadhyay, Anirban; Mondal, Arun; Mukherjee, Sandip; Khatua, Dipam; Ghosh, Subhajit; Mitra, Debasish; Ghosh, Tuhin

    2014-08-01

    In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.

  11. Why looking at the whole hippocampus is not enough – a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer’s disease diagnosis.

    Directory of Open Access Journals (Sweden)

    Aleksandra eMaruszak

    2014-03-01

    Full Text Available The hippocampus is one of the earliest affected brain regions in Alzheimer´s disease (AD and its dysfunction is believed to underlie the core feature of the disease- memory impairment. Given that hippocampal volume is one of the best AD biomarkers, our review focuses on distinct subfields within the hippocampus, pinpointing regions that might enhance the predictive value of current diagnostic methods. Our review presents how changes in hippocampal volume, shape, symmetry and activation are reflected by cognitive impairment and how they are linked with neurogenesis alterations. Moreover, we revisit the functional differentiation along the anteroposterior longitudinal axis of the hippocampus and discuss its relevance for AD diagnosis. Finally, we indicate that apart from hippocampal subfield volumetry, the characteristic pattern of hippocampal hyperactivation associated with seizures and neurogenesis changes is another promising candidate for an early AD biomarker that could become also a target for early interventions.

  12. TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

    Energy Technology Data Exchange (ETDEWEB)

    Liu, J; Wu, Q.J.; Yin, F; Kirkpatrick, J; Cabrera, A [Duke University Medical Center, Durham, NC (United States); Ge, Y [University of North Carolina at Charlotte, Charlotte, NC (United States)

    2014-06-15

    Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH

  13. TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

    International Nuclear Information System (INIS)

    Liu, J; Wu, Q.J.; Yin, F; Kirkpatrick, J; Cabrera, A; Ge, Y

    2014-01-01

    Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH

  14. The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs.

    Directory of Open Access Journals (Sweden)

    Yida Hu

    Full Text Available In this study, we aimed to predict newly diagnosed patient responses to antiepileptic drugs (AEDs using resting-state functional magnetic resonance imaging tools to explore changes in spontaneous brain activity. We recruited 21 newly diagnosed epileptic patients, 8 drug-resistant (DR patients, 11 well-healed (WH patients, and 13 healthy controls. After a 12-month follow-up, 11 newly diagnosed epileptic patients who showed a poor response to AEDs were placed into the seizures uncontrolled (SUC group, while 10 patients were enrolled in the seizure-controlled (SC group. By calculating the amplitude of fractional low-frequency fluctuations (fALFF of blood oxygen level-dependent signals to measure brain activity during rest, we found that the SUC patients showed increased activity in the bilateral occipital lobe, particularly in the cuneus and lingual gyrus compared with the SC group and healthy controls. Interestingly, DR patients also showed increased activity in the identical cuneus and lingual gyrus regions, which comprise Brodmann's area 17 (BA17, compared with the SUC patients; however, these abnormalities were not observed in SC and WH patients. The receiver operating characteristic (ROC curves indicated that the fALFF value of BA17 could differentiate SUC patients from SC patients and healthy controls with sufficient sensitivity and specificity prior to the administration of medication. Functional connectivity analysis was subsequently performed to evaluate the difference in connectivity between BA17 and other brain regions in the SUC, SC and control groups. Regions nearby the cuneus and lingual gyrus were found positive connectivity increased changes or positive connectivity changes with BA17 in the SUC patients, while remarkably negative connectivity increased changes or positive connectivity decreased changes were found in the SC patients. Additionally, default mode network (DMN regions showed negative connectivity increased changes or

  15. Analysis of Orbital Lifetime Prediction Parameters in Preparation for Post-Mission Disposal

    Directory of Open Access Journals (Sweden)

    Ha–Yeon Choi

    2015-12-01

    Full Text Available Atmospheric drag force is an important source of perturbation of Low Earth Orbit (LEO orbit satellites, and solar activity is a major factor for changes in atmospheric density. In particular, the orbital lifetime of a satellite varies with changes in solar activity, so care must be taken in predicting the remaining orbital lifetime during preparation for post-mission disposal. In this paper, the System Tool Kit (STK® Long-term Orbit Propagator is used to analyze the changes in orbital lifetime predictions with respect to solar activity. In addition, the STK® Lifetime tool is used to analyze the change in orbital lifetime with respect to solar flux data generation, which is needed for the orbital lifetime calculation, and its control on the drag coefficient control. Analysis showed that the application of the most recent solar flux file within the Lifetime tool gives a predicted trend that is closest to the actual orbit. We also examine the effect of the drag coefficient, by performing a comparative analysis between varying and constant coefficients in terms of solar activity intensities.

  16. Social-cognitive theories for predicting physical activity behaviours of employed women with and without young children.

    Science.gov (United States)

    Tavares, Leonor S; Plotnikoff, Ronald C; Loucaides, Constantinos

    2009-03-01

    Chronic disease interventions for women have been understudied in the workplace domain. Understanding the role of cognitions in individual behaviour can help motivate change and suggest directions for achieving improvements in health. The purpose of this study was to identify psychosocial constructs and social-cognitive theories [e.g. Transtheoretical model (TTM), Theory of Planned Behaviour (TPB), Protection Motivation Theory (PMT) and Social Cognitive Theory (SCT)] that are most salient for explaining physical activity behaviour among employed women (n = 1183). Demographic information, and social-cognitive measures related to physical activity, intention and behaviours (e.g. stage of change, energy expenditure) were assessed. A series of multiple regression analyses predicting intention, energy expenditure and stage of change were conducted separately for: (1) women with young children (n = 302), and (2) women without young children (n = 881) for each of the respective social-cognitive theories. Although taken as a whole the results were relatively similar between the two sub-groups of women for each of the socio-cognitive theories examined in this study, differences were observed in the relative contributions of the theoretical constructs between the two sub-groups. Results also indicate that self-efficacy and intention were the strongest predictors of behaviour among both women with and without young children. The explained variances (R(2)) for the theories examined in this study for different sub-groups ranged from 16 to 60%, generally reflecting what has been reported in other studies within the physical activity domain. The results of this study could be useful in guiding future research and in designing physical activity intervention programs for these specific population groups. Integrating approaches of individual lifestyle change while addressing issues related to creating supportive environments for women in various life stages is a suggested strategy

  17. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

    Energy Technology Data Exchange (ETDEWEB)

    Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

  18. Predictive modelling of the spatial pattern of past and future forest cover changes in India

    Science.gov (United States)

    Reddy, C. Sudhakar; Singh, Sonali; Dadhwal, V. K.; Jha, C. S.; Rao, N. Rama; Diwakar, P. G.

    2017-02-01

    This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km2, which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880-2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multi-layer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.

  19. Uncertainty in Predicting CCN Activity of Aged and Primary Aerosols

    Science.gov (United States)

    Zhang, Fang; Wang, Yuying; Peng, Jianfei; Ren, Jingye; Collins, Don; Zhang, Renyi; Sun, Yele; Yang, Xin; Li, Zhanqing

    2017-11-01

    Understanding particle CCN activity in diverse atmospheres is crucial when evaluating aerosol indirect effects. Here aerosols measured at three sites in China were categorized as different types for attributing uncertainties in CCN prediction in terms of a comprehensive data set including size-resolved CCN activity, size-resolved hygroscopic growth factor, and chemical composition. We show that CCN activity for aged aerosols is unexpectedly underestimated 22% at a supersaturation (S) of 0.2% when using κ-Kohler theory with an assumption of an internal mixture with measured bulk composition that has typically resulted in an overestimate of the CCN activity in previous studies. We conclude that the underestimation stems from neglect of the effect of aging/coating on particle hygroscopicity, which is not considered properly in most current models. This effect enhanced the hygroscopicity parameter (κ) by between 11% (polluted conditions) and 30% (clean days), as indicated in diurnal cycles of κ based on measurements by different instruments. In the urban Beijing atmosphere heavily influenced by fresh emissions, the CCN activity was overestimated by 45% at S = 0.2%, likely because of inaccurate assumptions of particle mixing state and because of variability of chemical composition over the particle size range. For both fresh and aged aerosols, CCN prediction exhibits very limited sensitivity to κSOA, implying a critical role of other factors like mixing of aerosol components within and between particles in regulating CCN activity. Our findings could help improving CCN parameterization in climate models.

  20. Detection of cardiac activity changes from human speech

    Science.gov (United States)

    Tovarek, Jaromir; Partila, Pavol; Voznak, Miroslav; Mikulec, Martin; Mehic, Miralem

    2015-05-01

    Impact of changes in blood pressure and pulse from human speech is disclosed in this article. The symptoms of increased physical activity are pulse, systolic and diastolic pressure. There are many methods of measuring and indicating these parameters. The measurements must be carried out using devices which are not used in everyday life. In most cases, the measurement of blood pressure and pulse following health problems or other adverse feelings. Nowadays, research teams are trying to design and implement modern methods in ordinary human activities. The main objective of the proposal is to reduce the delay between detecting the adverse pressure and to the mentioned warning signs and feelings. Common and frequent activity of man is speaking, while it is known that the function of the vocal tract can be affected by the change in heart activity. Therefore, it can be a useful parameter for detecting physiological changes. A method for detecting human physiological changes by speech processing and artificial neural network classification is described in this article. The pulse and blood pressure changes was induced by physical exercises in this experiment. The set of measured subjects was formed by ten healthy volunteers of both sexes. None of the subjects was a professional athlete. The process of the experiment was divided into phases before, during and after physical training. Pulse, systolic, diastolic pressure was measured and voice activity was recorded after each of them. The results of this experiment describe a method for detecting increased cardiac activity from human speech using artificial neural network.

  1. Predicting Seagrass Occurrence in a Changing Climate Using Random Forests

    Science.gov (United States)

    Aydin, O.; Butler, K. A.

    2017-12-01

    Seagrasses are marine plants that can quickly sequester vast amounts of carbon (up to 100 times more and 12 times faster than tropical forests). In this work, we present an integrated GIS and machine learning approach to build a data-driven model of seagrass presence-absence. We outline a random forest approach that avoids the prevalence bias in many ecological presence-absence models. One of our goals is to predict global seagrass occurrence from a spatially limited training sample. In addition, we conduct a sensitivity study which investigates the vulnerability of seagrass to changing climate conditions. We integrate multiple data sources including fine-scale seagrass data from MarineCadastre.gov and the recently available globally extensive publicly available Ecological Marine Units (EMU) dataset. These data are used to train a model for seagrass occurrence along the U.S. coast. In situ oceans data are interpolated using Empirical Bayesian Kriging (EBK) to produce globally extensive prediction variables. A neural network is used to estimate probable future values of prediction variables such as ocean temperature to assess the impact of a warming climate on seagrass occurrence. The proposed workflow can be generalized to many presence-absence models.

  2. Space can substitute for time in predicting climate-change effects on biodiversity

    Science.gov (United States)

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-01-01

    “Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  3. Space can substitute for time in predicting climate-change effects on biodiversity.

    Science.gov (United States)

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-04

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  4. Changes In Growth Culture FDA Activity Under Changing Growth Conditions

    DEFF Research Database (Denmark)

    Jørgensen, Per Elberg; Eriksen, Thomas Juul; Jensen, Bjørn K.

    1992-01-01

    The FDA hydrolysis capacities and bacterial biomass concentrations (estimated by determination of ATP content) of growth cultures prepared from activated sludge and wastewater, were measured to find out whether the FDA activity would reflect bacterial biomass under different physiological states...... of the bacteria. The FDA activity/ATP ratio was calculated for different concentrations of autoclaved sludge. A faster decay rate of ATP relative to FDA hydrolysis activity was observed, thus causing changes in the ratio. Furthermore, comparison between values obtained from pure cultures and different soils...... revealed differences up to two orders of magnitude of the ratio. Based on these results it was concluded that the FDA activity should not be applied for measurements of viable biomass in environments in which different physiological conditions occur....

  5. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  6. Prediction of the impacts of climate changes on the stream flow of ...

    African Journals Online (AJOL)

    Abstract. Soil and Water Assessment Tool, (SWAT) model was used to predict the impacts of Climate Change on Ajali River watershed, Aguobu-Umumba, Ezeagu, Enugu State, Nigeria. The model was first used to simulate stream flow using observed data. After model run, parameterization, sensitivity analysis, the monthly ...

  7. Change in physical education motivation and physical activity behavior during middle school.

    Science.gov (United States)

    Cox, Anne E; Smith, Alan L; Williams, Lavon

    2008-11-01

    To test a mediational model of the relationships among motivation-related variables in middle-school physical education and leisure-time physical activity behavior. Sixth- and seventh-grade physical education students from five middle schools in the midwest United States completed a survey containing measures of study variables on two occasions, 1 year apart. Motivation-related constructs positively predicted leisure-time physical activity behavior. Enjoyment of activities in physical education and physical activity during class mediated the relationship between self-determined motivation in physical education and leisure-time physical activity. Perceived competence, autonomy, and relatedness were important antecedent variables in the model, with autonomy and relatedness showing less stability over time and positively predicting self-determined motivation. Students' leisure-time physical activity is linked to motivation-related experiences in physical education. Perceptions of competence, autonomy, and relatedness, self-determined motivation, enjoyment, and physical activity in the physical education setting directly or indirectly predict leisure-time physical activity. The associations suggest that more adaptive motivation corresponds to transfer of behavior across contexts. Also, the findings suggest that the efficacy of school-based physical activity interventions, within and outside of school, is linked to the degree of support for students' self-determined motivation.

  8. Motor Skill Competence and Perceived Motor Competence: Which Best Predicts Physical Activity among Girls?

    Science.gov (United States)

    Khodaverdi, Zeinab; Bahram, Abbas; Khalaji, Hassan; Kazemnejad, Anoshirvan

    2013-10-01

    The main purpose of this study was to determine which correlate, perceived motor competence or motor skill competence, best predicts girls' physical activity behavior. A sample of 352 girls (mean age=8.7, SD=0.3 yr) participated in this study. To assess motor skill competence and perceived motor competence, each child completed the Test of Gross Motor Development-2 and Physical Ability sub-scale of Marsh's Self-Description Questionnaire. Children's physical activity was assessed by the Physical Activity Questionnaire for Older Children. Multiple linear regression model was used to determine whether perceived motor competence or motor skill competence best predicts moderate-to-vigorous self-report physical activity. Multiple regression analysis indicated that motor skill competence and perceived motor competence predicted 21% variance in physical activity (R(2)=0.21, F=48.9, P=0.001), and motor skill competence (R(2)=0.15, ᵝ=0.33, P= 0.001) resulted in more variance than perceived motor competence (R(2)=0.06, ᵝ=0.25, P=0.001) in physical activity. Results revealed motor skill competence had more influence in comparison with perceived motor competence on physical activity level. We suggest interventional programs based on motor skill competence and perceived motor competence should be administered or implemented to promote physical activity in young girls.

  9. Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

    Science.gov (United States)

    Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha C.; Thompson, Jill; Zimmerman, Jess K.; Murphy, Lora

    2018-01-01

    Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

  10. Advancing Environmental Prediction Capabilities for the Polar Regions and Beyond during The Year of Polar Prediction

    Science.gov (United States)

    Werner, Kirstin; Goessling, Helge; Hoke, Winfried; Kirchhoff, Katharina; Jung, Thomas

    2017-04-01

    Environmental changes in polar regions open up new opportunities for economic and societal operations such as vessel traffic related to scientific, fishery and tourism activities, and in the case of the Arctic also enhanced resource development. The availability of current and accurate weather and environmental information and forecasts will therefore play an increasingly important role in aiding risk reduction and safety management around the poles. The Year of Polar Prediction (YOPP) has been established by the World Meteorological Organization's World Weather Research Programme as the key activity of the ten-year Polar Prediction Project (PPP; see more on www.polarprediction.net). YOPP is an internationally coordinated initiative to significantly advance our environmental prediction capabilities for the polar regions and beyond, supporting improved weather and climate services. Scheduled to take place from mid-2017 to mid-2019, the YOPP core phase covers an extended period of intensive observing, modelling, prediction, verification, user-engagement and education activities in the Arctic and Antarctic, on a wide range of time scales from hours to seasons. The Year of Polar Prediction will entail periods of enhanced observational and modelling campaigns in both polar regions. With the purpose to close the gaps in the conventional polar observing systems in regions where the observation network is sparse, routine observations will be enhanced during Special Observing Periods for an extended period of time (several weeks) during YOPP. This will allow carrying out subsequent forecasting system experiments aimed at optimizing observing systems in the polar regions and providing insight into the impact of better polar observations on forecast skills in lower latitudes. With various activities and the involvement of a wide range of stakeholders, YOPP will contribute to the knowledge base needed to managing the opportunities and risks that come with polar climate change.

  11. A GIS-model for predicting the impact of climate change on shore erosion in hydroelectric reservoirs

    International Nuclear Information System (INIS)

    Penner, L.A.; Zimmer, T.A.M.; St Laurent, M.

    2008-01-01

    Shoreline erosion affects inland lakes and hydroelectric reservoirs in several ways. This poster described a vector-based geographic information system (GIS) model designed to predict changes in shore zone geometry, top-of-bluff recession, and eroded sediment volumes. The model was designed for use in Manitoba Hydro's reservoirs in northern Manitoba, and simulated near-shore downcutting and bank recession caused by wind-generated waves. Parameters for the model included deep water wave energy, and water level fluctuations. Effective wave energy was seen as a function of the water level fluctuation range, wave conditions, and near-shore slope. The model was validated by field monitoring studies that included repeated shore zone transect surveys and sediment coring studies. Results of the study showed that the model provides a systematic method of predicting potential changes in erosion associated with climatic change. The volume and mass of eroded sediment predicted for the different modelling scenarios will be used as input data for future sedimentation models. tabs., figs

  12. New active drugs against liver stages of Plasmodium predicted by molecular topology.

    NARCIS (Netherlands)

    Mahmoudi, N.; Garcia-Domenech, R.; Galvez, J.; Farhati, K.; Franetich, J.F.; Sauerwein, R.W.; Hannoun, L.; Derouin, F.; Danis, M.; Mazier, D.

    2008-01-01

    We conducted a quantitative structure-activity relationship (QSAR) study based on a database of 127 compounds previously tested against the liver stage of Plasmodium yoelii in order to develop a model capable of predicting the in vitro antimalarial activities of new compounds. Topological indices

  13. Naturally-occurring changes in social-cognitive factors modify change in physical activity during early adolescence.

    Directory of Open Access Journals (Sweden)

    Rod K Dishman

    Full Text Available To determine whether naturally-occurring changes in children's motives and beliefs are associated with the steep decline in physical activity observed from childhood to early adolescence.Latent growth modeling was applied in longitudinal tests of social-cognitive influences, and their interactions, on physical activity in a large cohort of boys and girls evaluated annually between 5th and 7th grades.Measurement equivalence of motives and beliefs was confirmed between boys and girls. After adjustment for gender and maturity differences, physical activity declined less in children who reported the least decreases in self-efficacy for overcoming barriers to activity and perceived parental support. Physical activity also declined less in students who persistently felt they had more parental and friend support for activity compared to those who reported the largest decrease in support from friends. After further adjustment for race, the decline in physical activity was less in those who had the largest decrease in perceived barriers and maintained a favorable perception of their neighborhood environment. Changes in enjoyment and social motives were unrelated to change in physical activity.Using an objective measure of physical activity, we confirm that naturally-occurring changes in children's beliefs about barriers to physical activity and their ability to overcome them, as well as perceptions of their neighborhood environment and social support, are concurrent with age-related declines in children's physical activity. The longitudinal findings confirm these putative social-cognitive mediators as plausible, interacting targets of interventions designed to mitigate the marked decline in physical activity that occurs during the transition between elementary and middle schools.

