Strength of figure-ground activity in monkey primary visual cortex predicts saccadic reaction time in a delayed detection task.

PubMed

Supèr, Hans; Lamme, Victor A F

2007-06-01

When and where are decisions made? In the visual system a saccade, which is a fast shift of gaze toward a target in the visual scene, is the behavioral outcome of a decision. Current neurophysiological data and reaction time models show that saccadic reaction times are determined by a build-up of activity in motor-related structures, such as the frontal eye fields. These structures depend on the sensory evidence of the stimulus. Here we use a delayed figure-ground detection task to show that late modulated activity in the visual cortex (V1) predicts saccadic reaction time. This predictive activity is part of the process of figure-ground segregation and is specific for the saccade target location. These observations indicate that sensory signals are directly involved in the decision of when and where to look.

Predicting river travel time from hydraulic characteristics

USGS Publications Warehouse

Jobson, H.E.

2001-01-01

Predicting the effect of a pollutant spill on downstream water quality is primarily dependent on the water velocity, longitudinal mixing, and chemical/physical reactions. Of these, velocity is the most important and difficult to predict. This paper provides guidance on extrapolating travel-time information from one within bank discharge to another. In many cases, a time series of discharge (such as provided by a U.S. Geological Survey stream gauge) will provide an excellent basis for this extrapolation. Otherwise, the accuracy of a travel time extrapolation based on a resistance equation can be greatly improved by assuming the total flow area is composed of two parts, an active and an inactive area. For 60 reaches of 12 rivers with slopes greater than about 0.0002, travel times could be predicted to within about 10% by computing the active flow area using the Manning equation with n = 0.035 and assuming a constant inactive area for each reach. The predicted travel times were not very sensitive to the assumed values of bed slope or channel width.

Premotor neural correlates of predictive motor timing for speech production and hand movement: evidence for a temporal predictive code in the motor system.

PubMed

Johari, Karim; Behroozmand, Roozbeh

2017-05-01

The predictive coding model suggests that neural processing of sensory information is facilitated for temporally-predictable stimuli. This study investigated how temporal processing of visually-presented sensory cues modulates movement reaction time and neural activities in speech and hand motor systems. Event-related potentials (ERPs) were recorded in 13 subjects while they were visually-cued to prepare to produce a steady vocalization of a vowel sound or press a button in a randomized order, and to initiate the cued movement following the onset of a go signal on the screen. Experiment was conducted in two counterbalanced blocks in which the time interval between visual cue and go signal was temporally-predictable (fixed delay at 1000 ms) or unpredictable (variable between 1000 and 2000 ms). Results of the behavioral response analysis indicated that movement reaction time was significantly decreased for temporally-predictable stimuli in both speech and hand modalities. We identified premotor ERP activities with a left-lateralized parietal distribution for hand and a frontocentral distribution for speech that were significantly suppressed in response to temporally-predictable compared with unpredictable stimuli. The premotor ERPs were elicited approximately -100 ms before movement and were significantly correlated with speech and hand motor reaction times only in response to temporally-predictable stimuli. These findings suggest that the motor system establishes a predictive code to facilitate movement in response to temporally-predictable sensory stimuli. Our data suggest that the premotor ERP activities are robust neurophysiological biomarkers of such predictive coding mechanisms. These findings provide novel insights into the temporal processing mechanisms of speech and hand motor systems.

Which domains of childhood physical activity predict physical activity in adulthood? A 20-year prospective tracking study.

PubMed

Cleland, Verity; Dwyer, Terence; Venn, Alison

2012-06-01

It is important to examine how childhood physical activity is related to adult physical activity in order to best tailor physical activity-promotion strategies. The time- and resource-intensive nature of studies spanning childhood into adulthood means the understanding of physical activity trajectories over this time span is limited. This study aimed to determine whether childhood domain-specific physical activities predict domain-specific physical activity 20 years later in adulthood, and whether age and sex play a role in these trajectories. In 1985, 6412 children of age 9-15 years self-reported frequency and duration of discretionary sport and exercise (leisure activity), transport activity, school sport and physical education (PE) in the past week and number of sports played in the past year. In 2004-2006, 2201 of these participants (aged 26-36 years) completed the long International Physical Activity Questionnaire and/or wore a Yamax pedometer. Analyses included partial correlation coefficients and log-binomial regression. Childhood and adult activity were weakly correlated (r=-0.08-0.14). Total weekly physical activity in childhood did not predict adult activity. School PE predicted adult total weekly physical activity and daily steps (older females), while school sport demonstrated inconsistent associations. Leisure and transport activity in childhood predicted adult leisure activity among younger males and older females, respectively. Childhood past year sport participation positively predicted adult physical activity (younger males and older females). Despite modest associations between childhood and adult physical activity that varied by domain, age and sex, promoting a range of physical activities to children of all ages is warranted.

The Costs and Risks of Social Activism: A Study of Sanctuary Movement Activism.

ERIC Educational Resources Information Center

Wiltfang, Gregory L.; McAdam, Doug

1991-01-01

Among 141 activists with varying levels of participation in the sanctuary movement, biographical availability factors--younger age and greater discretionary time--best predict high-cost activism (more hours devoted to the movement), whereas ideological socialization factors best predict high-risk activism (direct contact with refugees). Contains…

Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

PubMed

Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

2005-09-01

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

Constructing realistic engrams: poststimulus activity of hippocampus and dorsal striatum predicts subsequent episodic memory.

PubMed

Ben-Yakov, Aya; Dudai, Yadin

2011-06-15

Encoding of real-life episodic memory commonly involves integration of information as the episode unfolds. Offline processing immediately following event offset is expected to play a role in encoding the episode into memory. In this study, we examined whether distinct human brain activity time-locked to the offset of short narrative audiovisual episodes could predict subsequent memory for the gist of the episodes. We found that a set of brain regions, most prominently the bilateral hippocampus and the bilateral caudate nucleus, exhibit memory-predictive activity time-locked to the stimulus offset. We propose that offline activity in these regions reflects registration to memory of integrated episodes.

Prediction of Therapy Tumor-Absorbed Dose Estimates in I-131 Radioimmunotherapy Using Tracer Data Via a Mixed-Model Fit to Time Activity

PubMed Central

Koral, Kenneth F.; Avram, Anca M.; Kaminski, Mark S.; Dewaraja, Yuni K.

2012-01-01

Abstract Background For individualized treatment planning in radioimmunotherapy (RIT), correlations must be established between tracer-predicted and therapy-delivered absorbed doses. The focus of this work was to investigate this correlation for tumors. Methods The study analyzed 57 tumors in 19 follicular lymphoma patients treated with I-131 tositumomab and imaged with SPECT/CT multiple times after tracer and therapy administrations. Instead of the typical least-squares fit to a single tumor's measured time-activity data, estimation was accomplished via a biexponential mixed model in which the curves from multiple subjects were jointly estimated. The tumor-absorbed dose estimates were determined by patient-specific Monte Carlo calculation. Results The mixed model gave realistic tumor time-activity fits that showed the expected uptake and clearance phases even with noisy data or missing time points. Correlation between tracer and therapy tumor-residence times (r=0.98; p<0.0001) and correlation between tracer-predicted and therapy-delivered mean tumor-absorbed doses (r=0.86; p<0.0001) were very high. The predicted and delivered absorbed doses were within±25% (or within±75 cGy) for 80% of tumors. Conclusions The mixed-model approach is feasible for fitting tumor time-activity data in RIT treatment planning when individual least-squares fitting is not possible due to inadequate sampling points. The good correlation between predicted and delivered tumor doses demonstrates the potential of using a pretherapy tracer study for tumor dosimetry-based treatment planning in RIT. PMID:22947086

Psychological factors related to physical education classes as predictors of students' intention to partake in leisure-time physical activity.

PubMed

Baena-Extremera, Antonio; Granero-Gallegos, Antonio; Ponce-de-León-Elizondo, Ana; Sanz-Arazuri, Eva; Valdemoros-San-Emeterio, María de Los Ángeles; Martínez-Molina, Marina

2016-04-01

In view of the rise in sedentary lifestyle amongst young people, knowledge regarding their intention to partake in physical activity can be decisive when it comes to instilling physical activity habits to improve the current and future health of school students. Therefore, the object of this study was to find a predictive model of the intention to partake in leisure- time physical activity based on motivation, satisfaction and competence. The sample consisted of 347 Spanish, male, high school students and 411 female students aged between 13 and 18 years old. We used a questionnaire made up of the Sport Motivation Scale, Sport Satisfaction Instrument, and the competence factor in the Basic Psychological Needs in Exercise Scale and Intention to Partake in Leisure-Time Physical Activity, all of them adapted to school Physical Education. We carried out confirmatory factor analyses and structural equation models. The intention to partake in leisure-time physical activity was predicted by competence and the latter by satisfaction/fun. Intrinsic motivation was revealed to be the best predictor of satisfaction/fun. Intrinsic motivation should be enhanced in order to predict an intention to partake in physical activity in Physical Education students.

Status of Cycle 23 Forecasts

NASA Technical Reports Server (NTRS)

Hathaway, D. H.

2000-01-01

A number of techniques for predicting solar activity on a solar cycle time scale are identified, described, and tested with historical data. Some techniques, e.g,, regression and curve-fitting, work well as solar activity approaches maximum and provide a month- by-month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but provide an estimate only of the amplitude of the cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. A combination of two uncorrelated geomagnetic precursor techniques provides the most accurate prediction for the amplitude of a solar activity cycle at a time well before activity minimum. This precursor method gave a smoothed sunspot number maximum of 154+21 for cycle 23. A mathematical function dependent upon the time of cycle initiation and the cycle amplitude then describes the level of solar activity for the complete cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between recent activity levels and this function. This Combined Solar Cycle Activity Forecast now gives a smoothed sunspot maximum of 140+20 for cycle 23. The success of the geomagnetic precursors in predicting future solar activity suggests that solar magnetic phenomena at latitudes above the sunspot activity belts are linked to solar activity, which occurs many years later in the lower latitudes.

Diversity of leisure-time sport activities in adolescence as a predictor of leisure-time physical activity in adulthood.

PubMed

Mäkelä, S; Aaltonen, S; Korhonen, T; Rose, R J; Kaprio, J

2017-12-01

Because sustained physical activity is important for a healthy life, this paper examined whether a greater diversity of sport activities during adolescence predicts higher levels of leisure-time physical activity (LTPA) in adulthood. From sport activity participation reported by 17-year-old twins, we formed five groups: 1, 2, 3, 4, and 5+ different sport activities. At follow-up in their mid-thirties, twins were divided into four activity classes based on LTPA, including active commuting. Multinomial regression analyses, adjusted for several confounders, were conducted separately for male (N=1288) and female (N=1770) participants. Further, conditional logistic regression analysis included 23 twin pairs discordant for both diversity of sport activities in adolescence and LTPA in adulthood. The diversity of leisure-time sport activities in adolescence had a significant positive association with adulthood LTPA among females. Membership in the most active adult quartile, compared to the least active quartile, was predicted by participation in 2, 3, 4, and 5+ sport activities in adolescence with odds ratios: 1.52 (P=.11), 1.86 (P=.02), 1.29 (P=.39), and 3.12 (P=5.4e-05), respectively. Within-pair analyses, limited by the small sample of twins discordant for both adolescent activities and adult outcomes, did not replicate the association. A greater diversity of leisure-time sport activities in adolescence predicts higher levels of LTPA in adulthood in females, but the causal nature of this association remains unresolved. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Sleep Quality Prediction From Wearable Data Using Deep Learning.

PubMed

Sathyanarayana, Aarti; Joty, Shafiq; Fernandez-Luque, Luis; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-11-04

The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. ©Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.11.2016.

Sleep Quality Prediction From Wearable Data Using Deep Learning

PubMed Central

Sathyanarayana, Aarti; Joty, Shafiq; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-01-01

Background The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463). Conclusions Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. PMID:27815231

Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning

PubMed Central

Cavanagh, James F.; Frank, Michael J.; Klein, Theresa J.; Allen, John J.B.

2009-01-01

Investigations into action monitoring have consistently detailed a fronto-central voltage deflection in the Event-Related Potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the Feedback Related Negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Medio-frontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations: with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. PMID:19969093

Role of enhanced synoptic activity and its interaction with intra-seasonal oscillations on the lower extended range prediction skill during 2015 monsoon season

NASA Astrophysics Data System (ADS)

Abhilash, S.; Mandal, R.; Dey, A.; Phani, R.; Joseph, S.; Chattopadhyay, R.; De, S.; Agarwal, N. K.; Sahai, A. K.; Devi, S. Sunitha; Rajeevan, M.

2018-01-01

Indian summer monsoon of 2015 was deficient with prominence of short-lived (long-lived) active (break) spells. The real-time extended range forecasts disseminated by Indian Institute of Tropical Meteorology using an indigenous ensemble prediction system (EPS) based on National Center for Environmental Predictions's climate forecast system could broadly predict these intraseasonal fluctuations at shorter time leads (i.e. up to 10 days), but failed to predict at longer leads (15-20 days). Considering the multi-scale nature of Indian Summer Monsoon system, this particular study aims to examine the inability of the EPS in predicting the active/break episodes at longer leads from the perspective of non-linear scale interaction between the synoptic, intraseasonal and seasonal scale. It is found that the 2015 monsoon season was dominated by synoptic scale disturbances that can hinder the prediction on extended range. Further, the interaction between synoptic scale disturbances and low frequency mode was prominent during the season, which might have contributed to the reduced prediction skill at longer leads.

Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits.

PubMed

Rosen, L D; Lim, A F; Felt, J; Carrier, L M; Cheever, N A; Lara-Ruiz, J M; Mendoza, J S; Rokkum, J

2014-06-01

The American Academy of Pediatrics recommends no screen time for children under the age of 2 and limited screen time for all children. However, no such guidelines have been proposed for preteens and teenagers. Further, research shows that children, preteens, and teenagers are using massive amounts of media and those with more screen time have been shown to have increased obesity, reduced physical activity, and decreased health. This study examined the impact of technology on four areas of ill-being-psychological issues, behavior problems, attention problems and physical health-among children (aged 4-8), preteens (9-12), and teenagers (13-18) by having 1030 parents complete an online, anonymous survey about their own and their child's behaviors. Measures included daily technology use, daily food consumption, daily exercise, and health. Hypothesis 1, which posited that unhealthy eating would predict impaired ill-being, was partially supported, particularly for children and preteens. Hypothesis 2, which posited that reduced physical activity would predict diminished health levels, was partially supported for preteens and supported for teenagers. Hypothesis 3, that increased daily technology use would predict ill-being after factoring out eating habits and physical activity, was supported. For children and preteens, total media consumption predicted illbeing while for preteens specific technology uses, including video gaming and electronic communication, predicted ill-being. For teenagers, nearly every type of technological activity predicted poor health. Practical implications were discussed in terms of setting limits and boundaries on technology use and encouraging healthy eating and physical activity at home and at school.

Physical activity, but not sedentary time, influences bone strength in late adolescence.

PubMed

Tan, Vina Ps; Macdonald, Heather M; Gabel, Leigh; McKay, Heather A

2018-03-20

Physical activity is essential for optimal bone strength accrual, but we know little about interactions between physical activity, sedentary time, and bone outcomes in older adolescents. Physical activity (by accelerometer and self-report) positively predicted bone strength and the distal and midshaft tibia in 15-year-old boys and girls. Lean body mass mediated the relationship between physical activity and bone strength in adolescents. To examine the influence of physical activity (PA) and sedentary time on bone strength, structure, and density in older adolescents. We used peripheral quantitative computed tomography to estimate bone strength at the distal tibia (8% site; bone strength index, BSI) and tibial midshaft (50% site; polar strength strain index, SSI p ) in adolescent boys (n = 86; 15.3 ± 0.4 years) and girls (n = 106; 15.3 ± 0.4 years). Using accelerometers (GT1M, Actigraph), we measured moderate-to-vigorous PA (MVPA Accel ), vigorous PA (VPA Accel ), and sedentary time in addition to self-reported MVPA (MVPA PAQ-A ) and impact PA (ImpactPA PAQ-A ). We examined relations between PA and sedentary time and bone outcomes, adjusting for ethnicity, maturity, tibial length, and total body lean mass. At the distal tibia, MVPA Accel and VPA Accel positively predicted BSI (explained 6-7% of the variance, p < 0.05). After adjusting for lean mass, only VPA Accel explained residual variance in BSI. At the tibial midshaft, MVPA Accel , but not VPA Accel , positively predicted SSI p (explained 3% of the variance, p = 0.01). Lean mass attenuated this association. MVPA PAQ-A and ImpactPA PAQ-A also positively predicted BSI and SSI p (explained 2-4% of the variance, p < 0.05), but only ImpactPA PAQ-A explained residual variance in BSI after accounting for lean mass. Sedentary time did not independently predict bone strength at either site. Greater tibial bone strength in active adolescents is mediated, in part, by lean mass. Despite spending most of their day in sedentary pursuits, adolescents' bone strength was not negatively influenced by sedentary time.

Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

NASA Astrophysics Data System (ADS)

Anggraeni, Novia Antika

2015-04-01

The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano's inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 - 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between -2.86 up to 5.49 days.

Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

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

Anggraeni, Novia Antika, E-mail: novia.antika.a@gmail.com

The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration ofmore » the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.« less

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

NASA Astrophysics Data System (ADS)

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.

Spatial variation in water loss predicts terrestrial salamander distribution and population dynamics.

PubMed

Peterman, W E; Semlitsch, R D

2014-10-01

Many patterns observed in ecology, such as species richness, life history variation, habitat use, and distribution, have physiological underpinnings. For many ectothermic organisms, temperature relationships shape these patterns, but for terrestrial amphibians, water balance may supersede temperature as the most critical physiologically limiting factor. Many amphibian species have little resistance to water loss, which restricts them to moist microhabitats, and may significantly affect foraging, dispersal, and courtship. Using plaster models as surrogates for terrestrial plethodontid salamanders (Plethodon albagula), we measured water loss under ecologically relevant field conditions to estimate the duration of surface activity time across the landscape. Surface activity time was significantly affected by topography, solar exposure, canopy cover, maximum air temperature, and time since rain. Spatially, surface activity times were highest in ravine habitats and lowest on ridges. Surface activity time was a significant predictor of salamander abundance, as well as a predictor of successful recruitment; the probability of a juvenile salamander occupying an area with high surface activity time was two times greater than an area with limited predicted surface activity. Our results suggest that survival, recruitment, or both are demographic processes that are affected by water loss and the ability of salamanders to be surface-active. Results from our study extend our understanding of plethodontid salamander ecology, emphasize the limitations imposed by their unique physiology, and highlight the importance of water loss to spatial population dynamics. These findings are timely for understanding the effects that fluctuating temperature and moisture conditions predicted for future climates will have on plethodontid salamanders.

'It is Time to Prepare the Next patient' Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies.

PubMed

Guédon, Annetje C P; Paalvast, M; Meeuwsen, F C; Tax, D M J; van Dijke, A P; Wauben, L S G L; van der Elst, M; Dankelman, J; van den Dobbelsteen, J J

2016-12-01

Operating Room (OR) scheduling is crucial to allow efficient use of ORs. Currently, the predicted durations of surgical procedures are unreliable and the OR schedulers have to follow the progress of the procedures in order to update the daily planning accordingly. The OR schedulers often acquire the needed information through verbal communication with the OR staff, which causes undesired interruptions of the surgical process. The aim of this study was to develop a system that predicts in real-time the remaining procedure duration and to test this prediction system for reliability and usability in an OR. The prediction system was based on the activation pattern of one single piece of equipment, the electrosurgical device. The prediction system was tested during 21 laparoscopic cholecystectomies, in which the activation of the electrosurgical device was recorded and processed in real-time using pattern recognition methods. The remaining surgical procedure duration was estimated and the optimal timing to prepare the next patient for surgery was communicated to the OR staff. The mean absolute error was smaller for the prediction system (14 min) than for the OR staff (19 min). The OR staff doubted whether the prediction system could take all relevant factors into account but were positive about its potential to shorten waiting times for patients. The prediction system is a promising tool to automatically and objectively predict the remaining procedure duration, and thereby achieve optimal OR scheduling and streamline the patient flow from the nursing department to the OR.

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

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.

Predicting Solar Activity Using Machine-Learning Methods

NASA Astrophysics Data System (ADS)

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.

Viscoelastic blood coagulation measurement with Sonoclot predicts postoperative bleeding in cardiac surgery after heparin reversal.

PubMed

Bischof, Dominique B; Ganter, Michael T; Shore-Lesserson, Linda; Hartnack, Sonja; Klaghofer, Richard; Graves, Kirk; Genoni, Michele; Hofer, Christoph K

2015-01-01

The aim of the study was to determine if Sonoclot with its sensitive glass bead-activated, viscoelastic test can predict postoperative bleeding in patients undergoing cardiac surgery at predefined time points. A prospective, observational clinical study. A teaching hospital, single center. Consecutive patients undergoing cardiac surgery (N = 300). Besides routine laboratory coagulation studies and heparin management with standard (kaolin) activated clotting time, additional native blood samples were analyzed on a Sonoclot using glass bead-activated tests. Glass bead-activated clotting time, clot rate, and platelet function were recorded immediately before anesthesia induction and at the end of surgery after heparin reversal but before chest closure. Primary outcome was postoperative blood loss (chest tube drainage at 4, 8, and 12 hours postoperatively). Secondary outcome parameters were transfusion requirements, need for surgical re-exploration, time of mechanical ventilation, length of intensive care unit and hospital stay, and hospital morbidity and mortality. Patients were categorized into "bleeders" and "nonbleeders." Patient characteristics, operations, preoperative standard laboratory parameters, and procedural times were comparable between bleeders and nonbleeders except for sex and age. Bleeders had higher rates of transfusions, surgical re-explorations, and complications. Only glass bead measurements by Sonoclot after heparin reversal before chest closure but not preoperatively were predictive for increased postoperative bleeding. Sonoclot with its glass bead-activated tests may predict the risk for postoperative bleeding in patients undergoing cardiac surgery at the end of surgery after heparin reversal but before chest closure. Copyright © 2015 Elsevier Inc. All rights reserved.

Less-structured time in children's daily lives predicts self-directed executive functioning.

PubMed

Barker, Jane E; Semenov, Andrei D; Michaelson, Laura; Provan, Lindsay S; Snyder, Hannah R; Munakata, Yuko

2014-01-01

Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.

Less-structured time in children's daily lives predicts self-directed executive functioning

PubMed Central

Barker, Jane E.; Semenov, Andrei D.; Michaelson, Laura; Provan, Lindsay S.; Snyder, Hannah R.; Munakata, Yuko

2014-01-01

Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6–7 year-old children's daily, annual, and typical schedules. We categorized children's activities as “structured” or “less-structured” based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up. PMID:25071617

Time spent in housework and leisure: links with parents' physiological recovery from work.

PubMed

Saxbe, Darby E; Repetti, Rena L; Graesch, Anthony P

2011-04-01

Spouses' balancing of housework and leisure activities at home may affect their recovery from work. This paper reports on a study of everyday family life in which 30 dual-earner couples were tracked around their homes by researchers who recorded their locations and activities every 10 min. For women, the most frequently pursued activities at home were housework, communication, and leisure; husbands spent the most time in leisure activities, followed by communication and housework. Spouses differed in their total time at home and their proportion of time devoted to leisure and housework activities, with wives observed more often in housework and husbands observed more often in leisure activities. Both wives and husbands who devoted more time to housework had higher levels of evening cortisol and weaker afternoon-to-evening recovery. For wives, husbands' increased housework time also predicted stronger evening cortisol recovery. When both spouses' activities were entered in the same model, leisure predicted husbands' evening cortisol, such that husbands who apportioned more time to leisure, and whose wives apportioned less time to leisure, showed stronger after-work recovery. These results suggest that the division of labor within couples may have implications for physical health.

Task relevance modulates the behavioural and neural effects of sensory predictions

PubMed Central

Friston, Karl J.; Nobre, Anna C.

2017-01-01

The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants’ brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling. PMID:29206225

The contribution of former work-related activity levels to predict physical activity and sedentary time during early retirement: moderating role of educational level and physical functioning.

PubMed

Van Dyck, Delfien; Cardon, Greet; Deforche, Benedicte; De Bourdeaudhuij, Ilse

2015-01-01

The transition to retirement introduces a decline in total physical activity and an increase in TV viewing time. Nonetheless, as more time becomes available, early retirement is an ideal stage to implement health interventions. Therefore, knowledge on specific determinants of physical activity and sedentary time is needed. Former work-related physical activity has been proposed as a potential determinant, but concrete evidence is lacking. The aim of this study was to examine if former work-related sitting, standing, walking or vigorous activities predict physical activity and sedentary time during early retirement. Additionally, moderating effects of educational level and physical functioning were examined. In total, 392 recently retired Belgian adults (>6 months, <5 years) completed the International Physical Activity Questionnaire, the SF-36 Health Survey and a questionnaire on sociodemographics and former work-related activities. Generalized linear regression analyses were conducted in R. Moderating effects were examined by adding cross-products to the models. More former work-related sitting was predictive of more screen time during retirement. Lower levels of former work-related vigorous activities and higher levels of former work-related walking were associated with respectively more cycling for transport and more walking for transport during retirement. None of the predictors significantly explained passive transportation, cycling and walking for recreation, and leisure-time moderate-to-vigorous physical activity during retirement. Several moderating effects were found, but the direction of the interactions was not univocal. Former-work related behaviors are of limited importance to explain physical activity during early retirement, so future studies should focus on other individual, social and environmental determinants. Nonetheless, adults who previously had a sedentary job had higher levels of screen time during retirement, so this is an important subgroup to focus on during interventions. Because of the inconsistent moderating effects of educational level and physical functioning, no clear recommendations can be formulated.

The Contribution of Former Work-Related Activity Levels to Predict Physical Activity and Sedentary Time during Early Retirement: Moderating Role of Educational Level and Physical Functioning

PubMed Central

Van Dyck, Delfien; Cardon, Greet; Deforche, Benedicte; De Bourdeaudhuij, Ilse

2015-01-01

Background The transition to retirement introduces a decline in total physical activity and an increase in TV viewing time. Nonetheless, as more time becomes available, early retirement is an ideal stage to implement health interventions. Therefore, knowledge on specific determinants of physical activity and sedentary time is needed. Former work-related physical activity has been proposed as a potential determinant, but concrete evidence is lacking. The aim of this study was to examine if former work-related sitting, standing, walking or vigorous activities predict physical activity and sedentary time during early retirement. Additionally, moderating effects of educational level and physical functioning were examined. Methods In total, 392 recently retired Belgian adults (>6 months, <5 years) completed the International Physical Activity Questionnaire, the SF-36 Health Survey and a questionnaire on sociodemographics and former work-related activities. Generalized linear regression analyses were conducted in R. Moderating effects were examined by adding cross-products to the models. Results More former work-related sitting was predictive of more screen time during retirement. Lower levels of former work-related vigorous activities and higher levels of former work-related walking were associated with respectively more cycling for transport and more walking for transport during retirement. None of the predictors significantly explained passive transportation, cycling and walking for recreation, and leisure-time moderate-to-vigorous physical activity during retirement. Several moderating effects were found, but the direction of the interactions was not univocal. Conclusions Former-work related behaviors are of limited importance to explain physical activity during early retirement, so future studies should focus on other individual, social and environmental determinants. Nonetheless, adults who previously had a sedentary job had higher levels of screen time during retirement, so this is an important subgroup to focus on during interventions. Because of the inconsistent moderating effects of educational level and physical functioning, no clear recommendations can be formulated. PMID:25826218

Relationship of physical activity to fundamental movement skills among adolescents.

PubMed

Okely, A D; Booth, M L; Patterson, J W

2001-11-01

To determine the relationship of participation in organized and nonorganized physical activity with fundamental movement skills among adolescents. Male and female children in Grade 8 (mean age, 13.3 yr) and Grade 10 (mean age, 15.3 yr) were assessed on six fundamental movement skills (run, vertical jump, catch, overhand throw, forehand strike, and kick). Physical activity was assessed using a self-report recall measure where students reported the type, duration, and frequency of participation in organized physical activity and nonorganized physical activity during a usual week. Multiple regression analysis indicated that fundamental movement skills significantly predicted time in organized physical activity, although the percentage of variance it could explain was small. This prediction was stronger for girls than for boys. Multiple regression analysis showed no relationship between time in nonorganized physical activity and fundamental movement skills. Fundamental movement skills are significantly associated with adolescents' participation in organized physical activity, but predict only a small portion of it.

Anticipatory eye movements evoked after active following versus passive observation of a predictable motion stimulus.

PubMed

Burke, M R; Barnes, G R

2008-12-15

We used passive and active following of a predictable smooth pursuit stimulus in order to establish if predictive eye movement responses are equivalent under both passive and active conditions. The smooth pursuit stimulus was presented in pairs that were either 'predictable' in which both presentations were matched in timing and velocity, or 'randomized' in which each presentation in the pair was varied in both timing and velocity. A visual cue signaled the type of response required from the subject; a green cue indicated the subject should follow both the target presentations (Go-Go), a pink cue indicated that the subject should passively observe the 1st target and follow the 2nd target (NoGo-Go), and finally a green cue with a black cross revealed a randomized (Rnd) trial in which the subject should follow both presentations. The results revealed better prediction in the Go-Go trials than in the NoGo-Go trials, as indicated by higher anticipatory velocity and earlier eye movement onset (latency). We conclude that velocity and timing information stored from passive observation of a moving target is diminished when compared to active following of the target. This study has significant consequences for understanding how visuomotor memory is generated, stored and subsequently released from short-term memory.

Broadening the trans-contextual model of motivation: A study with Spanish adolescents.

PubMed

González-Cutre, D; Sicilia, Á; Beas-Jiménez, M; Hagger, M S

2014-08-01

The original trans-contextual model of motivation proposed that autonomy support from teachers develops students' autonomous motivation in physical education (PE), and that autonomous motivation is transferred from PE contexts to physical activity leisure-time contexts, and predicts attitudes, perceived behavioral control and subjective norms, and forming intentions to participate in future physical activity behavior. The purpose of this study was to test an extended trans-contextual model of motivation including autonomy support from peers and parents and basic psychological needs in a Spanish sample. School students (n = 400) aged between 12 and 18 years completed measures of perceived autonomy support from three sources, autonomous motivation and constructs from the theory of planned behavior at three different points in time and in two contexts, PE and leisure-time. A path analysis controlling for past physical activity behavior supported the main postulates of the model. Autonomous motivation in a PE context predicted autonomous motivation in a leisure-time physical activity context, perceived autonomy support from teachers predicted satisfaction of basic psychological needs in PE, and perceived autonomy support from peers and parents predicted need satisfaction in leisure-time. This study provides a cross-cultural replication of the trans-contextual model of motivation and broadens it to encompass basic psychological needs. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Predicting forest insect flight activity: A Bayesian network approach

PubMed Central

Pawson, Stephen M.; Marcot, Bruce G.; Woodberry, Owen G.

2017-01-01

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. PMID:28953904

Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment.

PubMed

Breska, Assaf; Deouell, Leon Y

2017-02-01

Predicting the timing of upcoming events enables efficient resource allocation and action preparation. Rhythmic streams, such as music, speech, and biological motion, constitute a pervasive source for temporal predictions. Widely accepted entrainment theories postulate that rhythm-based predictions are mediated by synchronizing low-frequency neural oscillations to the rhythm, as indicated by increased phase concentration (PC) of low-frequency neural activity for rhythmic compared to random streams. However, we show here that PC enhancement in scalp recordings is not specific to rhythms but is observed to the same extent in less periodic streams if they enable memory-based prediction. This is inconsistent with the predictions of a computational entrainment model of stronger PC for rhythmic streams. Anticipatory change in alpha activity and facilitation of electroencephalogram (EEG) manifestations of response selection are also comparable between rhythm- and memory-based predictions. However, rhythmic sequences uniquely result in obligatory depression of preparation-related premotor brain activity when an on-beat event is omitted, even when it is strategically beneficial to maintain preparation, leading to larger behavioral costs for violation of prediction. Thus, while our findings undermine the validity of PC as a sign of rhythmic entrainment, they constitute the first electrophysiological dissociation, to our knowledge, between mechanisms of rhythmic predictions and of memory-based predictions: the former obligatorily lead to resonance-like preparation patterns (that are in line with entrainment), while the latter allow flexible resource allocation in time regardless of periodicity in the input. Taken together, they delineate the neural mechanisms of three distinct modes of preparation: continuous vigilance, interval-timing-based prediction and rhythm-based prediction.

Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment

PubMed Central

Deouell, Leon Y.

2017-01-01

Predicting the timing of upcoming events enables efficient resource allocation and action preparation. Rhythmic streams, such as music, speech, and biological motion, constitute a pervasive source for temporal predictions. Widely accepted entrainment theories postulate that rhythm-based predictions are mediated by synchronizing low-frequency neural oscillations to the rhythm, as indicated by increased phase concentration (PC) of low-frequency neural activity for rhythmic compared to random streams. However, we show here that PC enhancement in scalp recordings is not specific to rhythms but is observed to the same extent in less periodic streams if they enable memory-based prediction. This is inconsistent with the predictions of a computational entrainment model of stronger PC for rhythmic streams. Anticipatory change in alpha activity and facilitation of electroencephalogram (EEG) manifestations of response selection are also comparable between rhythm- and memory-based predictions. However, rhythmic sequences uniquely result in obligatory depression of preparation-related premotor brain activity when an on-beat event is omitted, even when it is strategically beneficial to maintain preparation, leading to larger behavioral costs for violation of prediction. Thus, while our findings undermine the validity of PC as a sign of rhythmic entrainment, they constitute the first electrophysiological dissociation, to our knowledge, between mechanisms of rhythmic predictions and of memory-based predictions: the former obligatorily lead to resonance-like preparation patterns (that are in line with entrainment), while the latter allow flexible resource allocation in time regardless of periodicity in the input. Taken together, they delineate the neural mechanisms of three distinct modes of preparation: continuous vigilance, interval-timing-based prediction and rhythm-based prediction. PMID:28187128

Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time.

PubMed

Schneegans, Sebastian; Bays, Paul M

2018-05-23

Short-term memories are thought to be maintained in the form of sustained spiking activity in neural populations. Decreases in recall precision observed with increasing number of memorized items can be accounted for by a limit on total spiking activity, resulting in fewer spikes contributing to the representation of each individual item. Longer retention intervals likewise reduce recall precision, but it is unknown what changes in population activity produce this effect. One possibility is that spiking activity becomes attenuated over time, such that the same mechanism accounts for both effects of set size and retention duration. Alternatively, reduced performance may be caused by drift in the encoded value over time, without a decrease in overall spiking activity. Human participants of either sex performed a variable-delay cued recall task with a saccadic response, providing a precise measure of recall latency. Based on a spike integration model of decision making, if the effects of set size and retention duration are both caused by decreased spiking activity, we would predict a fixed relationship between recall precision and response latency across conditions. In contrast, the drift hypothesis predicts no systematic changes in latency with increasing delays. Our results show both an increase in latency with set size, and a decrease in response precision with longer delays within each set size, but no systematic increase in latency for increasing delay durations. These results were quantitatively reproduced by a model based on a limited neural resource in which working memories drift rather than decay with time. SIGNIFICANCE STATEMENT Rapid deterioration over seconds is a defining feature of short-term memory, but what mechanism drives this degradation of internal representations? Here, we extend a successful population coding model of working memory by introducing possible mechanisms of delay effects. We show that a decay in neural signal over time predicts that the time required for memory retrieval will increase with delay, whereas a random drift in the stored value predicts no effect of delay on retrieval time. Testing these predictions in a multi-item memory task with an eye movement response, we identified drift as a key mechanism of memory decline. These results provide evidence for a dynamic spiking basis for working memory, in contrast to recent proposals of activity-silent storage. Copyright © 2018 Schneegans and Bays.

Prediction of energy expenditure and physical activity in preschoolers

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) ...

Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits

PubMed Central

Rosen, L.D.; Lim, A.F.; Felt, J.; Carrier, L.M.; Cheever, N.A.; Lara-Ruiz, J.M.; Mendoza, J.S.; Rokkum, J.

2015-01-01

The American Academy of Pediatrics recommends no screen time for children under the age of 2 and limited screen time for all children. However, no such guidelines have been proposed for preteens and teenagers. Further, research shows that children, preteens, and teenagers are using massive amounts of media and those with more screen time have been shown to have increased obesity, reduced physical activity, and decreased health. This study examined the impact of technology on four areas of ill-being–psychological issues, behavior problems, attention problems and physical health–among children (aged 4–8), preteens (9–12), and teenagers (13–18) by having 1030 parents complete an online, anonymous survey about their own and their child's behaviors. Measures included daily technology use, daily food consumption, daily exercise, and health. Hypothesis 1, which posited that unhealthy eating would predict impaired ill-being, was partially supported, particularly for children and preteens. Hypothesis 2, which posited that reduced physical activity would predict diminished health levels, was partially supported for preteens and supported for teenagers. Hypothesis 3, that increased daily technology use would predict ill-being after factoring out eating habits and physical activity, was supported. For children and preteens, total media consumption predicted illbeing while for preteens specific technology uses, including video gaming and electronic communication, predicted ill-being. For teenagers, nearly every type of technological activity predicted poor health. Practical implications were discussed in terms of setting limits and boundaries on technology use and encouraging healthy eating and physical activity at home and at school. PMID:25717216

Physical activity behavior predicts endogenous pain modulation in older adults.

PubMed

Naugle, Kelly M; Ohlman, Thomas; Naugle, Keith E; Riley, Zachary A; Keith, NiCole R

2017-03-01

Older adults compared with younger adults are characterized by greater endogenous pain facilitation and a reduced capacity to endogenously inhibit pain, potentially placing them at a greater risk for chronic pain. Previous research suggests that higher levels of self-reported physical activity are associated with more effective pain inhibition and less pain facilitation on quantitative sensory tests in healthy adults. However, no studies have directly tested the relationship between physical activity behavior and pain modulatory function in older adults. This study examined whether objective measures of physical activity behavior cross-sectionally predicted pain inhibitory function on the conditioned pain modulation (CPM) test and pain facilitation on the temporal summation (TS) test in healthy older adults. Fifty-one older adults wore an accelerometer on the hip for 7 days and completed the CPM and TS tests. Measures of sedentary time, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) were obtained from the accelerometer. Hierarchical linear regressions were conducted to determine the relationship of TS and CPM with levels of physical activity, while controlling for demographic, psychological, and test variables. The results indicated that sedentary time and LPA significantly predicted pain inhibitory function on the CPM test, with less sedentary time and greater LPA per day associated with greater pain inhibitory capacity. Additionally, MVPA predicted pain facilitation on the TS test, with greater MVPA associated with less TS of pain. These results suggest that different types of physical activity behavior may differentially impact pain inhibitory and facilitatory processes in older adults.

Modeling long-term human activeness using recurrent neural networks for biometric data.

PubMed

Kim, Zae Myung; Oh, Hyungrai; Kim, Han-Gyu; Lim, Chae-Gyun; Oh, Kyo-Joong; Choi, Ho-Jin

2017-05-18

With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user. The dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures-as well as a deep neural network and a simple regression model-were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user's activeness falls below a certain threshold. A preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user's activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user's future activeness with precision, for example, a trained RNN model could predict-with the precision of 84%-when the user would be less active within the next hour given the latest 15 min of his activeness data. This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.

Estimation of shelf life of natural rubber latex exam-gloves based on creep behavior.

PubMed

Das, Srilekha Sarkar; Schroeder, Leroy W

2008-05-01

Samples of full-length glove-fingers cut from chlorinated and nonchlorinated latex medical examination gloves were aged for various times at several fixed temperatures and 25% relative humidity. Creep testing was performed using an applied stress of 50 kPa on rectangular specimens (10 mm x 8 mm) of aged and unaged glove fingers as an assessment of glove loosening during usage. Variations in creep curves obtained were compared to determine the threshold aging time when the amount of creep became larger than the initial value. These times were then used in various models to estimate shelf lives at lower temperatures. Several different methods of extrapolation were used for shelf-life estimation and comparison. Neither Q-factor nor Arrhenius activation energies, as calculated from 10 degrees C interval shift factors, were constant over the temperature range; in fact, both decreased at lower temperatures. Values of Q-factor and activation energies predicted up to 5 years of shelf life. Predictions are more sensitive to values of activation energy as the storage temperature departs from the experimental aging data. Averaging techniques for prediction of average activation energy predicted the longest shelf life as the curvature is reduced. Copyright 2007 Wiley Periodicals, Inc.

Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

PubMed Central

LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

2014-01-01

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Mood and the market: can press reports of investors' mood predict stock prices?

PubMed

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.

Mood and the Market: Can Press Reports of Investors' Mood Predict Stock Prices?

PubMed Central

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

Correlates of daily leisure-time physical activity in a community sample: Narrow personality traits and practical barriers.

PubMed

Gallagher, Patrick; Yancy, William S; Denissen, Jaap J A; Kühnel, Anja; Voils, Corrine I

2013-12-01

Previous studies examining correlates of leisure time physical activity (LTPA) have identified personality factors that are correlated with LTPA and practical factors that impede LTPA. The purpose of the present study was to test how several narrow traits predict daily reports of LTPA and to test whether traits that predict LTPA moderate the effects of practical barriers. 1192 participants completed baseline measures of personality, then reported their LTPA and several situational and environmental factors daily for 25 days. We used generalized estimating equations to measure how personality traits, practical barriers, and interactions between these factors affected (1) the odds of engaging in LTPA and (2) the duration of daily LTPA. Higher standing on Activity and Discipline and lower standing on Assertiveness predicted greater odds of engaging in LTPA and longer duration of LTPA, and higher standing on Aesthetics predicted shorter duration of LTPA. Poor weather conditions and less leisure time were associated with less LTPA, and effects of these barriers were generally greater among participants 30 and older. In participants older than 30, poor weather was associated with less LTPA among those with lower standing on Activity but was not associated with LTPA among those high in Activity. Despite Discipline's overall positive association with LTPA, less leisure time and less routineness were greater barriers for those high in Discipline. Assessing narrow personality traits could help target LTPA interventions to individual patients' needs and could help identify important new personality dynamics that affect LTPA.

Temporal Relationship Between Insulin Sensitivity and the Pubertal Decline in Physical Activity in Peripubertal Hispanic and African American Females

PubMed Central

Spruijt-Metz, Donna; Belcher, Britni R.; Hsu, Ya-Wen; McClain, Arianna D.; Chou, Chih-Ping; Nguyen-Rodriguez, Selena; Weigensberg, Marc J.; Goran, Michael I.

2013-01-01

OBJECTIVE Little attention has been paid to possible intrinsic biological mechanisms for the decline in physical activity that occurs during puberty. This longitudinal observational study examined the association between baseline insulin sensitivity (SI) and declines in physical activity and increases in sedentary behavior in peripubertal minority females over a year. RESEARCH DESIGN AND METHODS Participants were Hispanic and African American girls (n = 55; 76% Hispanic; mean age 9.4 years; 36% obese). SI and other insulin indices were measured at baseline using the frequently sampled intravenous glucose tolerance test. Physical activity was measured on a quarterly basis by accelerometry and self-report. RESULTS Physical activity declined by 25% and time spent in sedentary behaviors increased by ∼13% over 1 year. Lower baseline SI predicted the decline in physical activity measured by accelerometry, whereas higher baseline acute insulin response to glucose predicted the decline in physical activity measured by self-report. Time spent in sedentary behavior increased by ~13% over 1 year, and this was predicted by lower baseline SI. All models controlled for adiposity, age, pubertal stage, and ethnicity. CONCLUSIONS When evaluated using a longitudinal design with strong outcome measures, this study suggests that lower baseline SI predicts a greater decline in physical activity in peripubertal minority females. PMID:23846812

Forecasting Geomagnetic Activity Using Kalman Filters

NASA Astrophysics Data System (ADS)

Veeramani, T.; Sharma, A.

2006-05-01

The coupling of energy from the solar wind to the magnetosphere leads to the geomagnetic activity in the form of storms and substorms and are characterized by indices such as AL, Dst and Kp. The geomagnetic activity has been predicted near-real time using local linear filter models of the system dynamics wherein the time series of the input solar wind and the output magnetospheric response were used to reconstruct the phase space of the system by a time-delay embedding technique. Recently, the radiation belt dynamics have been studied using a adaptive linear state space model [Rigler et al. 2004]. This was achieved by assuming a linear autoregressive equation for the underlying process and an adaptive identification of the model parameters using a Kalman filter approach. We use such a model for predicting the geomagnetic activity. In the case of substorms, the Bargatze et al [1985] data set yields persistence like behaviour when a time resolution of 2.5 minutes was used to test the model for the prediction of the AL index. Unlike the local linear filters, which are driven by the solar wind input without feedback from the observations, the Kalman filter makes use of the observations as and when available to optimally update the model parameters. The update procedure requires the prediction intervals to be long enough so that the forecasts can be used in practice. The time resolution of the data suitable for such forecasting is studied by taking averages over different durations.

Simulating wildfire spread behavior between two NASA Active Fire data timeframes

NASA Astrophysics Data System (ADS)

Adhikari, B.; Hodza, P.; Xu, C.; Minckley, T. A.

2017-12-01

Although NASA's Active Fire dataset is considered valuable in mapping the spatial distribution and extent of wildfires across the world, the data is only available at approximately 12-hour time intervals, creating uncertainties and risks associated with fire spread and behavior between the two Visible Infrared Imaging Radiometer Satellite (VIIRS) data collection timeframes. Our study seeks to close the information gap for the United States by using the latest Active Fire data collected for instance around 0130 hours as an ignition source and critical inputs to a wildfire model by uniquely incorporating forecasted and real-time weather conditions for predicting fire perimeter at the next 12 hour reporting time (i.e. around 1330 hours). The model ingests highly dynamic variables such as fuel moisture, temperature, relative humidity, wind among others, and prompts a Monte Carlo simulation exercise that uses a varying range of possible values for evaluating all possible wildfire behaviors. The Monte Carlo simulation implemented in this model provides a measure of the relative wildfire risk levels at various locations based on the number of times those sites are intersected by simulated fire perimeters. Model calibration is achieved using data at next reporting time (i.e. after 12 hours) to enhance the predictive quality at further time steps. While initial results indicate that the calibrated model can predict the overall geometry and direction of wildland fire spread, the model seems to over-predict the sizes of most fire perimeters possibly due to unaccounted fire suppression activities. Nonetheless, the results of this study show great promise in aiding wildland fire tracking, fighting and risk management.

A Synthesis of Solar Cycle Prediction Techniques

NASA Technical Reports Server (NTRS)

Hathaway, David H.; Wilson, Robert M.; Reichmann, Edwin J.

1999-01-01

A number of techniques currently in use for predicting solar activity on a solar cycle timescale are tested with historical data. Some techniques, e.g., regression and curve fitting, work well as solar activity approaches maximum and provide a month-by-month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but only provide an estimate of the amplitude of the cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. A combination of two uncorrelated geomagnetic precursor techniques provides a more accurate prediction for the amplitude of a solar activity cycle at a time well before activity minimum. This combined precursor method gives a smoothed sunspot number maximum of 154 plus or minus 21 at the 95% level of confidence for the next cycle maximum. A mathematical function dependent on the time of cycle initiation and the cycle amplitude is used to describe the level of solar activity month by month for the next cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between previous activity levels and this function. This Combined Solar Cycle Activity Forecast gives, as of January 1999, a smoothed sunspot maximum of 146 plus or minus 20 at the 95% level of confidence for the next cycle maximum.

Phenology prediction component of GypsES

Treesearch

Jesse A. Logan; Lukas P. Schaub; F. William Ravlin

1991-01-01

Prediction of phenology is an important component of most pest management programs, and considerable research effort has been expended toward development of predictive tools for gypsy moth phenology. Although phenological prediction is potentially valuable for timing of spray applications (e.g. Bt, or Gypcheck) and other management activities (e.g. placement and...

Sun Series program for the REEDA System. [predicting orbital lifetime using sunspot values

NASA Technical Reports Server (NTRS)

Shankle, R. W.

1980-01-01

Modifications made to data bases and to four programs in a series of computer programs (Sun Series) which run on the REEDA HP minicomputer system to aid NASA's solar activity predictions used in orbital life time predictions are described. These programs utilize various mathematical smoothing technique and perform statistical and graphical analysis of various solar activity data bases residing on the REEDA System.

Advancing atmospheric river forecasts into subseasonal-to-seasonal time scales

NASA Astrophysics Data System (ADS)

Baggett, Cory F.; Barnes, Elizabeth A.; Maloney, Eric D.; Mundhenk, Bryan D.

2017-07-01

Atmospheric rivers are elongated plumes of intense moisture transport that are capable of producing extreme and impactful weather. Along the West Coast of North America, they occasionally cause considerable mayhem—delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal time scales (3 to 5 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into subseasonal-to-seasonal time scales through knowledge of two of the atmosphere's most prominent oscillations, the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). Strong MJO and QBO activity modulates the frequency at which atmospheric rivers strike—offering an opportunity to improve subseasonal-to-seasonal forecast models and thereby skillfully predict atmospheric river activity up to 5 weeks in advance.

Surrogate screening models for the low physical activity criterion of frailty.

PubMed

Eckel, Sandrah P; Bandeen-Roche, Karen; Chaves, Paulo H M; Fried, Linda P; Louis, Thomas A

2011-06-01

Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized, semiquantitative questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women's Health and Aging Study (WHAS). Using data on men and women ages 65 and older from the CHS, we applied logistic regression models to rank activities by "relative influence" in predicting low physical activity.We considered subsets of the most influential activities as inputs to potential surrogate models (logistic regressions). We evaluated predictive accuracy and predictive validity using the area under receiver operating characteristic curves and assessed criterion validity using proportional hazards models relating frailty status (defined using the surrogate) to mortality. Walking for exercise and moderately strenuous household chores were highly influential for both genders. Women required fewer activities than men for accurate classification. The WHAS model (8 CHS activities) was an effective surrogate, but a surrogate using 6 activities (walking, chores, gardening, general exercise, mowing and golfing) was also highly predictive. We recommend a 6 activity questionnaire to assess physical activity for men and women. If efficiency is essential and the study involves only women, fewer activities can be included.

Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community

PubMed Central

Taylor, Sarah L.; Curry, Whitney B.; Knowles, Zoe R.; Noonan, Robert J.; McGrane, Bronagh; Fairclough, Stuart J.

2017-01-01

Background: Schools have been identified as important settings for health promotion through physical activity participation, particularly as children are insufficiently active for health. The aim of this study was to investigate the child and school-level influences on children′s physical activity levels and sedentary time during school hours in a sample of children from a low-income community; Methods: One hundred and eighty-six children (110 boys) aged 9–10 years wore accelerometers for 7 days, with 169 meeting the inclusion criteria of 16 h∙day−1 for a minimum of three week days. Multilevel prediction models were constructed to identify significant predictors of sedentary time, light, and moderate to vigorous physical activity during school hour segments. Child-level predictors (sex, weight status, maturity offset, cardiorespiratory fitness, physical activity self-efficacy, physical activity enjoyment) and school-level predictors (number on roll, playground area, provision score) were entered into the models; Results: Maturity offset, fitness, weight status, waist circumference-to-height ratio, sedentary time, moderate to vigorous physical activity, number of children on roll and playground area significantly predicted physical activity and sedentary time; Conclusions: Research should move towards considering context-specific physical activity and its correlates to better inform intervention strategies. PMID:28509887

Motor Development and Physical Activity: A Longitudinal Discordant Twin-Pair Study.

PubMed

Aaltonen, Sari; Latvala, Antti; Rose, Richard J; Pulkkinen, Lea; Kujala, Urho M; Kaprio, Jaakko; Silventoinen, Karri

2015-10-01

Previous longitudinal research suggests that motor proficiency in early life predicts physical activity in adulthood. Familial effects including genetic and environmental factors could explain the association, but no long-term follow-up studies have taken into account potential confounding by genetic and social family background. The present twin study investigated whether childhood motor skill development is associated with leisure-time physical activity levels in adulthood independent of family background. Altogether, 1550 twin pairs from the FinnTwin12 study and 1752 twin pairs from the FinnTwin16 study were included in the analysis. Childhood motor development was assessed by the parents' report of whether one of the co-twins had been ahead of the other in different indicators of motor skill development in childhood. Leisure-time physical activity (MET·h·d) was self-reported by the twins in young adulthood and adulthood. Statistical analyses included conditional and ordinary linear regression models within twin pairs. Using all activity-discordant twin pairs, the within-pair difference in a sum score of motor development in childhood predicted the within-pair difference in the leisure-time physical activity level in young adulthood (P < 0.001). Within specific motor development indicators, learning to stand unaided earlier in infancy predicted higher leisure-time MET values in young adulthood statistically significantly in both samples (FinnTwin12, P = 0.02; and FinnTwin16, P = 0.001) and also in the pooled data set of the FinnTwin12 and FinnTwin16 studies (P < 0.001). Having been more agile than the co-twin as a child predicted higher leisure-time MET values up to adulthood (P = 0.03). More advanced childhood motor development is associated with higher leisure-time MET values in young adulthood at least partly independent of family background in both men and women.

MOTOR DEVELOPMENT AND PHYSICAL ACTIVITY: A LONGITUDINAL DISCORDANT TWIN-PAIR STUDY

PubMed Central

Aaltonen, Sari; Latvala, Antti; Rose, Richard J.; Pulkkinen, Lea; Kujala, Urho M.; Kaprio, Jaakko; Silventoinen, Karri

2015-01-01

Introduction Previous longitudinal research suggests that motor proficiency in early life predicts physical activity in adulthood. Familial effects including genetic and environmental factors could explain the association, but no long-term follow-up studies have taken into account potential confounding by genetic and social family background. The present twin study investigated whether childhood motor skill development is associated with leisure-time physical activity levels in adulthood independent of family background. Methods Altogether, 1 550 twin pairs from the FinnTwin12 study and 1 752 twin pairs from the FinnTwin16 study were included in the analysis. Childhood motor development was assessed by the parents’ report of whether one of the co-twins had been ahead of the other in different indicators of motor skill development in childhood. Leisure-time physical activity (MET hours/day) was self-reported by the twins in young adulthood and adulthood. Statistical analyses included conditional and ordinary linear regression models within twin pairs. Results Using all activity-discordant twin pairs, the within-pair difference in a sum score of motor development in childhood predicted the within-pair difference in the leisure-time physical activity level in young adulthood (p<0.001). Within specific motor development indicators, learning to stand unaided earlier in infancy predicted higher leisure-time MET values in young adulthood statistically significantly in both samples (FinnTwin12 p=0.02, FinnTwin16 p=0.001) and also in the pooled dataset of the FinnTwin12 and FinnTwin16 studies (p<0.001). Having been more agile than the co-twin as a child predicted higher leisure-time MET values up to adulthood (p=0.03). Conclusions More advanced childhood motor development is associated with higher leisure-time MET values in young adulthood at least partly independent of family background, in both men and women. PMID:26378945

Incorporating Retention Time to Refine Models Predicting Thermal Regimes of Stream Networks Across New England

EPA Science Inventory

Thermal regimes are a critical factor in models predicting effects of watershed management activities on fish habitat suitability. We have assembled a database of lotic temperature time series across New England (> 7000 station-year combinations) from state and Federal data s...

Organized Activity Involvement among Urban Youth: Understanding Family- and Neighborhood- Level Characteristics as Predictors of Involvement.

PubMed

Anderson, Nicole A; Bohnert, Amy M; Governale, Amy

2018-02-22

Research examining factors that predict youth's involvement in organized activities is very limited, despite associations with positive outcomes. Using data from 1043 youth (49% female; 46.4% Hispanic, 35.4% African American, 14.0% Caucasian, and 4.2% other) from the Project on Human Development in Chicago Neighborhoods, this study examined how characteristics of parents (supervision, warmth) and neighborhoods (perceived neighborhood safety and collective efficacy) predict patterns of adolescents' involvement in organized activities concurrently (i.e., intensity) and longitudinally (i.e., type and breadth). Parental supervision predicted adolescents' participation in organized activities across multiple waves. Neighborhood violence was positively associated with concurrent participation in organized activities after controlling for socioeconomic status (SES), whereas higher neighborhood collective efficacy predicted greater breadth in organized activity participation across time. These findings have important implications regarding how to attract and sustain organized activity participation for low-income, urban youth.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

PubMed

Limongi, Roberto; Silva, Angélica M

2016-11-01

The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

PubMed

Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

2016-08-11

Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

The dorsal medial frontal cortex is sensitive to time on task, not response conflict or error likelihood.

PubMed

Grinband, Jack; Savitskaya, Judith; Wager, Tor D; Teichert, Tobias; Ferrera, Vincent P; Hirsch, Joy

2011-07-15

The dorsal medial frontal cortex (dMFC) is highly active during choice behavior. Though many models have been proposed to explain dMFC function, the conflict monitoring model is the most influential. It posits that dMFC is primarily involved in detecting interference between competing responses thus signaling the need for control. It accurately predicts increased neural activity and response time (RT) for incompatible (high-interference) vs. compatible (low-interference) decisions. However, it has been shown that neural activity can increase with time on task, even when no decisions are made. Thus, the greater dMFC activity on incompatible trials may stem from longer RTs rather than response conflict. This study shows that (1) the conflict monitoring model fails to predict the relationship between error likelihood and RT, and (2) the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task. Copyright © 2010 Elsevier Inc. All rights reserved.

Associations of physical activity and sedentary behaviour with metabolic syndrome in rural Australian adults.

PubMed

Mitchell, Braden L; Smith, Ashleigh E; Rowlands, Alex V; Parfitt, Gaynor; Dollman, James

2018-05-22

Associations between objectively measured sedentary behaviour, physical activity (PA) and metabolic syndrome (MetS)-classified using three different definitions were investigated in an inactive sample of rural Australian adults. Quantitative, cross-sectional. 171 adults (50.7±12.4years) from two rural South Australian regions underwent seven-day accelerometer activity monitoring and MetS classification using the National Cholesterol Education Program, the International Diabetes Federation and the Harmonized definitions. Associations between sedentary and activity variables and MetS (adjusted for age, sex, diet and smoking status) were modelled using logistic regression. In secondary modelling, associations of sedentary and activity outcomes for each MetS definition were assessed, adjusting for other activity and sedentary variables. Prediction differences across the definitions of MetS were directly compared using Akaike's Information Criterion. Sedentary behaviour increased MetS risk, whereas light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) reduced MetS risk, irrespective of definition. In secondary models, LPA predicted MetS independently of MVPA and total sedentary time. Time spent in sedentary bouts (>30min) predicted MetS independently of MVPA and the number of sedentary bouts predicted MetS independently of LPA and MVPA. Prediction differences for MetS definitions failed to reach the critical threshold for difference (>10). This study highlights the importance of sedentary behaviour and LPA on the prevalence of MetS in an inactive sample of rural Australian adults. Studies assessing the efficacy of increasing LPA on MetS in this population are needed. Minimal predictive differences across the three MetS definitions suggest evidence from previous studies can be considered cumulative. Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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.

Forward Inferences: From Activation to Long-Term Memory.

ERIC Educational Resources Information Center

Klin, Celia M.; Murray, John D.; Levine, William H.; Guzman, Alexandria E.

1999-01-01

Investigates the extent to which forward inferences are activated and encoded during reading, as well as their prevalence and their time course. Finds that inferences were encoded and retained in working memory in both high- and low-predictability conditions, and that high-predictability forward inferences were encoded into long-term memory.…

A Preliminary Investigation of Accelerometer-Derived Sleep and Physical Activity Following Sport-Related Concussion.

PubMed

Sufrinko, Alicia M; Howie, Erin K; Elbin, R J; Collins, Michael W; Kontos, Anthony P

2018-03-29

Describe changes in postconcussion activity levels and sleep throughout recovery in a sample of pediatric sport-related concussion (SRC) patients, and examine the predictive value of accelerometer-derived activity and sleep on subsequent clinical outcomes at a follow-up clinic visit. Outpatient concussion clinic. Twenty athletes aged 12 to 19 years with diagnosed SRC. Prospective study including visit 1 (<72 hours postinjury) and visit 2 (6-18 days postinjury). Linear regressions used to predict scores (ie, neurocognitive, vestibular/oculomotor) at visit 2 from accelerometer-derived data collected 0 to 6 days postinjury. Linear mixed models evaluated changes in activity and sleep across recovery. Symptom, neurocognitive, and vestibular/oculomotor scores; sleep and activity data (Actigraph GT3x+) RESULTS:: The maximum intensity of physical activity increased (P = .009) and time in bed decreased throughout recovery (P = .026). Several physical activity metrics from 0 to 6 days postinjury were predictive of worse vestibular/oculomotor scores at visit 2 (P < .05). Metrics indicative of poor sleep 0 to 6 days postinjury were associated with worse reaction time at visit 2 (P < .05). This exploratory study suggests physical activity and sleep change from the acute to subacute postinjury time period in adolescent SRC patients. In our small sample, excess physical activity and poor sleep the first week postinjury may be associated with worse outcomes at follow-up in the subacute stage of recovery. This study further supported the feasibility of research utilizing wearable technology in concussion patients, and future research in a large, diverse sample of concussion patients examined at concise time intervals postinjury is needed.

Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

PubMed

Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

2014-11-26

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.

Less Structured Movement Patterns Predict Severity of Positive Syndrome, Excitement, and Disorganization

PubMed Central

Walther, Sebastian

2014-01-01

Disorganized behavior is a key symptom of schizophrenia. The objective assessment of disorganized behavior is particularly challenging. Actigraphy has enabled the objective assessment of motor behavior in various settings. Reduced motor activity was associated with negative syndrome scores, but simple motor activity analyses were not informative on other symptom dimensions. The analysis of movement patterns, however, could be more informative for assessing schizophrenia symptom dimensions. Here, we use time series analyses on actigraphic data of 100 schizophrenia spectrum disorder patients. Actigraphy recording intervals were set at 2 s. Data from 2 defined 60-min periods were analyzed, and partial autocorrelations of the actigraphy time series indicated predictability of movements in each individual. Increased positive syndrome scores were associated with reduced predictability of movements but not with the overall amount of movement. Negative syndrome scores were associated with low activity levels but unrelated with predictability of movement. The factors disorganization and excitement were related to movement predictability but emotional distress was not. Thus, the predictability of objectively assessed motor behavior may be a marker of positive symptoms and disorganized behavior. This behavior could become relevant for translational research. PMID:23502433

Risk Assessment Using Cytochrome P450 Time-Dependent Inhibition Assays at Single Time and Concentration in the Early Stage of Drug Discovery.

PubMed

Kosaka, Mai; Kosugi, Yohei; Hirabayashi, Hideki

2017-09-01

In this article, we proposed a risk assessment strategy for CYP3A time-dependent inhibition (TDI) during drug discovery based on a thorough retrospective study of 13 reference drugs, some of which are known to have in vitro TDI potential but have unknown clinical relevance. First, the traditional parameter k inact /K I , recommended by regulatory authorities for necessity decision making in clinical drug-drug interaction (DDI) studies, was investigated as a predictive index for clinical TDI liability. The cutoff value of 1.1 for k inact /K I , established by the Food and Drug Administration, tended to produce false-positive prediction results for clinical DDI occurrence. The value of 1.25 recommended in the European Medicines Evaluation Agency draft guideline yielded better predictions with only 1 false negative for diltiazem. Second, to enable earlier risk assessment, remaining activity, defined as the residual CYP3A activity in vitro obtained in the screening conditions, was investigated as an alternative index. As a result, the ratios of unbound C max or area under the curve to remaining activity precisely predicted clinical DDI occurrence. In conclusion, we demonstrated the predictive power of k inact /K I and remaining activity values for clinical DDIs. These findings provide insights that enable TDI risk assessment, even during drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

The predictability of consumer visitation patterns

NASA Astrophysics Data System (ADS)

Krumme, Coco; Llorente, Alejandro; Cebrian, Manuel; Pentland, Alex ("Sandy"); Moro, Esteban

2013-04-01

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.

The predictability of consumer visitation patterns

PubMed Central

Krumme, Coco; Llorente, Alejandro; Cebrian, Manuel; Pentland, Alex ("Sandy"); Moro, Esteban

2013-01-01

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population. PMID:23598917

Chronobiology of interspecific interactions in a changing world.

PubMed

Kronfeld-Schor, Noga; Visser, Marcel E; Salis, Lucia; van Gils, Jan A

2017-11-19

Animals should time activities, such as foraging, migration and reproduction, as well as seasonal physiological adaptation, in a way that maximizes fitness. The fitness outcome of such activities depends largely on their interspecific interactions; the temporal overlap with other species determines when they should be active in order to maximize their encounters with food and to minimize their encounters with predators, competitors and parasites. To cope with the constantly changing, but predictable structure of the environment, organisms have evolved internal biological clocks, which are synchronized mainly by light, the most predictable and reliable environmental cue (but which can be masked by other variables), which enable them to anticipate and prepare for predicted changes in the timing of the species they interact with, on top of responding to them directly. Here, we review examples where the internal timing system is used to predict interspecific interactions, and how these interactions affect the internal timing system and activity patterns. We then ask how plastic these mechanisms are, how this plasticity differs between and within species and how this variability in plasticity affects interspecific interactions in a changing world, in which light, the major synchronizer of the biological clock, is no longer a reliable cue owing to the rapidly changing climate, the use of artificial light and urbanization.This article is part of the themed issue 'Wild clocks: integrating chronobiology and ecology to understand timekeeping in free-living animals'. © 2017 The Author(s).