  14. Which nerve conduction parameters can predict spontaneous electromyographic activity in carpal tunnel syndrome?

    Science.gov (United States)

    Chang, Chia-Wei; Lee, Wei-Ju; Liao, Yi-Chu; Chang, Ming-Hong

    2013-11-01

    We investigate electrodiagnostic markers to determine which parameters are the best predictors of spontaneous electromyographic (EMG) activity in carpal tunnel syndrome (CTS). We enrolled 229 patients with clinically proven and nerve conduction study (NCS)-proven CTS, as well as 100 normal control subjects. All subjects were evaluated using electrodiagnostic techniques, including median distal sensory latencies (DSLs), sensory nerve action potentials (SNAPs), distal motor latencies (DMLs), compound muscle action potentials (CMAPs), forearm median nerve conduction velocities (FMCVs) and wrist-palm motor conduction velocities (W-P MCVs). All CTS patients underwent EMG examination of the abductor pollicis brevis (APB) muscle, and the presence or absence of spontaneous EMG activities was recorded. Normal limits were determined by calculating the means ± 2 standard deviations from the control data. Associations between parameters from the NCS and EMG findings were investigated. In patients with clinically diagnosed CTS, abnormal median CMAP amplitudes were the best predictors of spontaneous activity during EMG examination (p95% (positive predictive rate >95%). If the median CMAP amplitude was higher than the normal limit (>4.9 mV), the rate of no spontaneous EMG activity was >94% (negative predictive rate >94%). An abnormal SNAP amplitude was the second best predictor of spontaneous EMG activity (p<0.001; OR 4.13; 95% CI 2.16-7.90), and an abnormal FMCV was the third best predictor (p=0.01; OR 2.10; 95% CI 1.20-3.67). No other nerve conduction parameters had significant power to predict spontaneous activity upon EMG examination. The CMAP amplitudes of the APB are the most powerful predictors of the occurrence of spontaneous EMG activity. Low CMAP amplitudes are strongly associated with spontaneous activity, whereas high CMAP amplitude are less associated with spontaneous activity, implying that needle EMG examination should be recommended for the detection of

  15. Varying responses of vegetation activity to climate changes on the Tibetan Plateau grassland.

    Science.gov (United States)

    Cong, Nan; Shen, Miaogen; Yang, Wei; Yang, Zhiyong; Zhang, Gengxin; Piao, Shilong

    2017-08-01

    Vegetation activity on the Tibetan Plateau grassland has been substantially enhanced as a result of climate change, as revealed by satellite observations of vegetation greenness (i.e., the normalized difference vegetation index, NDVI). However, little is known about the temporal variations in the relationships between NDVI and temperature and precipitation, and understanding this is essential for predicting how future climate change would affect vegetation activity. Using NDVI data and meteorological records from 1982 to 2011, we found that the inter-annual partial correlation coefficient between growing season (May-September) NDVI and temperature (R NDVI-T ) in a 15-year moving window for alpine meadow showed little change, likely caused by the increasing R NDVI-T in spring (May-June) and autumn (September) and decreasing R NDVI-T in summer (July-August). Growing season R NDVI-T for alpine steppe increased slightly, mainly due to increasing R NDVI-T in spring and autumn. The partial correlation coefficient between growing season NDVI and precipitation (R NDVI-P ) for alpine meadow increased slightly, mainly in spring and summer, and R NDVI-P for alpine steppe increased, mainly in spring. Moreover, R NDVI-T for the growing season was significantly higher in those 15-year windows with more precipitation for alpine steppe. R NDVI-P for the growing season was significantly higher in those 15-year windows with higher temperature, and this tendency was stronger for alpine meadow than for alpine steppe. These results indicate that the impact of warming on vegetation activity of Tibetan Plateau grassland is more positive (or less negative) during periods with more precipitation and that the impact of increasing precipitation is more positive (or less negative) during periods with higher temperature. Such positive effects of the interactions between temperature and precipitation indicate that the projected warmer and wetter future climate will enhance vegetation activity

  16. 1/f neural noise and electrophysiological indices of contextual prediction in aging.

    Science.gov (United States)

    Dave, S; Brothers, T A; Swaab, T Y

    2018-07-15

    Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations. Copyright © 2018 Elsevier B.V. All rights reserved.

  17. Active inference, communication and hermeneutics.

    Science.gov (United States)

    Friston, Karl J; Frith, Christopher D

    2015-07-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

    Directory of Open Access Journals (Sweden)

    Huixiao Hong

    2016-09-01

    Full Text Available Sunscreen products are predominantly regulated as over-the-counter (OTC drugs by the US FDA. The “active” ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.

  19. The motivation to be sedentary predicts weight change when sedentary behaviors are reduced

    OpenAIRE

    Epstein, Leonard H; Roemmich, James N; Cavanaugh, Meghan D; Paluch, Rocco A

    2011-01-01

    Abstract Background Obesity is correlated with a sedentary lifestyle, and the motivation to be active or sedentary is correlated with obesity. The present study tests the hypothesis that the motivation to be active or sedentary is correlated with weight change when children reduce their sedentary behavior. Methods The motivation to be active or sedentary, changes in weight, and accelerometer assessed physical activity were collected for 55 families with overweight/obese children who participa...

  20. Prediction of crack density and electrical resistance changes in indium tin oxide/polymer thin films under tensile loading

    KAUST Repository

    Mora Cordova, Angel

    2014-06-11

    We present unified predictions for the crack onset strain, evolution of crack density, and changes in electrical resistance in indium tin oxide/polymer thin films under tensile loading. We propose a damage mechanics model to quantify and predict such changes as an alternative to fracture mechanics formulations. Our predictions are obtained by assuming that there are no flaws at the onset of loading as opposed to the assumptions of fracture mechanics approaches. We calibrate the crack onset strain and the damage model based on experimental data reported in the literature. We predict crack density and changes in electrical resistance as a function of the damage induced in the films. We implement our model in the commercial finite element software ABAQUS using a user subroutine UMAT. We obtain fair to good agreement with experiments. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  1. The sequential structure of brain activation predicts skill.

    Science.gov (United States)

    Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

    2016-01-29

    In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Design and implementation of predictive filtering system for current reference generation of active power filter

    Energy Technology Data Exchange (ETDEWEB)

    Kilic, Tomislav; Milun, Stanko; Petrovic, Goran [FESB University of Split, Faculty of Electrical Engineering, Machine Engineering and Naval Architecture, R. Boskovica bb, 21000, Split (Croatia)

    2007-02-15

    The shunt active power filters are used to attenuate the harmonic currents in power systems by injecting equal but opposite compensating currents. Successful control of the active filters requires an accurate current reference. In this paper the current reference determination based on predictive filtering structure is presented. Current reference was obtained by taking the difference of load current and its fundamental harmonic. For fundamental harmonic determination with no time delay a combination of digital predictive filter and low pass filter is used. The proposed method was implemented on a laboratory prototype of a three-phase active power filter. The algorithm for current reference determination was adapted and implemented on DSP controller. Simulation and experimental results show that the active power filter with implemented predictive filtering structure gives satisfactory performance in power system harmonic attenuation. (author)

  3. Nonlinear model predictive control of a passenger vehicle for automated lane changes

    NARCIS (Netherlands)

    Acosta, A.F.; Marquez-Ruiz, A.; Espinosa, J.J.

    2017-01-01

    This article presents a nonlinear Model Predictive Control (MPC) for lane changes, based on a simplified Single Track Model (STM) of the vehicle. The STM includes the position of the vehicle in global coordinates as a state so that the position of the target lane can be specified to the MPC for

  4. Prediction of hospital mortality by changes in the estimated glomerular filtration rate (eGFR).

    LENUS (Irish Health Repository)

    Berzan, E

    2015-03-01

    Deterioration of physiological or laboratory variables may provide important prognostic information. We have studied whether a change in estimated glomerular filtration rate (eGFR) value calculated using the (Modification of Diet in Renal Disease (MDRD) formula) over the hospital admission, would have predictive value. An analysis was performed on all emergency medical hospital episodes (N = 61964) admitted between 1 January 2002 and 31 December 2011. A stepwise logistic regression model examined the relationship between mortality and change in renal function from admission to discharge. The fully adjusted Odds Ratios (OR) for 5 classes of GFR deterioration showed a stepwise increased risk of 30-day death with OR\\'s of 1.42 (95% CI: 1.20, 1.68), 1.59 (1.27, 1.99), 2.71 (2.24, 3.27), 5.56 (4.54, 6.81) and 11.9 (9.0, 15.6) respectively. The change in eGFR during a clinical episode, following an emergency medical admission, powerfully predicts the outcome.

  5. Episodic radon changes in subsurface soil gas along active faults and possible relation to earthquakes

    International Nuclear Information System (INIS)

    King, C.

    1980-01-01

    Subsurface soil gas along active faults in central California has been continuously monitored by the Track Etch method to test whether its radon-isotope content may show any premonitory changes useful for earthquake prediction. The monitoring network was installed in May 1975 and has since been gradually expanded to consist of more than 60 stations along a 380-km section of the San Andreas fault system between Santa Rosa and Cholame. This network has recorded several episodes, each lasting several weeks to several months, during which the radon concentration increased by a factor of approximately 2 above average along some long, but limited, fault segments (approx.100 km). These episodes occurred in different seasons and do not appear to be systematically related to changes in meteorological conditions. However, they coincided reasonably well in time and space with larger local earthquakes above a threshold magnitude of about 4.0. These episodic radon changes may be caused by a changing outgassing rate in the fault zones in response to some episodic strain changes, which incidentally caused the earthquakes

  6. Endosulfan induces changes in spontaneous swimming activity and acetylcholinesterase activity of Jenynsia multidentata (Anablepidae, Cyprinodontiformes)

    International Nuclear Information System (INIS)

    Ballesteros, M.L.; Durando, P.E.; Nores, M.L.; Diaz, M.P.; Bistoni, M.A.; Wunderlin, D.A.

    2009-01-01

    We assessed changes in spontaneous swimming activity and acetylcholinesterase (AchE) activity of Jenynsia multidentata exposed to Endosulfan (EDS). Females of J. multidentata were exposed to 0.072 and 1.4 μg L -1 EDS. Average speed and movement percentage were recorded during 48 h. We also exposed females to EDS at five concentrations between 0.072 and 1.4 μg L -1 during 24 h, and measured the AchE activity in brain and muscle. At 0.072 μg L -1 EDS swimming motility decreased relative to the control group after 45 h, while at 1.4 μg L -1 EDS swimming motility decreased after 24 h. AchE activity significantly decreased in muscle when J. multidentata were exposed to EDS above 0.072 μg L -1 , while no significant changes were observed in brain. Thus, changes in swimming activity and AchE activity in muscle are good biomarkers of exposure to EDS in J. multidentata. - This work reports changes observed in spontaneous swimming activity and AchE activity of Jenynsia multidentata exposed to sublethal concentrations of Endosulfan.

  7. Endosulfan induces changes in spontaneous swimming activity and acetylcholinesterase activity of Jenynsia multidentata (Anablepidae, Cyprinodontiformes)

    Energy Technology Data Exchange (ETDEWEB)

    Ballesteros, M.L. [Facultad de Ciencias Exactas, Fisicas y Naturales, Catedra Diversidad Animal II, Universidad Nacional de Cordoba, Av. Velez Sarsfield 299, 5000 Cordoba (Argentina); Durando, P.E. [Facultad de Ciencias Exactas, Fisicas y Naturales, Departamento de Biologia, Catedra de Fisiologia Animal, Universidad Nacional de San Juan, Complejo ' Islas Malvinas' , Av. Jose I. de la Roza y Meglioli, Rivadavia, San Juan (Argentina); Nores, M.L. [Facultad de Ciencias Medicas, Universidad Nacional de Cordoba-CONICET, Ciudad Universitaria, Cordoba (Argentina); Diaz, M.P. [Facultad de Ciencias Medicas, Catedra de Estadistica y Bioestadistica, Escuela de Nutricion, Universidad Nacional de Cordoba, Pabellon Chile, Ciudad Universitaria, 5000 Cordoba (Argentina); Bistoni, M.A., E-mail: mbistoni@com.uncor.ed [Facultad de Ciencias Exactas, Fisicas y Naturales, Catedra Diversidad Animal II, Universidad Nacional de Cordoba, Av. Velez Sarsfield 299, 5000 Cordoba (Argentina); Wunderlin, D.A. [Facultad de Ciencias Quimicas, Dto. Bioquimica Clinica-CIBICI, Universidad Nacional de Cordoba-CONICET, Haya de la Torre esq. Medina Allende, Ciudad Universitaria, 5000 Cordoba (Argentina)

    2009-05-15

    We assessed changes in spontaneous swimming activity and acetylcholinesterase (AchE) activity of Jenynsia multidentata exposed to Endosulfan (EDS). Females of J. multidentata were exposed to 0.072 and 1.4 mug L{sup -1} EDS. Average speed and movement percentage were recorded during 48 h. We also exposed females to EDS at five concentrations between 0.072 and 1.4 mug L{sup -1} during 24 h, and measured the AchE activity in brain and muscle. At 0.072 mug L{sup -1} EDS swimming motility decreased relative to the control group after 45 h, while at 1.4 mug L{sup -1} EDS swimming motility decreased after 24 h. AchE activity significantly decreased in muscle when J. multidentata were exposed to EDS above 0.072 mug L{sup -1}, while no significant changes were observed in brain. Thus, changes in swimming activity and AchE activity in muscle are good biomarkers of exposure to EDS in J. multidentata. - This work reports changes observed in spontaneous swimming activity and AchE activity of Jenynsia multidentata exposed to sublethal concentrations of Endosulfan.

  8. Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.

    Science.gov (United States)

    Tulabandhula, Theja; Rudin, Cynthia

    2014-06-01

    Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing.

  9. THE DEVELOPMENT AND USE OF A MODEL TO PREDICT SUSTAINABILITY OF CHANGE IN HEALTH CARE SETTINGS.

    Science.gov (United States)

    Molfenter, Todd; Ford, James H; Bhattacharya, Abhik

    2011-01-01

    Innovations adopted through organizational change initiatives are often not sustained leading to diminished quality, productivity, and consumer satisfaction. Research explaining variance in the use of adopted innovations in health care settings is sparse, suggesting the need for a theoretical model to guide research and practice. In this article, we describe the development of a hybrid conjoint decision theoretic model designed to predict the sustainability of organizational change in health care settings. An initial test of the model's predictive validity using expert scored hypothetic profiles resulted in an r-squared value of .77. The test of this model offers a theoretical base for future research on the sustainability of change in health care settings.

  10. Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.

    Directory of Open Access Journals (Sweden)

    Marharyta Petukh

    2015-07-01

    Full Text Available A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624 while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation.

  11. Application of the Transtheoretical Model to Predict Exercise Activities in the Students of Islamic Azad University of Sabzevar

    Directory of Open Access Journals (Sweden)

    M. Mohammadi

    2012-04-01

    Full Text Available Background: Based on report of World Health Organization (WHO, about 60-85% of the world's population fails to complete the recommended amount of physical activity required to induce health benefits. It is necessary to assess health status for designing and programming about exercise activities. In this study the effectiveness of Transtheoretical Model (TTM in predicting exercise activities among the students of Islmaic Azad University of Sabzevar was examined. Methods: In this cross sectional-Correlational study. A random (clustered sample of 234 university students in Islamic Azad university of Sabzevar, participated in the study. A standard instrument was used to measure the variables of interest based on transtheoretical model. Reliability and validity of the questionnaire was examined by a panel of experts and cronbach alpha (N=30, α=0.83-0.95. The data were analyzed by SPSS 16.00 statistical software using Path analysis based regression, t-test and ANOVA and Correlation. Results: According to the results, the average age of students was 22.5±3.8 years. The distribution of the participants according to the stages of change model was as follows: pre-contemplation 36.3%, contemplation 25.6%, preparation, 18.9%, action, 10.5% and maintenance 8.7%.These were significant differences between mean of self efficacy, process of change, decisional balance by sex (p<0.05 and stages of change (p<0.01. Behavioral process of change (β=0.399 and self efficacy (β=0.350 were the most important variables for improving levels of exercise. Conclusion: Because the most students (62% were at precontemplation, contemplation and preparation stages and the results showed that behavioral process of change perceived barriers and self efficacy are the most important predictors for improving levels of exercise. Thus, policies and programs to strengthen these factors to promote exercise activities among students is recommended.

  12. Body image and body change: predictive factors in an Iranian population.

    Science.gov (United States)

    Garrusi, Behshid; Garousi, Saeide; Baneshi, Mohammad R

    2013-08-01

    Body concerns and its health consequences such as eating disorders and harmful body change activities are mentioned in Asian countries. This study evaluates factors contributing to body image/shape changes in an Iranian population. In this cross-sectional study we focused on four main body change activity (diet, exercise, substance use, and surgery) and their risk factors such as demographic variables, Body Mass Index (BMI), Media, Body-Esteem, Perceived Socio-cultural Pressure, Body dissatisfaction and, Self-Esteem. Approximately, 1,200 individuals between 14-55 years old participated in this study. We used a multistage sampling method. In each region, the first household was selected at random. The probability of outcomes was estimated from logistic models. About 54.3% of respondents were females. The mean (SD) of age was 31.06 (10.24) years. Variables such as gender, age, BMI, use of media and socio cultural factors as, body dissatisfaction, body-esteem and pressure by relatives were the main factors that influenced body change methods. In particular we have seen that male are 53% less likely to follow surgical treatments, but 125% were more likely to use substances. Investigation of body concern and its health related problem should be assessed in cultural context. For effectiveness of interventional programs and reducing harmful body image/shape changes activities, socio-cultural background should be noted.

  13. Body image and body change: Predictive factors in an Iranian population

    Directory of Open Access Journals (Sweden)

    Behshid Garrusi

    2013-01-01

    Full Text Available Background: Body concerns and its health consequences such as eating disorders and harmful body change activities are mentioned in Asian countries. This study evaluates factors contributing to body image/shape changes in an Iranian population. Methods: In this cross-sectional study we focused on four main body change activity (diet, exercise, substance use, and surgery and their risk factors such as demographic variables, Body Mass Index (BMI, Media, Body-Esteem, Perceived Socio-cultural Pressure, Body dissatisfaction and, Self-Esteem. Approximately, 1,200 individuals between 14-55 years old participated in this study. We used a multistage sampling method. In each region, the first household was selected at random. The probability of outcomes was estimated from logistic models. Results: About 54.3% of respondents were females. The mean (SD of age was 31.06 (10.24 years. Variables such as gender, age, BMI, use of media and socio cultural factors as, body dissatisfaction, body-esteem and pressure by relatives were the main factors that influenced body change methods. In particular we have seen that male are 53% less likely to follow surgical treatments, but 125% were more likely to use substances. Conclusions: Investigation of body concern and its health related problem should be assessed in cultural context. For effectiveness of interventional programs and reducing harmful body image/shape changes activities, socio-cultural background should be noted.

  14. Body Image and Body Change: Predictive Factors in an Iranian Population

    Science.gov (United States)

    Garrusi, Behshid; Garousi, Saeide; Baneshi, Mohammad R.

    2013-01-01

    Background: Body concerns and its health consequences such as eating disorders and harmful body change activities are mentioned in Asian countries. This study evaluates factors contributing to body image/shape changes in an Iranian population. Methods: In this cross-sectional study we focused on four main body change activity (diet, exercise, substance use, and surgery) and their risk factors such as demographic variables, Body Mass Index (BMI), Media, Body-Esteem, Perceived Socio-cultural Pressure, Body dissatisfaction and, Self-Esteem. Approximately, 1,200 individuals between 14-55 years old participated in this study. We used a multistage sampling method. In each region, the first household was selected at random. The probability of outcomes was estimated from logistic models. Results: About 54.3% of respondents were females. The mean (SD) of age was 31.06 (10.24) years. Variables such as gender, age, BMI, use of media and socio cultural factors as, body dissatisfaction, body-esteem and pressure by relatives were the main factors that influenced body change methods. In particular we have seen that male are 53% less likely to follow surgical treatments, but 125% were more likely to use substances. Conclusions: Investigation of body concern and its health related problem should be assessed in cultural context. For effectiveness of interventional programs and reducing harmful body image/shape changes activities, socio-cultural background should be noted. PMID:24049621

  15. Ensembles-based predictions of climate change impacts on bioclimatic zones in Northeast Asia

    Science.gov (United States)

    Choi, Y.; Jeon, S. W.; Lim, C. H.; Ryu, J.

    2017-12-01

    Biodiversity is rapidly declining globally and efforts are needed to mitigate this continually increasing loss of species. Clustering of areas with similar habitats can be used to prioritize protected areas and distribute resources for the conservation of species, selection of representative sample areas for research, and evaluation of impacts due to environmental changes. In this study, Northeast Asia (NEA) was classified into 14 bioclimatic zones using statistical techniques, which are correlation analysis and principal component analysis (PCA), and the iterative self-organizing data analysis technique algorithm (ISODATA). Based on these bioclimatic classification, we predicted shift of bioclimatic zones due to climate change. The input variables include the current climatic data (1960-1990) and the future climatic data of the HadGEM2-AO model (RCP 4.5(2050, 2070) and 8.5(2050, 2070)) provided by WorldClim. Using these data, multi-modeling methods including maximum likelihood classification, random forest, and species distribution modelling have been used to project the impact of climate change on the spatial distribution of bioclimatic zones within NEA. The results of various models were compared and analyzed by overlapping each result. As the result, significant changes in bioclimatic conditions can be expected throughout the NEA by 2050s and 2070s. The overall zones moved upward and some zones were predicted to disappear. This analysis provides the basis for understanding potential impacts of climate change on biodiversity and ecosystem. Also, this could be used more effectively to support decision making on climate change adaptation.

  16. Forest processes and global environmental change: predicting the effects of individual and multiple stressors

    Science.gov (United States)

    John Aber; Ronald P. Neilson; Steve McNulty; James M. Lenihan; Dominque Bachelet; Raymond J. Drapek

    2001-01-01

    The purpose of this article is to review the state of prediction of forest ecosystem response to envisioned changes in the physical and chemical climate. These results are offered as one part of the forest sector analysis of the National Assessment of the Potential Consequences of Climate Variability and Change. This article has three sections. The first offers a very...

  17. Impact of climate change and human activity on the eco-environment. An analysis of the Xisha Islands

    International Nuclear Information System (INIS)

    Xu, Liqiang

    2015-01-01

    This study describes the fundamentals of assessing the vulnerability of coral islands, as well as environmental management and resource exploitation. Using seabird subfossils, such as bones, guano, eggshells etc., which have been well preserved on the Xisha Islands in the South China Sea, the author identifies the influences of climate change and human activity on seabird populations and diets. Understanding the past is of great importance for predicting the future, and seabird subfossils provide valuable information, which can be used to study changes in seabird ecology, paleoceanography and palaeoclimate. Furthermore, this study proposes examining the biogeochemical cycling of some elements present in the geosphere, hydrosphere, biosphere and atmosphere.