Activation timing of postural muscles of lower legs and prediction of postural disturbance during bilateral arm flexion in older adults.

PubMed

Yaguchi, Chie; Fujiwara, Katsuo; Kiyota, Naoe

2017-12-22

Activation timings of postural muscles of lower legs and prediction of postural disturbance were investigated in young and older adults during bilateral arm flexion in a self-timing task and an oddball task with different probabilities of target presentation. Arm flexion was started from a standing posture with hands suspended 10 cm below the horizontal level in front of the body, in which postural control focused on the ankles is important. Fourteen young and 14 older adults raised the arms in response to the target sound signal. Three task conditions were used: 15 and 45% probabilities of the target in the oddball task and self-timing. Analysis items were activation timing of postural muscles (erector spinae, biceps femoris, and gastrocnemius) with respect to the anterior deltoid (AD), and latency and amplitude of the P300 component of event-related brain potential. For young adults, all postural muscles were activated significantly earlier than AD under each condition, and time of preceding gastrocnemius activation was significantly longer in the order of the self-timing, 45 and 15% conditions. P300 latency was significantly shorter, and P300 amplitude was significantly smaller under the 45% condition than under the 15% condition. For older adults, although all postural muscles, including gastrocnemius, were activated significantly earlier than AD in the self-timing condition, only activation timing of gastrocnemius was not significantly earlier than that of AD in oddball tasks, regardless of target probability. No significant differences were found between 15 and 45% conditions in onset times of all postural muscles, and latency and amplitude of P300. These results suggest that during arm movement, young adults can achieve sufficient postural preparation in proportion to the probability of target presentation in the oddball task. Older adults can achieve postural control using ankle joints in the self-timing task. However, in the oddball task, older adults experience difficulty predicting the timing of target presentation, which could be related to deteriorated cognitive function, resulting in reduced use of the ankle joints for postural control.

Activity Patterns in Response to Symptoms in Patients Being Treated for Chronic Fatigue Syndrome: An Experience Sampling Methodology Study

PubMed Central

2016-01-01

Objective: Cognitive–behavioral models of chronic fatigue syndrome (CFS) propose that patients respond to symptoms with 2 predominant activity patterns—activity limitation and all-or-nothing behaviors—both of which may contribute to illness persistence. The current study investigated whether activity patterns occurred at the same time as, or followed on from, patient symptom experience and affect. Method: Twenty-three adults with CFS were recruited from U.K. CFS services. Experience sampling methodology (ESM) was used to assess fluctuations in patient symptom experience, affect, and activity management patterns over 10 assessments per day for a total of 6 days. Assessments were conducted within patients’ daily life and were delivered through an app on touchscreen Android mobile phones. Multilevel model analyses were conducted to examine the role of self-reported patient fatigue, pain, and affect as predictors of change in activity patterns at the same and subsequent assessment. Results: Current experience of fatigue-related symptoms and pain predicted higher patient activity limitation at the current and subsequent assessments whereas subjective wellness predicted higher all-or-nothing behavior at both times. Current pain predicted less all-or-nothing behavior at the subsequent assessment. In contrast to hypotheses, current positive affect was predictive of current activity limitation whereas current negative affect was predictive of current all-or-nothing behavior. Both activity patterns varied at the momentary level. Conclusions: Patient symptom experiences appear to be driving patient activity management patterns in line with the cognitive–behavioral model of CFS. ESM offers a useful method for examining multiple interacting variables within the context of patients’ daily life. PMID:27819461

Early visual responses predict conscious face perception within and between subjects during binocular rivalry

PubMed Central

Sandberg, Kristian; Bahrami, Bahador; Kanai, Ryota; Barnes, Gareth Robert; Overgaard, Morten; Rees, Geraint

2014-01-01

Previous studies indicate that conscious face perception may be related to neural activity in a large time window around 170-800ms after stimulus presentation, yet in the majority of these studies changes in conscious experience are confounded with changes in physical stimulation. Using multivariate classification on MEG data recorded when participants reported changes in conscious perception evoked by binocular rivalry between a face and a grating, we showed that only MEG signals in the 120-320ms time range, peaking at the M170 around 180ms and the P2m at around 260ms, reliably predicted conscious experience. Conscious perception could not only be decoded significantly better than chance from the sensors that showed the largest average difference, as previous studies suggest, but also from patterns of activity across groups of occipital sensors that individually were unable to predict perception better than chance. Additionally, source space analyses showed that sources in the early and late visual system predicted conscious perception more accurately than frontal and parietal sites, although conscious perception could also be decoded there. Finally, the patterns of neural activity associated with conscious face perception generalized from one participant to another around the times of maximum prediction accuracy. Our work thus demonstrates that the neural correlates of particular conscious contents (here, faces) are highly consistent in time and space within individuals and that these correlates are shared to some extent between individuals. PMID:23281780

A novel time series link prediction method: Learning automata approach

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

Brief report: understanding intention to be physically active and physical activity behaviour in adolescents from a low socio-economic status background: an application of the Theory of Planned Behaviour.

PubMed

Duncan, Michael J; Rivis, Amanda; Jordan, Caroline

2012-06-01

The aim of this brief report is to report on the utility of the Theory of Planned Behaviour (TPB) for predicting the physical activity intentions and behaviour of British adolescents from lower-than-average socio-economic backgrounds. A prospective questionnaire design was employed with 197, 13-14 year olds (76 males, 121 females). At time 1 participant completed standard measures of TPB variables. One week later (Time 2), participants completed the Physical Activity Questionnaire for Adolescents (PAQ-A) as a measure of physical activity behaviour. Hierarchical regression analyses showed that attitude and perceived behavioural control jointly accounted for 25% of the variance in intention (p = 0.0001). Perceived behavioural control emerged as the only significant predictor of physical activity behaviour and explained 3.7% of the variance (p = 0.001). Therefore, attitude and PBC successfully predicts intention towards physical activity and PBC predicts physical activity behaviour in British adolescents from lower-than-average socio-economic backgrounds. Copyright © 2011 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Psychological, interpersonal, and clinical factors predicting time spent on physical activity among Mexican patients with hypertension.

PubMed

Ybarra Sagarduy, José Luis; Camacho Mata, Dacia Yurima; Moral de la Rubia, José; Piña López, Julio Alfonso; Yunes Zárraga, José Luis Masud

2018-01-01

It is widely known that physical activity is the key to the optimal management and clinical control of hypertension. This research was conducted to identify factors that can predict the time spent on physical activity among Mexican adults with hypertension. This cross-sectional study was conducted among 182 Mexican patients with hypertension, who completed a set of self-administered questionnaires related to personality, social support, and medical adherence and health care behaviors, body mass index, and time since the disease diagnosis. Several path analyses were performed in order to test the predictors of the study behavior. Lower tolerance to frustration, more tolerance to ambiguity, more effective social support, and less time since the disease diagnosis predicted more time spent on physical activity, accounting for 13.3% of the total variance. The final model shows a good fit to the sample data ( p BS =0.235, χ 2 / gl =1.519, Jöreskog and Sörbom's Goodness of Fit Index =0.987, adjusted modality =0.962, Bollen's Incremental Fit Index =0.981, Bentler-Bonett Normed Fit Index =0.946, standardized root mean square residual =0.053). The performance of physical activity in patients with hypertension depends on a complex set of interactions between personal, interpersonal, and clinical variables. Understanding how these factors interact might enhance the design of interdisciplinary intervention programs so that quality of life of patients with hypertension improves and they might be able to manage and control their disease well.

Dynamo theory prediction of solar activity

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H.

1988-01-01

The dynamo theory technique to predict decadal time scale solar activity variations is introduced. The technique was developed following puzzling correlations involved with geomagnetic precursors of solar activity. Based upon this, a dynamo theory method was developed to predict solar activity. The method was used successfully in solar cycle 21 by Schatten, Scherrer, Svalgaard, and Wilcox, after testing with 8 prior solar cycles. Schatten and Sofia used the technique to predict an exceptionally large cycle, peaking early (in 1990) with a sunspot value near 170, likely the second largest on record. Sunspot numbers are increasing, suggesting that: (1) a large cycle is developing, and (2) that the cycle may even surpass the largest cycle (19). A Sporer Butterfly method shows that the cycle can now be expected to peak in the latter half of 1989, consistent with an amplitude comparable to the value predicted near the last solar minimum.

Counteracting Obstacles with Optimistic Predictions

ERIC Educational Resources Information Center

Zhang, Ying; Fishbach, Ayelet

2010-01-01

This research tested for counteractive optimism: a self-control strategy of generating optimistic predictions of future goal attainment in order to overcome anticipated obstacles in goal pursuit. In support of the counteractive optimism model, participants in 5 studies predicted better performance, more time invested in goal activities, and lower…

Can the theory of planned behaviour predict the physical activity behaviour of individuals?

PubMed

Hobbs, Nicola; Dixon, Diane; Johnston, Marie; Howie, Kate

2013-01-01

The theory of planned behaviour (TPB) can identify cognitions that predict differences in behaviour between individuals. However, it is not clear whether the TPB can predict the behaviour of an individual person. This study employs a series of n-of-1 studies and time series analyses to examine the ability of the TPB to predict physical activity (PA) behaviours of six individuals. Six n-of-1 studies were conducted, in which TPB cognitions and up to three PA behaviours (walking, gym workout and a personally defined PA) were measured twice daily for six weeks. Walking was measured by pedometer step count, gym attendance by self-report with objective validation of gym entry and the personally defined PA behaviour by self-report. Intra-individual variability in TPB cognitions and PA behaviour was observed in all participants. The TPB showed variable predictive utility within individuals and across behaviours. The TPB predicted at least one PA behaviour for five participants but had no predictive utility for one participant. Thus, n-of-1 designs and time series analyses can be used to test theory in an individual.

Time of Day Differences in Neural Reward Functioning in Healthy Young Men.

PubMed

Byrne, Jamie E M; Hughes, Matthew E; Rossell, Susan L; Johnson, Sheri L; Murray, Greg

2017-09-13

Reward function appears to be modulated by the circadian system, but little is known about the neural basis of this interaction. Previous research suggests that the neural reward response may be different in the afternoon; however, the direction of this effect is contentious. Reward response may follow the diurnal rhythm in self-reported positive affect, peaking in the early afternoon. An alternative is that daily reward response represents a type of prediction error, with neural reward activation relatively high at times of day when rewards are unexpected (i.e., early and late in the day). The present study measured neural reward activation in the context of a validated reward task at 10.00 h, 14.00 h, and 19.00 h in healthy human males. A region of interest BOLD fMRI protocol was used to investigate the diurnal waveform of activation in reward-related brain regions. Multilevel modeling found, as expected, a highly significant quadratic time-of-day effect focusing on the left putamen ( p < 0.001). Consistent with the "prediction error" hypothesis, activation was significantly higher at 10.00 h and 19.00 h compared with 14.00 h. It is provisionally concluded that the putamen may be particularly important in endogenous priming of reward motivation at different times of day, with the pattern of activation consistent with circadian-modulated reward expectancies in neural pathways (i.e., greater activation to reward stimuli at unexpected times of day). This study encourages further research into circadian modulation of reward and underscores the methodological importance of accounting for time of day in fMRI protocols. SIGNIFICANCE STATEMENT This is one of the first studies to use a repeated-measures imaging procedure to explore the diurnal rhythm of reward activation. Although self-reported reward (most often operationalized as positive affect) peaks in the afternoon, the present findings indicate that neural activation is lowest at this time. We conclude that the diurnal neural activation pattern may reflect a prediction error of the brain, where rewards at unexpected times (10.00 h and 19.00 h) elicit higher activation in reward brain regions than at expected (14.00 h) times. These data also have methodological significance, suggesting that there may be a time of day influence, which should be accounted for in neural reward studies. Copyright © 2017 the authors 0270-6474/17/378895-06$15.00/0.

Unpredicted Pitch Modulates Beta Oscillatory Power during Rhythmic Entrainment to a Tone Sequence.

PubMed

Chang, Andrew; Bosnyak, Dan J; Trainor, Laurel J

2016-01-01

Extracting temporal regularities in external stimuli in order to predict upcoming events is an essential aspect of perception. Fluctuations in induced power of beta band (15-25 Hz) oscillations in auditory cortex are involved in predictive timing during rhythmic entrainment, but whether such fluctuations are affected by prediction in the spectral (frequency/pitch) domain remains unclear. We tested whether unpredicted (i.e., unexpected) pitches in a rhythmic tone sequence modulate beta band activity by recording EEG while participants passively listened to isochronous auditory oddball sequences with occasional unpredicted deviant pitches at two different presentation rates. The results showed that the power in low-beta (15-20 Hz) was larger around 200-300 ms following deviant tones compared to standard tones, and this effect was larger when the deviant tones were less predicted. Our results suggest that the induced beta power activities in auditory cortex are consistent with a role in sensory prediction of both "when" (timing) upcoming sounds will occur as well as the prediction precision error of "what" (spectral content in this case). We suggest, further, that both timing and content predictions may co-modulate beta oscillations via attention. These findings extend earlier work on neural oscillations by investigating the functional significance of beta oscillations for sensory prediction. The findings help elucidate the functional significance of beta oscillations in perception.

Unpredicted Pitch Modulates Beta Oscillatory Power during Rhythmic Entrainment to a Tone Sequence

PubMed Central

Chang, Andrew; Bosnyak, Dan J.; Trainor, Laurel J.

2016-01-01

Extracting temporal regularities in external stimuli in order to predict upcoming events is an essential aspect of perception. Fluctuations in induced power of beta band (15–25 Hz) oscillations in auditory cortex are involved in predictive timing during rhythmic entrainment, but whether such fluctuations are affected by prediction in the spectral (frequency/pitch) domain remains unclear. We tested whether unpredicted (i.e., unexpected) pitches in a rhythmic tone sequence modulate beta band activity by recording EEG while participants passively listened to isochronous auditory oddball sequences with occasional unpredicted deviant pitches at two different presentation rates. The results showed that the power in low-beta (15–20 Hz) was larger around 200–300 ms following deviant tones compared to standard tones, and this effect was larger when the deviant tones were less predicted. Our results suggest that the induced beta power activities in auditory cortex are consistent with a role in sensory prediction of both “when” (timing) upcoming sounds will occur as well as the prediction precision error of “what” (spectral content in this case). We suggest, further, that both timing and content predictions may co-modulate beta oscillations via attention. These findings extend earlier work on neural oscillations by investigating the functional significance of beta oscillations for sensory prediction. The findings help elucidate the functional significance of beta oscillations in perception. PMID:27014138

Predicting Child Physical Activity and Screen Time: Parental Support for Physical Activity and General Parenting Styles

PubMed Central

Crain, A. Lauren; Senso, Meghan M.; Levy, Rona L.; Sherwood, Nancy E.

2014-01-01

Objective: To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Methods: Participants were children (6.9 ± 1.8 years) with a body mass index in the 70–95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Results: Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Conclusions: Parenting practices and styles should be considered jointly, offering implications for tailored interventions. PMID:24812256

Energy expenditure while playing active and inactive video games.

PubMed

Leatherdale, Scott T; Woodruff, Sarah J; Manske, Stephen R

2010-01-01

To examine energy expenditure (EE) when playing active and inactive videogames (VG). Predicted EE was measured among 51 undergraduate students while playing active and inactive VG (Ontario, Canada). Predicted EE was significantly higher playing the active VG compared to the inactive VG according to heart rate monitor (97.4 kcal vs 64.7 kcal) and SenseWear armband (192.4 kcal vs 42.3 kcal) estimates. Active VG may be a viable intervention tool for increasing EE among students who would otherwise be spending time in sedentary screen-based behaviors.

The Current Status of Unsteady CFD Approaches for Aerodynamic Flow Control

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Singer, Bart A.; Yamaleev, Nail; Vatsa, Veer N.; Viken, Sally A.; Atkins, Harold L.

2002-01-01

An overview of the current status of time dependent algorithms is presented. Special attention is given to algorithms used to predict fluid actuator flows, as well as other active and passive flow control devices. Capabilities for the next decade are predicted, and principal impediments to the progress of time-dependent algorithms are identified.

Predicting the magnetic vectors within coronal mass ejections arriving at Earth: 2. Geomagnetic response

NASA Astrophysics Data System (ADS)

Savani, N. P.; Vourlidas, A.; Richardson, I. G.; Szabo, A.; Thompson, B. J.; Pulkkinen, A.; Mays, M. L.; Nieves-Chinchilla, T.; Bothmer, V.

2017-02-01

This is a companion to Savani et al. (2015) that discussed how a first-order prediction of the internal magnetic field of a coronal mass ejection (CME) may be made from observations of its initial state at the Sun for space weather forecasting purposes (Bothmer-Schwenn scheme (BSS) model). For eight CME events, we investigate how uncertainties in their predicted magnetic structure influence predictions of the geomagnetic activity. We use an empirical relationship between the solar wind plasma drivers and Kp index together with the inferred magnetic vectors, to make a prediction of the time variation of Kp (Kp(BSS)). We find a 2σ uncertainty range on the magnetic field magnitude (|B|) provides a practical and convenient solution for predicting the uncertainty in geomagnetic storm strength. We also find the estimated CME velocity is a major source of error in the predicted maximum Kp. The time variation of Kp(BSS) is important for predicting periods of enhanced and maximum geomagnetic activity, driven by southerly directed magnetic fields, and periods of lower activity driven by northerly directed magnetic field. We compare the skill score of our model to a number of other forecasting models, including the NOAA/Space Weather Prediction Center (SWPC) and Community Coordinated Modeling Center (CCMC)/SWRC estimates. The BSS model was the most unbiased prediction model, while the other models predominately tended to significantly overforecast. The True skill score of the BSS prediction model (TSS = 0.43 ± 0.06) exceeds the results of two baseline models and the NOAA/SWPC forecast. The BSS model prediction performed equally with CCMC/SWRC predictions while demonstrating a lower uncertainty.

Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age

PubMed Central

Strath, Scott J; Kate, Rohit J; Keenan, Kevin G; Welch, Whitney A; Swartz, Ann M

2016-01-01

To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18–39, 40–64, 65 + years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of −0.03 to 0.01 METs, bias percent of −0.8 to 0.3%, and a rMSE range of 0.81–1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed. PMID:26449155

Liking More Means Doing More

PubMed Central

Hepler, Justin; Albarracin, Dolores

2018-01-01

Dispositional attitudes are an individual difference in the tendency to form positive versus negative attitudes. As positive (negative) attitudes promote active (inactive) responses to stimuli, we predicted that dispositional attitudes would be positively correlated with patterns of general action. In Study 1, participants reported all activities they engaged in during a 1-week period using a structured time use survey. Dispositional attitudes were positively correlated with the number of unique behaviors participants engaged in and with the total number of behaviors reported for the entire week. Study 2 replicated Study 1 using a free response time use survey. Overall, the results demonstrated that dispositional attitudes predict general action, such that the tendency to form positive (negative) attitudes predicts the tendency to engage in many (few) behaviors in daily life. This pattern occurred for both low effort and high effort behaviors. Implications for understanding activity patterns are discussed. PMID:29375723

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

PubMed

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.

Total ion chromatographic fingerprints combined with chemometrics and mass defect filter to predict antitumor components of Picrasma quassioids.

PubMed

Shi, Yuanyuan; Zhan, Hao; Zhong, Liuyi; Yan, Fangrong; Feng, Feng; Liu, Wenyuan; Xie, Ning

2016-07-01

A method of total ion chromatogram combined with chemometrics and mass defect filter was established for the prediction of active ingredients in Picrasma quassioides samples. The total ion chromatogram data of 28 batches were pretreated with wavelet transformation and correlation optimized warping to correct baseline drifts and retention time shifts. Then partial least squares regression was applied to construct a regression model to bridge the total ion chromatogram fingerprints and the antitumor activity of P. quassioides. Finally, the regression coefficients were used to predict the active peaks in total ion chromatogram fingerprints. In this strategy, mass defect filter was employed to classify and characterize the active peaks from a chemical point of view. A total of 17 constituents were predicted as the potential active compounds, 16 of which were identified as alkaloids by this developed approach. The results showed that the established method was not only simple and easy to operate, but also suitable to predict ultraviolet undetectable compounds and provide chemical information for the prediction of active compounds in herbs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

PubMed Central

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. PMID:26457111

Using ecological momentary assessment to examine antecedents and correlates of physical activity bouts in adults age 50+ years: a pilot study.

PubMed

Dunton, Genevieve Fridlund; Atienza, Audie A; Castro, Cynthia M; King, Abby C

2009-12-01

National recommendations supporting the promotion of multiple short (10+ minute) physical activity bouts each day to increase overall physical activity levels in middle-aged and older adults underscore the need to identify antecedents and correlates of such daily physical activity episodes. This pilot study used Ecological Momentary Assessment to examine the time-lagged and concurrent effects of empirically supported social, cognitive, affective, and physiological factors on physical activity among adults age 50+ years. Participants (N = 23) responded to diary prompts on a handheld computer four times per day across a 2-week period. Moderate-to-vigorous physical activity (MVPA), self-efficacy, positive and negative affect, control, demand, fatigue, energy, social interactions, and stressful events were assessed during each sequence. Multivariate results showed that greater self-efficacy and control predicted greater MVPA at each subsequent assessment throughout the day (p < 0.05). Also, having a positive social interaction was concurrently related to higher levels of MVPA (p = 0.052). Time-varying multidimensional individual processes predict within daily physical activity levels.

A Model of Contextual Motivation in Physical Education: Using Constructs from Self-Determination and Achievement Goal Theories To Predict Physical Activity Intentions.

ERIC Educational Resources Information Center

Standage, Martyn; Duda, Joan L.; Ntoumanis, Nikos

2003-01-01

Examines a study of student motivation in physical education that incorporated constructs from achievement goal and self-determination theories. Self-determined motivation was found to positively predict, whereas amotivation was a negative predictor of leisure-time physical activity intentions. (Contains 86 references and 3 tables.) (GCP)

Increasing discomfort tolerance predicts incentive senitization of exercise reinforcement: Preliminary results from a randomized controlled intervention to increase the reinforcing value of exercise in overweight to obese adu

USDA-ARS?s Scientific Manuscript database

Objective: The reinforcing (motivating) value of exercise/physical activity (RRVex) predicts usual exercise behavior and meeting of physical activity guidelines. Recent cross-sectional evidence suggests, for the first time, that greater tolerance for the discomfort experienced during exercise is ass...

Inferencing Processes After Right Hemisphere Brain Damage: Effects of Contextual Bias

PubMed Central

Blake, Margaret Lehman

2009-01-01

Purpose Comprehension deficits associated with right hemisphere brain damage (RHD) have been attributed to an inability to use context, but there is little direct evidence to support the claim. This study evaluated the effect of varying contextual bias on predictive inferencing by adults with RHD. Method Fourteen adults with no brain damage (NBD) and 14 with RHD read stories constructed with either high predictability or low predictability of a specific outcome. Reading time for a sentence that disconfirmed the target outcome was measured and compared with a control story context. Results Adults with RHD evidenced activation of predictive inferences only for highly predictive conditions, whereas NBD adults generated inferences in both high- and low-predictability stories. Adults with RHD were more likely than those with NBD to require additional time to integrate inferences in high-predictability conditions. The latter finding was related to working memory for the RHD group. Results are interpreted in light of previous findings obtained using the same stimuli. Conclusions RHD does not abolish the ability to use context. Evidence of predictive inferencing is influenced by task and strength of inference activation. Treatment considerations and cautions regarding interpreting results from one methodology are discussed. PMID:19252126

Socializing Agents for Sport and Physical Activities in Teenage Students: Comparative Studies in Samples From Costa Rica, Mexico, and Spain.

PubMed

Ruiz-Juan, Francisco; Baena-Extremera, Antonio; Granero-Gallegos, Antonio

2017-01-01

The aim of this study is to analyze a set of socializing agents for sport and physical activities and to establish their relationship with leisure time sport and physical activities behaviors and practice patterns in samples of teenage students with different sociocultural backgrounds. The sample included 2168 students in their first year of secondary education, 423 of them being from Costa Rica, 408 from Mexico, and 1337 from Spain (1052 male students, 1037 female students, and 79 students who did not specify gender) aged 11-16 years old ( M = 12.49; SD = .81). A validated questionnaire with questions about leisure time sport and physical activities and socializing agents was used. Descriptive, inferential, and multinomial logistic regression analyses were carried out with SPSS 17.0 to compare all three countries. Costa Rica had the most active students, best friends' inactivity, and unsupportive parents being the agents predicting inactivity and a low level of sport and physical activities. Mexico has a high dropout rate and inactive students exceed active ones; no agent predicts inactivity or sport and physical activities pattern. Spain has the highest level of sport and physical activities practice, and parents, siblings, and friends are predicting agents of inactivity together with unsupportive parents and friends.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

The Role of Habit and Perceived Control on Health Behavior among Pregnant Women.

PubMed

Mullan, Barbara; Henderson, Joanna; Kothe, Emily; Allom, Vanessa; Orbell, Sheina; Hamilton, Kyra

2016-05-01

Many pregnant women do not adhere to physical activity and dietary recommendations. Research investigating what psychological processes might predict physical activity and healthy eating (fruit and vegetable consumption) during pregnancy is scant. We explored the role of intention, habit, and perceived behavioral control as predictors of physical activity and healthy eating. Pregnant women (N = 195, Mage = 30.17, SDage = 4.46) completed questionnaires at 2 time points. At Time 1, participants completed measures of intention, habit, and perceived behavioral control. At Time 2, participants reported on their behavior (physical activity and healthy eating) within the intervening week. Regression analysis determined whether Time 1 variables predicted behavior at Time 2. Interaction terms also were tested. Final regression models indicated that only intention and habit explained significant variance in physical activity, whereas habit and the interaction between intention and habit explained significant variance in healthy eating. Simple slopes analysis indicated that the relationship between intention and healthy eating behavior was only significant at high levels of habit. Findings highlight the influence of habit on behavior and suggest that automaticity interventions may be useful in changing health behaviors during pregnancy.

Real-time position reconstruction with hippocampal place cells.

PubMed

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M A; Brotons-Mas, Jorge R; Edlinger, Günter; Bermúdez I Badia, S; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V

2011-01-01

Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.

Real-Time Position Reconstruction with Hippocampal Place Cells

PubMed Central

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M. A.; Brotons-Mas, Jorge R.; Edlinger, Günter; Bermúdez i Badia, S.; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V.

2011-01-01

Brain–computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5–6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral–neuronal feedback loops or for implementing neuroprosthetic control. PMID:21808603

Dynamic Monitoring of Cleanroom Fallout Using an Air Particle Counter

NASA Technical Reports Server (NTRS)

Perry, Radford

2011-01-01

The particle fallout limitations and periodic allocations for the James Webb Space Telescope are very stringent. Standard prediction methods are complicated by non-linearity and monitoring methods that are insufficiently responsive. A method for dynamically predicting the particle fallout in a cleanroom using air particle counter data was determined by numerical correlation. This method provides a simple linear correlation to both time and air quality, which can be monitored in real time. The summation of effects provides the program better understanding of the cleanliness and assists in the planning of future activities. Definition of fallout rates within a cleanroom during assembly and integration of contamination-sensitive hardware, such as the James Webb Space Telescope, is essential for budgeting purposes. Balancing the activity levels for assembly and test with the particle accumulation rate is paramount. The current approach to predicting particle fallout in a cleanroom assumes a constant air quality based on the rated class of a cleanroom, with adjustments for projected work or exposure times. Actual cleanroom class can also depend on the number of personnel present and the type of activities. A linear correlation of air quality and normalized particle fallout was determined numerically. An air particle counter (standard cleanroom equipment) can be used to monitor the air quality on a real-time basis and determine the "class" of the cleanroom (per FED-STD-209 or ISO-14644). The correlation function provides an area coverage coefficient per class-hour of exposure. The prediction of particle accumulations provides scheduling inputs for activity levels and cleanroom class requirements.

Early prediction of movie box office success based on Wikipedia activity big data.

PubMed

Mestyán, Márton; Yasseri, Taha; Kertész, János

2013-01-01

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Solar prediction analysis

NASA Technical Reports Server (NTRS)

Smith, Jesse B.

1992-01-01

Solar Activity prediction is essential to definition of orbital design and operational environments for space flight. This task provides the necessary research to better understand solar predictions being generated by the solar community and to develop improved solar prediction models. The contractor shall provide the necessary manpower and facilities to perform the following tasks: (1) review, evaluate, and assess the time evolution of the solar cycle to provide probable limits of solar cycle behavior near maximum end during the decline of solar cycle 22, and the forecasts being provided by the solar community and the techniques being used to generate these forecasts; and (2) develop and refine prediction techniques for short-term solar behavior flare prediction within solar active regions, with special emphasis on the correlation of magnetic shear with flare occurrence.

Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

PubMed

Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

2017-03-01

How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate-imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans. © 2016 John Wiley & Sons Ltd.

Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

USGS Publications Warehouse

Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T.; Yahn, Jeremiah; Porter, Warren P.

2017-01-01

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate-imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans.

Psychological, interpersonal, and clinical factors predicting time spent on physical activity among Mexican patients with hypertension

PubMed Central

Ybarra Sagarduy, José Luis; Camacho Mata, Dacia Yurima; Moral de la Rubia, José; Piña López, Julio Alfonso; Yunes Zárraga, José Luis Masud

2018-01-01

Background It is widely known that physical activity is the key to the optimal management and clinical control of hypertension. Purpose This research was conducted to identify factors that can predict the time spent on physical activity among Mexican adults with hypertension. Methods This cross-sectional study was conducted among 182 Mexican patients with hypertension, who completed a set of self-administered questionnaires related to personality, social support, and medical adherence and health care behaviors, body mass index, and time since the disease diagnosis. Several path analyses were performed in order to test the predictors of the study behavior. Results Lower tolerance to frustration, more tolerance to ambiguity, more effective social support, and less time since the disease diagnosis predicted more time spent on physical activity, accounting for 13.3% of the total variance. The final model shows a good fit to the sample data (pBS =0.235, χ2/gl =1.519, Jöreskog and Sörbom’s Goodness of Fit Index =0.987, adjusted modality =0.962, Bollen’s Incremental Fit Index =0.981, Bentler-Bonett Normed Fit Index =0.946, standardized root mean square residual =0.053). Conclusion The performance of physical activity in patients with hypertension depends on a complex set of interactions between personal, interpersonal, and clinical variables. Understanding how these factors interact might enhance the design of interdisciplinary intervention programs so that quality of life of patients with hypertension improves and they might be able to manage and control their disease well. PMID:29379276

Decomposing delta, theta, and alpha time–frequency ERP activity from a visual oddball task using PCA

PubMed Central

Bernat, Edward M.; Malone, Stephen M.; Williams, William J.; Patrick, Christopher J.; Iacono, William G.

2008-01-01

Objective Time–frequency (TF) analysis has become an important tool for assessing electrical and magnetic brain activity from event-related paradigms. In electrical potential data, theta and delta activities have been shown to underlie P300 activity, and alpha has been shown to be inhibited during P300 activity. Measures of delta, theta, and alpha activity are commonly taken from TF surfaces. However, methods for extracting relevant activity do not commonly go beyond taking means of windows on the surface, analogous to measuring activity within a defined P300 window in time-only signal representations. The current objective was to use a data driven method to derive relevant TF components from event-related potential data from a large number of participants in an oddball paradigm. Methods A recently developed PCA approach was employed to extract TF components [Bernat, E. M., Williams, W. J., and Gehring, W. J. (2005). Decomposing ERP time-frequency energy using PCA. Clin Neurophysiol, 116(6), 1314–1334] from an ERP dataset of 2068 17 year olds (979 males). TF activity was taken from both individual trials and condition averages. Activity including frequencies ranging from 0 to 14 Hz and time ranging from stimulus onset to 1312.5 ms were decomposed. Results A coordinated set of time–frequency events was apparent across the decompositions. Similar TF components representing earlier theta followed by delta were extracted from both individual trials and averaged data. Alpha activity, as predicted, was apparent only when time–frequency surfaces were generated from trial level data, and was characterized by a reduction during the P300. Conclusions Theta, delta, and alpha activities were extracted with predictable time-courses. Notably, this approach was effective at characterizing data from a single-electrode. Finally, decomposition of TF data generated from individual trials and condition averages produced similar results, but with predictable differences. Specifically, trial level data evidenced more and more varied theta measures, and accounted for less overall variance. PMID:17027110

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

PubMed

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.

How learning analytics can early predict under-achieving students in a blended medical education course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2017-07-01

Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

How Curriculum and Classroom Achievement Predict Teacher Time on Lecture- and Inquiry-Based Mathematics Activities

ERIC Educational Resources Information Center

Kaufman, Julia H.; Rita Karam; Pane, John F.; Junker, Brian W.

2012-01-01

This study drew on data from a large, randomized trial of Cognitive Tutor Algebra (CTA) in high-poverty settings to investigate how mathematics curricula and classroom achievement related to teacher reports of time spent on inquiry-based and lecture-based mathematics activities. We found that teachers using the CTA curriculum reported more time on…

Active public Facebook use and adolescents' feelings of loneliness: Evidence for a curvilinear relationship.

PubMed

Wang, Kexin; Frison, Eline; Eggermont, Steven; Vandenbosch, Laura

2018-06-09

Inconsistent results have been reported concerning the relationships between SNS usage and loneliness. The current two-wave panel study with a one year interval examined the possibility of reciprocal and curvilinear relationships between active public Facebook use and adolescents' social/emotional loneliness. Belgian adolescents from fifteen high schools participated (N = 1188, 55% male). The results showed a U-shaped relationship between (1) active Facebook use and social/emotional loneliness and (2) emotional loneliness and active Facebook use. Specifically, active Facebook use predicted decreased social/emotional loneliness among low to moderate users, while among heavy users, increased levels of social/emotional loneliness were predicted by active Facebook use. Emotional loneliness predicted higher active Facebook use among lonely adolescents. At the same time, emotional loneliness predicted decreased active Facebook use among adolescents who did not feel lonely. These findings stress to consider different types of loneliness, and reciprocal and curvilinear relationships in future social media research. Copyright © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Anticipatory activity in the human thalamus is predictive of reaction times.

PubMed

Nikulin, V V; Marzinzik, F; Wahl, M; Schneider, G-H; Kupsch, A; Curio, G; Klostermann, F

2008-09-09

Responding to environmental stimuli in a fast manner is a fundamental behavioral capacity. The pace at which one responds is known to be predetermined by cortical areas, but it remains to be shown if subcortical structures also take part in defining motor swiftness. As the thalamus has previously been implicated in behavioral control, we tested if neuronal activity at this level could also predict the reaction time of upcoming movements. To this end we simultaneously recorded electrical brain activity from the scalp and the ventral intermediate nucleus (VIM) of the thalamus in patients undergoing thalamic deep brain stimulation. Based on trial-to-trial analysis of a Go/NoGo task, we demonstrate that both cortical and thalamic neuronal activity prior to the delivery of upcoming Go stimulus correlates with the reaction time. This result goes beyond the demonstration of thalamic activity being associated with but potentially staying invariant to motor performance. In contrast, it indicates that the latencies at which we respond to environmental stimuli are not exclusively related to cortical pre-movement states but are also correlated with anticipatory thalamic activity.

Towards intensive parenting? Changes in the composition and determinants of mothers' and fathers' time with children 1992-2006.

PubMed

Craig, Lyn; Powell, Abigail; Smyth, Ciara

2014-09-01

Contemporary expectations of good parenting hold that focused, intensive parental attention is essential to children's development. Parental input is viewed as a key determinant in children's social, psychological and educational outcomes, with the early years particularly crucial. However, increased rates of maternal employment mean that more parents are juggling work and family commitments and have less non-work time available to devote to children. Yet studies find that parental childcare time has increased over recent decades. In this paper, we explore the detail of this trend using data from the Australian Bureau of Statistics (ABS) Time Use Survey (TUS), 1992 and 2006. To investigate whether discourses on intensive parenting are reflected in behaviour, we examine a greater range of parent-child activities than has been undertaken to date, looking at trends in active childcare time (disaggregated into talk-based, physical and accompanying care activities); time in childcare as a secondary activity; time spent in the company of children in leisure activities; and time spent in the company of children in total. We also investigate whether the influence of factors known to predict parental time with children (gender, education, employment status and the age of children) have changed over time. We contextualize our analyses within social and economic trends in Australia and find a compositional change in parental time, with more active childcare occurring within less overall time, which suggests more intensive, child-centred parenting. Fathers' parent-child time, particularly in physical care, increased more than mothers' (from a much lower base), and tertiary education no longer predicts significantly higher childcare time. © London School of Economics and Political Science 2014.

Adolescents' technology and face-to-face time use predict objective sleep outcomes.

PubMed

Tavernier, Royette; Heissel, Jennifer A; Sladek, Michael R; Grant, Kathryn E; Adam, Emma K

2017-08-01

The present study examined both within- and between-person associations between adolescents' time use (technology-based activities and face-to-face interactions with friends and family) and sleep behaviors. We also assessed whether age moderated associations between adolescents' time use with friends and family and sleep. Adolescents wore an actigraph monitor and completed brief evening surveys daily for 3 consecutive days. Adolescents (N=71; mean age=14.50 years old, SD=1.84; 43.7% female) were recruited from 3 public high schools in the Midwest. We assessed 8 technology-based activities (eg, texting, working on a computer), as well as time spent engaged in face-to-face interactions with friends and family, via questions on adolescents' evening surveys. Actigraph monitors assessed 3 sleep behaviors: sleep latency, sleep hours, and sleep efficiency. Hierarchical linear models indicated that texting and working on the computer were associated with shorter sleep, whereas time spent talking on the phone predicted longer sleep. Time spent with friends predicted shorter sleep latencies, while family time predicted longer sleep latencies. Age moderated the association between time spent with friends and sleep efficiency, as well as between family time and sleep efficiency. Specifically, longer time spent interacting with friends was associated with higher sleep efficiency but only among younger adolescents. Furthermore, longer family time was associated with higher sleep efficiency but only for older adolescents. Findings are discussed in terms of the importance of regulating adolescents' technology use and improving opportunities for face-to-face interactions with friends, particularly for younger adolescents. Copyright © 2017 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

Time and interference: Effects on working memory.

PubMed

Botto, Marta; Palladino, Paola

2016-05-01

This study tested predictions from the time-based resource-sharing (TBRS) model with a classical verbal working memory (WM) task, where target and non-target information interfere strongly with each other. Different predictions can be formulated according to the dominant perspectives (TBRS and interference hypothesis) on the role of inhibitory control in WM task performance. Here, we aimed to trace the activation of irrelevant information, examining priming effects in a lexical decision task immediately following WM recall. Results indicate the roles of both time and interference constraints in determining task performance. In particular, the role of time available seemed crucial at the highest WM loads (i.e., 3 and 4 memoranda). These were also associated with a higher activation of no-longer-relevant information but, in this case, independently from time available for processing. © 2015 The British Psychological Society.

Spatial representation of organic carbon and active-layer thickness of high latitude soils in CMIP5 earth system models

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

Mishra, Umakant; Drewniak, Beth; Jastrow, Julie D.

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (F.SMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for redudng existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Modellntercomparison Project Phase 5 { CMIP5) ESMs with those predicted from geospatial predictions, based on observation datamore » for the state of Alaska, USA. For the geospatial modeling. we used soil profile observations {585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m-2) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors. when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primaly factors leading to observed differences were ( 1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in F.SMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks.« less

Job Design and Ethnic Differences in Working Women’s Physical Activity

PubMed Central

Grzywacz, Joseph G.; Crain, A. Lauren; Martinson, Brian C.; Quandt, Sara A.

2014-01-01

Objective To document the role job control and schedule control play in shaping women’s physical activity, and how it delineates educational and racial variability in associations of job and social control with physical activity. Methods Prospective data were obtained from a community-based sample of working women (N = 302). Validated instruments measured job control and schedule control. Steps per day were assessed using New Lifestyles 800 activity monitors. Results Greater job control predicted more steps per day, whereas greater schedule control predicted fewer steps. Small indirect associations between ethnicity and physical activity were observed among women with a trade school degree or less but not for women with a college degree. Conclusions Low job control created barriers to physical activity among working women with a trade school degree or less. Greater schedule control predicted less physical activity, suggesting women do not use time “created” by schedule flexibility for personal health enhancement. PMID:24034681

Job design and ethnic differences in working women's physical activity.

PubMed

Grzywacz, Joseph G; Crain, A Lauren; Martinson, Brian C; Quandt, Sara A

2014-01-01

To document the role job control and schedule control play in shaping women's physical activity, and how it delineates educational and racial variability in associations of job and social control with physical activity. Prospective data were obtained from a community-based sample of working women (N = 302). Validated instruments measured job control and schedule control. Steps per day were assessed using New Lifestyles 800 activity monitors. Greater job control predicted more steps per day, whereas greater schedule control predicted fewer steps. Small indirect associations between ethnicity and physical activity were observed among women with a trade school degree or less but not for women with a college degree. Low job control created barriers to physical activity among working women with a trade school degree or less. Greater schedule control predicted less physical activity, suggesting women do not use time "created" by schedule flexibility for personal health enhancement.

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

PubMed

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.

Assessment and Prediction of Natural Hazards from Satellite Imagery

PubMed Central

Gillespie, Thomas W.; Chu, Jasmine; Frankenberg, Elizabeth; Thomas, Duncan

2013-01-01

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earth’s surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earth’s surface. High-resolution passive sensors have been used to successfully assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily ≤ 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space. PMID:25170186

HOW DO IMMIGRANTS SPEND THEIR TIME?

PubMed Central

Hamermesh, Daniel S.

2012-01-01

Sharp differences in time use by nativity emerge when activities are distinguished by incidence and intensity in recent U.S. data. A model with daily fixed costs for assimilating activities predicts immigrants are less likely than natives to undertake such activities on a given day; but those who do will spend relatively more time on them. Activities such as purchasing, education, and market work conform to the model. Other results suggest that fixed costs for assimilating activities are higher for immigrants with poor English proficiency or who originate in less developed countries. An analysis of comparable Australian data yields similar results. PMID:24443631

Individual differences in cognitive functioning predict effectiveness of a heads-up Lane Departure Warning for younger and older drivers

PubMed Central

Aksan, Nazan; Sager, Lauren; Hacker, Sarah; Lester, Benjamin; Dawson, Jeffrey; Rizzo, Matthew; Ebe, Kazutoshi; Foley, James

2016-01-01

The effectiveness of an idealized lane departure warning (LDW) was evaluated in an interactive fixed base driving simulator. Thirty-eight older (mean age = 77 years) and 40 younger drivers (mean age = 35 years) took four different drives/routes similar in road culture composition and hazards encountered with and without LDW. The four drives were administered over visits separated approximately by two weeks to examine changes in long-term effectiveness of LDW. Performance metrics were number of LDW activations and average correction time to each LDW. LDW reduced correction time to re-center the vehicle by 1.34 seconds on average (95% CI = 1.12–1.57 seconds) but did not reduce the number of times the drivers drifted enough in their lanes to activate the system (LDW activations). The magnitude of reductions in average correction RT was similar for older and younger drivers and did not change with repeated exposures across visits. The contribution of individual differences in basic visual and motor function, as well as cognitive function to safety gains from LDW was also examined. Cognitive speed of processing predicted lane keeping performance for older and younger drivers. Differences in memory, visuospatial construction, and executive function tended to predict performance differences among older but not younger drivers. Cognitive functioning did not predict changes in the magnitude of safety benefits from LDW over time. Implications are discussed with respect to real-world safety systems. PMID:27898370

Individual differences in cognitive functioning predict effectiveness of a heads-up lane departure warning for younger and older drivers.

PubMed

Aksan, Nazan; Sager, Lauren; Hacker, Sarah; Lester, Benjamin; Dawson, Jeffrey; Rizzo, Matthew; Ebe, Kazutoshi; Foley, James

2017-02-01

The effectiveness of an idealized lane departure warning (LDW) was evaluated in an interactive fixed base driving simulator. Thirty-eight older (mean age=77years) and 40 younger drivers (mean age=35years) took four different drives/routes similar in road culture composition and hazards encountered with and without LDW. The four drives were administered over visits separated approximately by two weeks to examine changes in long-term effectiveness of LDW. Performance metrics were number of LDW activations and average correction time to each LDW. LDW reduced correction time to re-center the vehicle by 1.34s on average (95% CI=1.12-1.57s) but did not reduce the number of times the drivers drifted enough in their lanes to activate the system (LDW activations). The magnitude of reductions in average correction RT was similar for older and younger drivers and did not change with repeated exposures across visits. The contribution of individual differences in basic visual and motor function, as well as cognitive function to safety gains from LDW was also examined. Cognitive speed of processing predicted lane keeping performance for older and younger drivers. Differences in memory, visuospatial construction, and executive function tended to predict performance differences among older but not younger drivers. Cognitive functioning did not predict changes in the magnitude of safety benefits from LDW over time. Implications are discussed with respect to real-world safety systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting child physical activity and screen time: parental support for physical activity and general parenting styles.

PubMed

Langer, Shelby L; Crain, A Lauren; Senso, Meghan M; Levy, Rona L; Sherwood, Nancy E

2014-07-01

To examine relationships between parenting styles and practices and child moderate-to-vigorous physical activity (MVPA) and screen time. Participants were children (6.9 ± 1.8 years) with a body mass index in the 70-95th percentile and their parents (421 dyads). Parent-completed questionnaires assessed parental support for child physical activity (PA), parenting styles and child screen time. Children wore accelerometers to assess MVPA. Parenting style did not predict MVPA, but support for PA did (positive association). The association between support and MVPA, moreover, varied as a function of permissive parenting. For parents high in permissiveness, the association was positive (greater support was related to greater MVPA and therefore protective). For parents low in permissiveness, the association was neutral; support did not matter. Authoritarian and permissive parenting styles were both associated with greater screen time. Parenting practices and styles should be considered jointly, offering implications for tailored interventions. © The Author 2014. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design

PubMed Central

2017-01-01

Background 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. Objective 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. Methods 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. Results 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 of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Conclusions Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified. PMID:28619700

Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design.

PubMed

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 of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified. ©Armin Spreco, Olle Eriksson, Örjan Dahlström, Benjamin John Cowling, Toomas Timpka. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.06.2017.

Microenvironmental characteristics important for personal exposures to aldehydes in Sacramento, CA, and Milwaukee, WI

NASA Astrophysics Data System (ADS)

Raymer, J. H.; Akland, G.; Johnson, T. R.; Long, T.; Michael, L.; Cauble, L.; McCombs, M.

Oxygenated additives in gasoline are designed to decrease the ozone-forming hydrocarbons and total air toxics, yet they can increase the emissions of aldehydes and thus increase human exposure to these toxic compounds. This paper describes a study conducted to characterize targeted aldehydes in microenvironments in Sacramento, CA, and Milwaukee, WI, and to improve our understanding of the impact of the urban environment on human exposure to air toxics. Data were obtained from microenvironmental concentration measurements, integrated, 24-h personal measurements, indoor and outdoor pollutant monitors at the participants' residences, from ambient pollutant monitors at fixed-site locations in each city, and from real-time diaries and questionnaires completed by the technicians and participants. As part of this study, a model to predict personal exposures based on individual time/activity data was developed for comparison to measured concentrations. Predicted concentrations were generally within 25% of the measured concentrations. The microenvironments that people encounter daily provide for widely varying exposures to aldehydes. The activities that occur in those microenvironments can modulate the aldehyde concentrations dramatically, especially for environments such as "indoor at home." By considering personal activity, location (microenvironment), duration in the microenvironment, and a knowledge of the general concentrations of aldehydes in the various microenvironments, a simple model can do a reasonably good job of predicting the time-averaged personal exposures to aldehydes, even in the absence of monitoring data. Although concentrations of aldehydes measured indoors at the participants' homes tracked well with personal exposure, there were instances where personal exposures and indoor concentrations differed significantly. Key to the ability to predict exposure based on time/activity data is the quality and completeness of the microenvironmental characterizations for the chemicals of interest. Consistent with many earlier studies, personal exposures are difficult to predict using data from regional outdoor monitors.

Sex differences in theory-based predictors of leisure time physical activity in a population-based sample of adults with spinal cord injury.

PubMed

Stapleton, Jessie N; Martin Ginis, Kathleen A

2014-09-01

To examine sex differences in theory-based predictors of leisure time physical activity (LTPA) among men and women with spinal cord injury, and secondarily, to identify factors that might explain any sex differences in social cognitions. A secondary analysis of Study of Health and Activity in People with Spinal Cord Injury survey data. Community. Community-dwelling men (n=536) and women (n=164) recruited from 4 rehabilitation and research centers. Not applicable. Subjective norms, attitudes, barrier self-efficacy, perceived controllability (PC), and intentions. Men had stronger PC and barrier self-efficacy than women. Hierarchical regression analyses revealed that social support significantly predicted PC for both sexes, and health, pain, and physical independence also significantly predicted PC for men. Social support, health, and pain significantly predicted barrier self-efficacy for men. Social support was the only significant predictor of barrier self-efficacy for women. Women felt significantly less control over their physical activity behavior and had lower confidence to overcome barriers to physical activity than did men. Although social support predicted PC and barrier self-efficacy in both men and women, men seemed to take additional factors into consideration when formulating their control beliefs for LTPA. Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

The Influence of Pre-stimulus EEG Activity on Reaction Time During a Verbal Sternberg Task is Related to Musical Expertise.

PubMed

Klein, Carina; Diaz Hernandez, Laura; Koenig, Thomas; Kottlow, Mara; Elmer, Stefan; Jäncke, Lutz

2016-01-01

Previous work highlighted the possibility that musical training has an influence on cognitive functioning. The suggested reason for this influence is the strong recruitment of attention, planning, and working memory functions during playing a musical instrument. The purpose of the present work was twofold, namely to evaluate the general relationship between pre-stimulus electrophysiological activity and cognition, and more specifically the influence of musical expertise on working memory functions. With this purpose in mind, we used covariance mapping analyses to evaluate whether pre-stimulus electroencephalographic activity is predictive for reaction time during a visual working memory task (Sternberg paradigm) in musicians and non-musicians. In line with our hypothesis, we replicated previous findings pointing to a general predictive value of pre-stimulus activity for working memory performance. Most importantly, we also provide first evidence for an influence of musical expertise on working memory performance that could distinctively be predicted by pre-stimulus spectral power. Our results open novel perspectives for better comprehending the vast influences of musical expertise on cognition.

Predicting Solar Cycle 24 Using a Geomagnetic Precursor Pair

NASA Technical Reports Server (NTRS)

Pesnell, W. Dean

2014-01-01

We describe using Ap and F(10.7) as a geomagnetic-precursor pair to predict the amplitude of Solar Cycle 24. The precursor is created by using F(10.7) to remove the direct solar-activity component of Ap. Four peaks are seen in the precursor function during the decline of Solar Cycle 23. A recurrence index that is generated by a local correlation of Ap is then used to determine which peak is the correct precursor. The earliest peak is the most prominent but coincides with high levels of non-recurrent solar activity associated with the intense solar activity of October and November 2003. The second and third peaks coincide with some recurrent activity on the Sun and show that a weak cycle precursor closely following a period of strong solar activity may be difficult to resolve. A fourth peak, which appears in early 2008 and has recurrent activity similar to precursors of earlier solar cycles, appears to be the "true" precursor peak for Solar Cycle 24 and predicts the smallest amplitude for Solar Cycle 24. To determine the timing of peak activity it is noted that the average time between the precursor peak and the following maximum is approximately equal to 6.4 years. Hence, Solar Cycle 24 would peak during 2014. Several effects contribute to the smaller prediction when compared with other geomagnetic-precursor predictions. During Solar Cycle 23 the correlation between sunspot number and F(10.7) shows that F(10.7) is higher than the equivalent sunspot number over most of the cycle, implying that the sunspot number underestimates the solar-activity component described by F(10.7). During 2003 the correlation between aa and Ap shows that aa is 10 % higher than the value predicted from Ap, leading to an overestimate of the aa precursor for that year. However, the most important difference is the lack of recurrent activity in the first three peaks and the presence of significant recurrent activity in the fourth. While the prediction is for an amplitude of Solar Cycle 24 of 65 +/- 20 in smoothed sunspot number, a below-average amplitude for Solar Cycle 24, with maximum at 2014.5+/-0.5, we conclude that Solar Cycle 24 will be no stronger than average and could be much weaker than average.

Adsorption of selected pharmaceuticals and an endocrine disrupting compound by granular activated carbon. 2. Model prediction.

PubMed

Yu, Zirui; Peldszus, Sigrid; Huck, Peter M

2009-03-01

The adsorption of two representative pharmaceutically active compounds (PhACs)-naproxen and carbamazepine and one endocrine disrupting compound (EDC)-nonylphenol was studied in pilot-scale granular activated carbon (GAC) adsorbers using post-sedimentation (PS) water from a full-scale drinking water treatment plant. Acidic naproxen broke through fastest while nonylphenol was removed best, which was consistent with the degree to which fouling affected compound removals. Model predictions and experimental data were generally in good agreement for all three compounds, which demonstrated the effectiveness and robustness of the pore and surface diffusion model (PSDM) used in combination with the time-variable parameter approach for predicting removals at environmentally relevant concentrations (i.e., ng/L range). Sensitivity analyses suggested that accurate determination of film diffusion coefficients was critical for predicting breakthrough for naproxen and carbamazepine, in particular when high removals are targeted. Model simulations demonstrated that GAC carbon usage rates (CURs) for naproxen were substantially influenced by the empty bed contact time (EBCT) at the investigated conditions. Model-based comparisons between GAC CURs and minimum CURs for powdered activated carbon (PAC) applications suggested that PAC would be most appropriate for achieving 90% removal of naproxen, whereas GAC would be more suitable for nonylphenol.

Effective equilibrium states in mixtures of active particles driven by colored noise

NASA Astrophysics Data System (ADS)

Wittmann, René; Brader, J. M.; Sharma, A.; Marconi, U. Marini Bettolo

2018-01-01

We consider the steady-state behavior of pairs of active particles having different persistence times and diffusivities. To this purpose we employ the active Ornstein-Uhlenbeck model, where the particles are driven by colored noises with exponential correlation functions whose intensities and correlation times vary from species to species. By extending Fox's theory to many components, we derive by functional calculus an approximate Fokker-Planck equation for the configurational distribution function of the system. After illustrating the predicted distribution in the solvable case of two particles interacting via a harmonic potential, we consider systems of particles repelling through inverse power-law potentials. We compare the analytic predictions to computer simulations for such soft-repulsive interactions in one dimension and show that at linear order in the persistence times the theory is satisfactory. This work provides the toolbox to qualitatively describe many-body phenomena, such as demixing and depletion, by means of effective pair potentials.

Trial-to-trial Adaptation: Parsing out the Roles of Cerebellum and BG in Predictive Motor Timing.

PubMed

Lungu, Ovidiu V; Bares, Martin; Liu, Tao; Gomez, Christopher M; Cechova, Ivica; Ashe, James

2016-07-01

We previously demonstrated that predictive motor timing (i.e., timing requiring visuomotor coordination in anticipation of a future event, such as catching or batting a ball) is impaired in patients with spinocerebellar ataxia (SCA) types 6 and 8 relative to healthy controls. Specifically, SCA patients had difficulties postponing their motor response while estimating the target kinematics. This behavioral difference relied on the activation of both cerebellum and striatum in healthy controls, but not in cerebellar patients, despite both groups activating certain parts of cerebellum during the task. However, the role of these two key structures in the dynamic adaptation of the motor timing to target kinematic properties remained unexplored. In the current paper, we analyzed these data with the aim of characterizing the trial-by-trial changes in brain activation. We found that in healthy controls alone, and in comparison with SCA patients, the activation in bilateral striatum was exclusively associated with past successes and that in the left putamen, with maintaining a successful performance across successive trials. In healthy controls, relative to SCA patients, a larger network was involved in maintaining a successful trial-by-trial strategy; this included cerebellum and fronto-parieto-temporo-occipital regions that are typically part of attentional network and action monitoring. Cerebellum was also part of a network of regions activated when healthy participants postponed their motor response from one trial to the next; SCA patients showed reduced activation relative to healthy controls in both cerebellum and striatum in the same contrast. These findings support the idea that cerebellum and striatum play complementary roles in the trial-by-trial adaptation in predictive motor timing. In addition to expanding our knowledge of brain structures involved in time processing, our results have implications for the understanding of BG disorders, such as Parkinson disease where feedback processing or reward learning is affected.

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

NASA Astrophysics Data System (ADS)

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.

Start time variability and predictability in railroad train and engine freight and passenger service employees.

DOT National Transportation Integrated Search

2014-04-01

Start time variability in work schedules is often hypothesized to be a cause of railroad employee fatigue because unpredictable work start times prevent employees from planning sleep and personal activities. This report examines work start time diffe...

An active balance board system with real-time control of stiffness and time-delay to assess mechanisms of postural stability.

PubMed

Cruise, Denise R; Chagdes, James R; Liddy, Joshua J; Rietdyk, Shirley; Haddad, Jeffrey M; Zelaznik, Howard N; Raman, Arvind

2017-07-26

Increased time-delay in the neuromuscular system caused by neurological disorders, concussions, or advancing age is an important factor contributing to balance loss (Chagdes et al., 2013, 2016a,b). We present the design and fabrication of an active balance board system that allows for a systematic study of stiffness and time-delay induced instabilities in standing posture. Although current commercial balance boards allow for variable stiffness, they do not allow for manipulation of time-delay. Having two controllable parameters can more accurately determine the cause of balance deficiencies, and allows us to induce instabilities even in healthy populations. An inverted pendulum model of human posture on such an active balance board predicts that reduced board rotational stiffness destabilizes upright posture through board tipping, and limit cycle oscillations about the upright position emerge as feedback time-delay is increased. We validate these two mechanisms of instability on the designed balance board, showing that rotational stiffness and board time-delay induced the predicted postural instabilities in healthy, young adults. Although current commercial balance boards utilize control of rotational stiffness, real-time control of both stiffness and time-delay on an active balance board is a novel and innovative manipulation to reveal balance deficiencies and potentially improve individualized balance training by targeting multiple dimensions contributing to standing balance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Do self- or parent-reported dietary, physical activity, and sedentary behaviors predict worsening obesity in children?

PubMed

Dorsey, Karen B; Mauldon, Maria; Magraw, Ruth; Yu, Sunkyung; Krumholz, Harlan M

2010-10-01

To determine whether information gathered during routine healthcare visits regarding obesity related risk factors and risk behaviors predicts increases in BMI z-score over time among overweight and obese children. Medical records from 168 overweight and 441 obese patients seen for repeated visits between September 2003 and April 2006 were examined for reported dietary, physical activity, and sedentary behaviors, family history of obesity and diabetes mellitus, documented Acanthosis nigricans, and BMI values. Random-effects regression analysis was done to determine whether demographic, familial, or behavioral data predicted changes in BMI z-score over time. The presence of A nigricans and a family history of obesity were associated with an increase in BMI z-score (beta=0.56, SE=0.09, P<.001 and beta=0.31, SE=0.13, P=.021). These risk factors explained 8% and 7% of the variation in BMI z-score respectively. Self- or parent-reported dietary and physical activity behaviors did not predict change in BMI z-score. Our findings suggest that the risk factors and self- or parent-reported risk behaviors routinely assessed by pediatric clinicians have limited ability to predict future growth trends, demonstrating the difficulty in determining which patients have the greatest risk of progression of obesity. Copyright (c) 2010 Mosby, Inc. All rights reserved.

Possibility of Earthquake-prediction by analyzing VLF signals

NASA Astrophysics Data System (ADS)

Ray, Suman; Chakrabarti, Sandip Kumar; Sasmal, Sudipta

2016-07-01

Prediction of seismic events is one of the most challenging jobs for the scientific community. Conventional ways for prediction of earthquakes are to monitor crustal structure movements, though this method has not yet yield satisfactory results. Furthermore, this method fails to give any short-term prediction. Recently, it is noticed that prior to any seismic event a huge amount of energy is released which may create disturbances in the lower part of D-layer/E-layer of the ionosphere. This ionospheric disturbance may be used as a precursor of earthquakes. Since VLF radio waves propagate inside the wave-guide formed by lower ionosphere and Earth's surface, this signal may be used to identify ionospheric disturbances due to seismic activity. We have analyzed VLF signals to find out the correlations, if any, between the VLF signal anomalies and seismic activities. We have done both the case by case study and also the statistical analysis using a whole year data. In both the methods we found that the night time amplitude of VLF signals fluctuated anomalously three days before the seismic events. Also we found that the terminator time of the VLF signals shifted anomalously towards night time before few days of any major seismic events. We calculate the D-layer preparation time and D-layer disappearance time from the VLF signals. We have observed that this D-layer preparation time and D-layer disappearance time become anomalously high 1-2 days before seismic events. Also we found some strong evidences which indicate that it may possible to predict the location of epicenters of earthquakes in future by analyzing VLF signals for multiple propagation paths.

Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

PubMed

Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

2015-12-01

It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01). Copyright © 2015 Elsevier Inc. All rights reserved.

Network-based Prediction of Lotic Thermal Regimes Across New England

EPA Science Inventory

Thermal regimes are a critical factor in models predicting effects of watershed management activities on fish habitat suitability. We have assembled a database of lotic temperature time series across New England (> 7000 station-year combinations) from state and Federal data sour...

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure.

PubMed

Correa, John B; Apolzan, John W; Shepard, Desti N; Heil, Daniel P; Rood, Jennifer C; Martin, Corby K

2016-07-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

PubMed Central

Correa, John B.; Apolzan, John W.; Shepard, Desti N.; Heil, Daniel P.; Rood, Jennifer C.; Martin, Corby K.

2016-01-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland–Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity. PMID:27270210

Relationship of early infant state measures to behavior over the first year of life in the tufted capuchin monkey (Cebus apella).

PubMed

Byrne, G; Suomi, S J

1998-01-01

Data on activity states were collected from 29 group-housed capuchin monkey (Cebus apella) infants for 3 h each week from birth to 11 weeks of age. The amounts of time spent in sleeping/drowsy, alert-quiet, and alert-active states were measured in these subjects. Videotaped observations of these infants were recorded 3 times/week in the home cage over the first year of life and were scored for a number of social and exploratory behaviors. The extent to which early infant activity state scores predicted later behavior in the home cage was examined. Infant state measures correlated significantly with home cage behavior during months 2-6 in that infants that had been more active in early infancy spent more time alone, with other animals, and in exploration and play and less time with mothers than did quieter infants. Early state measures were less successful in predicting home cage scores beyond 8 months of age, whereas differences in behavior attributable to housing variables became more salient in the latter part of the first year. There was also a negative correlation between mother and infant activity in months 2 and 3, in that more sedentary mothers tended to have more active infants.

The time course of activity in dorsolateral prefrontal cortex and anterior cingulate cortex during top-down attentional control.

PubMed

Silton, Rebecca Levin; Heller, Wendy; Towers, David N; Engels, Anna S; Spielberg, Jeffrey M; Edgar, J Christopher; Sass, Sarah M; Stewart, Jennifer L; Sutton, Bradley P; Banich, Marie T; Miller, Gregory A

2010-04-15

A network of brain regions has been implicated in top-down attentional control, including left dorsolateral prefrontal cortex (LDLPFC) and dorsal anterior cingulate cortex (dACC). The present experiment evaluated predictions of the cascade-of-control model (Banich, 2009), which predicts that during attentionally-demanding tasks, LDLPFC imposes a top-down attentional set which precedes late-stage selection performed by dACC. Furthermore, the cascade-of-control model argues that dACC must increase its activity to compensate when top-down control by LDLPFC is poor. The present study tested these hypotheses using fMRI and dense-array ERP data collected from the same 80 participants in separate sessions. fMRI results guided ERP source modeling to characterize the time course of activity in LDLPFC and dACC. As predicted, dACC activity subsequent to LDLPFC activity distinguished congruent and incongruent conditions on the Stroop task. Furthermore, when LDLPFC activity was low, the level of dACC activity was related to performance outcome. These results demonstrate that dACC responds to attentional demand in a flexible manner that is dependent on the level of LDLPFC activity earlier in a trial. Overall, results were consistent with the temporal course of regional brain function proposed by the cascade-of-control model. Copyright 2009 Elsevier Inc. All rights reserved.

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

NASA Astrophysics Data System (ADS)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

Age differences in cognitive performance in later life: relationships to self-reported health and activity life style.

PubMed

Hultsch, D F; Hammer, M; Small, B J

1993-01-01

The predictive relationships among individual differences in self-reported physical health and activity life style and performance on an array of information processing and intellectual ability measures were examined. A sample of 484 men and women aged 55 to 86 years completed a battery of cognitive tasks measuring verbal processing time, working memory, vocabulary, verbal fluency, world knowledge, word recall, and text recall. Hierarchical regression was used to predict performance on these tasks from measures of self-reported physical health, alcohol and tobacco use, and level of participation in everyday activities. The results indicated: (a) individual differences in self-reported health and activity predicted performance on multiple cognitive measures; (b) self-reported health was more predictive of processing resource variables than knowledge-based abilities; (c) interaction effects indicated that participation in cognitively demanding activities was more highly related to performance on some measures for older adults than for middle-aged adults; and (d) age-related differences in performance on multiple measures were attenuated by partialing individual differences in self-reported health and activity.

Postural time-to-contact as a precursor of visually induced motion sickness.