  18. Impact of climate change and human activity on the eco-environment. An analysis of the Xisha Islands

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Liqiang [Heifei Univ. of Technology (China). School of Resources and Environmental Engineering

    2015-06-01

    This study describes the fundamentals of assessing the vulnerability of coral islands, as well as environmental management and resource exploitation. Using seabird subfossils, such as bones, guano, eggshells etc., which have been well preserved on the Xisha Islands in the South China Sea, the author identifies the influences of climate change and human activity on seabird populations and diets. Understanding the past is of great importance for predicting the future, and seabird subfossils provide valuable information, which can be used to study changes in seabird ecology, paleoceanography and palaeoclimate. Furthermore, this study proposes examining the biogeochemical cycling of some elements present in the geosphere, hydrosphere, biosphere and atmosphere.

  19. Diverse models for the prediction of CDK4 inhibitory activity of ...

    Indian Academy of Sciences (India)

    employed for development of models for the prediction of CDK4 inhibitory activity using a dataset comprising of 52 analogues of ... index; molecular connectivity index; connective eccentricity topochemical index. 1. ... 80% of human cancers.

  20. Climate change and predicting soil loss from rainfall

    Science.gov (United States)

    Kinnell, Peter

    2017-04-01

    Conceptually, rainfall has a certain capacity to cause soil loss from an eroding area while soil surfaces have a certain resistance to being eroded by rainfall. The terms "rainfall erosivity' and "soil erodibility" are frequently used to encapsulate the concept and in the Revised Universal Soil Loss Equation (RUSLE), the most widely used soil loss prediction equation in the world, average annual values of the R "erosivity" factor and the K "erodibility" factor provide a basis for accounting for variation in rainfall erosion associated with geographic variations of climate and soils. In many applications of RUSLE, R and K are considered to be independent but in reality they are not. In RUSLE2, provision has been made to take account of the fact that K values determined using soil physical factors have to be adjusted for variations in climate because runoff is not directly included as a factor in determining R. Also, the USLE event erosivity index EI30 is better related to accounting for event sediment concentration than event soil loss. While the USLE-M, a modification of the USLE which includes runoff as a factor in determining the event erosivity index provides better estimates of event soil loss when event runoff is known, runoff prediction provides a challenge to modelling event soil loss as climate changes

  1. Body-Related Shame and Guilt Predict Physical Activity in Breast Cancer Survivors Over Time.

    Science.gov (United States)

    Castonguay, Andrée L; Wrosch, Carsten; Pila, Eva; Sabiston, Catherine M

    2017-07-01

    To test body-related shame and guilt as predictors of breast cancer survivors' (BCS') moderate to vigorous intensity physical activity (MVPA) during six months and to examine motivational regulations as mediators of this association.
. Prospective study.
. Survivors were recruited through advertisements and oncologist referrals from medical clinics and hospitals in Montreal, Quebec, Canada.
. 149 female BCS.
. Self-reports of body-related shame and guilt, motivational regulations, and MVPA were measured among BCS at baseline. MVPA was assessed a second time six months later. Residual change scores were used.
. Body-related shame and guilt; external, introjected, and autonomous (identified and intrinsic) motivational regulations; MVPA.
. In the multiple mediation models, body-related shame was associated with low levels of MVPA, as well as external, introjected, and autonomous motivational regulations. Guilt was related to high levels of MVPA and introjected and autonomous motivational regulations. Indirect effects linked shame, guilt, and MVPA via autonomous motivation. Only body-related shame was a significant predictor of six-month changes in MVPA.
. Based on these results, the specific emotions of shame and guilt contextualized to the body differentially predict BCS' health motivations and behavior over time.
. Survivorship programs may benefit from integrating intervention strategies aimed at reducing body-related shame and helping women manage feelings of guilt to improve physical activity.

  2. Change in attachment predicts change in emotion regulation particularly among 5-HTTLPR short-allele homozygotes.

    Science.gov (United States)

    Viddal, Kristine Rensvik; Berg-Nielsen, Turid Suzanne; Belsky, Jay; Wichstrøm, Lars

    2017-07-01

    In view of the theory that the attachment relationship provides a foundation for the development of emotion regulation, here, we evaluated (a) whether change in attachment security from 4 to 6 years predicts change in emotion regulation from 6 to 8 years and (b) whether 5-HTTLPR moderates this relation in a Norwegian community sample (n = 678, 99.7% Caucasian). Attachment was measured with the Manchester Child Attachment Story Task, and teachers completed the Emotion Regulation Checklist. Attachment security was modestly stable, with children becoming more secure over time. Regression analyses revealed that increased attachment security from 4 to 6 forecasted increases in emotion regulation from 6 to 8 and decreased attachment security forecasted decreases in emotion regulation. This effect was strongest among the 5-HTTLPR short-allele homozygotes and, according to competitive model fitting, in a differential-susceptibility manner. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  3. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    Science.gov (United States)

    Lawrence N. Hudson; Joseph Wunderle M.; And Others

    2016-01-01

    The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to...

  4. Has growth mixture modeling improved our understanding of how early change predicts psychotherapy outcome?

    Science.gov (United States)

    Koffmann, Andrew

    2017-03-02

    Early change in psychotherapy predicts outcome. Seven studies have used growth mixture modeling [GMM; Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class-latent growth modeling. In L. M. Collins & A. G. Sawyers (Eds.), New methods for the analysis of change (pp. 291-322). Washington, DC: American Psychological Association] to identify patient classes based on early change but have yielded conflicting results. Here, we review the earlier studies and apply GMM to a new data set. In a university-based training clinic, 251 patients were administered the Outcome Questionnaire-45 [Lambert, M. J., Hansen, N. B., Umphress, V., Lunnen, K., Okiishi, J., Burlingame, G., … Reisinger, C. W. (1996). Administration and scoring manual for the Outcome Questionnaire (OQ 45.2). Wilmington, DE: American Professional Credentialing Services] at each psychotherapy session. We used GMM to identify class structure based on change in the first six sessions and examined trajectories as predictors of outcome. The sample was best described as a single class. There was no evidence of autoregressive trends in the data. We achieved better fit to the data by permitting latent variables some degree of kurtosis, rather than to assume multivariate normality. Treatment outcome was predicted by the amount of early improvement, regardless of initial level of distress. The presence of sudden early gains or losses did not further improve outcome prediction. Early improvement is an easily computed, powerful predictor of psychotherapy outcome. The use of GMM to investigate the relationship between change and outcome is technically complex and computationally intensive. To date, it has not been particularly informative.

  5. Activity Prediction of Schiff Base Compounds using Improved QSAR Models of Cinnamaldehyde Analogues and Derivatives

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2015-10-01

    Full Text Available In past work, QSAR (quantitative structure-activity relationship models of cinnamaldehyde analogues and derivatives (CADs have been used to predict the activities of new chemicals based on their mass concentrations, but these approaches are not without shortcomings. Therefore, molar concentrations were used instead of mass concentrations to determine antifungal activity. New QSAR models of CADs against Aspergillus niger and Penicillium citrinum were established, and the molecular design of new CADs was performed. The antifungal properties of the designed CADs were tested, and the experimental Log AR values were in agreement with the predicted Log AR values. The results indicate that the improved QSAR models are more reliable and can be effectively used for CADs molecular design and prediction of the activity of CADs. These findings provide new insight into the development and utilization of cinnamaldehyde compounds.

  6. Withdrawal-Related Changes in Delay Discounting Predict Short-Term Smoking Abstinence.

    Science.gov (United States)

    Miglin, Rickie; Kable, Joseph W; Bowers, Maureen E; Ashare, Rebecca L

    2017-06-01

    Impulsive decision making is associated with smoking behavior and reflects preferences for smaller, immediate rewards and intolerance of temporal delays. Nicotine withdrawal may alter impulsive decision making and time perception. However, little is known about whether withdrawal-related changes in decision making and time perception predict smoking relapse. Forty-five smokers (14 female) completed two laboratory sessions, one following 24-hour abstinence and one smoking-as-usual (order counterbalanced; biochemically verified abstinence). During each visit, participants completed measures of time perception, decision making (ie, discount rates), craving, and withdrawal. Following the second laboratory session, subjects underwent a well-validated model of short-term abstinence (quit week) with small monetary incentives for each day of biochemically confirmed abstinence. Smokers significantly overestimated time during abstinence, compared to smoking-as-usual (p = .021), but there were no abstinence effects on discount rates (p = .6). During the quit week, subjects were abstinent for 3.5 days (SD = 2.15) and smoked a total of 12.9 cigarettes (SD = 15.8). Importantly, higher discount rates (ie, preferences for immediate rewards) during abstinence (abstinence minus smoking difference score) predicted greater number of days abstinent (p = .01) and fewer cigarettes smoked during the quit week (p = .02). Withdrawal-related change in time reproduction did not predict relapse (p = .2). These data suggest that individuals who have a greater preference for immediate rewards during abstinence (vs. smoking-as-usual) may be more successful at maintaining short-term abstinence when provided with frequent (eg, daily) versus less frequent incentive schedules (eg, 1 month). Abstinence-induced changes in decision making may be important for identifying smokers who may benefit from interventions that incentivize abstinence such as contingency management (CM). The present results

  7. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

    Science.gov (United States)

    Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

    2018-05-17

    In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Predicting effects of environmental change on river inflows to ...

    Science.gov (United States)

    Estuarine river watersheds provide valued ecosystem services to their surrounding communities including drinking water, fish habitat, and regulation of estuarine water quality. However, the provisioning of these services can be affected by changes in the quantity and quality of river water, such as those caused by altered landscapes or shifting temperatures or precipitation. We used the ecohydrology model, VELMA, in the Trask River watershed to simulate the effects of environmental change scenarios on estuarine river inputs to Tillamook Bay (OR) estuary. The Trask River watershed is 453 km2 and contains extensive agriculture, silviculture, urban, and wetland areas. VELMA was parameterized using existing spatial datasets of elevation, soil type, land use, air temperature, precipitation, river flow, and water quality. Simulated land use change scenarios included alterations in the distribution of the nitrogen-fixing tree species Alnus rubra, and comparisons of varying timber harvest plans. Scenarios involving spatial and temporal shifts in air temperature and precipitation trends were also simulated. Our research demonstrates the utility of ecohydrology models such as VELMA to aid in watershed management decision-making. Model outputs of river water flow, temperature, and nutrient concentrations can be used to predict effects on drinking water quality, salmonid populations, and estuarine water quality. This modeling effort is part of a larger framework of

  9. Structural maturation and brain activity predict future working memory capacity during childhood development.

    Science.gov (United States)

    Ullman, Henrik; Almeida, Rita; Klingberg, Torkel

    2014-01-29

    Human working memory capacity develops during childhood and is a strong predictor of future academic performance, in particular, achievements in mathematics and reading. Predicting working memory development is important for the early identification of children at risk for poor cognitive and academic development. Here we show that structural and functional magnetic resonance imaging data explain variance in children's working memory capacity 2 years later, which was unique variance in addition to that predicted using cognitive tests. While current working memory capacity correlated with frontoparietal cortical activity, the future capacity could be inferred from structure and activity in basal ganglia and thalamus. This gives a novel insight into the neural mechanisms of childhood development and supports the idea that neuroimaging can have a unique role in predicting children's cognitive development.

  10. Cod Gadus morhua and climate change: processes, productivity and prediction

    DEFF Research Database (Denmark)

    Brander, Keith

    2010-01-01

    the causes. Investigation of cod Gadus morhua populations across the whole North Atlantic Ocean has shown large-scale patterns of change in productivity due to lower individual growth and condition, caused by large-scale climate forcing. If a population is being heavily exploited then a drop in productivity......Environmental factors act on individual fishes directly and indirectly. The direct effects on rates and behaviour can be studied experimentally and in the field, particularly with the advent of ever smarter tags for tracking fishes and their environment. Indirect effects due to changes in food......, predators, parasites and diseases are much more difficult to estimate and predict. Climate can affect all life-history stages through direct and indirect processes and although the consequences in terms of growth, survival and reproductive output can be monitored, it is often difficult to determine...

  11. Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities

    Science.gov (United States)

    Schemm, J. E.; Long, L.; Baxter, S.

    2013-12-01

    Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities Jae-Kyung E. Schemm, Lindsey Long and Stephen Baxter Climate Prediction Center, NCEP/NWS/NOAA Predictability of intraseasonal tropical storm (TS) activities is assessed using the 1999-2010 CFSv2 hindcast suite. Weekly TS activities in the CFSv2 45-day forecasts were determined using the TS detection and tracking method devised by Carmago and Zebiak (2002). The forecast periods are divided into weekly intervals for Week 1 through Week 6, and also the 30-day mean. The TS activities in those intervals are compared to the observed activities based on the NHC HURDAT and JTWC Best Track datasets. The CFSv2 45-day hindcast suite is made of forecast runs initialized at 00, 06, 12 and 18Z every day during the 1999 - 2010 period. For predictability evaluation, forecast TS activities are analyzed based on 20-member ensemble forecasts comprised of 45-day runs made during the most recent 5 days prior to the verification period. The forecast TS activities are evaluated in terms of the number of storms, genesis locations and storm tracks during the weekly periods. The CFSv2 forecasts are shown to have a fair level of skill in predicting the number of storms over the Atlantic Basin with the temporal correlation scores ranging from 0.73 for Week 1 forecasts to 0.63 for Week 6, and the average RMS errors ranging from 0.86 to 1.07 during the 1999-2010 hurricane season. Also, the forecast track density distribution and false alarm statistics are compiled using the hindcast analyses. In real-time applications of the intraseasonal TS activity forecasts, the climatological TS forecast statistics will be used to make the model bias corrections in terms of the storm counts, track distribution and removal of false alarms. An operational implementation of the weekly TS activity prediction is planned for early 2014 to provide an objective input for the CPC's Global Tropical Hazards

  12. Predictive accuracy of changes in transvaginal sonographic cervical length over time for preterm birth: a systematic review and metaanalysis.

    Science.gov (United States)

    Conde-Agudelo, Agustin; Romero, Roberto

    2015-12-01

    To determine the accuracy of changes in transvaginal sonographic cervical length over time in predicting preterm birth in women with singleton and twin gestations. PubMed, Embase, Cinahl, Lilacs, and Medion (all from inception to June 30, 2015), bibliographies, Google scholar, and conference proceedings. Cohort or cross-sectional studies reporting on the predictive accuracy for preterm birth of changes in cervical length over time. Two reviewers independently selected studies, assessed the risk of bias, and extracted the data. Summary receiver-operating characteristic curves, pooled sensitivities and specificities, and summary likelihood ratios were generated. Fourteen studies met the inclusion criteria, of which 7 provided data on singleton gestations (3374 women) and 8 on twin gestations (1024 women). Among women with singleton gestations, the shortening of cervical length over time had a low predictive accuracy for preterm birth at predictive accuracy for preterm birth at predictive accuracies for preterm birth of cervical length shortening over time and the single initial and/or final cervical length measurement in 8 of 11 studies that provided data for making these comparisons. In the largest and highest-quality study, a single measurement of cervical length obtained at 24 or 28 weeks of gestation was significantly more predictive of preterm birth than any decrease in cervical length between these gestational ages. Change in transvaginal sonographic cervical length over time is not a clinically useful test to predict preterm birth in women with singleton or twin gestations. A single cervical length measurement obtained between 18 and 24 weeks of gestation appears to be a better test to predict preterm birth than changes in cervical length over time. Published by Elsevier Inc.

  13. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

    Science.gov (United States)

    In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

  14. Is Personality Fixed? Personality Changes as Much as "Variable" Economic Factors and More Strongly Predicts Changes to Life Satisfaction

    Science.gov (United States)

    Boyce, Christopher J.; Wood, Alex M.; Powdthavee, Nattavudh

    2013-01-01

    Personality is the strongest and most consistent cross-sectional predictor of high subjective well-being. Less predictive economic factors, such as higher income or improved job status, are often the focus of applied subjective well-being research due to a perception that they can change whereas personality cannot. As such there has been limited…

  15. Approaches to predicting potential impacts of climate change on forest disease: An example with Armillaria root disease

    Science.gov (United States)

    Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist

    2011-01-01

    Climate change will likely have dramatic impacts on forest health because many forest trees could become maladapted to climate. Furthermore, climate change will have additional impacts on forest health through changes in the distribution and severity of forest disease. Methods are needed to predict the influence of climate change on forest disease so that appropriate...

  16. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    Science.gov (United States)

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

  17. Prediction of flare activity of stellar aggregates. I. Theoretical part

    International Nuclear Information System (INIS)

    Mnatsakanyan, M.A.; Mirzoyan, A.L.

    1989-01-01

    The problem is posed of predicting the number n k (t) of flare stars that have exhibited precisely k flares by the time t on the basis of data on these quantities known during the total time T of observations of the aggregate. The problem posed by Ambartsumyan of determining the distribution function f(ν) of the true frequency of stellar flares from known chronology of these data is equivalent to the limiting form of their formulation - prediction in the future over an infinitely long time. An exact analytic solution of the problem obtained without any assumption about the function f(ν) is given. It permits prediction of the steady flare activity of the aggregate into both the future and the (known) past. It follows from this solution that prediction into the future is in principle impossible to times that exceed the doubled time 2T of the available observations (this means that the problem of determining of the function f(ν) cannot be solved). Moreover, because of the unavoidable fluctuations in the observational data n k (T), such prediction is limited to even shorter times, and these are shorter the larger the value of k. Prediction into the past and into the future on the basis of the data n k (T) at the present time and its possible errors due to small fluctuations in these data are illustrated for the examples of the Pleiades and the Orion aggregate

  18. A Trap Motion in Validating Muscle Activity Prediction from Musculoskeletal Model using EMG

    NARCIS (Netherlands)

    Wibawa, A. D.; Verdonschot, N.; Halbertsma, J.P.K.; Burgerhof, J.G.M.; Diercks, R.L.; Verkerke, G. J.

    2016-01-01

    Musculoskeletal modeling nowadays is becoming the most common tool for studying and analyzing human motion. Besides its potential in predicting muscle activity and muscle force during active motion, musculoskeletal modeling can also calculate many important kinetic data that are difficult to measure

  19. Hydrological response to land cover changes and human activities in arid regions using a geographic information system and remote sensing.

    Directory of Open Access Journals (Sweden)

    Shereif H Mahmoud

    Full Text Available The hydrological response to land cover changes induced by human activities in arid regions has attracted increased research interest in recent decades. The study reported herein assessed the spatial and quantitative changes in surface runoff resulting from land cover change in the Al-Baha region of Saudi Arabia between 1990 and 2000 using an ArcGIS-surface runoff model and predicted land cover and surface runoff depth in 2030 using Markov chain analysis. Land cover maps for 1990 and 2000 were derived from satellite images using ArcGIS 10.1. The findings reveal a 26% decrease in forest and shrubland area, 28% increase in irrigated cropland, 1.5% increase in sparsely vegetated land and 0.5% increase in bare soil between 1990 and 2000. Overall, land cover changes resulted in a significant decrease in runoff depth values in most of the region. The decrease in surface runoff depth ranged from 25-106 mm/year in a 7020-km2 area, whereas the increase in such depth reached only 10 mm/year in a 243-km2 area. A maximum increase of 73 mm/year was seen in a limited area. The surface runoff depth decreased to the greatest extent in the central region of the study area due to the huge transition in land cover classes associated with the construction of 25 rainwater harvesting dams. The land cover prediction revealed a greater than twofold increase in irrigated cropland during the 2000-2030 period, whereas forest and shrubland are anticipated to occupy just 225 km2 of land area by 2030, a significant decrease from the 747 km2 they occupied in 2000. Overall, changes in land cover are predicted to result in an annual increase in irrigated cropland and dramatic decline in forest area in the study area over the next few decades. The increase in surface runoff depth is likely to have significant implications for irrigation activities.

  20. Predicting protein folding rate change upon point mutation using residue-level coevolutionary information.

    Science.gov (United States)

    Mallik, Saurav; Das, Smita; Kundu, Sudip

    2016-01-01

    Change in folding kinetics of globular proteins upon point mutation is crucial to a wide spectrum of biological research, such as protein misfolding, toxicity, and aggregations. Here we seek to address whether residue-level coevolutionary information of globular proteins can be informative to folding rate changes upon point mutations. Generating residue-level coevolutionary networks of globular proteins, we analyze three parameters: relative coevolution order (rCEO), network density (ND), and characteristic path length (CPL). A point mutation is considered to be equivalent to a node deletion of this network and respective percentage changes in rCEO, ND, CPL are found linearly correlated (0.84, 0.73, and -0.61, respectively) with experimental folding rate changes. The three parameters predict the folding rate change upon a point mutation with 0.031, 0.045, and 0.059 standard errors, respectively. © 2015 Wiley Periodicals, Inc.

  1. Climate change impact on economical and industrial activities

    Science.gov (United States)

    Parey, Sylvie; Bernardara, Pietro; Donat, Markus G.

    2010-05-01

    Climate change is underway and even if mitigation measures are successfully implemented, societies will have to adapt to new climatic conditions in the near future and further. This session had been proposed to gather different studies dedicated to the climate change impact on some human activities, and discuss the possible ways of adaptation. Climate change is often presented in terms of global mean temperature evolutions, but what is important for adaptation concerns the local evolutions, and rather of the variability and extremes than of the mean of the involved meteorological parameters. In the session, studies and applications will be presented, covering different economical and industrial activities, such as energy production, (re-) insurance and risk assessment, water management or tourism.