PubMed

Li, Ruixuan; Walter, Hannah; Curry, Christopher; Rath, Ruth; Peterson, Nicolette; Stoffregen, Thomas A

2018-06-01

The postural instability theory of motion sickness predicts that subjective symptoms of motion sickness will be preceded by unstable control of posture. In previous studies, this prediction has been confirmed with measures of the spatial magnitude and the temporal dynamics of postural activity. In the present study, we examine whether precursors of visually induced motion sickness might exist in postural time-to-contact, a measure of postural activity that is related to the risk of falling. Standing participants were exposed to oscillating visual motion stimuli in a standard laboratory protocol. Both before and during exposure to visual motion stimuli, we monitored the kinematics of the body's center of pressure. We predicted that postural activity would differ between participants who reported motion sickness and those who did not, and that these differences would exist before participants experienced subjective symptoms of motion sickness. During exposure to visual motion stimuli, the multifractality of sway differed between the Well and Sick groups. Postural time-to-contact differed between the Well and Sick groups during exposure to visual motion stimuli, but also before exposure to any motion stimuli. The results provide a qualitatively new type of support for the postural instability theory of motion sickness.

Prediction of Losartan-Active Carboxylic Acid Metabolite Exposure Following Losartan Administration Using Static and Physiologically Based Pharmacokinetic Models.

PubMed

Nguyen, Hoa Q; Lin, Jian; Kimoto, Emi; Callegari, Ernesto; Tse, Susanna; Obach, R Scott

2017-09-01

The aim of this study was to evaluate a strategy based on static and dynamic physiologically based pharmacokinetic (PBPK) modeling for the prediction of metabolite and parent drug area under the time-concentration curve ratio (AUC m /AUC p ) and their PK profiles in humans using in vitro data when active transport processes are involved in disposition. The strategy was applied to losartan and its pharmacologically active metabolite carboxylosartan as test compounds. Hepatobiliary transport including transport-mediated uptake, canilicular and basolateral efflux, and metabolic clearance estimates were obtained from in vitro studies using human liver microsomes and sandwich-cultured hepatocytes. Human renal clearance of carboxylosartan was estimated from dog renal clearance using allometric scaling approach. All clearance mechanisms were mechanistically incorporated in a static model to predict the relative exposure of carboxylosartan versus losartan (AUC m /AUC p ). The predicted AUC m /AUC p were consistent with the observed data following intravenous and oral administration of losartan. Moreover, the in vitro parameters were used as initial parameters in PBPK permeability-limited disposition models to predict the concentration-time profiles for both parent and its active metabolite after oral administration of losartan. The PBPK model was able to recover the plasma profiles of both losartan and carboxylosartan, further substantiating the validity of this approach. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Ongoing activity in temporally coherent networks predicts intra-subject fluctuation of response time to sporadic executive control demands.

PubMed

Nozawa, Takayuki; Sugiura, Motoaki; Yokoyama, Ryoichi; Ihara, Mizuki; Kotozaki, Yuka; Miyauchi, Carlos Makoto; Kanno, Akitake; Kawashima, Ryuta

2014-01-01

Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person's response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color-word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute "cognitive readiness," which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance.

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

PubMed

Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

2017-12-01

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

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

USGS Publications Warehouse

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.

Prediction, Assessment of the Rift Valley Fever Activity in East and Southern Africa 2006–2008 and Possible Vector Control Strategies

PubMed Central

Anyamba, Assaf; Linthicum, Kenneth J.; Small, Jennifer; Britch, Seth C.; Pak, Edwin; de La Rocque, Stephane; Formenty, Pierre; Hightower, Allen W.; Breiman, Robert F.; Chretien, Jean-Paul; Tucker, Compton J.; Schnabel, David; Sang, Rosemary; Haagsma, Karl; Latham, Mark; Lewandowski, Henry B.; Magdi, Salih Osman; Mohamed, Mohamed Ally; Nguku, Patrick M.; Reynes, Jean-Marc; Swanepoel, Robert

2010-01-01

Historical outbreaks of Rift Valley fever (RVF) since the early 1950s have been associated with cyclical patterns of the El Niño/Southern Oscillation (ENSO) phenomenon, which results in elevated and widespread rainfall over the RVF endemic areas of Africa. Using satellite measurements of global and regional elevated sea surface temperatures, elevated rainfall, and satellite derived-normalized difference vegetation index data, we predicted with lead times of 2–4 months areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa, Sudan, and Southern Africa at different time periods from September 2006 to March 2008. Predictions were confirmed by entomological field investigations of virus activity and by reported cases of RVF in human and livestock populations. This represents the first series of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation into the future. PMID:20682905

The EXCITE Trial: Predicting a Clinically Meaningful Motor Activity Log Outcome

PubMed Central

Park, Si-Woon; Wolf, Steven L.; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S.

2013-01-01

Background and Objective This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. Methods A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Results Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Conclusions Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT. PMID:18780883

The EXCITE Trial: Predicting a clinically meaningful motor activity log outcome.

PubMed

Park, Si-Woon; Wolf, Steven L; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S

2008-01-01

This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT.

Gross-alpha and gross-beta activities in airborne particulate samples. Analysis and prediction models.

PubMed

Dueñas, C; Fernández, M C; Carretero, J; Liger, E; Cañete, S

2001-04-01

Measurements of gross-alpha and gross-beta activities were made every week during the years 1992-1997 for airborne particulate samples collected using air filters at a clear site. The data are sufficiently numerous to allow the examination of variations in time and by these measurements to establish several features that should be important in understanding any trends of atmospheric radioactivity. Two models were used to predict the gross-alpha and gross-beta activities. A good agreement between the results of these models and the measurements was highlighted.

A survey of social media data analysis for physical activity surveillance.

PubMed

Liu, Sam; Young, Sean D

2018-07-01

Social media data can provide valuable information regarding people's behaviors and health outcomes. Previous studies have shown that social media data can be extracted to monitor and predict infectious disease outbreaks. These same approaches can be applied to other fields including physical activity research and forensic science. Social media data have the potential to provide real-time monitoring and prediction of physical activity level in a given region. This tool can be valuable to public health organizations as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (e.g. surveys, observational studies). As a result, this tool could help public health organizations better mobilize and target physical activity interventions. The first part of this paper aims to describe current approaches (e.g. topic modeling, sentiment analysis and social network analysis) that could be used to analyze social media data to provide real-time monitoring of physical activity level. The second aim of this paper was to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research. Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Effective and ineffective coping strategies in a low-autonomy work environment.

PubMed

Britt, Thomas W; Crane, Monique; Hodson, Stephanie E; Adler, Amy B

2016-04-01

The authors examined the effectiveness of different coping strategies in buffering the negative effects of uncontrollable stressors and predicting mental health symptoms in a low-autonomy work environment using a longitudinal design. Soldiers in training indicated the extent to which they engaged in various coping strategies to deal with stressors related to the training environment at 4 different points in time. Factor analyses of soldiers in 2 different countries (i.e., United States and Australia) yielded 5 coping dimensions: active coping, acceptance of demands, seeking social support, humor, and denial/self-criticism. Among U.S. soldiers in basic training, acceptance of demands and denial/self-criticism interacted with the magnitude of basic-training stressors to predict mental health symptoms (depression and anxiety) at 3 different points during training while controlling for symptoms at the immediate prior time period. Acceptance buffered soldiers from the negative effects of the stressors, whereas denial/self-criticism exacerbated the effects of the stressors. The results of LGC models also indicated that the slopes of acceptance and active coping were negatively related to the slope of mental health symptoms across training, whereas the slope for denial/self-criticism was positively related to the slope of symptoms. Active coping was less predictive of functioning in the face of stressors and in the prediction of symptoms over time. The results demonstrated that in a low-autonomy occupational setting, acceptance coping was more effective in facilitating good mental health outcomes compared with other coping strategies considered important in prior research (e.g., active coping). (c) 2016 APA, all rights reserved).

Correlation of tissue concentrations of the pyrethroid bifenthrin with neurotoxicity in the rat.

PubMed

Scollon, Edward J; Starr, James M; Crofton, Kevin M; Wolansky, Marcelo J; DeVito, Michael J; Hughes, Michael F

2011-11-28

The potential for human exposure to pyrethroid pesticides has prompted pharmacodynamic and pharmacokinetic research to better characterize risk. This work tested the hypothesis that blood and brain concentrations of the pyrethroid bifenthrin are predictive of neurotoxic effects. Adult male Long Evans rats received a single oral dose of bifenthrin dissolved in corn oil. Using figure-eight mazes, motor activity was measured for 1h at 4- and 7-h following exposure to bifenthrin (0-16mg/kg or 0-9mg/kg, respectively; n=4-8/group). Whole blood and brains were collected immediately following motor activity assays. Bifenthrin concentrations in blood and brain were quantified using HPLC/MS/MS. Bifenthrin exposure decreased motor activity from 20% to 70% in a dose-dependent manner at both time points. The relationship between motor activity data and administered dose, and blood and brain bifenthrin concentrations were described using a sigmoidal E(max) model. The relationships between motor activity and administered dose or blood concentrations were different between the 4- and 7-h time points. The relationship between motor activity and brain concentration was not significantly different between the two time points. These data suggest that momentary brain concentration of bifenthrin may be a more precise dose metric for predicting behavioral effects because the relationship between brain concentration and locomotor activity is independent of the time of exposure. Published by Elsevier Ireland Ltd.

Flare Prediction Using Photospheric and Coronal Image Data

NASA Astrophysics Data System (ADS)

Jonas, E.; Shankar, V.; Bobra, M.; Recht, B.

2016-12-01

We attempt to forecast M-and X-class solar flares using a machine-learning algorithm and five years of image data from both the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) instruments aboard the Solar Dynamics Observatory. HMI is the first instrument to continuously map the full-disk photospheric vector magnetic field from space (Schou et al., 2012). The AIA instrument maps the transition region and corona using various ultraviolet wavelengths (Lemen et al., 2012). HMI and AIA data are taken nearly simultaneously, providing an opportunity to study the entire solar atmosphere at a rapid cadence. Most flare forecasting efforts described in the literature use some parameterization of solar data - typically of the photospheric magnetic field within active regions. These numbers are considered to capture the information in any given image relevant to predicting solar flares. In our approach, we use HMI and AIA images of solar active regions and a deep convolutional kernel network to predict solar flares. This is effectively a series of shallow-but-wide random convolutional neural networks stacked and then trained with a large-scale block-weighted least squares solver. This algorithm automatically determines which patterns in the image data are most correlated with flaring activity and then uses these patterns to predict solar flares. Using the recently-developed KeystoneML machine learning framework, we construct a pipeline to process millions of images in a few hours on commodity cloud computing infrastructure. This is the first time vector magnetic field images have been combined with coronal imagery to forecast solar flares. This is also the first time such a large dataset of solar images, some 8.5 terabytes of images that together capture over 3000 active regions, has been used to forecast solar flares. We evaluate our method using various flare prediction windows defined in the literature (e.g. Ahmed et al., 2013) and a novel per-hour time series we've constructed which more closely mimics the demands of an operational solar flare prediction system. We estimate the performance of our algorithm using the True Skill Statistic (TSS; Bloomfield et al., 2012). We find that our algorithm gives a high TSS score and predictive abilities.

Young Zanzibari children with iron deficiency, iron deficiency anemia, stunting, or malaria have lower motor activity scores and spend less time in locomotion.

PubMed

Olney, Deanna K; Pollitt, Ernesto; Kariger, Patricia K; Khalfan, Sabra S; Ali, Nadra S; Tielsch, James M; Sazawal, Sunil; Black, Robert; Mast, Darrell; Allen, Lindsay H; Stoltzfus, Rebecca J

2007-12-01

Motor activity improves cognitive and social-emotional development through a child's exploration of his or her physical and social environment. This study assessed anemia, iron deficiency, hemoglobin (Hb), length-for-age Z-score (LAZ), and malaria infection as predictors of motor activity in 771 children aged 5-19 mo. Trained observers conducted 2- to 4-h observations of children's motor activity in and around their homes. Binary logistic regression assessed the predictors of any locomotion. Children who did not locomote during the observation (nonmovers) were excluded from further analyses. Linear regression evaluated the predictors of total motor activity (TMA) and time spent in locomotion for all children who locomoted during the observation combined (movers) and then separately for crawlers and walkers. Iron deficiency (77.0%), anemia (58.9%), malaria infection (33.9%), and stunting (34.6%) were prevalent. Iron deficiency with and without anemia, Hb, LAZ, and malaria infection significantly predicted TMA and locomotion in all movers. Malaria infection significantly predicted less TMA and locomotion in crawlers. In walkers, iron deficiency anemia predicted less activity and locomotion, whereas higher Hb and LAZ significantly predicted more activity and locomotion, even after controlling for attained milestone. Improvements in iron status and growth and prevention or effective treatment of malaria may improve children's motor, cognitive, and social-emotional development either directly or through improvements in motor activity. However, the relative importance of these factors is dependent on motor development, with malaria being important for the younger, less developmentally advanced children and Hb and LAZ becoming important as children begin to attain walking skills.

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

PubMed

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.

Gamma-band activation predicts both associative memory and cortical plasticity

PubMed Central

Headley, Drew B.; Weinberger, Norman M.

2011-01-01

Gamma-band oscillations are a ubiquitous phenomenon in the nervous system and have been implicated in multiple aspects of cognition. In particular, the strength of gamma oscillations at the time a stimulus is encoded predicts its subsequent retrieval, suggesting that gamma may reflect enhanced mnemonic processing. Likewise, activity in the gamma-band can modulate plasticity in vitro. However, it is unclear whether experience-dependent plasticity in vivo is also related to gamma-band activation. The aim of the present study is to determine whether gamma activation in primary auditory cortex modulates both the associative memory for an auditory stimulus during classical conditioning and its accompanying specific receptive field plasticity. Rats received multiple daily sessions of single tone/shock trace and two-tone discrimination conditioning, during which local field potentials and multiunit discharges were recorded from chronically implanted electrodes. We found that the strength of tone-induced gamma predicted the acquisition of associative memory 24 h later, and ceased to predict subsequent performance once asymptote was reached. Gamma activation also predicted receptive field plasticity that specifically enhanced representation of the signal tone. This concordance provides a long-sought link between gamma oscillations, cortical plasticity and the formation of new memories. PMID:21900554

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

PubMed Central

Basit, Nada; Wechsler, Harry

2011-01-01

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets. PMID:22007208

GT-CATS: Tracking Operator Activities in Complex Systems

NASA Technical Reports Server (NTRS)

Callantine, Todd J.; Mitchell, Christine M.; Palmer, Everett A.

1999-01-01

Human operators of complex dynamic systems can experience difficulties supervising advanced control automation. One remedy is to develop intelligent aiding systems that can provide operators with context-sensitive advice and reminders. The research reported herein proposes, implements, and evaluates a methodology for activity tracking, a form of intent inferencing that can supply the knowledge required for an intelligent aid by constructing and maintaining a representation of operator activities in real time. The methodology was implemented in the Georgia Tech Crew Activity Tracking System (GT-CATS), which predicts and interprets the actions performed by Boeing 757/767 pilots navigating using autopilot flight modes. This report first describes research on intent inferencing and complex modes of automation. It then provides a detailed description of the GT-CATS methodology, knowledge structures, and processing scheme. The results of an experimental evaluation using airline pilots are given. The results show that GT-CATS was effective in predicting and interpreting pilot actions in real time.

Forecasting the peak of the present solar activity cycle 24

NASA Astrophysics Data System (ADS)

Hamid, R. H.; Marzouk, B. A.

2018-06-01

Solar forecasting of the level of sun Activity is very important subject for all space programs. Most predictions are based on the physical conditions prevailing at or before the solar cycle minimum preceding the maximum in question. Our aim is to predict the maximum peak of cycle 24 using precursor techniques in particular those using spotless event, geomagnetic aamin. index and solar flux F10.7. Also prediction of exact date of the maximum (Tr) is taken in consideration. A study of variation over previous spotless event for cycles 7-23 and that for even cycles (8-22) are carried out for the prediction. Linear correlation between maximum of solar cycles (RM) and spotless event around the preceding minimum gives R24t = 88.4 with rise time Tr = 4.6 years. For the even cycles R24E = 77.9 with rise time Tr = 4.5 y's. Based on the average aamin. index for cycles (12-23), we estimate the expected amplitude for cycle 24 to be Raamin = 99.4 and 98.1 with time rise of Traamin = 4.04 & 4.3 years for both the total and even cycles in consecutive. The application of the data of solar flux F10.7 which cover only cycles (19-23) was taken in consideration and gives predicted maximum amplitude R24 10.7 = 126 with rise time Tr107 = 3.7 years, which are over estimation. Our result indicating to somewhat weaker of cycle 24 as compared to cycles 21-23.

Predicting Thermal Regimes of Stream Networks Across New England: Natural and Anthropogenic Influences

EPA Science Inventory

Thermal regime is a critical factor in models predicting joint effects of watershed management activities and climate change on habitat suitability for fish. We used a database of lotic temperature time series across New England (> 7000 station-year combinations) from state a...

Isolation predicts compositional change after discrete disturbances in a global meta-study

USDA-ARS?s Scientific Manuscript database

Globally, anthropogenic disturbances are occurring at unprecedented rates and over extensive spatial and temporal scales. Human activities also affect natural disturbances, prompting shifts in their timing and intensities. Thus, there is an urgent need to understand and predict the response of ecosy...

An EEG Finger-Print of fMRI deep regional activation.

PubMed

Meir-Hasson, Yehudit; Kinreich, Sivan; Podlipsky, Ilana; Hendler, Talma; Intrator, Nathan

2014-11-15

This work introduces a general framework for producing an EEG Finger-Print (EFP) which can be used to predict specific brain activity as measured by fMRI at a given deep region. This new approach allows for improved EEG spatial resolution based on simultaneous fMRI activity measurements. Advanced signal processing and machine learning methods were applied on EEG data acquired simultaneously with fMRI during relaxation training guided by on-line continuous feedback on changing alpha/theta EEG measure. We focused on demonstrating improved EEG prediction of activation in sub-cortical regions such as the amygdala. Our analysis shows that a ridge regression model that is based on time/frequency representation of EEG data from a single electrode, can predict the amygdala related activity significantly better than a traditional theta/alpha activity sampled from the best electrode and about 1/3 of the times, significantly better than a linear combination of frequencies with a pre-defined delay. The far-reaching goal of our approach is to be able to reduce the need for fMRI scanning for probing specific sub-cortical regions such as the amygdala as the basis for brain-training procedures. On the other hand, activity in those regions can be characterized with higher temporal resolution than is obtained by fMRI alone thus revealing additional information about their processing mode. Copyright © 2013 Elsevier Inc. All rights reserved.

Space Environment Modelling with the Use of Artificial Intelligence Methods

NASA Astrophysics Data System (ADS)

Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

1996-12-01

Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore also handled with the modules in the Lund Space Weather Model. Interesting Links: Lund Space Weather and AI Center

Developing a Degree-Day Model to Predict Billbug (Coleoptera: Curculionidae) Seasonal Activity in Utah and Idaho Turfgrass.

PubMed

Dupuy, Madeleine M; Powell, James A; Ramirez, Ricardo A

2017-10-01

Billbugs are native pests of turfgrass throughout North America, primarily managed with preventive, calendar-based insecticide applications. An existing degree-day model (lower development threshold of 10°C, biofix 1 March) developed in the eastern United States for bluegrass billbug, Sphenophorus parvulus (Gyllenhal; Coleoptera: Curculionidae), may not accurately predict adult billbug activity in the western United States, where billbugs occur as a species complex. The objectives of this study were 1) to track billbug phenology and species composition in managed Utah and Idaho turfgrass and 2) to evaluate model parameters that best predict billbug activity, including those of the existing bluegrass billbug model. Tracking billbugs with linear pitfall traps at two sites each in Utah and Idaho, we confirmed a complex of three univoltine species damaging turfgrass consisting of (in descending order of abundance) bluegrass billbug, hunting billbug (Sphenophorus venatus vestitus Chittenden; Coleoptera: Curculionidae), and Rocky Mountain billbug (Sphenophorus cicatristriatus Fabraeus; Coleoptera: Curculionidae). This complex was active from February through mid-October, with peak activity in mid-June. Based on linear regression analysis, we found that the existing bluegrass billbug model was not robust in predicting billbug activity in Utah and Idaho. Instead, the model that best predicts adult activity of the billbug complex accumulates degree-days above 3°C after 13 January. This model predicts adult activity levels important for management within 11 d of observed activity at 77% of sites. In conjunction with outreach and cooperative networking, this predictive degree-day model may assist end users to better time monitoring efforts and insecticide applications against billbug pests in Utah and Idaho by predicting adult activity. © The Author 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Group Time: Taking a "Humor Break" at Group Time

ERIC Educational Resources Information Center

Church, Ellen Booth

2005-01-01

January is a perfect time to insert a strong dose of humor into group time gatherings. Oftentimes, children have tired of the predictable pattern of group meetings and need some change. Humor-filled group time activities can be the best secret remedy. Not only will children become more interested in the group time meetings (and therefore listen…

Sub-seasonal precipitation during the South Asian summer monsoon onset period

NASA Astrophysics Data System (ADS)

Takaya, Y.; Yamaguchi, M.

2017-12-01

The South Asian summer monsoon (SASM) has a great impact on human activities (e.g., agriculture and health), thus skillful prediction of the SASM is highly anticipated. In particular, precipitation amount and timing of a rainy season onset are of great importance for crop planning. This study examines the performance of precipitation prediction during the onset period of the SASM using the WWRP/WCRP sub-seasonal to seasonal prediction project (S2S) dataset. Preliminary verification of ECMWF model reforecasts against the GSMaP precipitation analysis produced by Japan Aerospace Exploration Agency (JAXA) shows that a predictive skill of precipitation is reasonably high in a sub-seasonal time-range. It is also found that the predictive skill of precipitation in the South Asia is relatively higher around the onset period, consistent with our previous finding using the latest JMA seasonal prediction system (JMA/MRI-CPS2). The results suggest that state-of-the-art operational models have the capability to provide useful SASM onset predictions at a sub-seasonal time scale. In the presentation, we will also discuss the inherent potential predictability, feasibility of prediction of the monsoon onset and relevant processes.

An age-specific biokinetic model for iodine

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

Leggett, Richard Wayne

This study reviews age-specific biokinetic data for iodine in humans and extends to pre-adult ages the baseline parameter values of the author’s previously published model for systemic iodine in adult humans. Compared with the ICRP’s current age-specific model for iodine introduced in Publication 56 (1989), the present model provides a more detailed description of the behavior of iodine in the human body; predicts greater cumulative (integrated) activity in the thyroid for short-lived isotopes of iodine; predicts similar cumulative activity in the thyroid for isotopes with half-time greater than a few hours; and, for most iodine isotopes, predicts much greater cumulativemore » activity in salivary glands, stomach wall, liver, and kidneys.« less

An age-specific biokinetic model for iodine

DOE PAGES

Leggett, Richard Wayne

2017-10-26

This study reviews age-specific biokinetic data for iodine in humans and extends to pre-adult ages the baseline parameter values of the author’s previously published model for systemic iodine in adult humans. Compared with the ICRP’s current age-specific model for iodine introduced in Publication 56 (1989), the present model provides a more detailed description of the behavior of iodine in the human body; predicts greater cumulative (integrated) activity in the thyroid for short-lived isotopes of iodine; predicts similar cumulative activity in the thyroid for isotopes with half-time greater than a few hours; and, for most iodine isotopes, predicts much greater cumulativemore » activity in salivary glands, stomach wall, liver, and kidneys.« less

Comparison of ionospheric F2 peak parameters foF2 and hmF2 with IRI2001 at Hainan

NASA Astrophysics Data System (ADS)

Wang, X.; Shi, J. K.; Wang, G. J.; Gong, Y.

2009-06-01

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with quarter-hourly time interval resolution for the diurnal variation, obtained with DPS4 digisonde at Hainan (19.5°N, 109.1°E; Geomagnetic coordinates: 178.95°E, 8.1°N) are used to investigate the low-latitude ionospheric variations and comparisons with the International Reference Ionosphere (IRI) model predictions. The data used for the present study covers the period from February 2002 to April 2007, which is characterized by a wide range of solar activity, ranging from high solar activity (2002) to low solar activity (2007). The results show that (1) Generally, IRI predictions follow well the diurnal and seasonal variation patterns of the experimental values of foF2, especially in the summer of 2002. However, there are systematic deviation between experimental values and IRI predictions with either CCIR or URSI coefficients. Generally IRI model greatly underestimate the values of foF2 from about noon to sunrise of next day, especially in the afternoon, and slightly overestimate them from sunrise to about noon. It seems that there are bigger deviations between IRI Model predictions and the experimental observations for the moderate solar activity. (2) Generally the IRI-predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the experimental results, but there is a relatively good agreement in summer at low solar activity. The deviation between the IRI-predicted hmF2 using CCIR M(3000)F2 and observed hmF2 is bigger from noon to sunset and around sunrise especially at high solar activity. The occurrence time of hmF2 peak (about 1200 LT) of the IRI model predictions is earlier than that of observations (around 1500 LT). The agreement between the IRI hmF2 obtained with the measured M(3000)F2 and the observed hmF2 is very good except that IRI overestimates slightly hmF2 in the daytime in summer at high solar activity and underestimates it in the nighttime with lower values near sunrise at low solar activity.

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

PubMed

Mosley, Emma; Laborde, Sylvain; Kavanagh, Emma

2017-10-01

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

Advanced techniques for determining long term compatibility of materials with propellants

NASA Technical Reports Server (NTRS)

Green, R. L.; Stebbins, J. P.; Smith, A. W.; Pullen, K. E.

1973-01-01

A method for the prediction of propellant-material compatibility for periods of time up to ten years is presented. Advanced sensitive measurement techniques used in the prediction method are described. These include: neutron activation analysis, radioactive tracer technique, and atomic absorption spectroscopy with a graphite tube furnace sampler. The results of laboratory tests performed to verify the prediction method are presented.

GEWEX America Prediction Project (GAPP) Science and Implementation Plan

NASA Technical Reports Server (NTRS)

2004-01-01

The purpose of this Science and Implementation Plan is to describe GAPP science objectives and the activities required to meet these objectives, both specifically for the near-term and more generally for the longer-term. The GEWEX Americas Prediction Project (GAPP) is part of the Global Energy and Water Cycle Experiment (GEWEX) initiative that is aimed at observing, understanding and modeling the hydrological cycle and energy fluxes at various time and spatial scales. The mission of GAPP is to demonstrate skill in predicting changes in water resources over intraseasonal-to-interannual time scales, as an integral part of the climate system.

Parenting Style as a Predictor of Adolescent Weight and Weight-Related Behaviors

PubMed Central

Berge, Jerica M.; Wall, Melanie; Loth, Katie; Neumark-Sztainer, Dianne

2009-01-01

Purpose: Current research indicates that specific parenting styles are associated with adolescent overweight, dietary intake and physical activity, but the majority of research has been cross-sectional making it difficult to determine the temporal order of these associations. The current study adds to the previous research by examining 5-year longitudinal associations between parenting style and adolescent weight and weight-related behaviors. Methods: Data from Project EAT, a population-based study with adolescents from diverse ethic and socioeconomic backgrounds were used. Adolescents (N = 2516) from 31 Minnesota schools completed in-class assessments in 1999 (Time 1) and mailed surveys in 2004 (Time 2). Multiple linear regression models were used to predict mean levels of adolescent outcomes at Time 2 from parenting style at Time 1. Results: Time 1 maternal authoritative parenting style predicted lower BMI in adolescent sons and daughters at Time 2. Time 1 paternal permissive parenting style predicted more fruits and vegetables intake in daughters at Time 2. Significant associations were not found between parenting style and adolescent physical activity. Conclusions: Findings suggest that authoritative parenting style may play a protective role related to adolescent overweight and that the dimension of warmth/caring in the parent/adolescent relationship may be important in relation to female adolescent healthy dietary intake. Further exploration of opposite sex parent/adolescent dyad patterns related to parenting style and adolescent weight and weight-related behaviors is warranted. PMID:20307821

Physical activity in Black breast cancer survivors: implications for quality of life and mood at baseline and 6-month follow-up.

PubMed

Diggins, Allyson D; Hearn, Lauren E; Lechner, Suzanne C; Annane, Debra; Antoni, Michael H; Whitehead, Nicole Ennis

2017-06-01

The present study sought to examine the influence of physical activity on quality of life and negative mood in a sample of Black breast cancer survivors to determine if physical activity (dichotomized) predicted mean differences in negative mood and quality of life in this population. Study participants include 114 women diagnosed with breast cancer (any stage of disease, any type of breast cancer) recruited to participate in an adaptive cognitive-behavioral stress management intervention. The mean body mass index of the sample at baseline was 31.39 (standard deviation = 7.17). A multivariate analysis of covariance (MANCOVA) was conducted to determine if baseline physical activity predicted mean differences in negative mood and quality of life at baseline and at follow ups while controlling for relevant covariates. A one-way MANCOVA revealed a significant multivariate effect by physical activity group for the combined dependent variables at Time 2 (post 10-week intervention), p = .039. The second one-way MANCOVA revealed a significant multivariate effect at Time 3 (6 months after Time 2), p = .034. Specifically, Black breast cancer survivors who engaged in physical activity experienced significantly lower negative mood and higher social/family well-being at Time 2 and higher spiritual and functional well-being at Times 2 and 3. Results show that baseline physical activity served protective functions for breast cancer survivors over time. Developing culturally relevant physical activity interventions specifically for Black breast cancer survivors may prove vital to improving quality of life and mood in this population. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

PubMed

Aissa, Oualid; Moulahoum, Samir; Colak, Ilhami; Babes, Badreddine; Kabache, Nadir

2017-10-12

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.

Prediction of Normal Organ Absorbed Doses for [177Lu]Lu-PSMA-617 Using [44Sc]Sc-PSMA-617 Pharmacokinetics in Patients With Metastatic Castration Resistant Prostate Carcinoma.

PubMed

Khawar, Ambreen; Eppard, Elisabeth; Sinnes, Jean Phlippe; Roesch, Frank; Ahmadzadehfar, Hojjat; Kürpig, Stefan; Meisenheimer, Michael; Gaertner, Florian C; Essler, Markus; Bundschuh, Ralph A

2018-04-23

In vivo pharmacokinetic analysis of [Sc]Sc-PSMA-617 was used to determine the normal organ-absorbed doses that may result from therapeutic activity of [Lu]Lu-PSMA-617 and to predict the maximum permissible activity of [Lu]Lu-PSMA-617 for patients with metastatic castration-resistant prostate carcinoma. Pharmacokinetics of [Sc]Sc-PSMA-617 was evaluated in 5 patients with metastatic castration-resistant prostate carcinoma using dynamic PET/CT, followed by 3 static PET/CT acquisitions and blood sample collection over 19.5 hours, as well as urine sample collection at 2 time points. Total activity measured in source organs by PET imaging, as well as counts per milliliter measured in blood and urine samples, was decay corrected back to the time of injection using the half-life of Sc. Afterward, forward decay correction using the half-life of Lu was performed, extrapolating the pharmacokinetics of [Sc]Sc-PSMA-617 to that of [Lu]Lu-PSMA-617. Source organs residence times and organ-absorbed doses for [Lu]Lu-PSMA-617 were calculated using OLINDA/EXM software. Bone marrow self-dose was determined with indirect blood-based method, and urinary bladder contents residence time was estimated by trapezoidal approximation. The maximum permissible activity of [Lu]Lu-PSMA-617 was calculated for each patient considering external beam radiotherapy toxicity limits for radiation absorbed doses to kidneys, bone marrow, salivary glands, and whole body. The predicted mean organ-absorbed doses were highest in the kidneys (0.44 mSv/MBq), followed by the salivary glands (0.23 mSv/MBq). The maximum permissible activity was highly variable among patients; limited by whole body-absorbed dose (1 patient), marrow-absorbed dose (1 patient), and kidney-absorbed dose (3 patients). [Sc]Sc-PSMA-617 PET/CT imaging is feasible and allows theoretical extrapolation of the pharmacokinetics of [Sc]Sc-PSMA-617 to that of [Lu]Lu-PSMA-617, with the intent of predicting normal organ-absorbed doses and maximum permissible activity in patients scheduled for therapy with [Lu]Lu-PSMA-617.

The Predictive Brain State: Asynchrony in Disorders of Attention?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

It is postulated that a key function of attention in goal-oriented behavior is to reduce performance variability by generating anticipatory neural activity that can be synchronized with expected sensory information. A network encompassing the prefrontal cortex, parietal lobe, and cerebellum may be critical in the maintenance and timing of such predictive neural activity. Dysfunction of this temporal process may constitute a fundamental defect in attention, causing working memory problems, distractibility, and decreased awareness. PMID:19074688

Adsorption of selected pharmaceuticals and an endocrine disrupting compound by granular activated carbon. 2. Model prediction

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

Yu, Z.; Peldszus, S.; Huck, P.M.