  2. Predicting Effects of Water Regime Changes on Waterbirds: Insights from Staging Swans.

    Science.gov (United States)

    Nolet, Bart A; Gyimesi, Abel; van Krimpen, Roderick R D; de Boer, Willem F; Stillman, Richard A

    2016-01-01

    Predicting the environmental impact of a proposed development is notoriously difficult, especially when future conditions fall outside the current range of conditions. Individual-based approaches have been developed and applied to predict the impact of environmental changes on wintering and staging coastal bird populations. How many birds make use of staging sites is mostly determined by food availability and accessibility, which in the case of many waterbirds in turn is affected by water level. Many water systems are regulated and water levels are maintained at target levels, set by management authorities. We used an individual-based modelling framework (MORPH) to analyse how different target water levels affect the number of migratory Bewick's swans Cygnus columbianus bewickii staging at a shallow freshwater lake (Lauwersmeer, the Netherlands) in autumn. As an emerging property of the model, we found strong non-linear responses of swan usage to changes in water level, with a sudden drop in peak numbers as well as bird-days with a 0.20 m rise above the current target water level. Such strong non-linear responses are probably common and should be taken into account in environmental impact assessments.

  3. Predicting Effects of Water Regime Changes on Waterbirds: Insights from Staging Swans.

    Directory of Open Access Journals (Sweden)

    Bart A Nolet

    Full Text Available Predicting the environmental impact of a proposed development is notoriously difficult, especially when future conditions fall outside the current range of conditions. Individual-based approaches have been developed and applied to predict the impact of environmental changes on wintering and staging coastal bird populations. How many birds make use of staging sites is mostly determined by food availability and accessibility, which in the case of many waterbirds in turn is affected by water level. Many water systems are regulated and water levels are maintained at target levels, set by management authorities. We used an individual-based modelling framework (MORPH to analyse how different target water levels affect the number of migratory Bewick's swans Cygnus columbianus bewickii staging at a shallow freshwater lake (Lauwersmeer, the Netherlands in autumn. As an emerging property of the model, we found strong non-linear responses of swan usage to changes in water level, with a sudden drop in peak numbers as well as bird-days with a 0.20 m rise above the current target water level. Such strong non-linear responses are probably common and should be taken into account in environmental impact assessments.

  4. Predictive Duty Cycle Control of Three-Phase Active-Front-End Rectifiers

    DEFF Research Database (Denmark)

    Song, Zhanfeng; Tian, Yanjun; Chen, Wei

    2016-01-01

    This paper proposed an on-line optimizing duty cycle control approach for three-phase active-front-end rectifiers, aiming to obtain the optimal control actions under different operating conditions. Similar to finite control set model predictive control strategy, a cost function previously...

  5. Active Mirror Predictive and Requirements Verification Software (AMP-ReVS)

    Science.gov (United States)

    Basinger, Scott A.

    2012-01-01

    This software is designed to predict large active mirror performance at various stages in the fabrication lifecycle of the mirror. It was developed for 1-meter class powered mirrors for astronomical purposes, but is extensible to other geometries. The package accepts finite element model (FEM) inputs and laboratory measured data for large optical-quality mirrors with active figure control. It computes phenomenological contributions to the surface figure error using several built-in optimization techniques. These phenomena include stresses induced in the mirror by the manufacturing process and the support structure, the test procedure, high spatial frequency errors introduced by the polishing process, and other process-dependent deleterious effects due to light-weighting of the mirror. Then, depending on the maturity of the mirror, it either predicts the best surface figure error that the mirror will attain, or it verifies that the requirements for the error sources have been met once the best surface figure error has been measured. The unique feature of this software is that it ties together physical phenomenology with wavefront sensing and control techniques and various optimization methods including convex optimization, Kalman filtering, and quadratic programming to both generate predictive models and to do requirements verification. This software combines three distinct disciplines: wavefront control, predictive models based on FEM, and requirements verification using measured data in a robust, reusable code that is applicable to any large optics for ground and space telescopes. The software also includes state-of-the-art wavefront control algorithms that allow closed-loop performance to be computed. It allows for quantitative trade studies to be performed for optical systems engineering, including computing the best surface figure error under various testing and operating conditions. After the mirror manufacturing process and testing have been completed, the

  6. Burrowing Behavior of a Deposit Feeding Bivalve Predicts Change in Intertidal Ecosystem State

    NARCIS (Netherlands)

    Compton, T.J.; Bodnar, W.; Koolhaas, A.; Dekinga, A.; Holthuijsen, S.; Ten Horn, J.; McSweeney, N.; van Gils, J.A.; Piersma, T.

    2016-01-01

    Behavior has a predictive power that is often underutilized as a tool for signaling ecological change. The burrowing behavior of the deposit feeding bivalve Macoma balthica reflects a typical food-safety trade-off. The choice to live close to the sediment surface comes at a risk of predation and is

  7. Burrowing behavior of a deposit feeding bivalve predicts change in intertidal ecosystem state

    NARCIS (Netherlands)

    Compton, Tanya J.; Bodnar, Wanda; Koolhaas, Anita; Dekinga, Anne; Holthuijsen, Sander; ten Horn, Job; McSweeney, Niamh; van Gils, Jan; Piersma, Theunis

    2016-01-01

    Behavior has a predictive power that is often underutilized as a tool for signaling ecological change. The burrowing behavior of the deposit feeding bivalve Macoma balthica reflects a typical food-safety trade-off. The choice to live close to the sediment surface comes at a risk of predation and is

  8. Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

    Directory of Open Access Journals (Sweden)

    Ivanka Jerić

    2011-11-01

    Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.

  9. How can we predict microstructural changes caused by the multiscale irradiation process occurred in materials having complicated and hierarchical structures?

    International Nuclear Information System (INIS)

    Morishita, Kazunori; Watanabe, Yoshiyuki; Yoshimatsu, Jun-ichi

    2008-01-01

    Challenging efforts are discussed to establish an advanced methodology for prediction of material's property and performance changes by irradiation, which will be necessary by all means for the advanced reactor maintenance technology in the future. The changes of material's properties and performance caused by irradiation, such as irradiation-induced hardening, ductility loss, and material's degradation leading to reduction in reactor lifetime, are primarily determined by microstructural changes in materials during irradiation, where athermal lattice defects are continuously produced by collisions between an irradiating particle and a target material atom, and subsequently the defects are aggregated via diffusion in the form of dislocation loops, voids, and solute precipitation. These radiation damage processes are in essence multiscale phenomena, which involve varying time- and length-scales, from ballistic binary collisions to collective atomic motion in the thermal spike stage followed by the thermal activation process. In this report, the multiscale modeling approach is proposed to understand the processes in materials having complicated and hierarchical structures. (author)

  10. Climate change and related activities

    International Nuclear Information System (INIS)

    1992-01-01

    The production and consumption of energy contributes to the concentration of greenhouse gases in the atmosphere and is the focus of other environmental concerns as well. Yet the use of energy contributes to worldwide economic growth and development. If we are to achieve environmentally sound economic growth, we must develop and deploy energy technologies that contribute to global stewardship. The Department of Energy carries out an aggressive scientific research program to address some of the key uncertainties associated with the climate change issue. Of course, research simply to study the science of global climate change is not enough. At the heart of any regime of cost-effective actions to address the possibility of global climate change will be a panoply of new technologies-technologies both to provide the services we demand and to use energy more efficiently than in the past. These, too, are important areas of responsibility for the Department. This report is a brief description of the Department's activities in scientific research, technology development, policy studies, and international cooperation that are directly related to or have some bearing on the issue of global climate change

  11. New England observed and predicted August stream/river temperature maximum daily rate of change points

    Data.gov (United States)

    U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted August stream/river temperature maximum negative rate of change in New England based on a...

  12. Transitioning to adolescence: how changes in child personality and overreactive parenting predict adolescent adjustment problems.

    Science.gov (United States)

    van den Akker, Alithe L; Deković, Maja; Prinzie, Peter

    2010-01-01

    The present study examined how changes in child Big Five personality characteristics and overreactive parenting during the transition from childhood to adolescence predict adolescent adjustment problems. The sample included 290 children, aged 8-9 years. At three moments, with 2-year intervals, mothers, fathers, and a teacher reported on the child's personality, and mothers and fathers reported on their parenting behavior. At the third measurement moment, mothers, fathers, and children reported on the child's adjustment problems. Rank-order stability of the personality dimensions and overreactive parenting were high. Univariate latent growth models revealed mean-level decreases for extraversion, conscientiousness, and imagination. Mean levels of benevolence, emotional stability, and overreactive parenting were stable. Multivariate latent growth models revealed that decreases in extraversion and emotional stability predicted internalizing problems, whereas decreases in benevolence, conscientiousness, and emotional stability predicted externalizing problems. Increases in overreactive parenting predicted externalizing, but not internalizing problems. The associations were similar for boys and girls. The results indicate that changes in child personality and overreactive parenting during the transition to adolescence are associated with adolescent adjustment problems. Overall, child personality was more important than overreactive parenting, and children were more likely to "act out" than to "withdraw" in reaction to overreactive parenting.

  13. Do Urinary Cystine Parameters Predict Clinical Stone Activity?

    Science.gov (United States)

    Friedlander, Justin I; Antonelli, Jodi A; Canvasser, Noah E; Morgan, Monica S C; Mollengarden, Daniel; Best, Sara; Pearle, Margaret S

    2018-02-01

    An accurate urinary predictor of stone recurrence would be clinically advantageous for patients with cystinuria. A proprietary assay (Litholink, Chicago, Illinois) measures cystine capacity as a potentially more reliable estimate of stone forming propensity. The recommended capacity level to prevent stone formation, which is greater than 150 mg/l, has not been directly correlated with clinical stone activity. We investigated the relationship between urinary cystine parameters and clinical stone activity. We prospectively followed 48 patients with cystinuria using 24-hour urine collections and serial imaging, and recorded stone activity. We compared cystine urinary parameters at times of stone activity with those obtained during periods of stone quiescence. We then performed correlation and ROC analysis to evaluate the performance of cystine parameters to predict stone activity. During a median followup of 70.6 months (range 2.2 to 274.6) 85 stone events occurred which could be linked to a recent urine collection. Cystine capacity was significantly greater for quiescent urine than for stone event urine (mean ± SD 48 ± 107 vs -38 ± 163 mg/l, p stone activity (r = -0.29, p r = -0.88, p r = -0.87, p stone quiescence. Decreasing the cutoff to 90 mg/l or greater improved sensitivity to 25.2% while maintaining specificity at 90.9%. Our results suggest that the target for capacity should be lower than previously advised. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  14. Switch region for pathogenic structural change in conformational disease and its prediction.

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2010-01-01

    Full Text Available Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1 influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1 it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2 it provides an ideal target for clinical treatment.

  15. Selenium deficiency risk predicted to increase under future climate change.

    Science.gov (United States)

    Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

    2017-03-14

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

  16. Predicting the initial freezing point and water activity of meat products from composition data

    NARCIS (Netherlands)

    Sman, van der R.G.M.; Boer, E.P.J.

    2005-01-01

    In this paper we predict the water activity and initial freezing point of food products (meat and fish) based on their composition. The prediction is based on thermodynamics (the Clausius-Clapeyron equation, the Ross equation and an approximation of the Pitzer equation). Furthermore, we have taken

  17. Can changes in psychosocial factors and residency explain the decrease in physical activity during the transition from high school to college or university?

    Science.gov (United States)

    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

  18. Bleomycin induces molecular changes directly relevant to idiopathic pulmonary fibrosis: a model for "active" disease.

    Science.gov (United States)

    Peng, Ruoqi; Sridhar, Sriram; Tyagi, Gaurav; Phillips, Jonathan E; Garrido, Rosario; Harris, Paul; Burns, Lisa; Renteria, Lorena; Woods, John; Chen, Leena; Allard, John; Ravindran, Palanikumar; Bitter, Hans; Liang, Zhenmin; Hogaboam, Cory M; Kitson, Chris; Budd, David C; Fine, Jay S; Bauer, Carla M T; Stevenson, Christopher S

    2013-01-01

    The preclinical model of bleomycin-induced lung fibrosis, used to investigate mechanisms related to idiopathic pulmonary fibrosis (IPF), has incorrectly predicted efficacy for several candidate compounds suggesting that it may be of limited value. As an attempt to improve the predictive nature of this model, integrative bioinformatic approaches were used to compare molecular alterations in the lungs of bleomycin-treated mice and patients with IPF. Using gene set enrichment analysis we show for the first time that genes differentially expressed during the fibrotic phase of the single challenge bleomycin model were significantly enriched in the expression profiles of IPF patients. The genes that contributed most to the enrichment were largely involved in mitosis, growth factor, and matrix signaling. Interestingly, these same mitotic processes were increased in the expression profiles of fibroblasts isolated from rapidly progressing, but not slowly progressing, IPF patients relative to control subjects. The data also indicated that TGFβ was not the sole mediator responsible for the changes observed in this model since the ALK-5 inhibitor SB525334 effectively attenuated some but not all of the fibrosis associated with this model. Although some would suggest that repetitive bleomycin injuries may more effectively model IPF-like changes, our data do not support this conclusion. Together, these data highlight that a single bleomycin instillation effectively replicates several of the specific pathogenic molecular changes associated with IPF, and may be best used as a model for patients with active disease.

  19. Bleomycin induces molecular changes directly relevant to idiopathic pulmonary fibrosis: a model for "active" disease.

    Directory of Open Access Journals (Sweden)

    Ruoqi Peng

    Full Text Available The preclinical model of bleomycin-induced lung fibrosis, used to investigate mechanisms related to idiopathic pulmonary fibrosis (IPF, has incorrectly predicted efficacy for several candidate compounds suggesting that it may be of limited value. As an attempt to improve the predictive nature of this model, integrative bioinformatic approaches were used to compare molecular alterations in the lungs of bleomycin-treated mice and patients with IPF. Using gene set enrichment analysis we show for the first time that genes differentially expressed during the fibrotic phase of the single challenge bleomycin model were significantly enriched in the expression profiles of IPF patients. The genes that contributed most to the enrichment were largely involved in mitosis, growth factor, and matrix signaling. Interestingly, these same mitotic processes were increased in the expression profiles of fibroblasts isolated from rapidly progressing, but not slowly progressing, IPF patients relative to control subjects. The data also indicated that TGFβ was not the sole mediator responsible for the changes observed in this model since the ALK-5 inhibitor SB525334 effectively attenuated some but not all of the fibrosis associated with this model. Although some would suggest that repetitive bleomycin injuries may more effectively model IPF-like changes, our data do not support this conclusion. Together, these data highlight that a single bleomycin instillation effectively replicates several of the specific pathogenic molecular changes associated with IPF, and may be best used as a model for patients with active disease.

  20. Structure-based prediction of free energy changes of binding of PTP1B inhibitors

    Science.gov (United States)

    Wang, Jing; Ling Chan, Shek; Ramnarayan, Kal

    2003-08-01

    The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.

  1. Using Prediction Markets to Generate Probability Density Functions for Climate Change Risk Assessment

    Science.gov (United States)

    Boslough, M.

    2011-12-01

    Climate-related uncertainty is traditionally presented as an error bar, but it is becoming increasingly common to express it in terms of a probability density function (PDF). PDFs are a necessary component of probabilistic risk assessments, for which simple "best estimate" values are insufficient. Many groups have generated PDFs for climate sensitivity using a variety of methods. These PDFs are broadly consistent, but vary significantly in their details. One axiom of the verification and validation community is, "codes don't make predictions, people make predictions." This is a statement of the fact that subject domain experts generate results using assumptions within a range of epistemic uncertainty and interpret them according to their expert opinion. Different experts with different methods will arrive at different PDFs. For effective decision support, a single consensus PDF would be useful. We suggest that market methods can be used to aggregate an ensemble of opinions into a single distribution that expresses the consensus. Prediction markets have been shown to be highly successful at forecasting the outcome of events ranging from elections to box office returns. In prediction markets, traders can take a position on whether some future event will or will not occur. These positions are expressed as contracts that are traded in a double-action market that aggregates price, which can be interpreted as a consensus probability that the event will take place. Since climate sensitivity cannot directly be measured, it cannot be predicted. However, the changes in global mean surface temperature are a direct consequence of climate sensitivity, changes in forcing, and internal variability. Viable prediction markets require an undisputed event outcome on a specific date. Climate-related markets exist on Intrade.com, an online trading exchange. One such contract is titled "Global Temperature Anomaly for Dec 2011 to be greater than 0.65 Degrees C." Settlement is based

  2. Role of spontaneous physical activity in prediction of susceptibility to activity based anorexia in male and female rats.

    Science.gov (United States)

    Perez-Leighton, Claudio E; Grace, Martha; Billington, Charles J; Kotz, Catherine M

    2014-08-01

    Anorexia nervosa (AN) is a chronic eating disorder affecting females and males, defined by body weight loss, higher physical activity levels and restricted food intake. Currently, the commonalities and differences between genders in etiology of AN are not well understood. Animal models of AN, such as activity-based anorexia (ABA), can be helpful in identifying factors determining individual susceptibility to AN. In ABA, rodents are given an access to a running wheel while food restricted, resulting in paradoxical increased physical activity levels and weight loss. Recent studies suggest that different behavioral traits, including voluntary exercise, can predict individual weight loss in ABA. A higher inherent drive for movement may promote development and severity of AN, but this hypothesis remains untested. In rodents and humans, drive for movement is defined as spontaneous physical activity (SPA), which is time spent in low-intensity, non-volitional movements. In this paper, we show that a profile of body weight history and behavioral traits, including SPA, can predict individual weight loss caused by ABA in male and female rats with high accuracy. Analysis of the influence of SPA on ABA susceptibility in males and females rats suggests that either high or low levels of SPA increase the probability of high weight loss in ABA, but with larger effects in males compared to females. These results suggest that the same behavioral profile can identify individuals at-risk of AN for both male and female populations and that SPA has predictive value for susceptibility to AN. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Comparison of semantic and episodic memory BOLD fMRI activation in predicting cognitive decline in older adults.

    Science.gov (United States)

    Hantke, Nathan; Nielson, Kristy A; Woodard, John L; Breting, Leslie M Guidotti; Butts, Alissa; Seidenberg, Michael; Carson Smith, J; Durgerian, Sally; Lancaster, Melissa; Matthews, Monica; Sugarman, Michael A; Rao, Stephen M

    2013-01-01

    Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults. The present fMRI study examined the relative sensitivity of semantic memory (SM) versus episodic memory (EM) activation tasks for predicting cognitive decline. Seventy-eight cognitively intact elders underwent neuropsychological testing at entry and after an 18-month interval, with participants classified as cognitively "Stable" or "Declining" based on ≥ 1.0 SD decline in performance. Baseline fMRI scanning involved SM (famous name discrimination) and EM (name recognition) tasks. SM and EM fMRI activation, along with Apolipoprotein E (APOE) ε4 status, served as predictors of cognitive outcome using a logistic regression analysis. Twenty-seven (34.6%) participants were classified as Declining and 51 (65.4%) as Stable. APOE ε4 status alone significantly predicted cognitive decline (R(2) = .106; C index = .642). Addition of SM activation significantly improved prediction accuracy (R(2) = .285; C index = .787), whereas the addition of EM did not (R(2) = .212; C index = .711). In combination with APOE status, SM task activation predicts future cognitive decline better than EM activation. These results have implications for use of fMRI in prevention clinical trials involving the identification of persons at-risk for age-associated memory loss and Alzheimer's disease.

  4. NASA's Earth Observing System: The Transition from Climate Monitoring to Climate Change Prediction

    Science.gov (United States)

    King, Michael D.; Herring, David D.

    1998-01-01

    Earth's 4.5 billion year history is a study in change. Natural geological forces have been rearranging the surface features and climatic conditions of our planet since its beginning. There is scientific evidence that some of these natural changes have not only led to mass extinctions of species (e.g., dinosaurs), but have also severely impacted human civilizations. For instance, there is evidence that a relatively sudden climate change caused a 300-year drought that contributed to the downfall of Akkadia, one of the most powerful empires in the Middle-East region around 2200 BC. More recently, the "little ice age" from 1200-1400 AD forced the Vikings to abandon Greenland when temperatures there dropped by about 1.5 C, rendering it too difficult to grow enough crops to sustain the population. Today, there is compelling scientific evidence that human activities have attained the magnitude of a geological force and are speeding up the rate of global change. For example, carbon dioxide levels have risen 30 percent since the industrial revolution and about 40 percent of the world's land surface has been transformed by humans. We don't understand the cause-and-effect relationships among Earth's land, ocean, and atmosphere well enough to predict what, if any, impacts these rapid changes will have on future climate conditions. We need to make many measurements all over the world, over a long period of time, in order to assemble the information needed to construct accurate computer models that will enable us to forecast climate change. In 1988, the Earth System Sciences Committee, sponsored by NASA, issued a report calling for an integrated, long-term strategy for measuring the vital signs of Earth's climate system. The report urged that the measurements must all be intimately coupled with focused process studies, they must facilitate development of Earth system models, and they must be stored in an information system that ensures open access to consistent, long-term data

  5. Leisure Activities and Change in Cognitive Stability: A Multivariate Approach

    Directory of Open Access Journals (Sweden)

    Nathalie Mella

    2017-03-01

    Full Text Available Aging is traditionally associated with cognitive decline, attested by slower reaction times and poorer performance in various cognitive tasks, but also by an increase in intraindividual variability (IIV in cognitive performance. Results concerning how lifestyle activities protect from cognitive decline are mixed in the literature and all focused on how it affects mean performance. However, IIV has been proven to be an index more sensitive to age differences, and very little is known about the relationships between lifestyle activities and change in IIV in aging. This longitudinal study explores the association between frequency of physical, social, intellectual, artistic, or cultural activities and age-related change in various cognitive abilities, considering both mean performance and IIV. Ninety-six participants, aged 64–93 years, underwent a battery of cognitive tasks at four measurements over a seven-year period, and filled out a lifestyle activity questionnaire. Linear multilevel models were used to analyze the associations between change in cognitive performance and five types of activities. Results showed that the practice of leisure activities was more strongly associated with IIV than with mean performance, both when considering overall level and change in performance. Relationships with IIV were dependent of the cognitive tasks considered and overall results showed protective effects of cultural, physical and intellectual activities on IIV. These results underline the need for considering IIV in the study of age-related cognitive change.

  6. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    OpenAIRE

    Hudson, LN; Newbold, T; Contu, S; Hill, SLL; Lysenko, I; De Palma, A; Phillips, HRP; Alhusseini, TI; Bedford, FE; Bennett, DJ; Booth, H; Burton, VJ; Chng, CWT; Choimes, A; Correia, DLP

    2017-01-01

    The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make free...

  7. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    OpenAIRE

    Hudson, L. N.; Newbold, T.; Contu, S.; Hill, S. L.; Lysenko, I.; De Palma, A.; Phillips, H. R.; Alhusseini, T. I.; Bedford, F. E.; Bennett, D. J.; Booth, H.; Burton, V. J.; Chng, C. W.; Choimes, A.; Correia, D. L.

    2017-01-01

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make free...

  8. Final technical report. Can microbial functional traits predict the response and resilience of decomposition to global change?

    Energy Technology Data Exchange (ETDEWEB)

    Allison, Steven D. [Univ. of California, Irvine, CA (United States)

    2015-09-24

    The role of specific micro-organisms in the carbon cycle, and their responses to environmental change, are unknown in most ecosystems. This knowledge gap limits scientists’ ability to predict how important ecosystem processes, like soil carbon storage and loss, will change with climate and other environmental factors. The investigators addressed this knowledge gap by transplanting microbial communities from different environments into new environments and measuring the response of community composition and carbon cycling over time. Using state-of-the-art sequencing techniques, computational tools, and nanotechnology, the investigators showed that microbial communities on decomposing plant material shift dramatically with natural and experimentally-imposed drought. Microbial communities also shifted in response to added nitrogen, but the effects were smaller. These changes had implications for carbon cycling, with lower rates of carbon loss under drought conditions, and changes in the efficiency of decomposition with nitrogen addition. Even when transplanted into the same conditions, microbial communities from different environments remained distinct in composition and functioning for up to one year. Changes in functioning were related to differences in enzyme gene content across different microbial groups. Computational approaches developed for this project allowed the conclusions to be tested more broadly in other ecosystems, and new computer models will facilitate the prediction of microbial traits and functioning across environments. The data and models resulting from this project benefit the public by improving the ability to predict how microbial communities and carbon cycling functions respond to climate change, nutrient enrichment, and other large-scale environmental changes.

  9. Planning for subacute care: predicting demand using acute activity data.

    Science.gov (United States)

    Green, Janette P; McNamee, Jennifer P; Kobel, Conrad; Seraji, Md Habibur R; Lawrence, Suanne J

    2016-01-01

    Objective The aim of the present study was to develop a robust model that uses the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals. Methods The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre. Results The predictive model comprises tables of probabilities that patients will require rehabilitation or GEM care after an acute episode, with columns defined by age group and rows defined by grouped Australian Refined (AR)-DRGs. Conclusions The existing concept of rehabilitation-sensitive DRGs was revised and extended. When applied to national data, the model provided a conservative estimate of 83% of the activity actually provided. An example demonstrates the application of the model for service planning. What is known about the topic? Health service planning is core business for jurisdictions and local areas. With populations ageing and an acknowledgement of the underservicing of subacute care, it is timely to find improved methods of estimating demand for this type of care. Traditionally, age-sex standardised utilisation rates for individual DRGs have been applied to Australian Bureau of Statistics (ABS) population projections to predict the future need for subacute services. Improved predictions became possible when some AR-DRGs were designated 'rehabilitation-sensitive'. This improved methodology has been used in several Australian jurisdictions. What does this paper add? This paper presents a new tool, or model, to predict demand for rehabilitation and GEM services based on in-patient acute activity. In this model, the

  10. Development of a predictive model to determine micropollutant removal using granular activated carbon

    Directory of Open Access Journals (Sweden)

    D. J. de Ridder

    2009-12-01

    Full Text Available The occurrence of organic micropollutants in drinking water and its sources has opened up a field of study related to monitoring concentration levels in water sources, evaluating their toxicity and estimating their removal in drinking water treatment processes. Because a large number of organic micropollutants is currently present (although in relatively low concentrations in drinking water sources, a method should be developed to select which micropollutants has to be evaluated with priority. In this paper, a screening model is presented that can predict solute removal by activated carbon, in ultrapure water and in natural water. Solute removal prediction is based on a combination of solute hydrophobicity (expressed as log D, the pH corrected log Kow, solute charge and the carbon dose. Solute molecular weight was also considered as model input parameter, but this solute property appeared to relate insufficiently to solute removal.

    Removal of negatively charged solutes by preloaded activated carbon was reduced while the removal of positively charged solutes was increased, compared with freshly regenerated activated carbon. Differences in charged solute removal by freshly regenerated activated carbon were small, indicating that charge interactions are an important mechanism in adsorption onto preloaded carbon. The predicted solute removal was within 20 removal-% deviation of experimentally measured values for most solutes.

  11. Sound induced activity in voice sensitive cortex predicts voice memory ability

    Directory of Open Access Journals (Sweden)

    Rebecca eWatson

    2012-04-01

    Full Text Available The ‘temporal voice areas’ (TVAs (Belin et al., 2000 of the human brain show greater neuronal activity in response to human voices than to other categories of nonvocal sounds. However, a direct link between TVA activity and voice perceptionbehaviour has not yet been established. Here we show that a functional magnetic resonance imaging (fMRI measure of activity in the TVAs predicts individual performance at a separately administered voice memory test. This relation holds whengeneral sound memory ability is taken into account. These findings provide the first evidence that the TVAs are specifically involved in voice cognition.

  12. PASS assisted prediction and pharmacological evaluation of novel nicotinic analogs for nootropic activity in mice.

    Science.gov (United States)

    Khurana, Navneet; Ishar, Mohan Pal Singh; Gajbhiye, Asmita; Goel, Rajesh Kumar

    2011-07-15

    The aim of present study is to predict the probable nootropic activity of novel nicotine analogues with the help of computer program, PASS (prediction of activity spectra for substances) and evaluate the same. Two compounds from differently substituted pyridines were selected for synthesis and evaluation of nootropic activity based on their high probable activity (Pa) value predicted by PASS computer program. Evaluation of nootropic activity of compounds after acute and chronic treatment was done with transfer latency (TL) and step down latency (SDL) methods which showed significant nootropic activity. The effect on scopolamine induced amnesia was also observed along with their acetylcholine esterase inhibitory activity which also showed positive results which strengthened their efficacy as nootropic agents through involvement of cholinergic system. This nootropic effect was similar to the effect of nicotine and donepezil used as standard drugs. Muscle coordination and locomotor activity along with their addiction liability, safety and tolerability studies were also evaluated. These studies showed that these compounds are well tolerable and safe over a wide range of doses tested along with the absence of withdrawal effect which is present in nicotine due to its addiction liability. The study showed that these compounds are true nicotine analogs with desirable efficacy and safety profile for their use as effective nootropic agents. Copyright © 2011 Elsevier B.V. All rights reserved.

  13. Multinationals' Political Activities on Climate Change

    International Nuclear Information System (INIS)

    Kolk, A.; Pinkse, J.

    2007-01-01

    This article explores the international dimensions of multinationals' corporate political activities, focusing on an international issue - climate change - being implemented differently in a range of countries. Analyzing data from Financial Times Global 500 firms, it examines the influence on types and process of multinationals' political strategies, reckoning with institutional contexts and issue saliency. Findings show that the type of political activities can be characterized as an information strategy to influence policy makers toward market-based solutions, not so much withholding action on emission reduction. Moreover, multinationals pursue self-regulation, targeting a broad range of political actors. The process of political strategy is mostly one of collective action. International differences particularly surface in the type of political actors aimed at, with U.S. and Australian firms focusing more on non-government actors (voluntary programs) than European and Japanese firms. Influencing home-country (not host-country) governments is the main component of international political strategy on climate change

  14. Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

    Directory of Open Access Journals (Sweden)

    Zhiming Lin

    2015-01-01

    Full Text Available Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P=0.005, CRP (AUC = 0.75; P=0.017, and ASDAS (AUC = 0.778, P=0.007 significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P=0.040 significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2=6.87, P=0.009, and wald χ2=5.171, P=0.023. Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients.

  15. Within-person Changes in Individual Symptoms of Depression Predict Subsequent Depressive Episodes in Adolescents: A Prospective Study

    Science.gov (United States)

    Kouros, Chrystyna D.; Morris, Matthew C.; Garber, Judy

    2015-01-01

    The current longitudinal study examined which individual symptoms of depression uniquely predicted a subsequent Major Depressive Episode (MDE) in adolescents, and whether these relations differed by sex. Adolescents (N=240) were first interviewed in grade 6 (M=11.86 years old; SD = 0.56; 54% female; 81.5% Caucasian) and then annually through grade 12 regarding their individual symptoms of depression as well as the occurrence of MDEs. Individual symptoms of depression were assessed with the Children’s Depression Rating Scale-Revised (CDRS-R) and depressive episodes were assessed with the Longitudinal Interval Follow-up Evaluation (LIFE). Results showed that within-person changes in sleep problems and low self-esteem/excessive guilt positively predicted an increased likelihood of an MDE for both boys and girls. Significant sex differences also were found. Within-person changes in anhedonia predicted an increased likelihood of a subsequent MDE among boys, whereas irritability predicted a decreased likelihood of a future MDE among boys, and concentration difficulties predicted a decreased likelihood of an MDE in girls. These results identified individual depressive symptoms that predicted subsequent depressive episodes in male and female adolescents, and may be used to guide the early detection, treatment, and prevention of depressive disorders in youth. PMID:26105209

  16. Approaches to predicting potential impacts of climate change on forest disease: an example with Armillaria root disease

    Science.gov (United States)

    Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist

    2009-01-01

    Predicting climate change influences on forest diseases will foster forest management practices that minimize adverse impacts of diseases. Precise locations of accurately identified pathogens and hosts must be documented and spatially referenced to determine which climatic factors influence species distribution. With this information, bioclimatic models can predict the...

  17. Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

    Directory of Open Access Journals (Sweden)

    Avval Zhila Mohajeri

    2015-01-01

    Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

  18. Learning from sensory and reward prediction errors during motor adaptation.

    Science.gov (United States)

    Izawa, Jun; Shadmehr, Reza

    2011-03-01

    Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

  19. Analysis of Spring Flow Change in the Jinan City under Influences of Recent Human Activities

    Science.gov (United States)

    Liu, Xiaomeng; Hu, Litang; Sun, Kangning

    2018-06-01

    Jinan city, the capital of Shandong Province in China, is famous for its beautiful springs. With the rapid development of the economy in recent years, water demand in Jinan city has been increasing rapidly. The over-exploitation of groundwater has caused a decline in groundwater level and, notably, dried up springs under extreme climate conditions. To keep the springs gushing perennially and sustainably use groundwater resources, the local government has implemented many measures to restore the water table, such as the Sponge City Construction Project in Jinan. Focusing on changes in spring flow and its impact factors in Jinan, this paper analyzes the changes in observed spring flow in the most recent 50 years and then discusses the causes of decreases in the spring flow with the consideration of climate and human activities. Spring flow in the study area was changed from the natural state to a period of multiwater source management. The artificial neural network (ANN) model was developed to demonstrate the relationship among spring flow, precipitation, and groundwater abstraction to predict the variations of spring flow under the conditions of climate change and human activities. The good agreement between the simulated and observed results indicates that both precipitation and exploitation are important influence factors. However the effective infiltration of precipitation into groundwater is the most influential factor. The results can provide guidance for groundwater resource protection in the Jinan spring catchment.

  20. Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity

    International Nuclear Information System (INIS)

    Morales, Aliuska Helguera; Perez, Miguel Angel Cabrera; Combes, Robert D.; Gonzalez, Maykel Perez

    2006-01-01

    Several nitrocompounds have been screened for carcinogenicity in rodents, but this is a lengthy and expensive process, taking two years and typically costing 2.5 million dollars, and uses large numbers of animals. There is, therefore, much impetus to develop suitable alternative methods. One possible way of predicting carcinogenicity is to use quantitative structure-activity relationships (QSARs). QSARs have been widely utilized for toxicity testing, thereby contributing to a reduction in the need for experimental animals. This paper describes the results of applying a TOPological substructural molecular design (TOPS-MODE) approach for predicting the rodent carcinogenicity of nitrocompounds. The model described 79.10% of the experimental variance, with a standard deviation of 0.424. The predictive power of the model was validated by leave-one-out validation, with a determination coefficient of 0.666. In addition, this approach enabled the contribution of different fragments to carcinogenic potency to be assessed, thereby making the relationships between structure and carcinogenicity to be transparent. It was found that the carcinogenic activity of the chemicals analysed was increased by the presence of a primary amine group bonded to the aromatic ring, a manner that was proportional to the ring aromaticity. The nitro group bonded to an aromatic carbon atom is a more important determinant of carcinogenicity than the nitro group bonded to an aliphatic carbon. Finally, the TOPS-MODE approach was compared with four other predictive models, but none of these could explain more than 66% of the variance in the carcinogenic potency with the same number of variables

  1. Preparatory neural activity predicts performance on a conflict task.

    Science.gov (United States)

    Stern, Emily R; Wager, Tor D; Egner, Tobias; Hirsch, Joy; Mangels, Jennifer A

    2007-10-24

    Advance preparation has been shown to improve the efficiency of conflict resolution. Yet, with little empirical work directly linking preparatory neural activity to the performance benefits of advance cueing, it is not clear whether this relationship results from preparatory activation of task-specific networks, or from activity associated with general alerting processes. Here, fMRI data were acquired during a spatial Stroop task in which advance cues either informed subjects of the upcoming relevant feature of conflict stimuli (spatial or semantic) or were neutral. Informative cues decreased reaction time (RT) relative to neutral cues, and cues indicating that spatial information would be task-relevant elicited greater activity than neutral cues in multiple areas, including right anterior prefrontal and bilateral parietal cortex. Additionally, preparatory activation in bilateral parietal cortex and right dorsolateral prefrontal cortex predicted faster RT when subjects responded to spatial location. No regions were found to be specific to semantic cues at conventional thresholds, and lowering the threshold further revealed little overlap between activity associated with spatial and semantic cueing effects, thereby demonstrating a single dissociation between activations related to preparing a spatial versus semantic task-set. This relationship between preparatory activation of spatial processing networks and efficient conflict resolution suggests that advance information can benefit performance by leading to domain-specific biasing of task-relevant information.

  2. Prediction of crack density and electrical resistance changes in indium tin oxide/polymer thin films under tensile loading

    KAUST Repository

    Mora Cordova, Angel; Khan, Kamran; El Sayed, Tamer

    2014-01-01

    We present unified predictions for the crack onset strain, evolution of crack density, and changes in electrical resistance in indium tin oxide/polymer thin films under tensile loading. We propose a damage mechanics model to quantify and predict

  3. Physics-Based Predictions for Coherent Change Detection Using X-Band Synthetic Aperture Radar

    Directory of Open Access Journals (Sweden)

    Mark Preiss

    2005-12-01

    Full Text Available A theoretical model is developed to describe the interferometric coherency between pairs of SAR images of rough soil surfaces. The model is derived using a dyadic form for surface reflectivity in the Kirchhoff approximation. This permits the combination of Kirchhoff theory and spotlight synthetic aperture radar (SAR image formation theory. The resulting model is used to describe the interferometric coherency between pairs of SAR images of rough soil surfaces. The theoretical model is applied to SAR images formed before and after surface changes observed by a repeat-pass SAR system. The change in surface associated with a tyre track following vehicle passage is modelled and SAR coherency estimates are obtained. Predicted coherency distributions for both the change and no-change scenarios are used to estimate receiver operator curves for the detection of the changes using a high-resolution, X-band SAR system.

  4. Narrative Changes Predict a Decrease in Symptoms in CBT for Depression: An Exploratory Study.

    Science.gov (United States)

    Gonçalves, Miguel M; Silva, Joana Ribeiro; Mendes, Inês; Rosa, Catarina; Ribeiro, António P; Batista, João; Sousa, Inês; Fernandes, Carlos F

    2017-07-01

    Innovative moments (IMs) are new and more adjusted ways of thinking, acting, feeling and relating that emerge during psychotherapy. Previous research on IMs has provided sustainable evidence that IMs differentiate recovered from unchanged psychotherapy cases. However, studies with cognitive behavioural therapy (CBT) are so far absent. The present study tests whether IMs can be reliably identified in CBT and examines if IMs and symptoms' improvement are associated. The following variables were assessed in each session from a sample of six cases of CBT for depression (a total of 111 sessions): (a) symptomatology outcomes (Outcome Questionnaire-OQ-10) and (b) IMs. Two hierarchical linear models were used: one to test whether IMs predicted a symptom decrease in the next session and a second one to test whether symptoms in one session predicted the emergence of IMs in the next session. Innovative moments were better predictors of symptom decrease than the reverse. A higher proportion of a specific type of IMs-reflection 2-in one session predicted a decrease in symptoms in the next session. Thus, when clients further elaborated this type of IM (in which clients describe positive contrasts or elaborate on changes processes), a reduction in symptoms was observed in the next session. A higher expression and elaboration of reflection 2 IMs appear to have a facilitative function in the reduction of depressive symptoms in this sample of CBT. Copyright © 2016 John Wiley & Sons, Ltd. Elaborating innovative moments (IMs) that are new ways of thinking, feeling, behaving and relating, in the therapeutic dialogue, may facilitate change. IMs that are more predictive of amelioration of symptoms in CBT are the ones focused on contrasts between former problematic patterns and new adjusted ones; and the ones in which the clients elaborate on processes of change. Therapists may integrate these kinds of questions (centred on contrasts and centred on what allowed change from the client

  5. Early prediction of outcome of activities of daily living after stroke: a systematic review

    OpenAIRE

    Veerbeek, J.M.; Kwakkel, G.; Wegen, van, E.E.H.; Ket, J.C.F.; Heijmans, M.W.

    2011-01-01

    BACKGROUND AND PURPOSE-Knowledge about robust and unbiased factors that predict outcome of activities of daily living (ADL) is paramount in stroke management. This review investigates the methodological quality of prognostic studies in the early poststroke phase for final ADL to identify variables that are predictive or not predictive for outcome of ADL after stroke. METHODS-PubMed, Ebsco/Cinahl and Embase were systematically searched for prognostic studies in which stroke patients were inclu...

  6. Dynamic response of airborne infections to climate change: predictions for varicella

    Science.gov (United States)

    Baker, R.; Mahmud, A. S.; Metcalf, C. J. E.

    2017-12-01

    Characterizing how climate change will alter the burden of infectious diseases has clear applications for public health policy. Despite our uniquely detailed understanding of the transmission process for directly transmitted infections, the impact of climate variables on these infections remains understudied. We develop a novel methodology for estimating the causal relationship between climate and directly transmitted infections, which combines an epidemiological model of disease transmission with panel regression techniques. Our method allows us to move beyond correlational approaches to studying the link between climate and infectious diseases. Further, we can generate semi-mechanistic projections of incidence across climate scenarios. We illustrate our approach using 30 years of reported cases of varicella, a common airborne childhood infection, across 32 states in Mexico. We find significantly increased varicella transmission in drier conditions. We use this to map potential changes in the magnitude and variability of varicella incidence in Mexico as a result of projected changes in future climate conditions. Our results indicate that the predicted decrease in humidity in Mexico towards the end of the century will increase incidence of varicella, all else equal, and that these changes in incidence will be non-uniform across the year.

  7. You'll change more than I will: Adults' predictions about their own and others' future preferences.

    Science.gov (United States)

    Renoult, Louis; Kopp, Leia; Davidson, Patrick S R; Taler, Vanessa; Atance, Cristina M

    2016-01-01

    It has been argued that adults underestimate the extent to which their preferences will change over time. We sought to determine whether such mispredictions are the result of a difficulty imagining that one's own current and future preferences may differ or whether it also characterizes our predictions about the future preferences of others. We used a perspective-taking task in which we asked young people how much they liked stereotypically young-person items (e.g., Top 40 music, adventure vacations) and stereotypically old-person items (e.g., jazz, playing bridge) now, and how much they would like them in the distant future (i.e., when they are 70 years old). Participants also made these same predictions for a generic same-age, same-sex peer. In a third condition, participants predicted how much a generic older (i.e., age 70) same-sex adult would like items from both categories today. Participants predicted less change between their own current and future preferences than between the current and future preferences of a peer. However, participants estimated that, compared to a current older adult today, their peer would like stereotypically young items more in the future and stereotypically old items less. The fact that peers' distant-future estimated preferences were different from the ones they made for "current" older adults suggests that even though underestimation of change of preferences over time is attenuated when thinking about others, a bias still exists.