The adsorption of two representative pharmaceutically active compounds (PhACs) naproxen and carbamazepine and one endocrine disrupting compound (EDC) nonylphenol was studied in pilot-scale granular activated carbon (GAC) adsorbers using post-sedimentation (PS) water from a full-scale drinking water treatment plant. The GAC adsorbents were coal-based Calgon Filtrasorb 400 and coconut shell-based PICA CTIF TE. Acidic naproxen broke through fastest while nonylphenol was removed best, which was consistent with the degree to which fouling affected compound removals. Model predictions and experimental data were generally in good agreement for all three compounds, which demonstrated the effectiveness and robustness of the pore and surfacemore » diffusion model (PSDM) used in combination with the time-variable parameter approach for predicting removals at environmentally relevant concentrations (i.e., ng/L range). Sensitivity analyses suggested that accurate determination of film diffusion coefficients was critical for predicting breakthrough for naproxen and carbamazepine, in particular when high removals are targeted. Model simulations demonstrated that GAC carbon usage rates (CURs) for naproxen were substantially influenced by the empty bed contact time (EBCT) at the investigated conditions. Model-based comparisons between GAC CURs and minimum CURs for powdered activated carbon (PAC) applications suggested that PAC would be most appropriate for achieving 90% removal of naproxen, whereas GAC would be more suitable for nonylphenol. 25 refs., 4 figs., 1 tab.« less

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

PubMed Central

Van Dyck, Delfien; Cardon, Greet

2017-01-01

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 perceived land use mix access (p = 0.09) predicted an increase in car use (adjusted R2 = 0.06). A few moderating effects, mainly of educational level, were found. Discussion Walkability characteristics (perceived residential density) and self-efficacy at the start of retirement are the most important predictors of longitudinal changes in active transportation and leisure-time physical activity. Few moderating effects were found, so health interventions at the start of retirement focusing on self-efficacy and specific walkability characteristics could be effective to increase physical activity in recently retired adults. No firm conclusions can be drawn on the importance of the examined predictors to explain change in car use and screen time, possibly other factors like the home environment, or automatic processes and habit strength are more important to explain sedentary behaviors. PMID:28507817

Patterns of Children's Participation in Unorganized Physical Activity

ERIC Educational Resources Information Center

Findlay, Leanne C.; Garner, Rochelle E.; Kohen, Dafna E.

2010-01-01

Children's leisure-time or unorganized physical activity is associated with positive physical and mental health, yet there is little information available on tracking and predicting participation throughout the childhood and adolescent years. The purpose of the current study was to explore patterns of unorganized physical activity participation of…

ENSO-based probabilistic forecasts of March-May U.S. tornado and hail activity

NASA Astrophysics Data System (ADS)

Lepore, Chiara; Tippett, Michael K.; Allen, John T.

2017-09-01

Extended logistic regression is used to predict March-May severe convective storm (SCS) activity based on the preceding December-February (DJF) El Niño-Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross-validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state.

Hebbian learning and predictive mirror neurons for actions, sensations and emotions

PubMed Central

Keysers, Christian; Gazzola, Valeria

2014-01-01

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system. PMID:24778372

Hebbian learning and predictive mirror neurons for actions, sensations and emotions.

PubMed

Keysers, Christian; Gazzola, Valeria

2014-01-01

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

PubMed Central

Mestyán, Márton; Yasseri, Taha; Kertész, János

2013-01-01

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia. PMID:23990938

Cognitive Impairment Precedes and Predicts Functional Impairment in Mild Alzheimer's Disease.

PubMed

Liu-Seifert, Hong; Siemers, Eric; Price, Karen; Han, Baoguang; Selzler, Katherine J; Henley, David; Sundell, Karen; Aisen, Paul; Cummings, Jeffrey; Raskin, Joel; Mohs, Richard

2015-01-01

The temporal relationship of cognitive deficit and functional impairment in Alzheimer's disease (AD) is not well characterized. Recent analyses suggest cognitive decline predicts subsequent functional decline throughout AD progression. To better understand the relationship between cognitive and functional decline in mild AD using autoregressive cross-lagged (ARCL) panel analyses in several clinical trials. Data included placebo patients with mild AD pooled from two multicenter, double-blind, Phase 3 solanezumab (EXPEDITION/2) or semagacestat (IDENTITY/2) studies, and from AD patients participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitive and functional outcomes were assessed using AD Assessment Scale-Cognitive subscale (ADAS-Cog), AD Cooperative Study-Activities of Daily Living instrumental subscale (ADCS-iADL), or Functional Activities Questionnaire (FAQ), respectively. ARCL panel analyses evaluated relationships between cognitive and functional impairment over time. In EXPEDITION, ARCL panel analyses demonstrated cognitive scores significantly predicted future functional impairment at 5 of 6 time points, while functional scores predicted subsequent cognitive scores in only 1 of 6 time points. Data from IDENTITY and ADNI programs yielded consistent results whereby cognition predicted subsequent function, but not vice-versa. Analyses from three databases indicated cognitive decline precedes and predicts subsequent functional decline in mild AD dementia, consistent with previously proposed hypotheses, and corroborate recent publications using similar methodologies. Cognitive impairment may be used as a predictor of future functional impairment in mild AD dementia and can be considered a critical target for prevention strategies to limit future functional decline in the dementia process.

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Predicting thermal regimes of stream networks across the Chesapeake Bay Watershed: Natural and anthropogenic influences

EPA Science Inventory

Thermal regimes are a critical factor in models predicting joint effects of watershed management activities and climate change on fish habitat suitability. We have compiled a database of lotic temperature time series across the Chesapeake Bay Watershed (725 station-year combinat...

Deepened Extinction following Compound Stimulus Presentation: Noradrenergic Modulation

ERIC Educational Resources Information Center

Janak, Patricia H.; Corbit, Laura H.

2011-01-01

Behavioral extinction is an active form of new learning involving the prediction of nonreward where reward has previously been present. The expression of extinction learning can be disrupted by the presentation of reward itself or reward-predictive stimuli (reinstatement) as well as the passage of time (spontaneous recovery) or contextual changes…

Activity Profile and Energy Expenditure Among Active Older Adults, British Columbia, 2011-2012.

PubMed

Madden, Kenneth M; Ashe, Maureen C; Chase, Jocelyn M

2015-07-16

Time spent by young adults in moderate to vigorous activity predicts daily caloric expenditure. In contrast, caloric expenditure among older adults is best predicted by time spent in light activity. We examined highly active older adults to examine the biggest contributors to energy expenditure in this population. Fifty-four community-dwelling men and women aged 65 years or older (mean, 71.4 y) were enrolled in this cross-sectional observational study. All were members of the Whistler Senior Ski Team, and all met current American guidelines for physical activity. Activity levels (sedentary, light, and moderate to vigorous) were recorded by accelerometers worn continuously for 7 days. Caloric expenditure was measured using accelerometry, galvanic skin response, skin temperature, and heat flux. Significant variables were entered into a stepwise multivariate linear model consisting of activity level, age, and sex. The average (standard deviation [SD]) daily nonlying sedentary time was 564 (92) minutes (9.4 [1.5] h) per day. The main predictors of higher caloric expenditure were time spent in moderate to vigorous activity (standardized β = 0.42 [SE, 0.08]; P < .001) and male sex (standardized β = 1.34 [SE, 0.16]; P < .001). A model consisting of only moderate to vigorous physical activity and sex explained 68% of the variation in caloric expenditure. An increase in moderate to vigorous physical activity by 1 minute per day was associated with an additional 16 kcal expended in physical activity. The relationship between activity intensity and caloric expenditure in athletic seniors is similar to that observed in young adults. Active older adults still spend a substantial proportion of the day engaged in sedentary behaviors.

Activity Profile and Energy Expenditure Among Active Older Adults, British Columbia, 2011–2012

PubMed Central

Ashe, Maureen C.; Chase, Jocelyn M.

2015-01-01

Introduction Time spent by young adults in moderate to vigorous activity predicts daily caloric expenditure. In contrast, caloric expenditure among older adults is best predicted by time spent in light activity. We examined highly active older adults to examine the biggest contributors to energy expenditure in this population. Methods Fifty-four community-dwelling men and women aged 65 years or older (mean, 71.4 y) were enrolled in this cross-sectional observational study. All were members of the Whistler Senior Ski Team, and all met current American guidelines for physical activity. Activity levels (sedentary, light, and moderate to vigorous) were recorded by accelerometers worn continuously for 7 days. Caloric expenditure was measured using accelerometry, galvanic skin response, skin temperature, and heat flux. Significant variables were entered into a stepwise multivariate linear model consisting of activity level, age, and sex. Results The average (standard deviation [SD]) daily nonlying sedentary time was 564 (92) minutes (9.4 [1.5] h) per day. The main predictors of higher caloric expenditure were time spent in moderate to vigorous activity (standardized β = 0.42 [SE, 0.08]; P < .001) and male sex (standardized β = 1.34 [SE, 0.16]; P < .001). A model consisting of only moderate to vigorous physical activity and sex explained 68% of the variation in caloric expenditure. An increase in moderate to vigorous physical activity by 1 minute per day was associated with an additional 16 kcal expended in physical activity. Conclusion The relationship between activity intensity and caloric expenditure in athletic seniors is similar to that observed in young adults. Active older adults still spend a substantial proportion of the day engaged in sedentary behaviors. PMID:26182147

Solar activity simulation and forecast with a flux-transport dynamo

NASA Astrophysics Data System (ADS)

Macario-Rojas, Alejandro; Smith, Katharine L.; Roberts, Peter C. E.

2018-06-01

We present the assessment of a diffusion-dominated mean field axisymmetric dynamo model in reproducing historical solar activity and forecast for solar cycle 25. Previous studies point to the Sun's polar magnetic field as an important proxy for solar activity prediction. Extended research using this proxy has been impeded by reduced observational data record only available from 1976. However, there is a recognised need for a solar dynamo model with ample verification over various activity scenarios to improve theoretical standards. The present study aims to explore the use of helioseismology data and reconstructed solar polar magnetic field, to foster the development of robust solar activity forecasts. The research is based on observationally inferred differential rotation morphology, as well as observed and reconstructed polar field using artificial neural network methods via the hemispheric sunspot areas record. Results show consistent reproduction of historical solar activity trends with enhanced results by introducing a precursor rise time coefficient. A weak solar cycle 25, with slow rise time and maximum activity -14.4% (±19.5%) with respect to the current cycle 24 is predicted.

Self-expansion and flow in couples' momentary experiences: an experience sampling study.

PubMed

Graham, James M

2008-09-01

The self-expansion model of close relationships posits that when couples engage in exciting and activating conjoint activities, they feel connected with their partners and more satisfied with their relationships. In the present study, the experience sampling method was used to examine the predictions of the self-expansion model in couples' momentary experiences. In addition, the author generated several new hypotheses by integrating the self-expansion model with existing research on flow. Over the course of 1 week, 20 couples were signaled at quasi-random intervals to provide data on 1,265 unique experiences. The results suggest that the level of activation experienced during an activity was positively related to experience-level relationship quality. This relationship was consistent across free-time and nonfree-time contexts and was mediated by positive affect. Activation was not found to predict later affect unless the level of activation exceeded what was typical for the individual. Also examined was the influence of interpersonal context and activity type on self-expansion. The results support the self-expansion model and suggest that it could be considered under the broader umbrella of flow.

Participation in Organized Activities Protects Against Adolescents' Risky Substance Use, Even Beyond Development in Conscientiousness.

PubMed

McCabe, Kira O; Modecki, Kathryn L; Barber, Bonnie L

2016-11-01

Adolescents are at a significant risk for binge drinking and illicit drug use. One way to protect against these behaviors is through participation in extracurricular activities. However, there is a debate about whether highly conscientious adolescents are more likely to participate in activities, which raises the concern of a confound. To disentangle these relationships, we tested the latent trajectories of substance use and personality across 3 years, with participation in activities and sports as time-varying predictors. We surveyed 687 adolescents (55 % female, 85.4 % Caucasian) in Western Australia schools across 3 years. At Time 1, the students were in Year 10 1 (mean age 15 years). The results showed that participation in activities and conscientiousness are related, but each uniquely predicts slower growth in substance use. Across waves, participation in activities predicted less risky substance use a year later, over and above conscientiousness development. These results suggest that there may be unique benefits of participation in activities that protect against risky substance use.

Brain activity in valuation regions while thinking about the future predicts individual discount rates.

PubMed

Cooper, Nicole; Kable, Joseph W; Kim, B Kyu; Zauberman, Gal

2013-08-07

People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future--specifically, to judge the subjective length of future time intervals--predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future.

Brain Activity in Valuation Regions while Thinking about the Future Predicts Individual Discount Rates

PubMed Central

Cooper, Nicole; Kim, B. Kyu; Zauberman, Gal

2013-01-01

People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future—specifically, to judge the subjective length of future time intervals—predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future. PMID:23926268

A PC-based system for predicting movement from deep brain signals in Parkinson's disease.

PubMed

Loukas, Constantinos; Brown, Peter

2012-07-01

There is much current interest in deep brain stimulation (DBS) of the subthalamic nucleus (STN) for the treatment of Parkinson's disease (PD). This type of surgery has enabled unprecedented access to deep brain signals in the awake human. In this paper we present an easy-to-use computer based system for recording, displaying, archiving, and processing electrophysiological signals from the STN. The system was developed for predicting self-paced hand-movements in real-time via the online processing of the electrophysiological activity of the STN. It is hoped that such a computerised system might have clinical and experimental applications. For example, those sites within the STN most relevant to the processing of voluntary movement could be identified through the predictive value of their activities with respect to the timing of future movement. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Sources of self-efficacy for physical activity.

PubMed

Warner, Lisa M; Schüz, Benjamin; Wolff, Julia K; Parschau, Linda; Wurm, Susanne; Schwarzer, Ralf

2014-11-01

The effects of self-efficacy beliefs on physical activity are well documented, but much less is known about the origins of self-efficacy beliefs. This article proposes scales to assess the sources of self-efficacy for physical activity aims and to comparatively test their predictive power for physical activity via self-efficacy over time to detect the principal sources of self-efficacy beliefs for physical activity. A study of 1,406 German adults aged 16-90 years was conducted to construct scales to assess the sources of self-efficacy for physical activity (Study 1). In Study 2, the scales' predictive validity for self-efficacy and physical activity was tested in a sample of 310 older German adults. Short, reliable and valid instruments to measure six sources of self-efficacy for physical activity were developed that enable researchers to comparatively test the predictive value of the sources of self-efficacy. The results suggest that mastery experience, self-persuasion, and reduction in negative affective states are the most important predictors of self-efficacy for physical activity in community-dwelling older adults. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Gender differences in leisure-time versus non-leisure-time physical activity among Saudi adolescents.

PubMed

Al-Sobayel, Hana; Al-Hazzaa, Hazzaa M; Abahussain, Nanda A; Qahwaji, Dina M; Musaiger, Abdulrahman O

2015-01-01

The aim of the study was to examine the gender differences and predictors of leisure versus non-leisure time physical activities among Saudi adolescents aged 14-19 years. The multistage stratified cluster random sampling technique was used. A sample of 1,388 males and 1,500 females enrolled in secondary schools in three major cities in Saudi Arabia was included. Anthropometric measurements were performed and Body Mass Index was calculated. Physical activity, sedentary behaviours and dietary habits were measured using a self-reported validated questionnaire. The total time spent in leisure and non-leisure physical activity per week was 90 and 77 minutes, respectively. The males spent more time per week in leisure-time physical activities than females. Females in private schools spent more time during the week in leisure-time physical activities, compared to females in Stateschools. There was a significant difference between genders by obesity status interaction in leisure-time physical activity. Gender, and other factors, predicted total duration spent in leisure-time and non-leisure-time physical activity. The study showed that female adolescents are much less active than males, especially in leisure-time physical activities. Programmes to promote physical activity among adolescents are urgently needed, with consideration of gender differences.

Time-integrated activity coefficient estimation for radionuclide therapy using PET and a pharmacokinetic model: A simulation study on the effect of sampling schedule and noise

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

Hardiansyah, Deni

2016-09-15

Purpose: The aim of this study was to investigate the accuracy of PET-based treatment planning for predicting the time-integrated activity coefficients (TIACs). Methods: The parameters of a physiologically based pharmacokinetic (PBPK) model were fitted to the biokinetic data of 15 patients to derive assumed true parameters and were used to construct true mathematical patient phantoms (MPPs). Biokinetics of 150 MBq {sup 68}Ga-DOTATATE-PET was simulated with different noise levels [fractional standard deviation (FSD) 10%, 1%, 0.1%, and 0.01%], and seven combinations of measurements at 30 min, 1 h, and 4 h p.i. PBPK model parameters were fitted to the simulated noisymore » PET data using population-based Bayesian parameters to construct predicted MPPs. Therapy simulations were performed as 30 min infusion of {sup 90}Y-DOTATATE of 3.3 GBq in both true and predicted MPPs. Prediction accuracy was then calculated as relative variability v{sub organ} between TIACs from both MPPs. Results: Large variability values of one time-point protocols [e.g., FSD = 1%, 240 min p.i., v{sub kidneys} = (9 ± 6)%, and v{sub tumor} = (27 ± 26)%] show inaccurate prediction. Accurate TIAC prediction of the kidneys was obtained for the case of two measurements (1 and 4 h p.i.), e.g., FSD = 1%, v{sub kidneys} = (7 ± 3)%, and v{sub tumor} = (22 ± 10)%, or three measurements, e.g., FSD = 1%, v{sub kidneys} = (7 ± 3)%, and v{sub tumor} = (22 ± 9)%. Conclusions: {sup 68}Ga-DOTATATE-PET measurements could possibly be used to predict the TIACs of {sup 90}Y-DOTATATE when using a PBPK model and population-based Bayesian parameters. The two time-point measurement at 1 and 4 h p.i. with a noise up to FSD = 1% allows an accurate prediction of the TIACs in kidneys.« less

Knee alignment can help predict sedentary behaviour in children: a pilot study.

PubMed

Shultz, S P; Kagawa, M; Fink, P W; Hills, A P

2014-10-01

The purpose of this pilot study was to introduce knee alignment as a potential predictor of sedentary activity levels in boys and girls. Dual energy x-ray absorptiometry (DXA) and anthropometric assessment were conducted on 47 children (21 boys and 26 girls; 5-14 y) and their gender-matched parent. Body Mass Index (BMI) and abdominal-to-height ratio were calculated. Lower extremity alignment was determined by anatomic tibiofemoral angle (TFA) measurements from DXA images. Time spent in moderate-to-vigorous physical activity and sedentary activities were obtained from a parent-reported questionnaire. Stepwise multiple regression analyses identified anthropometric, musculoskeletal, and activity factors of parents and children for predicting total time spent in sedentary behaviour. Weight, total sedentary time of parents and TFA are moderate predictors of sedentary behaviour in children (R2=0.469). When stratifying for gender, TFA and total sedentary time of the parent, as well as waist circumference, are the most useful predictors of sedentary behaviour in boys (R2=0.648). However, weight is the only predictor of sedentary behaviour in girls (R2=0.479). Negative associations between TFA and sedentary behaviour indicate that even slight variations in musculoskeletal alignment may influence a child's motivation to be physically active. Although growth and development is complicated by many potentialities, this pilot study suggests that orthopaedic factors should also be considered when evaluating physical activity in children.

Prediction of new bioactive molecules using a Bayesian belief network.

PubMed

Abdo, Ammar; Leclère, Valérie; Jacques, Philippe; Salim, Naomie; Pupin, Maude

2014-01-27

Natural products and synthetic compounds are a valuable source of new small molecules leading to novel drugs to cure diseases. However identifying new biologically active small molecules is still a challenge. In this paper, we introduce a new activity prediction approach using Bayesian belief network for classification (BBNC). The roots of the network are the fragments composing a compound. The leaves are, on one side, the activities to predict and, on another side, the unknown compound. The activities are represented by sets of known compounds, and sets of inactive compounds are also used. We calculated a similarity between an unknown compound and each activity class. The more similar activity is assigned to the unknown compound. We applied this new approach on eight well-known data sets extracted from the literature and compared its performance to three classical machine learning algorithms. Experiments showed that BBNC provides interesting prediction rates (from 79% accuracy for high diverse data sets to 99% for low diverse ones) with a short time calculation. Experiments also showed that BBNC is particularly effective for homogeneous data sets but has been found to perform less well with structurally heterogeneous sets. However, it is important to stress that we believe that using several approaches whenever possible for activity prediction can often give a broader understanding of the data than using only one approach alone. Thus, BBNC is a useful addition to the computational chemist's toolbox.

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes.

PubMed

Brondeel, Ruben; Pannier, Bruno; Chaix, Basile

2015-12-01

Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests. The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips. The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.

The subthalamic nucleus during decision-making with multiple alternatives.

PubMed

Keuken, Max C; Van Maanen, Leendert; Bogacz, Rafal; Schäfer, Andreas; Neumann, Jane; Turner, Robert; Forstmann, Birte U

2015-10-01

Several prominent neurocomputational models predict that an increase of choice alternatives is modulated by increased activity in the subthalamic nucleus (STN). In turn, increased STN activity allows prolonged accumulation of information. At the same time, areas in the medial frontal cortex such as the anterior cingulate cortex (ACC) and the pre-SMA are hypothesized to influence the information processing in the STN. This study set out to test concrete predictions of STN activity in multiple-alternative decision-making using a multimodal combination of 7 Tesla structural and functional Magnetic Resonance Imaging, and ancestral graph (AG) modeling. The results are in line with the predictions in that increased STN activity was found with an increasing amount of choice alternatives. In addition, our study shows that activity in the ACC is correlated with activity in the STN without directly modulating it. This result sheds new light on the information processing streams between medial frontal cortex and the basal ganglia. © 2015 Wiley Periodicals, Inc.

Anticipated affective consequences of physical activity adoption and maintenance.

PubMed

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.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

PubMed

Freedson, Patty S; Lyden, Kate; Kozey-Keadle, Sarah; Staudenmayer, John

2011-12-01

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Gender and Facebook motives as predictors of specific types of Facebook use: A latent growth curve analysis in adolescence.

PubMed

Frison, Eline; Eggermont, Steven

2016-10-01

Despite increasing evidence that specific types of Facebook use (i.e., active private, active public, and passive Facebook use) are differently related to adolescents' well-being, little is known how these types function over the course of adolescence and whether gender and Facebook motives may predict the initial level and changes in these types over time. To address these gaps, Flemish adolescents (ages 12-19) were questioned at three different time points, with six months in between (NTime1 = 1866). Latent growth curve models revealed that active private Facebook use increased over the course of adolescence, whereas public Facebook use decreased. Passive Facebook use, however, remained stable. In addition, gender and Facebook motives were related to initial levels of specific types of Facebook use, and predictive of dynamic change in specific types of Facebook use over time. The discussion focuses on the understanding and implications of these findings. Copyright © 2016. Published by Elsevier Ltd.

Real-time control of walking using recordings from dorsal root ganglia.

PubMed

Holinski, B J; Everaert, D G; Mushahwar, V K; Stein, R B

2013-10-01

The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.

A Study of the Relationship of Parenting Styles, Child Temperament, and Operatory Behavior in Healthy Children.

PubMed

Tsoi, Amanda K; Wilson, Stephen; Thikkurissy, S

2018-05-11

The purpose of this study was to assess the relationship of the child's temperament, parenting styles, and parents' prediction of their child's behavior in the dental setting. Subjects were healthy children 4-12 years of age attending a dental clinic. A Parenting Styles and Dimensions Questionnaire (PSDQ) was given to parents to determine their parenting style. Parents completed the Emotionality, Activity, Sociability Temperament (EAS) survey to measure their child's temperament. Parents were asked to predict their child's behavior using the Frankl Scale. Data analysis included 113 parent/child dyads. Parents accurately predicted their child's behavior 58% of the time. Significant correlations were noted between parent's predictions of behavior and emotionality (r = -.497, p < .001), activity (r = -.217, p < .009), and shyness (r = -.282, p < .002) of EAS. Significant correlations were found between actual behavior and emotionality (r = -.586, p < .001), activity (r = -.196, p < .03), and shyness (r = -.281, p < .003). Parenting style scores did not correlate to predicted or actual behavior; however, categories of PSDQ were related to parental predictions of behavior. Relationships between temperament and parenting may aid in predicting children's behavior in the operatory.

A Framework for Assessing Operational Madden–Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project

DOE PAGES

Gottschalck, J.; Wheeler, M.; Weickmann, K.; ...

2010-09-01

The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii)more » facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the international body that fosters the development of atmospheric models for NWP and climate studies. The Climate Prediction Center (CPC) within the National Centers for Environmental Prediction (NCEP) is hosting the acquisition of the forecast data, application of the MJO diagnostic, and real-time display of the standardized forecasts. The activity has contributed to the production of 1–2-week operational outlooks at NCEP and activities at other centers. Further enhancements of the diagnostic's implementation, including more extensive analysis, comparison, illustration, and verification of the contributions from the participating centers, will increase the usefulness and application of these forecasts and potentially lead to more skillful predictions of the MJO and indirectly extratropical and other weather variability (e.g., tropical cyclones) influenced by the MJO. The purpose of this article is to inform the larger scientific and operational forecast communities of the MJOWG forecast effort and invite participation from additional operational centers.« less

Predicting emissions from oil and gas operations in the Uinta Basin, Utah.

PubMed

Wilkey, Jonathan; Kelly, Kerry; Jaramillo, Isabel Cristina; Spinti, Jennifer; Ring, Terry; Hogue, Michael; Pasqualini, Donatella

2016-05-01

In this study, emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin are predicted (with uncertainty estimates) from 2015-2019 using a Monte-Carlo model of (a) drilling and production activity, and (b) emission factors. Cross-validation tests against actual drilling and production data from 2010-2014 show that the model can accurately predict both types of activities, returning median results that are within 5% of actual values for drilling, 0.1% for oil production, and 4% for gas production. A variety of one-time (drilling) and ongoing (oil and gas production) emission factors for greenhouse gases, methane, and volatile organic compounds (VOCs) are applied to the predicted oil and gas operations. Based on the range of emission factor values reported in the literature, emissions from well completions are the most significant source of emissions, followed by gas transmission and production. We estimate that the annual average VOC emissions rate for the oil and gas industry over the 2010-2015 time period was 44.2E+06 (mean) ± 12.8E+06 (standard deviation) kg VOCs per year (with all applicable emissions reductions). On the same basis, over the 2015-2019 period annual average VOC emissions from oil and gas operations are expected to drop 45% to 24.2E+06 ± 3.43E+06 kg VOCs per year, due to decreases in drilling activity and tighter emission standards. This study improves upon previous methods for estimating emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin by tracking one-time and ongoing emission events on a well-by-well basis. The proposed method has proven highly accurate at predicting drilling and production activity and includes uncertainty estimates to describe the range of potential emissions inventory outcomes. If similar input data are available in other oil and gas producing regions, then the method developed here could be applied to those regions as well.

Physical workload, leisure-time physical activity, obesity and smoking as predictors of multisite musculoskeletal pain. A 2-year prospective study of kitchen workers.

PubMed

Haukka, Eija; Ojajärvi, Anneli; Takala, Esa-Pekka; Viikari-Juntura, Eira; Leino-Arjas, Päivi

2012-07-01

The aim of this prospective study was to examine the role of physical workload, leisure-time physical activity, obesity and smoking in predicting the occurrence and course of multisite musculoskeletal pain (MSP). Data on physical and psychosocial workload, lifestyle factors and MSP were based on questionnaire surveys of 385 Finnish female kitchen workers. MSP (defined as pain at three or more of seven sites) during the past 3 months was measured repeatedly at 3-month intervals over 2 years. Four different patterns (trajectories) in the course of MSP were identified. The authors analysed whether the determinants at baseline predicted the occurrence of MSP (1) at the 2-year follow-up and (2) over the total of nine measurements during the 2 years by exploiting the MSP trajectories. Logistic regression was used. High physical workload at baseline was an independent predictor of MSP at the 2-year follow-up (OR 3.8, 95% CI 1.7 to 8.5) in a model allowing for age, psychosocial factors at work and lifestyle. High physical workload (OR 2.0, 95% CI 1.0 to 4.0) and moderate (OR 2.4, 95% CI 1.2 to 4.9) or low (OR 2.3, 95% CI 1.1 to 4.7) physical activity predicted persistent MSP. Obesity (OR 2.8, 95% CI 1.0 to 7.8) predicted an increased, and not being obese (OR 3.7, 95% CI 1.1 to 12.7) a decreased, prevalence of MSP in models similarly including all covariates. Smoking had no effect. The results emphasise the importance of high physical workload, low to moderate physical activity and obesity as potential modifiable risk factors for the occurrence and course of MSP over time.

Perceptual Distortions in Pitch and Time Reveal Active Prediction and Support for an Auditory Pitch-Motion Hypothesis

PubMed Central

Henry, Molly J.; McAuley, J. Devin

2013-01-01

A number of accounts of human auditory perception assume that listeners use prior stimulus context to generate predictions about future stimulation. Here, we tested an auditory pitch-motion hypothesis that was developed from this perspective. Listeners judged either the time change (i.e., duration) or pitch change of a comparison frequency glide relative to a standard (referent) glide. Under a constant-velocity assumption, listeners were hypothesized to use the pitch velocity (Δf/Δt) of the standard glide to generate predictions about the pitch velocity of the comparison glide, leading to perceptual distortions along the to-be-judged dimension when the velocities of the two glides differed. These predictions were borne out in the pattern of relative points of subjective equality by a significant three-way interaction between the velocities of the two glides and task. In general, listeners’ judgments along the task-relevant dimension (pitch or time) were affected by expectations generated by the constant-velocity standard, but in an opposite manner for the two stimulus dimensions. When the comparison glide velocity was faster than the standard, listeners overestimated time change, but underestimated pitch change, whereas when the comparison glide velocity was slower than the standard, listeners underestimated time change, but overestimated pitch change. Perceptual distortions were least evident when the velocities of the standard and comparison glides were matched. Fits of an imputed velocity model further revealed increasingly larger distortions at faster velocities. The present findings provide support for the auditory pitch-motion hypothesis and add to a larger body of work revealing a role for active prediction in human auditory perception. PMID:23936462

Perceptual distortions in pitch and time reveal active prediction and support for an auditory pitch-motion hypothesis.

PubMed

Henry, Molly J; McAuley, J Devin

2013-01-01

A number of accounts of human auditory perception assume that listeners use prior stimulus context to generate predictions about future stimulation. Here, we tested an auditory pitch-motion hypothesis that was developed from this perspective. Listeners judged either the time change (i.e., duration) or pitch change of a comparison frequency glide relative to a standard (referent) glide. Under a constant-velocity assumption, listeners were hypothesized to use the pitch velocity (Δf/Δt) of the standard glide to generate predictions about the pitch velocity of the comparison glide, leading to perceptual distortions along the to-be-judged dimension when the velocities of the two glides differed. These predictions were borne out in the pattern of relative points of subjective equality by a significant three-way interaction between the velocities of the two glides and task. In general, listeners' judgments along the task-relevant dimension (pitch or time) were affected by expectations generated by the constant-velocity standard, but in an opposite manner for the two stimulus dimensions. When the comparison glide velocity was faster than the standard, listeners overestimated time change, but underestimated pitch change, whereas when the comparison glide velocity was slower than the standard, listeners underestimated time change, but overestimated pitch change. Perceptual distortions were least evident when the velocities of the standard and comparison glides were matched. Fits of an imputed velocity model further revealed increasingly larger distortions at faster velocities. The present findings provide support for the auditory pitch-motion hypothesis and add to a larger body of work revealing a role for active prediction in human auditory perception.

Modeling Interdependent and Periodic Real-World Action Sequences

PubMed Central

Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions. PMID:29780977

Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

NASA Astrophysics Data System (ADS)

Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

2000-11-01

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

Seizure Prediction and its Applications

PubMed Central

Iasemidis, Leon D.

2011-01-01

Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity, that may remain localized and/or spread and severely disrupt the brain’s normal multi-task and multi-processing function. Epileptic seizures are the hallmarks of such activity and had been considered unpredictable. It is only recently that research on the dynamics of seizure generation by analysis of the brain’s electrographic activity (EEG) has shed ample light on the predictability of seizures, and illuminated the way to automatic, prospective, long-term prediction of seizures. The ability to issue warnings in real time of impending seizures (e.g., tens of minutes prior to seizure occurrence in the case of focal epilepsy), may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a simple warning to the patient, in order to avert seizure-associated injuries, to intervention by automatic timely administration of an appropriate stimulus, for example of a chemical nature like an anti-epileptic drug (AED), electromagnetic nature like vagus nerve stimulation (VNS), deep brain stimulation (DBS), transcranial direct current (TDC) or transcranial magnetic stimulation (TMS), and/or of another nature (e.g., ultrasonic, cryogenic, biofeedback operant conditioning). It is thus expected that seizure prediction could readily become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems. PMID:21939848

Metabolic profiling and predicting the free radical scavenging activity of guava (Psidium guajava L.) leaves according to harvest time by 1H-nuclear magnetic resonance spectroscopy.