  8. Influence of predicted climage change elements on Z. ...

    Science.gov (United States)

    Global climate change (GCC) is expected to have pronounced impacts on estuarine and marine habitats including sea level rise, increased storm intensity, increased air and water temperatures, changes in upwelling dynamics and ocean acidification. All of these elements are likely to impact the growth and potential distribution of the non-indigenous seagrass Zostera japonica both within the State of Washington and within the region. Understanding how Z. japonica will respond to GCC requires a thorough understanding of plant physiology and predictions of GCC effects. Furthermore, Washington State is proposing to list Z. japonica as a “noxious weed” which will allow the state to use herbicide controls for management. We present data from manipulative experiments designed to better understand how Z. japonica photosynthetic physiology responds to temperature, salinity and light. We found that Z. japonica is well adapted to moderate temperatures and salinity with maximum photosynthesis of salinity of 20. The Coos Bay population had greater Pmax and saturation irradiance (Ik) than the Padilla bay population (p < 0.001) and tolerates daily exposure to both freshwater and marine water, suggesting that this population tolerates fairly extreme environmental fluctuations. Extreme temperatures (35 °C) were generally lethal to Z. japonica populations from Padilla, Coos and Yaquina Bays. High salinity (35) had lower mortality than either salinity of 5 or 20 (p = 0.0

  9. Decadal Recruitment and Mortality of Ponderosa pine Predicted for the 21st Century Under five Downscaled Climate Change Scenarios

    Science.gov (United States)

    Ironside, K. E.; Cole, K. L.; Eischeid, J. K.; Garfin, G. M.; Shaw, J. D.; Cobb, N. S.

    2008-12-01

    Ponderosa pine (Pinus ponderosa var. scopulorum) is the dominant conifer in higher elevation regions of the southwestern United States. Because this species is so prominent, southwestern montane ecosystems will be significantly altered if this species is strongly affected by future climate changes. These changes could be highly challenging for land management agencies. In order to model the consequences of future climates, 20th Century recruitment events and mortality for ponderosa pine were characterized using measures of seasonal water balance (precipitation - potential evapotranspiration). These relationships, assuming they will remain unchanged, were then used to predict 21st Century changes in ponderosa pine occurrence in the southwest. Twenty-one AR4 IPCC General Circulation Model (GCM) A1B simulation results were ranked on their ability to simulate the later 20th Century (1950-2000 AD) precipitation seasonality, spatial patterns, and quantity in the western United States. Among the top ranked GCMs, five were selected for downscaling to a 4 km grid that represented a range in predictions in terms of changes in water balance. Predicted decadal changes in southwestern ponderosa pine for the 21st Century for these five climate change scenarios were calculated using a multiple quadratic logistic regression model. Similar models of other western tree species (Pinus edulis, Yucca brevifolia) predicted severe contractions, especially in the southern half of their ranges. However, the results for Ponderosa pine suggested future expansions throughout its range to both higher and lower elevations, as well as very significant expansions northward.

  10. Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design.

    Science.gov (United States)

    Spreco, Armin; Eriksson, Olle; Dahlström, Örjan; Cowling, Benjamin John; Timpka, Toomas

    2017-06-15

    Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic "big data" from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning

  11. Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China

    Directory of Open Access Journals (Sweden)

    Huiran Han

    2015-04-01

    Full Text Available Land use and land cover (LULC models are essential for analyzing LULC change and predicting land use requirements and are valuable for guiding reasonable land use planning and management. However, each LULC model has its own advantages and constraints. In this paper, we explore the characteristics of LULC change and simulate future land use demand by combining a CLUE-S model with a Markov model to deal with some shortcomings of existing LULC models. Using Beijing as a case study, we describe the related driving factors from land-adaptive variables, regional spatial variables and socio-economic variables and then simulate future land use scenarios from 2010 to 2020, which include a development scenario (natural development and rapid development and protection scenarios (ecological and cultivated land protection. The results indicate good consistency between predicted results and actual land use situations according to a Kappa statistic. The conversion of cultivated land to urban built-up land will form the primary features of LULC change in the future. The prediction for land use demand shows the differences under different scenarios. At higher elevations, the geographical environment limits the expansion of urban built-up land, but the conversion of cultivated land to built-up land in mountainous areas will be more prevalent by 2020; Beijing, however, still faces the most pressure in terms of ecological and cultivated land protection.

  12. Altered Coupling between Motion-Related Activation and Resting-State Brain Activity in the Ipsilesional Sensorimotor Cortex after Cerebral Stroke

    Directory of Open Access Journals (Sweden)

    Jianping Hu

    2017-07-01

    Full Text Available Functional connectivity maps using resting-state functional magnetic resonance imaging (rs-fMRI can closely resemble task fMRI activation patterns, suggesting that resting-state brain activity may predict task-evoked activation or behavioral performance. However, this conclusion was mostly drawn upon a healthy population. It remains unclear whether the predictive ability of resting-state brain activity for task-evoked activation would change under different pathological conditions. This study investigated dynamic changes of coupling between patterns of resting-state functional connectivity (RSFC and motion-related activation in different stages of cerebral stroke. Twenty stroke patients with hand motor function impairment were involved. rs-fMRI and hand motion-related fMRI data were acquired in the acute, subacute, and early chronic stages of cerebral stroke on a 3-T magnetic resonance (MR scanner. Sixteen healthy participants were enrolled as controls. For each subject, an activation map of the affected hand was first created using general linear model analysis on task fMRI data, and then an RSFC map was determined by seeding at the peak region of hand motion activation during the intact hand task. We then measured the extent of coupling between the RSFC maps and motion-related activation maps. Dynamic changes of the coupling between the two fMRI maps were estimated using one-way repeated measures analysis of variance across the three stages. Moreover, imaging parameters were correlated with motor performances. Data analysis showed that there were different coupling patterns between motion-related activation and RSFC maps associating with the affected motor regions during the acute, subacute, and early chronic stages of stroke. Coupling strengths increased as the recovery from stroke progressed. Coupling strengths were correlated with hand motion performance in the acute stage, while coupling recovery was negatively correlated with the recovery

  13. Changes in Weight, Sedentary Behaviour and Physical Activity during the School Year and Summer Vacation

    Directory of Open Access Journals (Sweden)

    Chiaki Tanaka

    2018-05-01

    Full Text Available Background: To examine bidirectional associations between body weight and objectively assessed sedentary behaviour (SB and physical activity (PA during the school year and summer vacation. Methods: Participants were 209 Japanese boys and girls (9.0 ± 1.8 years at baseline. SB and PA were measured using triaxial accelerometry that discriminated between ambulatory and non-ambulatory PA, screen time measured by questionnaire during the school-term was evaluated in May and the summer vacation, and relative body weight measured in May and just after the end of summer vacation. Results: There were no significant relationships between changes in SB or PA and changes in body weight. However, higher relative body weight at baseline was associated with decreased non-ambulatory moderate PA (p = 0.049, but this association was slightly diminished after adjusting for change in SB (p = 0.056. Longer screen time at baseline was also associated with increased relative body weight (p = 0.033. Conclusions: The present study revealed that body weight might be particularly influential on non-ambulatory moderate PA while SB, PA or changes in these variables did not predict changes in body weight. Moreover, screen time during the school year is a predictor of change in relative body weight during the subsequent summer vacation.

  14. Leisure activity associated with cognitive ability level, but not cognitive change

    DEFF Research Database (Denmark)

    Gow, Alan John; Avlund, Kirsten; Mortensen, Erik L

    2014-01-01

    Although activity participation is promoted as cognitively protective, critical questions of causality remain. In a cohort followed every 5 years from age 75 to 85 years, potential reciprocal associations between level and change in leisure activity participation and level and change in cognitive...... abilities were examined. Participants in the Glostrup 1914 Cohort, a longitudinal study of aging, completed standardized cognitive ability tests and reported their leisure activity participation (11 activities defined a leisure activity score) at ages 75, 80, and 85. Higher leisure activity was associated...... with higher cognitive ability (significant correlations ranged from 0.15 to 0.31, p cognitive ability declined significantly. Growth curve models, which provided latent variables for level of and 10-year change in both leisure activity...

  15. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-12-28

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

  17. Accurate cut-offs for predicting endoscopic activity and mucosal healing in Crohn's disease with fecal calprotectin

    Directory of Open Access Journals (Sweden)

    Juan María Vázquez-Morón

    Full Text Available Background: Fecal biomarkers, especially fecal calprotectin, are useful for predicting endoscopic activity in Crohn's disease; however, the cut-off point remains unclear. The aim of this paper was to analyze whether faecal calprotectin and M2 pyruvate kinase are good tools for generating highly accurate scores for the prediction of the state of endoscopic activity and mucosal healing. Methods: The simple endoscopic score for Crohn's disease and the Crohn's disease activity index was calculated for 71 patients diagnosed with Crohn's. Fecal calprotectin and M2-PK were measured by the enzyme-linked immunosorbent assay test. Results: A fecal calprotectin cut-off concentration of ≥ 170 µg/g (sensitivity 77.6%, specificity 95.5% and likelihood ratio +17.06 predicts a high probability of endoscopic activity, and a fecal calprotectin cut-off of ≤ 71 µg/g (sensitivity 95.9%, specificity 52.3% and likelihood ratio -0.08 predicts a high probability of mucosal healing. Three clinical groups were identified according to the data obtained: endoscopic activity (calprotectin ≥ 170, mucosal healing (calprotectin ≤ 71 and uncertainty (71 > calprotectin < 170, with significant differences in endoscopic values (F = 26.407, p < 0.01. Clinical activity or remission modified the probabilities of presenting endoscopic activity (100% vs 89% or mucosal healing (75% vs 87% in the diagnostic scores generated. M2-PK was insufficiently accurate to determine scores. Conclusions: The highly accurate scores for fecal calprotectin provide a useful tool for interpreting the probabilities of presenting endoscopic activity or mucosal healing, and are valuable in the specific clinical context.

  18. Mood and the market: can press reports of investors' mood predict stock prices?

    Science.gov (United States)

    Cohen-Charash, Yochi; Scherbaum, Charles A; Kammeyer-Mueller, John D; Staw, Barry M

    2013-01-01

    We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders' affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors' emotion on a given trading day, could predict the next day's opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices.

  19. Pitch Syntax Violations Are Linked to Greater Skin Conductance Changes, Relative to Timbral Violations - The Predictive Role of the Reward System in Perspective of Cortico-subcortical Loops.

    Science.gov (United States)

    Gorzelańczyk, Edward J; Podlipniak, Piotr; Walecki, Piotr; Karpiński, Maciej; Tarnowska, Emilia

    2017-01-01

    According to contemporary opinion emotional reactions to syntactic violations are due to surprise as a result of the general mechanism of prediction. The classic view is that, the processing of musical syntax can be explained by activity of the cerebral cortex. However, some recent studies have indicated that subcortical brain structures, including those related to the processing of emotions, are also important during the processing of syntax. In order to check whether emotional reactions play a role in the processing of pitch syntax or are only the result of the general mechanism of prediction, the comparison of skin conductance levels reacting to three types of melodies were recorded. In this study, 28 subjects listened to three types of short melodies prepared in Musical Instrument Digital Interface Standard files (MIDI) - tonally correct, tonally violated (with one out-of-key - i.e., of high information content), and tonally correct but with one note played in a different timbre. The BioSemi ActiveTwo with two passive Nihon Kohden electrodes was used. Skin conductance levels were positively correlated with the presented stimuli (timbral changes and tonal violations). Although changes in skin conductance levels were also observed in response to the change in timbre, the reactions to tonal violations were significantly stronger. Therefore, despite the fact that timbral change is at least as equally unexpected as an out-of-key note, the processing of pitch syntax mainly generates increased activation of the sympathetic part of the autonomic nervous system. These results suggest that the cortico-subcortical loops (especially the anterior cingulate - limbic loop) may play an important role in the processing of musical syntax.

  20. Graph-based representation of behavior in detection and prediction of daily living activities.

    Science.gov (United States)

    Augustyniak, Piotr; Ślusarczyk, Grażyna

    2018-04-01

    Various surveillance systems capture signs of human activities of daily living (ADLs) and store multimodal information as time line behavioral records. In this paper, we present a novel approach to the analysis of a behavioral record used in a surveillance system designed for use in elderly smart homes. The description of a subject's activity is first decomposed into elementary poses - easily detectable by dedicated intelligent sensors - and represented by the share coefficients. Then, the activity is represented in the form of an attributed graph, where nodes correspond to elementary poses. As share coefficients of poses are expressed as attributes assigned to graph nodes, their change corresponding to a subject's action is represented by flow in graph edges. The behavioral record is thus a time series of graphs, which tiny size facilitates storage and management of long-term monitoring results. At the system learning stage, the contribution of elementary poses is accumulated, discretized and probability-ordered leading to a finite list representing the possible transitions between states. Such a list is independently built for each room in the supervised residence, and employed for assessment of the current action in the context of subject's habits and a room purpose. The proposed format of a behavioral record, applied to an adaptive surveillance system, is particularly advantageous for representing new activities not known at the setup stage, for providing a quantitative measure of transitions between poses and for expressing the difference between a predicted and actual action in a numerical way. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  1. Anticipated affective consequences of physical activity adoption and maintenance.

    Science.gov (United States)

    Dunton, Genevieve Fridlund; Vaughan, Elaine

    2008-11-01

    The expected emotional consequences of future actions are thought to play an important role in health behavior change. This research examined whether anticipated affective consequences of success and failure vary across stages of physical activity change and differentially predict physical activity adoption as compared to maintenance. Using a prospective design over a 3-month period, a community sample of 329 healthy, middle-aged adults were assessed at 2 time points. Anticipated positive and negative emotions, stage of behavior change (precontemplation [PC], contemplation [C], preparation [P], action [A], maintenance [M]), and level of physical activity. At baseline, anticipated positive emotions were greater in C versus PC, whereas anticipated negative emotions were greater in M versus A and in M versus P. Higher anticipated positive but not negative emotions predicted physical activity adoption and maintenance after 3 months. Although the expected affective consequences of future success and failure differentiated among individuals in the early and later stages of physical activity change, respectively; only the anticipated affective consequences of success predicted future behavior.

  2. Hippocampus activation related to 'real-time' processing of visuospatial change

    NARCIS (Netherlands)

    Beudel, M.; Leenders, K. L.; de Jong, B. M.

    2016-01-01

    The delay associated with cerebral processing time implies a lack of real-time representation of changes in the observed environment. To bridge this gap for motor actions in a dynamical environment, the brain uses predictions of the most plausible future reality based on previously provided

  3. PASS-Predicted Hepatoprotective Activity of Caesalpinia sappan in Thioacetamide-Induced Liver Fibrosis in Rats

    Directory of Open Access Journals (Sweden)

    Farkaad A. Kadir

    2014-01-01

    Full Text Available The antifibrotic effects of traditional medicinal herb Caesalpinia sappan (CS extract on liver fibrosis induced by thioacetamide (TAA and the expression of transforming growth factor β1 (TGF-β1, α-smooth muscle actin (αSMA, and proliferating cell nuclear antigen (PCNA in rats were studied. A computer-aided prediction of antioxidant and hepatoprotective activities was primarily performed with the Prediction Activity Spectra of the Substance (PASS Program. Liver fibrosis was induced in male Sprague Dawley rats by TAA administration (0.03% w/v in drinking water for a period of 12 weeks. Rats were divided into seven groups: control, TAA, Silymarin (SY, and CS 300 mg/kg body weight and 100 mg/kg groups. The effect of CS on liver fibrogenesis was determined by Masson’s trichrome staining, immunohistochemical analysis, and western blotting. In vivo determination of hepatic antioxidant activities, cytochrome P450 2E1 (CYP2E1, and matrix metalloproteinases (MPPS was employed. CS treatment had significantly increased hepatic antioxidant enzymes activity in the TAA-treated rats. Liver fibrosis was greatly alleviated in rats when treated with CS extract. CS treatment was noted to normalize the expression of TGF-β1, αSMA, PCNA, MMPs, and TIMP1 proteins. PASS-predicted plant activity could efficiently guide in selecting a promising pharmaceutical lead with high accuracy and required antioxidant and hepatoprotective properties.

  4. Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, Ian B.; Arendt, Dustin L.; Bell, Eric B.; Volkova, Svitlana

    2017-05-17

    Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word’s contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus – VKontakte collected during the Russia-Ukraine crisis in 2014 – 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.

  5. A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

    Science.gov (United States)

    Vitousek, Sean; Barnard, Patrick; Limber, Patrick W.; Erikson, Li; Cole, Blake

    2017-01-01

    We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.

  6. Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

    Science.gov (United States)

    Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

    2018-01-01

    Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

  7. Predictive value of European Scleroderma Group Activity Index in an early scleroderma cohort.

    Science.gov (United States)

    Nevskaya, Tatiana; Baron, Murray; Pope, Janet E

    2017-07-01

    To estimate the effect of disease activity, as measured by the European Scleroderma Research Group Activity Index (EScSG-AI), on the risk of subsequent organ damage in a large systemic sclerosis (SSc) cohort. Of 421 SSc patients from the Canadian Scleroderma Research Group database with disease duration of ⩽ 3 years, 197 who had no evidence of end-stage organ damage initially and available 3 year follow-up were included. Disease activity was assessed by the EScSG-AI with two variability measures: the adjusted mean EScSG-AI (the area under the curve of the EScSG-AI over the observation period) and persistently active disease/flare. Outcomes were based on the Medsger severity scale and included accrual of a new severity score (Δ ⩾ 1) overall and within organ systems or reaching a significant level of deterioration in health status. After adjustment for covariates, the adjusted mean EScSG-AI was the most consistent predictor of risk across the study outcomes over 3 years in dcSSc: disease progression defined as Δ ⩾ 1 in any major internal organ, significant decline in forced vital capacity and diffusing capacity of carbon monoxide, severity of visceral disease and HAQ Disability Index worsening. In multivariate analysis, progression of lung disease was predicted solely by adjusted mean EScSG-AI, while the severity of lung disease was predicted the adjusted mean EScSG-AI, older age, modified Rodnan skin score (mRSS) and initial severity. The EScSG-AI was associated with patient- and physician-assessed measures of health status and overpowered the mRSS in predicting disease outcomes. Disease activity burden quantified with the adjusted mean EScSG-AI predicted the risk of deterioration in health status and severe organ involvement in dcSSc. The EScSG-AI is more responsive when done repeatedly and averaged. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email

  8. The influence of active region information on the prediction of solar flares: an empirical model using data mining

    Directory of Open Access Journals (Sweden)

    M. Núñez

    2005-11-01

    Full Text Available Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL, for automatically discovering correlations between solar flares and active region data, in order to predict the former. The goal of BPL is to predict the interval of time to the next solar flare and provide a confidence value for the associated prediction. The discovered correlations are described in terms of easy-to-read rules. The results indicate that active region dynamics is essential for predicting solar flares.

  9. Changes in predicted protein disorder tendency may contribute to disease risk

    Directory of Open Access Journals (Sweden)

    Hu Yang

    2011-12-01

    Full Text Available Abstract Background Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort. Results Using the exonic single nucleotide variations (SNVs identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk. Conclusions After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.

  10. Effects of predicted climatic changes on distribution of organic contaminants in brackish water mesocosms.

    Science.gov (United States)

    Ripszam, M; Gallampois, C M J; Berglund, Å; Larsson, H; Andersson, A; Tysklind, M; Haglund, P

    2015-06-01

    Predicted consequences of future climate change in the northern Baltic Sea include increases in sea surface temperatures and terrestrial dissolved organic carbon (DOC) runoff. These changes are expected to alter environmental distribution of anthropogenic organic contaminants (OCs). To assess likely shifts in their distributions, outdoor mesocosms were employed to mimic pelagic ecosystems at two temperatures and two DOC concentrations, current: 15°C and 4 mg DOCL(-1) and, within ranges of predicted increases, 18°C and 6 mg DOCL(-1), respectively. Selected organic contaminants were added to the mesocosms to monitor changes in their distribution induced by the treatments. OC partitioning to particulate matter and sedimentation were enhanced at the higher DOC concentration, at both temperatures, while higher losses and lower partitioning of OCs to DOC were observed at the higher temperature. No combined effects of higher temperature and DOC on partitioning were observed, possibly because of the balancing nature of these processes. Therefore, changes in OCs' fates may largely depend on whether they are most sensitive to temperature or DOC concentration rises. Bromoanilines, phenanthrene, biphenyl and naphthalene were sensitive to the rise in DOC concentration, whereas organophosphates, chlorobenzenes (PCBz) and polychlorinated biphenyls (PCBs) were more sensitive to temperature. Mitotane and diflufenican were sensitive to both temperature and DOC concentration rises individually, but not in combination. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Change in self-esteem predicts depressive symptoms at follow-up after intensive multimodal psychotherapy for major depression.

    Science.gov (United States)

    Dinger, Ulrike; Ehrenthal, Johannes C; Nikendei, Christoph; Schauenburg, Henning

    2017-09-01

    Reduced self-esteem is a core symptom of depression, but few studies have investigated within-treatment change of self-esteem as a predictor of long-term outcome in depression. This study investigated change in self-esteem during 8 weeks of multimodal, psychodynamically oriented psychotherapy for 40 depressed patients and tested whether it would predict outcome 6 months after termination. Data was drawn from a randomized clinical pilot trial on day-clinic versus inpatient psychotherapy for depression. Findings supported the association between change in self-esteem and follow-up depression severity, even when controlling for within-treatment symptom change. Change in self-esteem was not related to overall symptoms and interpersonal problems at follow-up. Thus, change in self-esteem may be an important variable in preventing relapse for depression. Self-esteem is related to depressive symptoms and interpersonal problems. Improvement of self-esteem during psychotherapy correlates with improvements of symptoms and interpersonal problems. Change of self-esteem during psychotherapy predicts depressive symptoms 6 months after termination of therapy. When treating depressed patients, psychotherapists should work towards an improvement of self-esteem in order to prevent relapse. Copyright © 2017 John Wiley & Sons, Ltd.

  12. Physical activity is associated with changes in knee cartilage microstructure.

    Science.gov (United States)

    Halilaj, E; Hastie, T J; Gold, G E; Delp, S L

    2018-06-01

    The purpose of this study was to determine if there is an association between objectively measured physical activity and longitudinal changes in knee cartilage microstructure. We used accelerometry and T 2 -weighted magnetic resonance imaging (MRI) data from the Osteoarthritis Initiative, restricting the analysis to men aged 45-60 years, with a body mass index (BMI) of 25-27 kg/m 2 and no radiographic evidence of knee osteoarthritis. After computing 4-year changes in mean T 2 relaxation time for six femoral cartilage regions and mean daily times spent in the sedentary, light, moderate, and vigorous activity ranges, we performed canonical correlation analysis (CCA) to find a linear combination of times spent in different activity intensity ranges (Activity Index) that was maximally correlated with a linear combination of regional changes in cartilage microstructure (Cartilage Microstructure Index). We used leave-one-out pre-validation to test the robustness of the model on new data. Nineteen subjects satisfied the inclusion criteria. CCA identified an Activity Index and a Cartilage Microstructure Index that were significantly correlated (r = .82, P microstructural changes in different cartilage regions than it is with univariate or cumulative changes, likely because this index separates the effect of activity, which is greater in the medial loadbearing region, from that of patient-specific natural aging. Copyright © 2018 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

  13. Activism or "Slacktivism?": Digital Media and Organizing for Social Change

    Science.gov (United States)

    Glenn, Cerise L.

    2015-01-01

    The influence of social media and technological developments has changed how groups and organizations advocating for social change generate awareness and participation in their causes. In this single class activity students will (a) analyze notions of activism and "slacktivism" from scholarly and popular sources to apply these concepts…

  14. M-ficolin levels reflect disease activity and predict remission in early rheumatoid arthritis

    DEFF Research Database (Denmark)

    Ammitzbøll, Christian Gytz; Thiel, Steffen; Jensenius, Jens Christian

    2013-01-01

    To assess plasma M-ficolin concentrations in disease-modifying antirheumatic drug (DMARD)-naive patients with early rheumatoid arthritis (RA), to investigate the correlation of M-ficolin concentrations with disease activity markers, and to determine the predictive value of M-ficolin with respect...... to the Disease Activity Score in 28 joints (DAS28)....

  15. Validity of predicting left ventricular end systolic pressure changes following an acute bout of exercise.

    Science.gov (United States)

    Kappus, Rebecca M; Ranadive, Sushant M; Yan, Huimin; Lane, Abbi D; Cook, Marc D; Hall, Grenita; Harvey, I Shevon; Wilund, Kenneth R; Woods, Jeffrey A; Fernhall, Bo

    2013-01-01

    Left ventricular end systolic pressure (LV ESP) is important in assessing left ventricular performance and is usually derived from prediction equations. It is unknown whether these equations are accurate at rest or following exercise in a young, healthy population. Measured LV ESP vs. LV ESP values from the prediction equations were compared at rest, 15 min and 30 min following peak aerobic exercise in 60 participants. LV ESP was obtained by applanation tonometry at rest, 15 min post and 30 min post peak cycle exercise. Measured LV ESP was significantly lower (p<0.05) at all time points in comparison to the two calculated values. Measured LV ESP decreased significantly from rest at both the post15 and post30 time points (p<0.05) and changed differently in comparison to the calculated values (significant interaction; p<0.05). The two LV ESP equations were also significantly different from each other (p<0.05) and changed differently over time (significant interaction; p<0.05). The two commonly used prediction equations did not accurately predict either resting or post exercise LV ESP in a young, healthy population. Thus, LV ESP needs to be individually determined in young, healthy participants. Non-invasive measurement through applanation tonometry appears to allow for a more accurate determination of LV ESP. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  16. Dynamic changes in brain activity during prism adaptation.

    Science.gov (United States)

    Luauté, Jacques; Schwartz, Sophie; Rossetti, Yves; Spiridon, Mona; Rode, Gilles; Boisson, Dominique; Vuilleumier, Patrik

    2009-01-07

    Prism adaptation does not only induce short-term sensorimotor plasticity, but also longer-term reorganization in the neural representation of space. We used event-related fMRI to study dynamic changes in brain activity during both early and prolonged exposure to visual prisms. Participants performed a pointing task before, during, and after prism exposure. Measures of trial-by-trial pointing errors and corrections allowed parametric analyses of brain activity as a function of performance. We show that during the earliest phase of prism exposure, anterior intraparietal sulcus was primarily implicated in error detection, whereas parieto-occipital sulcus was implicated in error correction. Cerebellum activity showed progressive increases during prism exposure, in accordance with a key role for spatial realignment. This time course further suggests that the cerebellum might promote neural changes in superior temporal cortex, which was selectively activated during the later phase of prism exposure and could mediate the effects of prism adaptation on cognitive spatial representations.

  17. Anxiety Sensitivity Uniquely Predicts Exercise Behaviors in Young Adults Seeking to Increase Physical Activity.

    Science.gov (United States)

    Moshier, Samantha J; Szuhany, Kristin L; Hearon, Bridget A; Smits, Jasper A J; Otto, Michael W

    2016-01-01

    Individuals with elevated levels of anxiety sensitivity (AS) may be motivated to avoid aversive emotional or physical states, and therefore may have greater difficulty achieving healthy behavioral change. This may be particularly true for exercise, which produces many of the somatic sensations within the domain of AS concerns. Cross-sectional studies show a negative association between AS and exercise. However, little is known about how AS may prospectively affect attempts at behavior change in individuals who are motivated to increase their exercise. We recruited 145 young adults who self-identified as having a desire to increase their exercise behavior. Participants completed a web survey assessing AS and additional variables identified as important for behavior change-impulsivity, grit, perceived behavioral control, and action planning-and set a specific goal for exercising in the next week. One week later, a second survey assessed participants' success in meeting their exercise goals. We hypothesized that individuals with higher AS would choose lower exercise goals and would complete less exercise at the second survey. AS was not significantly associated with exercise goal level, but significantly and negatively predicted exercise at Time 2 and was the only variable to offer significant prediction beyond consideration of baseline exercise levels. These results underscore the importance of considering AS in relation to health behavior intentions. This is particularly apt given the absence of prediction offered by other traditional predictors of behavior change. © The Author(s) 2015.

  18. Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

    Science.gov (United States)

    Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

    2018-05-15

    An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

  19. Physical Activity Status and Position of Governmental Employees in Changing Stage Based on the Trans-Theoretical Model in Hamadan, Iran.

    Science.gov (United States)

    Abdi, Jalal; Eftekhar, Hassan; Mahmoodi, Mahmood; Shojayzadeh, Davood; Sadeghi, Roya

    2015-02-24

    Physical inactivity is the fourth leading risk factor for death worldwide. Given the key role of employees as valuable human resources and increasing sedentary life style among them, the aim of this study was to evaluate physical activity status and position of governmental employees in changing stage based on the Trans-Theoretical Model (TTM) in Hamadan, Iran, in 2014.This descriptive-analytical study was performed on 1200 government employees selected using proportional stratified random sampling. Data collection was performed using a three-section questionnaire containing demographic characteristics, SQUASH (Short questionnaire to assess health-enhancing physical activity) questionnaire and Marcus et al's five-part algoritm. Data were analyzed by multiple linear and logistic regression, Chi-square, T-test and ANOVA using SPSS-20. The mean age of the participants was 38.12±8.04 years. About a half of the employees were in the preparatory stage of TTM.49.2% and 50.8% of the sample were classified as active and inactive, respectively .Associations between physical activity status and exercise stage of change were found. The associations between exercise stage of change and age, sex, work experience, education and marital status were significant (pphysical activity (PA) status and accounted for 31.2% of variance in PA (adjusted R2=0.312, R2 change=0.01). The results of this study showed that TTM was useful to evaluate and predict physical activity behavior among the Iranian governmental employees and can be utilized by health planners to inform appropriate intervention strategies, specifically in work place.

  20. Behavioral change theories can inform the prediction of young adults' adoption of a plant-based diet.

    Science.gov (United States)

    Wyker, Brett A; Davison, Kirsten K

    2010-01-01

    Drawing on the Theory of Planned Behavior (TPB) and the Transtheoretical Model (TTM), this study (1) examines links between stages of change for following a plant-based diet (PBD) and consuming more fruits and vegetables (FV); (2) tests an integrated theoretical model predicting intention to follow a PBD; and (3) identifies associated salient beliefs. Cross-sectional. Large public university in the northeastern United States. 204 college students. TPB and TTM constructs were assessed using validated scales. Outcome, normative, and control beliefs were measured using open-ended questions. The overlap between stages of change for FV consumption and adopting a PBD was assessed using Spearman rank correlation analysis and cross-tab comparisons. The proposed model predicting adoption of a PBD was tested using structural equation modeling (SEM). Salient beliefs were coded using automatic response coding software. No association was found between stages of change for FV consumption and following a PBD. Results from SEM analyses provided support for the proposed model predicting intention to follow a PBD. Gender differences in salient beliefs for following a PBD were found. Results demonstrate the potential for effective theory-driven and stage-tailored public health interventions to promote PBDs. Copyright 2010 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

  1. Surfing depth on a behaviour change website: predictors and effects on behaviour.

    Science.gov (United States)

    Jacobs, Nele; De Bourdeaudhuij, Ilse; Claes, Neree

    2010-03-01

    The primary objectives of the present study were to gain insight into website use and to predict the surfing depth on a behaviour change website and its effect on behaviour. Two hundred eight highly educated adults from the intervention condition of a randomised trial received access to a medical intervention, individual coaching (by e-mail, post, telephone or face-to-face) and a behaviour change website. Website use (e.g. surfing depth, page view duration) was registered. Online questionnaires for physical activity and fat intake were filled out at baseline and after 6 months. Hierarchical linear regression was used to predict surfing depth and its effect on behaviour. Seventy-five per cent of the participants visited the website. Fifty-one and fifty-six per cent consulted the physical activity and fat intake feedback, respectively. The median surfing depth was 2. The total duration of interventions by e-mail predicted deeper surfing (beta=0.36; pSurfing depth did not predict changes in fat intake (beta=-0.07; p=0.45) or physical activity (beta=-0.03; p=0.72). Consulting the physical activity feedback led to more physical activity (beta=0.23; p=0.01). The findings from the present study can be used to guide future website development and improve the information architecture of behaviour change websites.

  2. Ensemble-based Regional Climate Prediction: Political Impacts

    Science.gov (United States)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  3. Potential for thermal tolerance to mediate climate change effects on three members of a cool temperate lizard genus, Niveoscincus.

    Science.gov (United States)

    Caldwell, Amanda J; While, Geoffrey M; Beeton, Nicholas J; Wapstra, Erik

    2015-08-01

    Climatic changes are predicted to be greater in higher latitude and mountainous regions but species specific impacts are difficult to predict. This is partly due to inter-specific variance in the physiological traits which mediate environmental temperature effects at the organismal level. We examined variation in the critical thermal minimum (CTmin), critical thermal maximum (CTmax) and evaporative water loss rates (EWL) of a widespread lowland (Niveoscincus ocellatus) and two range restricted highland (N. microlepidotus and N. greeni) members of a cool temperate Tasmanian lizard genus. The widespread lowland species had significantly higher CTmin and CTmax and significantly lower EWL than both highland species. Implications of inter-specific variation in thermal tolerance for activity were examined under contemporary and future climate change scenarios. Instances of air temperatures below CTmin were predicted to decline in frequency for the widespread lowland and both highland species. Air temperatures of high altitude sites were not predicted to exceed the CTmax of either highland species throughout the 21st century. In contrast, the widespread lowland species is predicted to experience air temperatures in excess of CTmax on 1 or 2 days by three of six global circulation models from 2068-2096. To estimate climate change effects on activity we reran the thermal tolerance models using minimum and maximum temperatures selected for activity. A net gain in available activity time was predicted under climate change for all three species; while air temperatures were predicted to exceed maximum temperatures selected for activity with increasing frequency, the change was not as great as the predicted decline in air temperatures below minimum temperatures selected for activity. We hypothesise that the major effect of rising air temperatures under climate change is an increase in available activity period for both the widespread lowland and highland species. The

  4. Prediction of objectively measured physical activity and sedentariness among blue-collar workers using survey questionnaires

    DEFF Research Database (Denmark)

    Gupta, Nidhi; Heiden, Marina; Mathiassen, Svend Erik

    2016-01-01

    responded to a questionnaire containing information about personal and work related variables, available in most large epidemiological studies and surveys. Workers also wore accelerometers for 1-4 days measuring time spent sedentary and in physical activity, defined as non-sedentary time. Least......-squares linear regression models were developed, predicting objectively measured exposures from selected predictors in the questionnaire. RESULTS: A full prediction model based on age, gender, body mass index, job group, self-reported occupational physical activity (OPA), and self-reported occupational sedentary...

  5. Land use change and prediction in the Baimahe Basin using GIS and CA-Markov model

    International Nuclear Information System (INIS)

    Wang, Shixu; Zhang, Zulu; Wang, Xue

    2014-01-01

    Using ArcGIS and IDRISI, land use dynamics and Shannon entropy information were applied in this paper to analyze the quantity and structure change in the Baimahe Basin from 1996 to 2008. A CA-Markov model was applied to predict the land use patterns in 2020. Results showed that farmland, forest and construction land are the dominant land use types in the Baimahe Basin. From 1996 to 2008, areas of farmland and forest decreased and other land use types increased, with construction land increasing the most. The prediction results showed that the changes in land use patterns from 2008 to 2020 would be the same with those from 1996 to 2008. Main changes are the transiting out of farmland and forest and the transiting in of construction land. The order degree of the whole basin goes on decreasing. Measures of farmland protection and grain for green projects should be adopted to enhance the stability of land use system in the Baimahe Basin in order to promote regional sustainable development

  6. Predicting incentives to change among adolescents with substance abuse disorder.

    Science.gov (United States)

    Breda, Carolyn; Heflinger, Craig Anne

    2004-05-01

    While interest in understanding the incentives to change among individuals with substance abuse disorders is growing, little is known about incentives among adolescents with substance abuse disorders who are participating in formal services. The present research assesses the degree and nature of motivation and treatment readiness among adolescents admitted to substance abuse services, and whether such factors vary across significant subgroups of youth based on their social, legal, or clinical profiles. Data are based on interviews with 249 youth between 12 and 18 years of age who have been admitted to either inpatient, residential, or outpatient substance abuse treatment. Measures are adapted from an instrument developed to assess multiple domains of motivation to change (e.g., intrinsic and extrinsic motivation, treatment readiness). Results suggest that the incentive to change among adolescents with substance-abusing behavior is modest at best, regardless of dimension. Nonetheless, ethnicity, type of substance use, and psychopathology significantly predict incentives to change, though the predictors depend on which dimension is considered. The most robust predictor of incentives is the severity of negative consequences associated with youth's substance use--the greater the severity, the greater the incentives. Findings underscore the need to examine the utility and dimensionality of incentive for treatment planning, while at the same time, they identify factors that treatment planners can consider as they seek ways to enhance incentives and help adolescents with substance use disorders attain positive outcomes.

  7. GRIND2-based 3D-QSAR and prediction of activity spectra for symmetrical bis-pyridinium salts with promastigote antileishmanial activity.

    Science.gov (United States)

    Diniz, Evelyn Mirella Lopes Pina; Tomich de Paula da Silva, Carlos Henrique; Gómez-Perez, Verónica; Federico, Leonardo Bruno; Campos Rosa, Joaquín María

    2017-08-01

    Leishmaniasis is a major group of neglected tropical diseases caused by the protozoan parasite Leishmania. About 12 million people are affected in 98 countries and 350 million people worldwide are at risk of infection. Current leishmaniasis treatments rely on a relatively small arsenal of drugs, including amphotericin B, pentamidine and others, which in general have some type of inconvenience. Recently, we have synthesized antileishmanial bis-pyridinium derivatives and symmetrical bis-pyridinium cyclophanes. These compounds are considered structural analogues of pentamidine, where the amidino moiety, protonated at physiological pH, is replaced by a positively charged nitrogen atom as a pyridinium ring. In this work, a statistically significant GRIND2-based 3D-QSAR model was built and biological activity predictions were in silico carried out allowing rationalization of the different activities recently obtained against Leishmania donovani (in L. donovani promastigotes) for a data set of 19 bis-pyridinium compounds. We will emphasize the most important structural requirements to improve the biological activity and probable interactions with the biological receptor as a guide for lead and prototype optimization. In addition, since no information about the actual biological target for this series of active compounds is provided, we have used Prediction of Activity Spectra for Biologically Active Substances to propose our compounds as potential nicotinic α6β3β4α5 receptor antagonists. This proposal is reinforced by the high structural similarity observed between our compounds and several anthelmintic drugs in current clinical use, which have the same drug action mechanism here predicted. Such new findings would be confirmed with further and additional experimental assays.

  8. Change in BMI accurately predicted by social exposure to acquaintances.

    Science.gov (United States)

    Oloritun, Rahman O; Ouarda, Taha B M J; Moturu, Sai; Madan, Anmol; Pentland, Alex Sandy; Khayal, Inas

    2013-01-01

    Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (pchange in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

  9. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers

    International Nuclear Information System (INIS)

    Ellis, Katherine; Lanckriet, Gert; Kerr, Jacqueline; Godbole, Suneeta; Wing, David; Marshall, Simon

    2014-01-01

    Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6 min bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE. (paper)

  10. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.

    Science.gov (United States)

    Ellis, Katherine; Kerr, Jacqueline; Godbole, Suneeta; Lanckriet, Gert; Wing, David; Marshall, Simon

    2014-11-01

    Wrist accelerometers are being used in population level surveillance of physical activity (PA) but more research is needed to evaluate their validity for correctly classifying types of PA behavior and predicting energy expenditure (EE). In this study we compare accelerometers worn on the wrist and hip, and the added value of heart rate (HR) data, for predicting PA type and EE using machine learning. Forty adults performed locomotion and household activities in a lab setting while wearing three ActiGraph GT3X+ accelerometers (left hip, right hip, non-dominant wrist) and a HR monitor (Polar RS400). Participants also wore a portable indirect calorimeter (COSMED K4b2), from which EE and metabolic equivalents (METs) were computed for each minute. We developed two predictive models: a random forest classifier to predict activity type and a random forest of regression trees to estimate METs. Predictions were evaluated using leave-one-user-out cross-validation. The hip accelerometer obtained an average accuracy of 92.3% in predicting four activity types (household, stairs, walking, running), while the wrist accelerometer obtained an average accuracy of 87.5%. Across all 8 activities combined (laundry, window washing, dusting, dishes, sweeping, stairs, walking, running), the hip and wrist accelerometers obtained average accuracies of 70.2% and 80.2% respectively. Predicting METs using the hip or wrist devices alone obtained root mean square errors (rMSE) of 1.09 and 1.00 METs per 6 min bout, respectively. Including HR data improved MET estimation, but did not significantly improve activity type classification. These results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using accelerometers. The wrist accelerometer proved more useful in predicting activities with significant arm movement, while the hip accelerometer was superior for predicting locomotion and estimating EE.

  11. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  12. Changing the world through shareholder activism?

    Directory of Open Access Journals (Sweden)

    Joakim Sandberg

    2011-05-01

    Full Text Available As one of the more progressive facets of the socially responsibleinvestment (SRI movement, shareholder activism isgenerally recommended or justified on the grounds that itcan create social change. But how effective are differentkinds of activist campaigns likely to be in this regard? Thisarticle outlines the full range of different ways in whichshareholder activism could make a difference by carefullygoing through, first, all the more specific lines of actiontypically included under the shareholder activismumbrella and, second, all of the different ways in which ithas been suggested that these could influence the activitiesof commercial companies. It is argued that – althoughmuch more empirical research is needed in the area – thereare at least theoretical reasons for thinking that it will bedifficult to influence companies through the standardactions of filing or voting on shareholder resolutions.However, some alternative strategies open to activists mayallow them to increase their efficacy. It is specificallyargued that even individual investors could be able to pushfor corporate change through devising a radically selfsacrificialcampaign that manages to get the attention ofpowerful forces outside the corporate sphere.

  13. Five-year change in physical activity is associated with changes in cardiovascular disease risk factors: the Inter99 study

    DEFF Research Database (Denmark)

    Aadahl, Mette; von Huth Smith, L; Pisinger, Charlotte

    2009-01-01

    OBJECTIVE: To evaluate whether five-year changes in self-reported physical activity level were associated with changes in waist circumference, weight, serum lipids and blood pressure. METHODS: In the Inter99 study (1999-2006) in Copenhagen, Denmark, 4039 men and women (30-60 years) answered quest....... Change in physical activity level induced a significant change in HDL concentration in men only. Women's use of hormone replacement therapy may partly explain this gender difference.......OBJECTIVE: To evaluate whether five-year changes in self-reported physical activity level were associated with changes in waist circumference, weight, serum lipids and blood pressure. METHODS: In the Inter99 study (1999-2006) in Copenhagen, Denmark, 4039 men and women (30-60 years) answered...... questions on lifestyle and provided blood samples and anthropometric measures at baseline and after five years. Multiple regression analyses were performed with five-year value of each cardiovascular biomarker as outcome and change in physical activity level as explanatory variable. RESULTS: Approximately...

  14. Modeling behavioral thermoregulation in a climate change sentinel.

    Science.gov (United States)

    Moyer-Horner, Lucas; Mathewson, Paul D; Jones, Gavin M; Kearney, Michael R; Porter, Warren P

    2015-12-01

    When possible, many species will shift in elevation or latitude in response to rising temperatures. However, before such shifts occur, individuals will first tolerate environmental change and then modify their behavior to maintain heat balance. Behavioral thermoregulation allows animals a range of climatic tolerances and makes predicting geographic responses under future warming scenarios challenging. Because behavioral modification may reduce an individual's fecundity by, for example, limiting foraging time and thus caloric intake, we must consider the range of behavioral options available for thermoregulation to accurately predict climate change impacts on individual species. To date, few studies have identified mechanistic links between an organism's daily activities and the need to thermoregulate. We used a biophysical model, Niche Mapper, to mechanistically model microclimate conditions and thermoregulatory behavior for a temperature-sensitive mammal, the American pika (Ochotona princeps). Niche Mapper accurately simulated microclimate conditions, as well as empirical metabolic chamber data for a range of fur properties, animal sizes, and environmental parameters. Niche Mapper predicted pikas would be behaviorally constrained because of the need to thermoregulate during the hottest times of the day. We also showed that pikas at low elevations could receive energetic benefits by being smaller in size and maintaining summer pelage during longer stretches of the active season under a future warming scenario. We observed pika behavior for 288 h in Glacier National Park, Montana, and thermally characterized their rocky, montane environment. We found that pikas were most active when temperatures were cooler, and at sites characterized by high elevations and north-facing slopes. Pikas became significantly less active across a suite of behaviors in the field when temperatures surpassed 20°C, which supported a metabolic threshold predicted by Niche Mapper. In general

  15. Mood and the Market: Can Press Reports of Investors' Mood Predict Stock Prices?

    Science.gov (United States)

    Scherbaum, Charles A.; Kammeyer-Mueller, John D.

    2013-01-01

    We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders' affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors' emotion on a given trading day, could predict the next day's opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices. PMID:24015202

  16. The climatic change induced by human activities

    International Nuclear Information System (INIS)

    Balairon Ruiz, L.

    2004-01-01

    The climate of the Earth is a changing climate. Along their history many natural climate changes have existed in all time scales. At the present time we use the term climate changes have existed in all time scales. At the present time we use the term climate change in a restricted way, understanding that we have referring to a singular change that has their origin in the modification of the natural composition of the atmosphere. The increase of greenhouse gases from the second half the XVIII century, is due to the human activities of fossil fuels burning to obtain energy and to industrial and agricultural activities needing for the development of a world which population has been duplicated between 1960 and 2000, until overcoming the 6,000 million inhabitants. In particular, the concentrations of carbon dioxide-CO 2 have increased in a 34%. The more recent emission scenarios proposed by the IPCC (SRES, 2000) are based on hypothesis about the population evolution, the energy consumption and the word patterns of development, which are grouped in four families dominated as A1, A2, B1 and B2. The answer for these scenarios from a range of climate models results in an increase of the world average surface atmospheric temperature between 1,4 degree centigrade and 5,8 degree centigrade and a corresponding sea level rise understood between 9 cm and 88 cm. The changes in the precipitation patterns show us that could be above to the current one in high and media latitudes and below in subtropical latitudes, with exceptions highly depending of the model used. (Author)

  17. Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

    Science.gov (United States)

    Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

    2017-03-01

    Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent.

  18. The use of specialisation indices to predict vulnerability of coral-feeding butterflyfishes to environmental change

    KAUST Repository

    Lawton, Rebecca J.; Pratchett, Morgan S.; Berumen, Michael L.

    2011-01-01

    , the utility of these different indices to predict vulnerability to future environmental change is unknown. Here we compare the performance of specialisation indices using coral-feeding butterflyfishes as a model group. Our aims were to 1) quantify the dietary

  19. Changing patterns of brain activation during maze learning.

    Science.gov (United States)

    Van Horn, J D; Gold, J M; Esposito, G; Ostrem, J L; Mattay, V; Weinberger, D R; Berman, K F

    1998-05-18

    Recent research has found that patterns of brain activation involving the frontal cortex during novel task performance change dramatically following practice and repeat performance. Evidence for differential left vs. right frontal lobe activation, respectively, during episodic memory encoding and retrieval has also been reported. To examine these potentially related issues regional cerebral blood flow (rCBF) was measured in 15 normal volunteers using positron emission tomography (PET) during the naive and practiced performance of a maze task paradigm. SPM analysis indicated a largely right-sided, frontal lobe activation during naive performance. Following training and practice, performance of the same maze task elicited a more posterior pattern of rCBF activation involving posterior cingulate and precuneus. The change in the pattern of rCBF activation between novel and practiced task conditions agrees with results found in previous studies using repeat task methodology, and indicates that the neural circuitry required for encoding novel task information differs from that required when the same task has become familiar and information is being recalled. The right-sided preponderance of activation during naive performance may relate to task novelty and the spatially-based nature of the stimuli, whereas posterior areas activated during repeat performance are those previously found to be associated with visuospatial memory recall. Activation of these areas, however, does not agree with previously reported findings of left-sided activation during verbal episodic memory encoding and right-sided activation during retrieval, suggesting different neural substrates for verbal and visuospatial processing within memory. Copyright 1998 Elsevier Science B.V.

  20. Analysis and prediction of daily physical activity level data using autoregressive integrated moving average models

    NARCIS (Netherlands)

    Long, Xi; Pauws, S.C.; Pijl, M.; Lacroix, J.; Goris, A.H.C.; Aarts, R.M.

    2009-01-01

    Results are provided on predicting daily physical activity level (PAL) data from past data of participants of a physical activity lifestyle program aimed at promoting a healthier lifestyle consisting of more physical exercise. The PAL data quantifies the level of a person’s daily physical activity

  1. Changes in physical activity and all-cause mortality in COPD

    DEFF Research Database (Denmark)

    Vaes, Anouk W; Garcia-Aymerich, Judith; Marott, Jacob L

    2014-01-01

    -up was associated with an increased mortality risk in subjects with and without COPD. These observational data suggest that it is important to assess and encourage physical activity in the earliest stages of COPD in order to maintain a physical activity level that is as high as possible, as this is associated......Little is known about changes in physical activity in subjects with chronic obstructive pulmonary disease (COPD) and its impact on mortality. Therefore, we aimed to study changes in physical activity in subjects with and without COPD and the impact of physical activity on mortality risk. Subjects......, respectively) were included. COPD subjects with moderate or high baseline physical activity who reported low physical activity level at follow-up had the highest hazard ratios of mortality (1.73 and 2.35, respectively; both pphysical activity, no differences were...

  2. Predicted Changes in Climatic Niche and Climate Refugia of Conservation Priority Salamander Species in the Northeastern United States

    Directory of Open Access Journals (Sweden)

    William B. Sutton

    2014-12-01

    Full Text Available Global climate change represents one of the most extensive and pervasive threats to wildlife populations. Amphibians, specifically salamanders, are particularly susceptible to the effects of changing climates due to their restrictive physiological requirements and low vagility; however, little is known about which landscapes and species are vulnerable to climate change. Our study objectives included, (1 evaluating species-specific predictions (based on 2050 climate projections and vulnerabilities to climate change and (2 using collective species responses to identify areas of climate refugia for conservation priority salamanders in the northeastern United States. All evaluated salamander species were projected to lose a portion of their climatic niche. Averaged projected losses ranged from 3%–100% for individual species, with the Cow Knob Salamander (Plethodon punctatus, Cheat Mountain Salamander (Plethodon nettingi, Shenandoah Mountain Salamander (Plethodon virginia, Mabee’s Salamander (Ambystoma mabeei, and Streamside Salamander (Ambystoma barbouri predicted to lose at least 97% of their landscape-scale climatic niche. The Western Allegheny Plateau was predicted to lose the greatest salamander climate refugia richness (i.e., number of species with a climatically-suitable niche in a landscape patch, whereas the Central Appalachians provided refugia for the greatest number of species during current and projected climate scenarios. Our results can be used to identify species and landscapes that are likely to be further affected by climate change and potentially resilient habitats that will provide consistent climatic conditions in the face of environmental change.

  3. Spontaneous local alpha oscillations predict motion-induced blindness.

    Science.gov (United States)

    Händel, Barbara F; Jensen, Ole

    2014-11-01

    Bistable visual illusions are well suited for exploring the neuronal states of the brain underlying changes in perception. In this study, we investigated oscillatory activity associated with 'motion-induced blindness' (MIB), which denotes the perceptual disappearance of salient target stimuli when a moving pattern is superimposed on them (Bonneh et al., ). We applied an MIB paradigm in which illusory target disappearances would occur independently in the left and right hemifields. Both illusory and real target disappearance were followed by an alpha lateralization with weaker contralateral than ipsilateral alpha activity (~10 Hz). However, only the illusion showed early alpha lateralization in the opposite direction, which preceded the alpha effect present for both conditions and coincided with the estimated onset of the illusion. The duration of the illusory disappearance was further predicted by the magnitude of this early lateralization when considered over subjects. In the gamma band (60-80 Hz), we found an increase in activity contralateral relative to ipsilateral only after a real disappearance. Whereas early alpha activity was predictive of onset and length of the illusory percept, gamma activity showed no modulation in relation to the illusion. Our study demonstrates that the spontaneous changes in visual alpha activity have perceptual consequences. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  4. Variation in dietary intake and physical activity pattern as predictors of change in body mass index (BMI) Z-score among Brazilian adolescents.

    Science.gov (United States)

    Enes, Carla C; Slater, Betzabeth

    2013-06-01

    To assess whether changes in dietary intake and physical activity pattern are associated with the annual body mass index (BMI) z-score change among adolescents. The study was conducted in public schools in the city of Piracicaba, Sao Paulo, Brazil, with a probabilistic sample of 431 adolescents participating in wave I (2004) (hereafter, baseline) and 299 in wave II (2005) (hereafter, follow-up). BMI, usual food intake, physical activity, screen time, sexual maturation and demographic variables were assessed twice. The association between annual change in food intake, physical activity, screen time, and annual BMI z-score changes were assessed by multiple regression. The study showed a positive variation in BMI z-score over one-year. Among variables related to physical activity pattern only playing videogame and using computer increased over the year. The intake of fruits and vegetables and sugar-sweetened beverages increased over one year, while the others variables showed a reduction. An increased consumption of fatty foods (β = 0.04, p = 0.04) and sweetened natural fruit juices (β = 0.05, p = 0.03) was positively associated with the rise in BMI z-score. Unhealthy dietary habits can predict the BMI z-score gain more than the physical activity pattern. The intake of fatty foods and sweetened fruit juices is associated with the BMI z-score over one year.

  5. Variation in dietary intake and physical activity pattern as predictors of change in body mass index (BMI Z-score among Brazilian adolescents*

    Directory of Open Access Journals (Sweden)

    Carla C. Enes

    2013-06-01

    Full Text Available Objective: To assess whether changes in dietary intake and physical activity pattern are associated with the annual body mass index (BMI z-score change among adolescents. Methods: The study was conducted in public schools in the city of Piracicaba, Sao Paulo, Brazil, with a probabilistic sample of 431 adolescents participating in wave I (2004 (hereafter, baseline and 299 in wave II (2005 (hereafter, follow-up. BMI, usual food intake, physical activity, screen time, sexual maturation and demographic variables were assessed twice. The association between annual change in food intake, physical activity, screen time, and annual BMI z-score changes were assessed by multiple regression. Results: The study showed a positive variation in BMI z-score over one-year. Among variables related to physical activity pattern only playing videogame and using computer increased over the year. The intake of fruits and vegetables and sugar-sweetened beverages increased over one year, while the others variables showed a reduction. An increased consumption of fatty foods (β = 0.04, p = 0.04 and sweetened natural fruit juices (β = 0.05, p = 0.03 was positively associated with the rise in BMI z-score. Conclusions: Unhealthy dietary habits can predict the BMI z-score gain more than the physical activity pattern. The intake of fatty foods and sweetened fruit juices is associated with the BMI z-score over one year.

  6. A novel method for predicting activity of cis-regulatory modules, based on a diverse training set.

    Science.gov (United States)

    Yang, Wei; Sinha, Saurabh

    2017-01-01

    With the rapid emergence of technologies for locating cis-regulatory modules (CRMs) genome-wide, the next pressing challenge is to assign precise functions to each CRM, i.e. to determine the spatiotemporal domains or cell-types where it drives expression. A popular approach to this task is to model the typical k-mer composition of a set of CRMs known to drive a common expression pattern, and assign that pattern to other CRMs exhibiting a similar k-mer composition. This approach does not rely on prior knowledge of transcription factors relevant to the CRM or their binding motifs, and is thus more widely applicable than motif-based methods for predicting CRM activity, but is also prone to false positive predictions. We present a novel strategy to improve the above-mentioned approach: to predict if a CRM drives a specific gene expression pattern, assess not only how similar the CRM is to other CRMs with similar activity but also to CRMs with distinct activities. We use a state-of-the-art statistical method to quantify a CRM's sequence similarity to many different training sets of CRMs, and employ a classification algorithm to integrate these similarity scores into a single prediction of the CRM's activity. This strategy is shown to significantly improve CRM activity prediction over current approaches. Our implementation of the new method, called IMMBoost, is freely available as source code, at https://github.com/weiyangedward/IMMBoost CONTACT: sinhas@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. Changes in automatic threat processing precede and predict clinical changes with exposure-based cognitive-behavior therapy for panic disorder.

    Science.gov (United States)

    Reinecke, Andrea; Waldenmaier, Lara; Cooper, Myra J; Harmer, Catherine J

    2013-06-01

    Cognitive behavioral therapy (CBT) is an effective treatment for emotional disorders such as anxiety or depression, but the mechanisms underlying successful intervention are far from understood. Although it has been a long-held view that psychopharmacological approaches work by directly targeting automatic emotional information processing in the brain, it is usually postulated that psychological treatments affect these processes only over time, through changes in more conscious thought cycles. This study explored the role of early changes in emotional information processing in CBT action. Twenty-eight untreated patients with panic disorder were randomized to a single session of exposure-based CBT or waiting group. Emotional information processing was measured on the day after intervention with an attentional visual probe task, and clinical symptoms were assessed on the day after intervention and at 4-week follow-up. Vigilance for threat information was decreased in the treated group, compared with the waiting group, the day after intervention, before reductions in clinical symptoms. The magnitude of this early effect on threat vigilance predicted therapeutic response after 4 weeks. Cognitive behavioral therapy rapidly affects automatic processing, and these early effects are predictive of later therapeutic change. Such results suggest very fast action on automatic processes mediating threat sensitivity, and they provide an early marker of treatment response. Furthermore, these findings challenge the notion that psychological treatments work directly on conscious thought processes before automatic information processing and imply a greater similarity between early effects of pharmacological and psychological treatments for anxiety than previously thought. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  8. Predicting physical activity energy expenditure in wheelchair users with a multisensor device.

    Science.gov (United States)

    Nightingale, T E; Walhin, J P; Thompson, D; Bilzon, J L J

    2015-01-01

    To assess the error in predicting physical activity energy expenditure (PAEE), using a multisensor device in wheelchair users, and to examine the efficacy of using an individual heart rate calibration (IC) method. 15 manual wheelchair users (36±10 years, 72±11 kg) completed 10 activities: resting, folding clothes, wheelchair propulsion on a 1% gradient (3456 and 7 km/h) and propulsion at 4 km/h (with an additional 8% of body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. Criterion PAEE was measured using a computerised indirect calorimetry system. Participants wore a combined accelerometer and heart rate monitor (Actiheart). They also performed an incremental arm crank ergometry test to exhaustion which permitted retrospective individual calibration of the Actiheart for the activity protocol. Linear regression analysis was conducted between criterion (indirect calorimetry) and estimated PAEE from the Actiheart using the manufacturer's proprietary algorithms (group calibration, GC) or IC. Bland-Altman plots were used and mean absolute error was calculated to assess the agreement between criterion values and estimated PAEE. Predicted PAEE was significantly (p<0.01) correlated with criterion PAEE (GC, r=0.76 and IC, r=0.95). The absolute bias ±95% limits of agreement were 0.51±3.75 and -0.22±0.96 kcal/min for GC and IC, respectively. Mean absolute errors across the activity protocol were 51.4±38.9% using GC and 16.8±15.8% using IC. PAEE can be accurately and precisely estimated using a combined accelerometer and heart rate monitor device, with integration of an IC. Interindividual variance in cardiovascular function and response to exercise is high in this population. Therefore, in manual wheelchair users, we advocate the use of an IC when using the Actiheart to predict PAEE.

  9. Theoretical prediction of thermodynamic activities of liquid Au-Sn-X (X=Bi, Sb, Zn) solder systems

    Energy Technology Data Exchange (ETDEWEB)

    Awe, O.E., E-mail: draweoe2004@yahoo.com [Department of Physics, University of Ibadan, Ibadan (Nigeria); Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife (Nigeria); Oshakuade, O.M. [Department of Physics, University of Ibadan, Ibadan (Nigeria)

    2017-02-15

    Molecular interaction volume model has been theoretically used to predict the thermodynamic activities of tin in Au-Sn-Bi and Au-Sn-Sb and the thermodynamic activity of zinc in Au-Sn-Zn at experimental temperatures 800 K, 873 K and 973 K, respectively. On the premise of agreement between the predicted and experimental values, we predicted the activities of the remaining two components in each of the three systems. This prediction was extended from three cross-sections to five cross-sections, and to temperature range 400–600 K, relevant for applications. Iso-activities were plotted. Results show that addition of tin reduces the tendency for chemical short range order in both Au-Sb and Au-Zn systems, while addition of gold and bismuth, respectively, reduce the tendency for chemical short range order in Sn-Sb and Au-Sn systems. Also, we found that, in the desired high-temperature region for applications, while a combination of chemical order and miscibility of components exist in both Au-Sn-Bi and Au-Sn-Zn systems, only chemical order exist in the Au-Sn-Sb system. Results, further show that increase in temperature reduces the phase separation tendency in Au-Sn-Bi system.

  10. Planning versus action: Different decision-making processes predict plans to change one's diet versus actual dietary behavior.

    Science.gov (United States)

    Kiviniemi, Marc T; Brown-Kramer, Carolyn R

    2015-05-01

    Most health decision-making models posit that deciding to engage in a health behavior involves forming a behavioral intention which then leads to actual behavior. However, behavioral intentions and actual behavior may not be functionally equivalent. Two studies examined whether decision-making factors predicting dietary behaviors were the same as or distinct from those predicting intentions. Actual dietary behavior was proximally predicted by affective associations with the behavior. By contrast, behavioral intentions were predicted by cognitive beliefs about behaviors, with no contribution of affective associations. This dissociation has implications for understanding individual regulation of health behaviors and for behavior change interventions. © The Author(s) 2015.

  11. Change in BMI accurately predicted by social exposure to acquaintances.

    Directory of Open Access Journals (Sweden)

    Rahman O Oloritun

    Full Text Available Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC and R(2. This study found a model that explains 68% (p<0.0001 of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as

  12. Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya.

    Science.gov (United States)

    Aryal, Achyut; Shrestha, Uttam Babu; Ji, Weihong; Ale, Som B; Shrestha, Sujata; Ingty, Tenzing; Maraseni, Tek; Cockfield, Geoff; Raubenheimer, David

    2016-06-01

    Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator-prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km(2)) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km(2) (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator-prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards - a species already facing energetic constraints due to the

  13. New England observed and predicted August stream/river temperature maximum positive daily rate of change points

    Data.gov (United States)

    U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted August stream/river temperature maximum positive daily rate of change in New England based on a...

  14. New England observed and predicted July stream/river temperature maximum positive daily rate of change points

    Data.gov (United States)

    U.S. Environmental Protection Agency — The shapefile contains points with associated observed and predicted July stream/river temperature maximum positive daily rate of change in New England based on a...

  15. New England observed and predicted July maximum negative stream/river temperature daily rate of change points

    Data.gov (United States)

    U.S. Environmental Protection Agency — The shapefile contains points with associated observed and