PubMed

Kim, So-Hyun; Cho, Somi K; Hyun, Sun-Hee; Park, Hae-Eun; Kim, Young-Suk; Choi, Hyung-Kyoon

2011-01-01

Guava leaves were classified and the free radical scavenging activity (FRSA) evaluated according to different harvest times by using the (1)H-NMR-based metabolomic technique. A principal component analysis (PCA) of (1)H-NMR data from the guava leaves provided clear clusters according to the harvesting time. A partial least squares (PLS) analysis indicated a correlation between the metabolic profile and FRSA. FRSA levels of the guava leaves harvested during May and August were high, and those leaves contained higher amounts of 3-hydroxybutyric acid, acetic acid, glutamic acid, asparagine, citric acid, malonic acid, trans-aconitic acid, ascorbic acid, maleic acid, cis-aconitic acid, epicatechin, protocatechuic acid, and xanthine than the leaves harvested during October and December. Epicatechin and protocatechuic acid among those compounds seem to have enhanced FRSA of the guava leaf samples harvested in May and August. A PLS regression model was established to predict guava leaf FRSA at different harvesting times by using a (1)H-NMR data set. The predictability of the PLS model was then tested by internal and external validation. The results of this study indicate that (1)H-NMR-based metabolomic data could usefully characterize guava leaves according to their time of harvesting.

Childhood adversities and socioeconomic position as predictors of leisure-time physical inactivity in early adulthood.

PubMed

Kestilä, Laura; Mäki-Opas, Tomi; Kunst, Anton E; Borodulin, Katja; Rahkonen, Ossi; Prättälä, Ritva

2015-02-01

Limited knowledge exists on how childhood social, health-related and economic circumstances predict adult physical inactivity. Our aim was a) to examine how various childhood adversities and living conditions predict leisure-time physical inactivity in early adulthood and b) to find out whether these associations are mediated through the respondent's own education. Young adults aged 18-29 were used from the Health 2000 Study of the Finnish. The cross-sectional data were based on interviews and questionnaires including retrospective information on childhood circumstances. The analyses were carried out on 68% of the original sample (N = 1894). The outcome measure was leisure-time physical inactivity. Only a few of the 11 childhood adversities were related with physical activity in early adulthood. Having been bullied at school was associated with physical inactivity independently of the other childhood circumstances and the respondent's own education. Low parental education predicted leisure-time physical inactivity in men and the association was mediated by the respondent's own education. Respondents with only primary or vocational education were more likely to be physically inactive during leisure-time compared with those with secondary or higher education. There is some evidence that few specific childhood adversities, especially bullying at school, have long-lasting effects on physical activity levels.

Solar wind control of auroral zone geomagnetic activity

NASA Technical Reports Server (NTRS)

Clauer, C. R.; Mcpherron, R. L.; Searls, C.; Kivelson, M. G.

1981-01-01

Solar wind magnetosphere energy coupling functions are analyzed using linear prediction filtering with 2.5 minute data. The relationship of auroral zone geomagnetic activity to solar wind power input functions are examined, and a least squares prediction filter, or impulse response function is designed from the data. Computed impulse response functions are observed to have characteristics of a low pass filter with time delay. The AL index is found well related to solar wind energy functions, although the AU index shows a poor relationship. High frequency variations of auroral indices and substorm expansions are not predictable with solar wind information alone, suggesting influence by internal magnetospheric processes. Finally, the epsilon parameter shows a poorer relationship with auroral geomagnetic activity than a power parameter, having a VBs solar wind dependency.

Do intensive care data on respiratory infections reflect influenza epidemics?

PubMed

Koetsier, Antonie; van Asten, Liselotte; Dijkstra, Frederika; van der Hoek, Wim; Snijders, Bianca E; van den Wijngaard, Cees C; Boshuizen, Hendriek C; Donker, Gé A; de Lange, Dylan W; de Keizer, Nicolette F; Peek, Niels

2013-01-01

Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics. We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003-2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI. Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51. ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities.

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

PubMed

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

A Model-based Approach to Controlling the ST-5 Constellation Lights-Out Using the GMSEC Message Bus and Simulink

NASA Technical Reports Server (NTRS)

Witt, Kenneth J.; Stanley, Jason; Shendock, Robert; Mandl, Daniel

2005-01-01

Space Technology 5 (ST-5) is a three-satellite constellation, technology validation mission under the New Millennium Program at NASA to be launched in March 2006. One of the key technologies to be validated is a lights-out, model-based operations approach to be used for one week to control the ST-5 constellation with no manual intervention. The ground architecture features the GSFC Mission Services Evolution Center (GMSEC) middleware, which allows easy plugging in of software components and a standardized messaging protocol over a software bus. A predictive modeling tool built on MatLab's Simulink software package makes use of the GMSEC standard messaging protocol to interface to the Advanced Mission Planning System (AMPS) Scenario Scheduler which controls all activities, resource allocation and real-time re-profiling of constellation resources when non-nominal events occur. The key features of this system, which we refer to as the ST-5 Simulink system, are as follows: Original daily plan is checked to make sure that predicted resources needed are available by comparing the plan against the model. As the plan is run in real-time, the system re-profiles future activities in real-time if planned activities do not occur in the predicted timeframe or fashion. Alert messages are sent out on the GMSEC bus by the system if future predicted problems are detected. This will allow the Scenario Scheduler to correct the situation before the problem happens. The predictive model is evolved automatically over time via telemetry updates thus reducing the cost of implementing and maintaining the models by an order of magnitude from previous efforts at GSFC such as the model-based system built for MAP in the mid-1990's. This paper will describe the key features, lessons learned and implications for future missions once this system is successfully validated on-orbit in 2006.

Predicting forest insect flight activity: A Bayesian network approach

Treesearch

Stephen M. Pawson; Bruce G. Marcot; Owen G. Woodberry

2017-01-01

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...

Predictability of CFSv2 in the tropical Indo-Pacific region, at daily and subseasonal time scales

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.

2018-06-01

The predictability of a coupled climate model is evaluated at daily and intraseasonal time scales in the tropical Indo-Pacific region during boreal summer and winter. This study has assessed the daily retrospective forecasts of the Climate Forecast System version 2 from the National Centers of Environmental Prediction for the period 1982-2010. The growth of errors in the forecasts of daily precipitation, monsoon intraseasonal oscillation (MISO) and the Madden-Julian oscillation (MJO) is studied. The seasonal cycle of the daily climatology of precipitation is reasonably well predicted except for the underestimation during the peak of summer. The anomalies follow the typical pattern of error growth in nonlinear systems and show no difference between summer and winter. The initial errors in all the cases are found to be in the nonlinear phase of the error growth. The doubling time of small errors is estimated by applying Lorenz error formula. For summer and winter, the doubling time of the forecast errors is in the range of 4-7 and 5-14 days while the doubling time of the predictability errors is 6-8 and 8-14 days, respectively. The doubling time in MISO during the summer and MJO during the winter is in the range of 12-14 days, indicating higher predictability and providing optimism for long-range prediction. There is no significant difference in the growth of forecasts errors originating from different phases of MISO and MJO, although the prediction of the active phase seems to be slightly better.

Real time wave forecasting using wind time history and numerical model

NASA Astrophysics Data System (ADS)

Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

PubMed

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

PubMed Central

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

Predicting College Student Success: A Historical and Predictive Examination of High School Activities and Accomplishments

ERIC Educational Resources Information Center

Davey, Carla Mae

2010-01-01

According to generational theorists, the interests and experiences of incoming students have fluctuated over time, with Millennial students being more engaged and accomplished than their predecessors. This project explored data from 1974-2007 to determine the actual trends in engagement and accomplishments for three generations of students. Over…

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

A neuronal model of predictive coding accounting for the mismatch negativity.

PubMed

Wacongne, Catherine; Changeux, Jean-Pierre; Dehaene, Stanislas

2012-03-14

The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA…), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

Effect of altitude on seasonal flight activity of Rhagoletis cerasi flies (Diptera: Tephritidae).

PubMed

Kovanci, O B; Kovanci, B

2006-08-01

The effect of altitudinal variation on the seasonal flight activity of Rhagoletis cerasi (Linnaeus) flies was evaluated along an altitudinal gradient from 150 to 1170 m in Mount Uludag, northwestern Turkey. The predicted dates of fly emergence, flight duration and dates of 5%, 50% and 95% cumulative fly catches at various altitudes were estimated from a degree-day model. Degree-day predictions were compared with those obtained from observations made with yellow sticky traps. The observed and predicted dates of appearance of adults were delayed by 1.4 and 2.0 days for every 100 m increase in altitude, respectively. The delay in phenology events was less at high altitudes than postulated by Hopkins' bioclimatic law, whether observed or predicted. The average absolute difference in predicted and observed dates of cumulative percentage catch of adults was 4.9 and 3.0 days in 1997 and 1998, respectively, but these differences were not significant. Prolonged flight activity was predicted and observed at higher altitudes, but the flight period lasted significantly longer than predicted. The observed flight period varied from 29 to 43 days in 1997 and from 36 to 52 days in 1998 between the lowest and highest altitude on the transect. Altitudinal variation between geographically close locations should be taken into account to properly time monitoring activities and hence to manage R. cerasi populations more effectively.

Bridging the gap between computation and clinical biology: validation of cable theory in humans

PubMed Central

Finlay, Malcolm C.; Xu, Lei; Taggart, Peter; Hanson, Ben; Lambiase, Pier D.

2013-01-01

Introduction: Computerized simulations of cardiac activity have significantly contributed to our understanding of cardiac electrophysiology, but techniques of simulations based on patient-acquired data remain in their infancy. We sought to integrate data acquired from human electrophysiological studies into patient-specific models, and validated this approach by testing whether electrophysiological responses to sequential premature stimuli could be predicted in a quantitatively accurate manner. Methods: Eleven patients with structurally normal hearts underwent electrophysiological studies. Semi-automated analysis was used to reconstruct activation and repolarization dynamics for each electrode. This S2 extrastimuli data was used to inform individualized models of cardiac conduction, including a novel derivation of conduction velocity restitution. Activation dynamics of multiple premature extrastimuli were then predicted from this model and compared against measured patient data as well as data derived from the ten-Tusscher cell-ionic model. Results: Activation dynamics following a premature S3 were significantly different from those after an S2. Patient specific models demonstrated accurate prediction of the S3 activation wave, (Pearson's R2 = 0.90, median error 4%). Examination of the modeled conduction dynamics allowed inferences into the spatial dispersion of activation delay. Further validation was performed against data from the ten-Tusscher cell-ionic model, with our model accurately recapitulating predictions of repolarization times (R2 = 0.99). Conclusions: Simulations based on clinically acquired data can be used to successfully predict complex activation patterns following sequential extrastimuli. Such modeling techniques may be useful as a method of incorporation of clinical data into predictive models. PMID:24027527

Factor analysis shows association between family activity environment and children's health behaviour.

PubMed

Hendrie, Gilly A; Coveney, John; Cox, David N

2011-12-01

To characterise the family activity environment in a questionnaire format, assess the questionnaire's reliability and describe its predictive ability by examining the relationships between the family activity environment and children's health behaviours - physical activity, screen time and fruit and vegetable intake. This paper describes the creation of a tool, based on previously validated scales, adapted from the food domain. Data are from 106 children and their parents (Adelaide, South Australia). Factor analysis was used to characterise factors within the family activity environment. Pearson-Product Moment correlations between the family environment and child outcomes, controlling for demographic variation, were examined. Three factors described the family activity environment - parental activity involvement, opportunity for role modelling and parental support for physical activity - and explained 37.6% of the variance. Controlling for demographic factors, the scale was significantly correlated with children's health behaviour - physical activity (r=0.27), screen time (r=-0.24) and fruit and vegetable intake (r=0.34). The family activity environment questionnaire shows high internal consistency and moderate predictive ability. This study has built on previous research by taking a more comprehensive approach to measuring the family activity environment. This research suggests the family activity environment should be considered in family-based health promotion interventions. © 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia.

Psychosocial correlates to high school girls' leisure-time physical activity: a test of the theory of planned behavior.

PubMed

Kerner, Matthew S; Kurrant, Anthony B

2003-12-01

This study was designed to test the efficacy of the theory of planned behavior in predicting intention to engage in leisure-time physical activity and leisure-time physical activity behavior of high school girls. Rating scales were used for assessing attitude to leisure-time physical activity, subjective norm, perceived control, and intention to engage in leisure-time physical activity among 129 ninth through twelfth graders. Leisure-time physical activity was obtained from 3-wk. diaries. The first hierarchical multiple regression indicated that perceived control added (R2 change = .033) to the contributions of attitude to leisure-time physical activity and subjective norm in accounting for 50.7% of the total variance of intention to engage in leisure-time physical activity. The second regression analysis indicated that almost 10% of the variance of leisure-time physical activity was explicated by intention to engage in leisure-time physical activity and perceived control, with perceived control contributing 6.4%. From both academic and theoretical standpoints, our findings support the theory of planned behavior, although quantitatively the variance of leisure-time physical activity was not well-accounted for. In addition, considering the small percentage increase in variance explained by the addition of perceived control explaining variance of intention to engage in leisure-time physical activity, the pragmatism of implementing the measure of perceived control is questionable for this population.

Associations Between Physical Fitness Indices and Working Memory in Breast Cancer Survivors and Age-Matched Controls

PubMed Central

Mackenzie, Michael J.; Zuniga, Krystle E.; Raine, Lauren B.; Awick, Elizabeth A.; Hillman, Charles H.; Kramer, Arthur F.

2016-01-01

Abstract Background: This study examined the effects of cardiorespiratory fitness, heart rate recovery, and physical activity on working memory in breast cancer survivors and age-matched controls. Method: Using a case-control design, 32 women who had received a breast cancer diagnosis and completed primary treatment within the past 36-months (11 radiation only; 21 chemotherapy) and 30 age-matched women with no previous cancer diagnosis completed a n-back continuous performance task commonly used as an assessment of working memory. In addition, cardiorespiratory fitness and heart rate recovery were measured during a submaximal graded exercise test and physical activity was measured using 7-days of accelerometer monitoring. Results: Breast cancer survivors who had received chemotherapy had poorer heart rate recovery (p = .010) and engaged in less physical activity than women who had received radiation only (p = .004) or non-cancer controls (p = .029). Cancer treatment (radiation; chemotherapy) predicted differences in reaction times on the 1-back working memory task (p = .029). However, more rapid heart rate recovery predicted shorter reaction times on the 1-back task in the age-matched control group (p = .002). All participants with greater cardiorespiratory fitness displayed greater accuracy independent of disease status on the 1-back task (p = .017). No significant group differences in reaction times were observed for 2-back target trials between breast cancer survivors and controls. However, greater total physical activity predicted shorter reaction times in breast cancer survivors (radiation, chemotherapy) on the 2-back task (p = .014). In addition, all participants who exhibited more rapid heart rate recovery demonstrated better greater accuracy regardless of disease status (p = .013). Conclusion: These findings support differences in physical activty participation, heart rate recovery, and 1- and 2-back working memory reaction times between breast cancer survivors and age-matched controls. Greater cardiorespiratory fitness, heart rate recovery, and physical activity were positively associated with better working memory performance across conditions. PMID:26418463

Artificial neural network modelling of uncertainty in gamma-ray spectrometry

NASA Astrophysics Data System (ADS)

Dragović, S.; Onjia, A.; Stanković, S.; Aničin, I.; Bačić, G.

2005-03-01

An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray spectrometry was developed and optimized. A three-layer feed-forward ANN with back-propagation learning algorithm was used to model uncertainties of measurement of activity levels of eight radionuclides ( 226Ra, 238U, 235U, 40K, 232Th, 134Cs, 137Cs and 7Be) in soil samples as a function of measurement time. It was shown that the neural network provides useful data even from small experimental databases. The performance of the optimized neural network was found to be very good, with correlation coefficients ( R2) between measured and predicted uncertainties ranging from 0.9050 to 0.9915. The correlation coefficients did not significantly deteriorate when the network was tested on samples with greatly different uranium-to-thorium ( 238U/ 232Th) ratios. The differences between measured and predicted uncertainties were not influenced by the absolute values of uncertainties of measured radionuclide activities. Once the ANN is trained, it could be employed in analyzing soil samples regardless of the 238U/ 232Th ratio. It was concluded that a considerable saving in time could be obtained using the trained neural network model for predicting the measurement times needed to attain the desired statistical accuracy.

Real-time evaluation of polyphenol oxidase (PPO) activity in lychee pericarp based on weighted combination of spectral data and image features as determined by fuzzy neural network.

PubMed

Yang, Yi-Chao; Sun, Da-Wen; Wang, Nan-Nan; Xie, Anguo

2015-07-01

A novel method of using hyperspectral imaging technique with the weighted combination of spectral data and image features by fuzzy neural network (FNN) was proposed for real-time prediction of polyphenol oxidase (PPO) activity in lychee pericarp. Lychee images were obtained by a hyperspectral reflectance imaging system operating in the range of 400-1000nm. A support vector machine-recursive feature elimination (SVM-RFE) algorithm was applied to eliminating variables with no or little information for the prediction from all bands, resulting in a reduced set of optimal wavelengths. Spectral information at the optimal wavelengths and image color features were then used respectively to develop calibration models for the prediction of PPO in pericarp during storage, and the results of two models were compared. In order to improve the prediction accuracy, a decision strategy was developed based on weighted combination of spectral data and image features, in which the weights were determined by FNN for a better estimation of PPO activity. The results showed that the combined decision model was the best among all of the calibration models, with high R(2) values of 0.9117 and 0.9072 and low RMSEs of 0.45% and 0.459% for calibration and prediction, respectively. These results demonstrate that the proposed weighted combined decision method has great potential for improving model performance. The proposed technique could be used for a better prediction of other internal and external quality attributes of fruits. Copyright © 2015 Elsevier B.V. All rights reserved.

Real-time Tsunami Inundation Prediction Using High Performance Computers

NASA Astrophysics Data System (ADS)

Oishi, Y.; Imamura, F.; Sugawara, D.

2014-12-01

Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the earthquake occurs took about 2 minutes, which would be sufficient for a practical tsunami inundation predictions. In the presentation, the computational performance of our faster-than-real-time tsunami inundation model will be shown, and preferable tsunami wave source analysis for an accurate inundation prediction will also be discussed.

Initial Aerodynamic and Acoustic Study of an Active Twist Rotor Using a Loosely Coupled CFD/CSD Method

NASA Technical Reports Server (NTRS)

Boyd, David D. Jr.

2009-01-01

Preliminary aerodynamic and performance predictions for an active twist rotor for a HART-II type of configuration are performed using a computational fluid dynamics (CFD) code, OVERFLOW2, and a computational structural dynamics (CSD) code, CAMRAD -II. These codes are loosely coupled to compute a consistent set of aerodynamics and elastic blade motions. Resultant aerodynamic and blade motion data are then used in the Ffowcs-Williams Hawkins solver, PSU-WOPWOP, to compute noise on an observer plane under the rotor. Active twist of the rotor blade is achieved in CAMRAD-II by application of a periodic torsional moment couple (of equal and opposite sign) at the blade root and tip at a specified frequency and amplitude. To provide confidence in these particular active twist predictions for which no measured data is available, the rotor system geometry and computational set up examined here are identical to that used in a previous successful Higher Harmonic Control (HHC) computational study. For a single frequency equal to three times the blade passage frequency (3P), active twist is applied across a range of control phase angles at two different amplitudes. Predicted results indicate that there are control phase angles where the maximum mid-frequency noise level and the 4P non -rotating hub vibrations can be reduced, potentially, both at the same time. However, these calculated reductions are predicted to come with a performance penalty in the form of a reduction in rotor lift-to-drag ratio due to an increase in rotor profile power.

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

PubMed

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 used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Changes of Linearity in MF2 Index with R12 and Solar Activity Maximum

NASA Astrophysics Data System (ADS)

Villanueva, L.

2013-05-01

Critical frequency of F2 layer is related to the solar activity, and the sunspot number has been the standard index for ionospheric prediction programs. This layer, being considered the most important in HF radio communications due to its highest electron density, determines the maximum frequency coming back from ground base transmitter signals, and shows irregular variation in time and space. Nowadays the spatial variation, better understood due to the availability of TEC measurements, let Space Weather Centers have observations almost in real time. However, it is still the most difficult layer to predict in time. Short time variations are improved in IRI model, but long term predictions are only related to the well-known CCIR and URSI coefficients and Solar activity R12 predictions, (or ionospheric indexes in regional models). The concept of the "saturation" of the ionosphere is based on data observations around 3 solar cycles before 1970, (NBS, 1968). There is a linear relationship among MUF (0Km) and R12, for smooth Sunspot numbers R12 less than 100, but constant for higher R12, so, no rise of MUF is expected for R12 higher than 100. This recommendation has been used in most of the known Ionospheric prediction programs for HF Radio communication. In this work, observations of smoothed ionospheric index MF2 related to R12 are presented to find common features of the linear relationship, which is found to persist in different ranges of R12 depending on the specific maximum level of each solar cycle. In the analysis of individual solar cycles, the lapse of linearity is less than 100 for a low solar cycle and higher than 100 for a high solar cycle. To improve ionospheric predictions we can establish levels for solar cycle maximum sunspot numbers R12 around low 100, medium 150 and high 200 and specify the ranges of linearity of MUF(0Km) related to R12 which is not only 100 as assumed for all the solar cycles. For lower levels of solar cycle, discussions of present observations are presented.

Nicotine Withdrawal Induces Neural Deficits in Reward Processing.

PubMed

Oliver, Jason A; Evans, David E; Addicott, Merideth A; Potts, Geoffrey F; Brandon, Thomas H; Drobes, David J

2017-06-01

Nicotine withdrawal reduces neurobiological responses to nonsmoking rewards. Insight into these reward deficits could inform the development of targeted interventions. This study examined the effect of withdrawal on neural and behavioral responses during a reward prediction task. Smokers (N = 48) attended two laboratory sessions following overnight abstinence. Withdrawal was manipulated by having participants smoke three regular nicotine (0.6 mg yield; satiation) or very low nicotine (0.05 mg yield; withdrawal) cigarettes. Electrophysiological recordings of neural activity were obtained while participants completed a reward prediction task that involved viewing four combinations of predictive and reward-determining stimuli: (1) Unexpected Reward; (2) Predicted Reward; (3) Predicted Punishment; (4) Unexpected Punishment. The task evokes a medial frontal negativity that mimics the phasic pattern of dopaminergic firing in ventral tegmental regions associated with reward prediction errors. Nicotine withdrawal decreased the amplitude of the medial frontal negativity equally across all trial types (p < .001). Exploratory analyses indicated withdrawal increased time to initiate the next trial following unexpected punishment trials (p < .001) and response time on reward trials during withdrawal was positively related to nicotine dependence (p < .001). Nicotine withdrawal had equivocal impact across trial types, suggesting reward processing deficits are unlikely to stem from changes in phasic dopaminergic activity during prediction errors. Effects on tonic activity may be more pronounced. Pharmacological interventions directly targeting the dopamine system and behavioral interventions designed to increase reward motivation and responsiveness (eg, behavioral activation) may aid in mitigating withdrawal symptoms and potentially improving smoking cessation outcomes. Findings from this study indicate nicotine withdrawal impacts reward processing signals that are observable in smokers' neural activity. This may play a role in the subjective aversive experience of nicotine withdrawal and potentially contribute to smoking relapse. Interventions that address abnormal responding to both pleasant and unpleasant stimuli may be particularly effective for alleviating nicotine withdrawal. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Parenting style as a predictor of adolescent weight and weight-related behaviors.

PubMed

Berge, Jerica M; Wall, Melanie; Loth, Katie; Neumark-Sztainer, Dianne

2010-04-01

Current research indicates that specific parenting styles are associated with adolescent overweight, dietary intake, and physical activity; but most of the research has been cross-sectional, making it difficult to determine the temporal order of these associations. The current study adds to the previous research by examining 5-year longitudinal associations between parenting style and adolescent weight and weight-related behaviors. Data from Project EAT, a population-based study with adolescents from diverse ethnic and socioeconomic backgrounds, were used. Adolescents (N = 2,516) from 31 Minnesota schools completed in-class assessments in 1999 (Time 1) and mailed surveys in 2004 (Time 2). Multiple linear regression models were used to predict mean levels of adolescent outcomes at Time 2 from parenting style at Time 1. Time 1 maternal authoritative parenting style predicted lower body mass index in adolescent sons and daughters at Time 2. Time 1 paternal permissive parenting style predicted more fruits and vegetables intake in daughters at Time 2. Significant associations were not found between parenting style and adolescent physical activity. Findings suggest that authoritative parenting style may play a protective role related to adolescent overweight and that the dimension of warmth and/or caring in the parent-adolescent relationship may be important in relation to female adolescent healthy dietary intake. Further exploration of opposite sex parent-adolescent dyad patterns related to parenting style and adolescent weight and weight-related behaviors is warranted. Copyright 2010 Society for Adolescent Medicine. Published by Elsevier Inc. All rights reserved.

The role of the time-kill kinetics assay as part of a preclinical modeling framework for assessing the activity of anti-tuberculosis drugs.

PubMed

Bax, Hannelore I; Bakker-Woudenberg, Irma A J M; de Vogel, Corné P; van der Meijden, Aart; Verbon, Annelies; de Steenwinkel, Jurriaan E M

2017-07-01

Novel treatment strategies for tuberculosis are urgently needed. Many different preclinical models assessing anti-tuberculosis drug activity are available, but it is yet unclear which combination of models is most predictive of clinical treatment efficacy. The aim of this study was to determine the role of our in vitro time kill-kinetics assay as an asset to a predictive preclinical modeling framework assessing anti-tuberculosis drug activity. The concentration- and time-dependent mycobacterial killing capacities of six anti-tuberculosis drugs were determined during exposure as single drugs or in dual, triple and quadruple combinations towards a Mycobacterium tuberculosis Beijing genotype strain and drug resistance was assessed. Streptomycin, rifampicin and isoniazid were most active against fast-growing M. tuberculosis. Isoniazid with rifampicin or high dose ethambutol were the only synergistic drug combinations. The addition of rifampicin or streptomycin to isoniazid prevented isoniazid resistance. In vitro ranking showed agreement with early bactericidal activity in tuberculosis patients for some but not all anti-tuberculosis drugs. The time-kill kinetics assay provides important information on the mycobacterial killing dynamics of anti-tuberculosis drugs during the early phase of drug exposure. As such, this assay is a valuable component of the preclinical modeling framework. Copyright © 2017 Elsevier Ltd. All rights reserved.

Single-digit Arabic numbers do not automatically activate magnitude representations in adults or in children: Evidence from the symbolic same–different task☆

PubMed Central

Wong, Becky; Szücs, Dénes

2013-01-01

We investigated whether the mere presentation of single-digit Arabic numbers activates their magnitude representations using a visually-presented symbolic same–different task for 20 adults and 15 children. Participants saw two single-digit Arabic numbers on a screen and judged whether the numbers were the same or different. We examined whether reaction time in this task was primarily driven by (objective or subjective) perceptual similarity, or by the numerical difference between the two digits. We reasoned that, if Arabic numbers automatically activate magnitude representations, a numerical function would best predict reaction time; but if Arabic numbers do not automatically activate magnitude representations, a perceptual function would best predict reaction time. Linear regressions revealed that a perceptual function, specifically, subjective visual similarity, was the best and only significant predictor of reaction time in adults and in children. These data strongly suggest that, in this task, single-digit Arabic numbers do not necessarily automatically activate magnitude representations in adults or in children. As the first study to date to explicitly study the developmental importance of perceptual factors in the symbolic same–different task, we found no significant differences between adults and children in their reliance on perceptual information in this task. Based on our findings, we propose that visual properties may play a key role in symbolic number judgements. PMID:24076332

The timing of associative memory formation: frontal lobe and anterior medial temporal lobe activity at associative binding predicts memory

PubMed Central

Hales, J. B.

2011-01-01

The process of associating items encountered over time and across variable time delays is fundamental for creating memories in daily life, such as for stories and episodes. Forming associative memory for temporally discontiguous items involves medial temporal lobe structures and additional neocortical processing regions, including prefrontal cortex, parietal lobe, and lateral occipital regions. However, most prior memory studies, using concurrently presented stimuli, have failed to examine the temporal aspect of successful associative memory formation to identify when activity in these brain regions is predictive of associative memory formation. In the current study, functional MRI data were acquired while subjects were shown pairs of sequentially presented visual images with a fixed interitem delay within pairs. This design allowed the entire time course of the trial to be analyzed, starting from onset of the first item, across the 5.5-s delay period, and through offset of the second item. Subjects then completed a postscan recognition test for the items and associations they encoded during the scan and their confidence for each. After controlling for item-memory strength, we isolated brain regions selectively involved in associative encoding. Consistent with prior findings, increased regional activity predicting subsequent associative memory success was found in anterior medial temporal lobe regions of left perirhinal and entorhinal cortices and in left prefrontal cortex and lateral occipital regions. The temporal separation within each pair, however, allowed extension of these findings by isolating the timing of regional involvement, showing that increased response in these regions occurs during binding but not during maintenance. PMID:21248058

Beyond endoscopic assessment in inflammatory bowel disease: real-time histology of disease activity by non-linear multimodal imaging

NASA Astrophysics Data System (ADS)

Chernavskaia, Olga; Heuke, Sandro; Vieth, Michael; Friedrich, Oliver; Schürmann, Sebastian; Atreya, Raja; Stallmach, Andreas; Neurath, Markus F.; Waldner, Maximilian; Petersen, Iver; Schmitt, Michael; Bocklitz, Thomas; Popp, Jürgen

2016-07-01

Assessing disease activity is a prerequisite for an adequate treatment of inflammatory bowel diseases (IBD) such as Crohn’s disease and ulcerative colitis. In addition to endoscopic mucosal healing, histologic remission poses a promising end-point of IBD therapy. However, evaluating histological remission harbors the risk for complications due to the acquisition of biopsies and results in a delay of diagnosis because of tissue processing procedures. In this regard, non-linear multimodal imaging techniques might serve as an unparalleled technique that allows the real-time evaluation of microscopic IBD activity in the endoscopy unit. In this study, tissue sections were investigated using the non-linear multimodal microscopy combination of coherent anti-Stokes Raman scattering (CARS), two-photon excited auto fluorescence (TPEF) and second-harmonic generation (SHG). After the measurement a gold-standard assessment of histological indexes was carried out based on a conventional H&E stain. Subsequently, various geometry and intensity related features were extracted from the multimodal images. An optimized feature set was utilized to predict histological index levels based on a linear classifier. Based on the automated prediction, the diagnosis time interval is decreased. Therefore, non-linear multimodal imaging may provide a real-time diagnosis of IBD activity suited to assist clinical decision making within the endoscopy unit.

A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

NASA Astrophysics Data System (ADS)

Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

2018-03-01

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

Induced changes in island fox (Urocyon littoralis) activity do not mitigate the extinction threat posed by a novel predator.

PubMed

Hudgens, Brian R; Garcelon, David K

2011-03-01

Prey response to novel predators influences the impacts on prey populations of introduced predators, bio-control efforts, and predator range expansion. Predicting the impacts of novel predators on native prey requires an understanding of both predator avoidance strategies and their potential to reduce predation risk. We examine the response of island foxes (Urocyon littoralis) to invasion by golden eagles (Aquila chrysaetos). Foxes reduced daytime activity and increased night time activity relative to eagle-naïve foxes. Individual foxes reverted toward diurnal tendencies following eagle removal efforts. We quantified the potential population impact of reduced diurnality by modeling island fox population dynamics. Our model predicted an annual population decline similar to what was observed following golden eagle invasion and predicted that the observed 11% reduction in daytime activity would not reduce predation risk sufficiently to reduce extinction risk. The limited effect of this behaviorally plastic predator avoidance strategy highlights the importance of linking behavioral change to population dynamics for predicting the impact of novel predators on resident prey populations.

Time spent on health-related activities by senior Australians with chronic diseases: what is the role of multimorbidity and comorbidity?

PubMed

Islam, M Mofizul; McRae, Ian S; Yen, Laurann; Jowsey, Tanisha; Valderas, Jose M

2015-06-01

To examine the effect of various morbidity clusters of chronic diseases on health-related time use and to explore factors associated with heavy time burden (more than 30 hours/month) of health-related activities. Using a national survey, data were collected from 2,540 senior Australians. Natural clusters were identified using cluster analysis and clinical clusters using clinical expert opinion. We undertook a set of linear regressions to model people's time use, and logistic regressions to model heavy time burden. Time use increases with the number of chronic diseases. Six of the 12 diseases are significantly associated with higher time use, with the highest effect for diabetes followed by depression; 18% reported a heavy time burden, with diabetes again being the most significant disease. Clusters and dominant comorbid groupings do not contribute to predicting time use or time burden. Total number of diseases and specific diseases are useful determinants of time use and heavy time burden. Dominant groupings and disease clusters do not predict time use. In considering time demands on patients and the need for care co-ordination, care providers need to be aware of how many and what specific diseases the patient faces. © 2015 Public Health Association of Australia.

Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

DOE PAGES

Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

2016-01-19

A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Kohler theory to predict themore » effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. Furthermore, the model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.« less

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Link Prediction in Evolving Networks Based on Popularity of Nodes.

PubMed

Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

2017-08-02

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

Earthquake Prediction in Large-scale Faulting Experiments

NASA Astrophysics Data System (ADS)

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

2004-12-01

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

Predicting activity approach based on new atoms similarity kernel function.

PubMed

Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

2015-07-01

Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Clinical factors are not the best predictors of quality of sexual life and sexual functioning in women with early stage breast cancer.

PubMed

Den Oudsten, Brenda L; Van Heck, Guus L; Van der Steeg, Alida F W; Roukema, Jan A; De Vries, Jolanda

2010-06-01

Few studies have prospectively assessed the impact of breast cancer (BC) on women's sexual lives. Therefore, this study examines the determinants of quality of sexual life (QOSL), sexual functioning (SF), and sexual enjoyment (SE) at 6 and 12 months after surgical treatment. All participants completed a measure of QOSL (The World Health Organization Quality of Life assessment instrument-100 (WHOQOL-100)-facet Sexual Activity) before diagnosis (Time-1), and 1 (Time-2), 3 (Time-3), 6 (Time-4) and 12 months (Time-5) after surgical treatment. At Time-1, women also completed questionnaires on personality (The State Trait Anxiety Inventory-trait, NEO-FFI), body image and self-esteem (WHOQOL-100), depressive symptoms (Center for Epidemiological Studies-Depression Scale), and fatigue (Fatigue Assessment Scale). Furthermore, SF and SE (The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Breast Cancer module) were measured from Time-2 onwards. At baseline, the analysis included 223 women with early stage BC. Clinical factors did not predict QOSL, SF or SE. In the final analyses, trait anxiety predicted QOSL and SF at Time-4 (p's<0.01). At Time-5, agreeableness predicted QOSL and SE (p's<0.05). Having a partner and age predicted SF, while SE was predicted by educational level (Time-4). In addition, fatigue predicted SE at Time-4 (p<0.05). In general, QOSL diminished across time, while SF improved. However, women with a mastectomy did not differ from women with breast conserving therapy. Mainly personality and psychological factors affect patients' sexuality after surgical treatment. Clinical factors did not predict QOSL, SF or SE. More knowledge in this field will help professionals to identify women who are at risk of experiencing sexual problems and consequently will contribute to provide adequate support. (c) 2009 John Wiley & Sons, Ltd.

Twist Model Development and Results from the Active Aeroelastic Wing F/A-18 Aircraft

NASA Technical Reports Server (NTRS)

Lizotte, Andrew M.; Allen, Michael J.

2007-01-01

Understanding the wing twist of the active aeroelastic wing (AAW) F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption. This technique produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results.

Twist Model Development and Results From the Active Aeroelastic Wing F/A-18 Aircraft

NASA Technical Reports Server (NTRS)

Lizotte, Andrew; Allen, Michael J.

2005-01-01

Understanding the wing twist of the active aeroelastic wing F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption and by using neural networks. These techniques produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results.

The Predictive Effects of Protection Motivation Theory on Intention and Behaviour of Physical Activity in Patients with Type 2 Diabetes.

PubMed

Ali Morowatisharifabad, Mohammad; Abdolkarimi, Mahdi; Asadpour, Mohammad; Fathollahi, Mahmood Sheikh; Balaee, Parisa

2018-04-15

Theory-based education tailored to target behaviour and group can be effective in promoting physical activity. The purpose of this study was to examine the predictive power of Protection Motivation Theory on intent and behaviour of Physical Activity in Patients with Type 2 Diabetes. This descriptive study was conducted on 250 patients in Rafsanjan, Iran. To examine the scores of protection motivation theory structures, a researcher-made questionnaire was used. Its validity and reliability were confirmed. The level of physical activity was also measured by the International Short - form Physical Activity Inventory. Its validity and reliability were also approved. Data were analysed by statistical tests including correlation coefficient, chi-square, logistic regression and linear regression. The results revealed that there was a significant correlation between all the protection motivation theory constructs and the intention to do physical activity. The results showed that the Theory structures were able to predict 60% of the variance of physical activity intention. The results of logistic regression demonstrated that increase in the score of physical activity intent and self - efficacy increased the chance of higher level of physical activity by 3.4 and 1.5 times, respectively OR = (3.39, 1.54). Considering the ability of protection motivation theory structures to explain the physical activity behaviour, interventional designs are suggested based on the structures of this theory, especially to improve self -efficacy as the most powerful factor in predicting physical activity intention and behaviour.

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

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

Volkova, Svitlana; Bell, Eric B.

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.

Factors Associated with Clinician Participation in TF-CBT Post-workshop Training Components.

PubMed

Pemberton, Joy R; Conners-Burrow, Nicola A; Sigel, Benjamin A; Sievers, Chad M; Stokes, Lauren D; Kramer, Teresa L

2017-07-01

For proficiency in an evidence-based treatment (EBT), mental health professionals (MHPs) need training activities extending beyond a one-time workshop. Using data from 178 MHPs participating in a statewide TF-CBT dissemination project, we used five variables assessed at the workshop, via multiple and logistic regression, to predict participation in three post-workshop training components. Perceived in-workshop learning and client-treatment mismatch were predictive of consultation call participation and case presentation respectively. Attitudes toward EBTs were predictive of trauma assessment utilization, although only with non-call participants removed from analysis. Productivity requirements and confidence in TF-CBT skills were not associated with participation in post-workshop activities.

Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Jedlovec, Gary J.

2012-01-01

NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.

Synergistic Effects of Planning and Self-Efficacy on Physical Activity

ERIC Educational Resources Information Center

Koring, Milena; Richert, Jana; Lippke, Sonia; Parschau, Linda; Reuter, Tabea; Schwarzer, Ralf

2012-01-01

Many individuals are motivated to improve their physical activity levels but often fail to act on their good intention. This study examines the roles of planning and self-efficacy in the prediction of physical activity. A total of 290 participants (77% women, mean age = 41.9 years) were surveyed three times. Intentions, planning, and physical…

Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models

NASA Astrophysics Data System (ADS)

Al-Ghraibah, Amani

Solar flares release stored magnetic energy in the form of radiation and can have significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict future flare activity on the basis of quantitative measures of the solar magnetic field. Accurate advanced warning of solar flare occurrence is an area of increasing concern and much research is ongoing in this area. Our previous work 111] utilized standard pattern recognition and classification techniques to determine (classify) whether a region is expected to flare within a predictive time window, using a Relevance Vector Machine (RVM) classification method. We extracted 38 features which describing the complexity of the photospheric magnetic field, the result classification metrics will provide the baseline against which we compare our new work. We find a true positive rate (TPR) of 0.8, true negative rate (TNR) of 0.7, and true skill score (TSS) of 0.49. This dissertation proposes three basic topics; the first topic is an extension to our previous work [111, where we consider a feature selection method to determine an appropriate feature subset with cross validation classification based on a histogram analysis of selected features. Classification using the top five features resulting from this analysis yield better classification accuracies across a large unbalanced dataset. In particular, the feature subsets provide better discrimination of the many regions that flare where we find a TPR of 0.85, a TNR of 0.65 sightly lower than our previous work, and a TSS of 0.5 which has an improvement comparing with our previous work. In the second topic, we study the prediction of solar flare size and time-to-flare using support vector regression (SVR). When we consider flaring regions only, we find an average error in estimating flare size of approximately half a GOES class. When we additionally consider non-flaring regions, we find an increased average error of approximately 3/4 a GOES class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a TPR. of 0.69 and a TNR of 0.86 for flare prediction, consistent with our previous studies of flare prediction using the same magnetic complexity features. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This conjecture is supported by our larger error rates of some 40 hours in the time-to-flare regression problem. The magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem. We also study the prediction of solar flare size and time-to-flare using two temporal features, namely the ▵- and ▵-▵-features, the same average size and time-to-flare regression error are found when these temporal features are used in size and time-to-flare prediction. In the third topic, we study the temporal evolution of active region magnetic fields using Hidden Markov Models (HMMs) which is one of the efficient temporal analyses found in literature. We extracted 38 features which describing the complexity of the photospheric magnetic field. These features are converted into a sequence of symbols using k-nearest neighbor search method. We study many parameters before prediction; like the length of the training window Wtrain which denotes to the number of history images use to train the flare and non-flare HMMs, and number of hidden states Q. In training phase, the model parameters of the HMM of each category are optimized so as to best describe the training symbol sequences. In testing phase, we use the best flare and non-flare models to predict/classify active regions as a flaring or non-flaring region using a sliding window method. The best prediction result is found where the length of the history training images are 15 images (i.e., Wtrain= 15) and the length of the sliding testing window is less than or equal to W train, the best result give a TPR of 0.79 consistent with previous flare prediction work, TNR of 0.87 arid TSS of 0.66, where both are higher than our previous flare prediction work. We find that the best number of hidden states which can describe the temporal evolution of the solar ARs is equal to five states, at the same time, a close resultant metrics are found using different number of states.

The heart of the story: peripheral physiology during narrative exposure predicts charitable giving.

PubMed

Barraza, Jorge A; Alexander, Veronika; Beavin, Laura E; Terris, Elizabeth T; Zak, Paul J

2015-02-01

Emotionally laden narratives are often used as persuasive appeals by charitable organizations. Physiological responses to a narrative may explain why some people respond to an appeal while others do not. In this study we tested whether autonomic and hormonal activity during a narrative predict subsequent narrative influence via charitable giving. Participants viewed a brief story of a father's experience with his 2-year-old son who has terminal cancer. After the story, participants were presented with an opportunity to donate some of their study earnings to a related charity. Measures derived from cardiac and electrodermal activity, including HF-HRV, significantly predicted donor status. Time-series GARCH models of physiology during the narrative further differentiated donors from non-donors. Moreover, cardiac activity and experienced concern were found to covary from moment-to-moment across the narrative. Our findings indicate that the physiological response to a stimulus, herein a narrative, can predict influence as indexed by stimulus-related behavior. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Guven, Erhan; Moniz, Linda; Babin, Steven M.; Chretien, Jean-Paul

2016-01-01

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article. PMID:27127415

Wind Prediction Accuracy for Air Traffic Management Decision Support Tools

NASA Technical Reports Server (NTRS)

Cole, Rod; Green, Steve; Jardin, Matt; Schwartz, Barry; Benjamin, Stan

2000-01-01

The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an optimal interpolation of the latest ACARS reports since the RUC run. This paper presents an overview of the study's results including the identification and use of new large mor wind-prediction accuracy metrics that are key to ATM DST performance.

Body size and activity times mediate mammalian responses to climate change.

PubMed

McCain, Christy M; King, Sarah R B

2014-06-01

Model predictions of extinction risks from anthropogenic climate change are dire, but still overly simplistic. To reliably predict at-risk species we need to know which species are currently responding, which are not, and what traits are mediating the responses. For mammals, we have yet to identify overarching physiological, behavioral, or biogeographic traits determining species' responses to climate change, but they must exist. To date, 73 mammal species in North America and eight additional species worldwide have been assessed for responses to climate change, including local extirpations, range contractions and shifts, decreased abundance, phenological shifts, morphological or genetic changes. Only 52% of those species have responded as expected, 7% responded opposite to expectations, and the remaining 41% have not responded. Which mammals are and are not responding to climate change is mediated predominantly by body size and activity times (phylogenetic multivariate logistic regressions, P < 0.0001). Large mammals respond more, for example, an elk is 27 times more likely to respond to climate change than a shrew. Obligate diurnal and nocturnal mammals are more than twice as likely to respond as mammals with flexible activity times (P < 0.0001). Among the other traits examined, species with higher latitudinal and elevational ranges were more likely to respond to climate change in some analyses, whereas hibernation, heterothermy, burrowing, nesting, and study location did not influence responses. These results indicate that some mammal species can behaviorally escape climate change whereas others cannot, analogous to paleontology's climate sheltering hypothesis. Including body size and activity flexibility traits into future extinction risk forecasts should substantially improve their predictive utility for conservation and management. © 2014 John Wiley & Sons Ltd.

Real-time control of walking using recordings from dorsal root ganglia

NASA Astrophysics Data System (ADS)

Holinski, B. J.; Everaert, D. G.; Mushahwar, V. K.; Stein, R. B.

2013-10-01

Objective. The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. Approach. In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. Main results. Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Significance. Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.

Real-time control of walking using recordings from dorsal root ganglia

PubMed Central

Holinski, B J; Everaert, D G; Mushahwar, V K; Stein, R B

2013-01-01

Objective The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. Approach In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the dorsal root ganglia. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modeled from recorded neural firing rates. These models were then used for closed-loop feedback. Main Results Overall, firing-rate based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48±13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. Significance Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development. PMID:23928579

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

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.

Temporal predictive mechanisms modulate motor reaction time during initiation and inhibition of speech and hand movement.

PubMed

Johari, Karim; Behroozmand, Roozbeh

2017-08-01

Skilled movement is mediated by motor commands executed with extremely fine temporal precision. The question of how the brain incorporates temporal information to perform motor actions has remained unanswered. This study investigated the effect of stimulus temporal predictability on response timing of speech and hand movement. Subjects performed a randomized vowel vocalization or button press task in two counterbalanced blocks in response to temporally-predictable and unpredictable visual cues. Results indicated that speech and hand reaction time was decreased for predictable compared with unpredictable stimuli. This finding suggests that a temporal predictive code is established to capture temporal dynamics of sensory cues in order to produce faster movements in responses to predictable stimuli. In addition, results revealed a main effect of modality, indicating faster hand movement compared with speech. We suggest that this effect is accounted for by the inherent complexity of speech production compared with hand movement. Lastly, we found that movement inhibition was faster than initiation for both hand and speech, suggesting that movement initiation requires a longer processing time to coordinate activities across multiple regions in the brain. These findings provide new insights into the mechanisms of temporal information processing during initiation and inhibition of speech and hand movement. Copyright © 2017 Elsevier B.V. All rights reserved.

Enzymatic detection of As(III) in aqueous solution using alginate immobilized pumpkin urease: optimization of process variables by response surface methodology.

PubMed

Talat, Mahe; Prakash, Om; Hasan, S H

2009-10-01

Urease immobilized on alginate was utilized to detect and quantify As(3+) in aqueous solution. Urease from the seeds of pumpkin (vegetable waste) was purified to apparent homogeneity by heat treatment and gel filtration (Sephadex G-200). Further enzyme was entrapped in 3.5% alginate beads. Urea hydrolysis by enzyme revealed a clear dependence on the concentration and interaction time of As(3+). The process variables effecting the quantitation of As(3+) was investigated using central composite design with Minitab 15 software. The predicted results were found in good agreement (R(2)=96.71%) with experimental results indicating the applicability of proposed model. The multiple regression analysis and ANOVA showed that enzyme activity decreased with increase of As(3+) concentration and interaction time. 3D plot and contour plot between As(3+) concentration and interaction time was helpful to predict residual activity of enzyme for a particular As(3+) at a particular time.

Several steps/day indicators predict changes in anthropometric outcomes: HUB city steps

USDA-ARS?s Scientific Manuscript database

Walking for exercise remains the most frequently reported leisure-time activity, likely because it is simple, inexpensive, and easily incorporated into most people’s lifestyle. Pedometers are simple, convenient, and economical tools that can be used to quantify step-determined physical activity. F...

Genetic predictors of exercise adherence in the Tiger study

USDA-ARS?s Scientific Manuscript database

Obesity established in adolescence strongly predicts obesity for the remainder of adult life, suggesting this is a critical time in which to establish healthy diet and physical activity behaviors. Many studies have reported low levels of habitual activity among this age group, however. We hypothesiz...

Effect of Caffeine on near Maximal Blood Pressure and Blood Pressure Recovery in Physically-Active, College-Aged Females

PubMed Central

CONNAHAN, LAURA E.; OTT, CHRISTOPHER A.; BARRY, VAUGHN W.

2017-01-01

The purpose of this study is to determine how caffeine affects exercise blood pressure (BP) and active and passive recovery BP after vigorous intensity exercise in physically active college-aged females. Fifteen physically active, ACSM stratified low-risk females (age (y): 23.53 ± 4.07, weight (kg): 60.34 ± 3.67, height (cm): 165.14 ± 7.20, BMI (kg/m2): 22.18 ± 1.55) participated in two Bruce protocol exercise tests. Before each test participants consumed 1) a placebo or 2) 3.3 mg·kg−1 of caffeine at least one hour before exercise in a counterbalanced double-blinded fashion. After reaching 85% of their age-predicted maximum heart rate, BP was taken and participants began an active (i.e. walking) recovery phase for 6 minutes followed by a passive (i.e. sitting) recovery phase. BP was assessed every two minutes in each phase. Recovery times were assessed until active and passive BP equaled 20 mmHg and 10 mmHg above resting, respectively. Participants completed each test 1–2 weeks a part. Maximal systolic and diastolic blood pressures were not significantly different between the two trials. Active recovery, passive recovery, and total recovery times were all significantly longer during the caffeine trial than the placebo trial. Furthermore, the time to reach age-predicted maximum heart rate was significantly shorter in the placebo trial than the caffeine trial. While caffeine consumption did not significantly affect maximal blood pressure, it did affect active and passive recovery time following vigorous intensity exercise in physically active females. Exercise endurance also improved after consuming caffeine in this population. PMID:28344739

Effect of Caffeine on near Maximal Blood Pressure and Blood Pressure Recovery in Physically-Active, College-Aged Females.

PubMed

Connahan, Laura E; Ott, Christopher A; Barry, Vaughn W

2017-01-01

The purpose of this study is to determine how caffeine affects exercise blood pressure (BP) and active and passive recovery BP after vigorous intensity exercise in physically active college-aged females. Fifteen physically active, ACSM stratified low-risk females (age (y): 23.53 ± 4.07, weight (kg): 60.34 ± 3.67, height (cm): 165.14 ± 7.20, BMI (kg/m 2 ): 22.18 ± 1.55) participated in two Bruce protocol exercise tests. Before each test participants consumed 1) a placebo or 2) 3.3 mg·kg -1 of caffeine at least one hour before exercise in a counterbalanced double-blinded fashion. After reaching 85% of their age-predicted maximum heart rate, BP was taken and participants began an active (i.e. walking) recovery phase for 6 minutes followed by a passive (i.e. sitting) recovery phase. BP was assessed every two minutes in each phase. Recovery times were assessed until active and passive BP equaled 20 mmHg and 10 mmHg above resting, respectively. Participants completed each test 1-2 weeks a part. Maximal systolic and diastolic blood pressures were not significantly different between the two trials. Active recovery, passive recovery, and total recovery times were all significantly longer during the caffeine trial than the placebo trial. Furthermore, the time to reach age-predicted maximum heart rate was significantly shorter in the placebo trial than the caffeine trial. While caffeine consumption did not significantly affect maximal blood pressure, it did affect active and passive recovery time following vigorous intensity exercise in physically active females. Exercise endurance also improved after consuming caffeine in this population.

Do time perspective and sensation-seeking predict quitting activity among smokers? Findings from the International Tobacco Control (ITC) Four Country Survey.

PubMed

Hall, Peter A; Fong, Geoffrey T; Yong, Hua-Hie; Sansone, Genevieve; Borland, Ron; Siahpush, Mohammad

2012-12-01

Personality factors such as time perspective and sensation-seeking have been shown to predict smoking uptake. However, little is known about the influences of these variables on quitting behavior, and no prior studies have examined the association cross-nationally in a large probability sample. In the current study it was hypothesized that future time perspective would enhance - while sensation-seeking would inhibit - quitting activity among smokers. It was anticipated that the effects would be similar across English speaking countries. Using a prospective cohort design, this cross-national study of adult smokers (N=8845) examined the associations among time perspective, sensation-seeking and quitting activity using the first three waves of data gathered from the International Tobacco Control Four Country Survey (ITC-4), a random digit dialed telephone survey of adult smokers from the United Kingdom, United States, Canada and Australia. Findings revealed that future time perspective (but not sensation-seeking) was a significant predictor of quitting attempts over the 8-month follow-up after adjusting for socio-demographic variables, factors known to inhibit quitting (e.g., perceived addiction, enjoyment of smoking, and perceived value of smoking), and factors known to enhance quitting (e.g., quit intention strength, perceived benefit of quitting, concerns about health effects of smoking). The latter, particularly intention, were significant mediators of the effect of time perspective on quitting activity. The effects of time perspective on quitting activity were similar across all four English speaking countries sampled. If these associations are causal in nature, it may be the case that interventions and health communications that enhance future-orientation may foster more quit attempts among current smokers. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method.

PubMed

Armutlu, Pelin; Ozdemir, Muhittin E; Uney-Yuksektepe, Fadime; Kavakli, I Halil; Turkay, Metin

2008-10-03

A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.

Testing an integrated behavioural and biomedical model of disability in N-of-1 studies with chronic pain.

PubMed

Quinn, Francis; Johnston, Marie; Johnston, Derek W

2013-01-01

Previous research has supported an integrated biomedical and behavioural model explaining activity limitations. However, further tests of this model are required at the within-person level, because while it proposes that the constructs are related within individuals, it has primarily been tested between individuals in large group studies. We aimed to test the integrated model at the within-person level. Six correlational N-of-1 studies in participants with arthritis, chronic pain and walking limitations were carried out. Daily measures of theoretical constructs were collected using a hand-held computer (PDA), the activity was assessed by self-report and accelerometer and the data were analysed using time-series analysis. The biomedical model was not supported as pain impairment did not predict activity, so the integrated model was supported partially. Impairment predicted intention to move around, while perceived behavioural control (PBC) and intention predicted activity. PBC did not predict activity limitation in the expected direction. The integrated model of disability was partially supported within individuals, especially the behavioural elements. However, results suggest that different elements of the model may drive activity (limitations) for different individuals. The integrated model provides a useful framework for understanding disability and suggests interventions, and the utility of N-of-1 methodology for testing theory is illustrated.

Perceived Control and Social Activity in Midlife and Older Age: A Reciprocal Association? Findings From the German Ageing Survey.

PubMed

Curtis, Rachel G; Huxhold, Oliver; Windsor, Tim D

2018-06-14

Perceived control may promote social activity in older adults because individuals with greater perceived control have greater confidence in their ability to achieve outcomes and are more likely to choose difficult activities, show persistence, and employ strategies to overcome challenges. Cross-sectional research has linked perceived control with social activity in life span and older adult samples but provides little insight into the direction of influence. We examined reciprocal associations between perceived control and social activity in order to establish temporal sequencing, which is one prerequisite for determining potential causation. Participants were 14,126 midlife and older adults from the German Ageing Survey. Using cross-lagged autoregressive modeling with age as the time metric (40-87 years), we examined reciprocal 3-year lagged associations between perceived control and social activity, while controlling for concurrent associations. Perceived control significantly predicted social activity 3 years later. Reciprocally, social activity significantly predicted perceived control 3 years later. The influence of perceived control on social activity was greater than the influence of social activity on perceived control. The finding that perceived control significantly predicts future social activity has potential implications for developing interventions aimed at promoting social activity in midlife and older adults.

An Experience Sampling Study of Physical Activity and Positive Affect: Investigating the Role of Situational Motivation and Perceived Intensity Across Time.

PubMed

Guérin, Eva; Fortier, Michelle S; Sweet, Shane N

2013-04-18

The nature of the association between physical activity and positive affect is complex, prompting experts to recommend continued examination of moderating variables. The main purpose of this 2-week field study was to examine the influence of situational motivational regulations from self-determination theory (SDT) on changes in positive affect from pre- to post- to 3-hours post-physical activity. Another purpose was to clarify the relationship between physical activity intensity [i.e., Ratings of Perceived Exertion (RPE)] and positive affect at the stated time points. This study employed an experience sampling design using electronic questionnaires. Sixty-six healthy and active, multiple-role women provided recurrent assessments of their physical activity, situational motivation, and positive affect in their everyday lives over a 14-day period. Specifically, measures were obtained at the three time points of interest (i.e., pre-, post-, 3-hours post-physical activity). The data were analyzed using multilevel modeling. Results showed that intrinsic motivation was related to post-physical activity positive affect while the influence of identified regulation appeared 3-hours post-physical activity. In addition, RPE, which was significantly predicted by levels of introjection, was more strongly associated with an increase in positive affect post-physical activity than three hours later. The theoretical implications of these findings vis-à vis SDT, namely in regards to a viable motivational sequence predicting the influence of physical activity on affective states, are discussed. The findings regarding the differential influences of RPE and motivational regulations carries applications for facilitating women's well-being.

Emotional attentional control predicts changes in diurnal cortisol secretion following exposure to a prolonged psychosocial stressor.

PubMed

Lenaert, Bert; Barry, Tom J; Schruers, Koen; Vervliet, Bram; Hermans, Dirk

2016-01-01

Hypothalamic-pituitary-adrenal (HPA) axis irregularities have been associated with several psychological disorders. Hence, the identification of individual difference variables that predict variations in HPA-axis activity represents an important challenge for psychiatric research. We investigated whether self-reported attentional control in emotionally demanding situations prospectively predicted changes in diurnal salivary cortisol secretion following exposure to a prolonged psychosocial stressor. Low ability to voluntarily control attention has previously been associated with anxiety and depressive symptomatology. Attentional control was assessed using the Emotional Attentional Control Scale. In students who were preparing for academic examination, salivary cortisol was assessed before (time 1) and after (time 2) examination. Results showed that lower levels of self-reported emotional attentional control at time 1 (N=90) predicted higher absolute diurnal cortisol secretion and a slower decline in cortisol throughout the day at time 2 (N=71). Difficulty controlling attention during emotional experiences may lead to chronic HPA-axis hyperactivity after prolonged exposure to stress. These results indicate that screening for individual differences may foster prediction of HPA-axis disturbances, paving the way for targeted disorder prevention. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

Longitudinal Relations Among Parental Monitoring Strategies, Knowledge, and Adolescent Delinquency in a Racially Diverse At-Risk Sample.

PubMed

Bendezú, Jason J; Pinderhughes, Ellen E; Hurley, Sean M; McMahon, Robert J; Racz, Sarah J

2016-04-04

Parents raising youth in high-risk communities at times rely on active, involved monitoring strategies in order to increase both knowledge about youth activities and the likelihood that adolescents will abstain from problem behavior. Key monitoring literature suggests that some of these active monitoring strategies predict increases in adolescent problem behavior rather than protect against it. However, this literature has studied racially homogenous, low-risk samples, raising questions about generalizability. With a diverse sample of youth (N = 753; 58% male; 46% Black) and families living in high-risk neighborhoods, bidirectional longitudinal relations were examined among three aspects of monitoring (parental discussions of daily activities, parental curfew rules, and adolescent communication with parents), parental knowledge, and youth delinquency. Parental discussion of daily activities was the strongest predictor of parental knowledge, which negatively predicted delinquency. However, these aspects of monitoring did not predict later delinquency. Findings were consistent across gender and race/urbanicity. Results highlight the importance of active and involved parental monitoring strategies in contexts where they are most needed.

Physics-based enzyme design: predicting binding affinity and catalytic activity.

PubMed

Sirin, Sarah; Pearlman, David A; Sherman, Woody

2014-12-01

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications. © 2014 Wiley Periodicals, Inc.

Climate Dynamics and Experimental Prediction (CDEP) and Regional Integrated Science Assessments (RISA) Programs at NOAA Office of Global Programs

NASA Astrophysics Data System (ADS)

Bamzai, A.

2003-04-01

This talk will highlight science and application activities of the CDEP and RISA programs at NOAA OGP. CDEP, through a set of Applied Research Centers (ARCs), supports NOAA's program of quantitative assessments and predictions of global climate variability and its regional implications on time scales of seasons to centuries. The RISA program consolidates results from ongoing disciplinary process research under an integrative framework. Examples of joint CDEP-RISA activities will be presented. Future directions and programmatic challenges will also be discussed.

Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

NASA Astrophysics Data System (ADS)

Niknazar, Hamid; Nasrabadi, Ali Motie

Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

The Complexity of Solar and Geomagnetic Indices

NASA Astrophysics Data System (ADS)

Pesnell, W. Dean

2017-08-01

How far in advance can the sunspot number be predicted with any degree of confidence? Solar cycle predictions are needed to plan long-term space missions. Fleets of satellites circle the Earth collecting science data, protecting astronauts, and relaying information. All of these satellites are sensitive at some level to solar cycle effects. Statistical and timeseries analyses of the sunspot number are often used to predict solar activity. These methods have not been completely successful as the solar dynamo changes over time and one cycle's sunspots are not a faithful predictor of the next cycle's activity. In some ways, using these techniques is similar to asking whether the stock market can be predicted. It has been shown that the Dow Jones Industrial Average (DJIA) can be more accurately predicted during periods when it obeys certain statistical properties than at other times. The Hurst exponent is one such way to partition the data. Another measure of the complexity of a timeseries is the fractal dimension. We can use these measures of complexity to compare the sunspot number with other solar and geomagnetic indices. Our concentration is on how trends are removed by the various techniques, either internally or externally. Comparisons of the statistical properties of the various solar indices may guide us in understanding how the dynamo manifests in the various indices and the Sun.

Limits on an energy dependence of the speed of light from a flare of the active galaxy PKS 2155-304.

PubMed

Aharonian, F; Akhperjanian, A G; Barres de Almeida, U; Bazer-Bachi, A R; Becherini, Y; Behera, B; Beilicke, M; Benbow, W; Bernlöhr, K; Boisson, C; Bochow, A; Borrel, V; Braun, I; Brion, E; Brucker, J; Brun, P; Bühler, R; Bulik, T; Büsching, I; Boutelier, T; Carrigan, S; Chadwick, P M; Charbonnier, A; Chaves, R C G; Chounet, L-M; Clapson, A C; Coignet, G; Costamante, L; Dalton, M; Degrange, B; Deil, C; Dickinson, H J; Djannati-Ataï, A; Domainko, W; Drury, L O'C; Dubois, F; Dubus, G; Dyks, J; Egberts, K; Emmanoulopoulos, D; Espigat, P; Farnier, C; Feinstein, F; Fiasson, A; Förster, A; Fontaine, G; Füssling, M; Gabici, S; Gallant, Y A; Gérard, L; Giebels, B; Glicenstein, J F; Glück, B; Goret, P; Hadjichristidis, C; Hauser, D; Hauser, M; Heinz, S; Heinzelmann, G; Henri, G; Hermann, G; Hinton, J A; Hoffmann, A; Hofmann, W; Holleran, M; Hoppe, S; Horns, D; Jacholkowska, A; de Jager, O C; Jung, I; Katarzyński, K; Kaufmann, S; Kendziorra, E; Kerschhaggl, M; Khangulyan, D; Khélifi, B; Keogh, D; Komin, Nu; Kosack, K; Lamanna, G; Lenain, J-P; Lohse, T; Marandon, V; Martin, J M; Martineau-Huynh, O; Marcowith, A; Maurin, D; McComb, T J L; Medina, C; Moderski, R; Moulin, E; Naumann-Godo, M; de Naurois, M; Nedbal, D; Nekrassov, D; Niemiec, J; Nolan, S J; Ohm, S; Olive, J-F; de Oña Wilhelmi, E; Orford, K J; Osborne, J L; Ostrowski, M; Panter, M; Pedaletti, G; Pelletier, G; Petrucci, P-O; Pita, S; Pühlhofer, G; Punch, M; Quirrenbach, A; Raubenheimer, B C; Raue, M; Rayner, S M; Renaud, M; Rieger, F; Ripken, J; Rob, L; Rosier-Lees, S; Rowell, G; Rudak, B; Ruppel, J; Sahakian, V; Santangelo, A; Schlickeiser, R; Schöck, F M; Schröder, R; Schwanke, U; Schwarzburg, S; Schwemmer, S; Shalchi, A; Skilton, J L; Sol, H; Spangler, D; Stawarz, Ł; Steenkamp, R; Stegmann, C; Superina, G; Tam, P H; Tavernet, J-P; Terrier, R; Tibolla, O; van Eldik, C; Vasileiadis, G; Venter, C; Vialle, J P; Vincent, P; Vivier, M; Völk, H J; Volpe, F; Wagner, S J; Ward, M; Zdziarski, A A; Zech, A

2008-10-24

In the past few decades, several models have predicted an energy dependence of the speed of light in the context of quantum gravity. For cosmological sources such as active galaxies, this minuscule effect can add up to measurable photon-energy dependent time lags. In this Letter a search for such time lags during the High Energy Stereoscopic System observations of the exceptional very high energy flare of the active galaxy PKS 2155-304 on 28 July 2006 is presented. Since no significant time lag is found, lower limits on the energy scale of speed of light modifications are derived.

The Predictive Brain State: Timing Deficiency in Traumatic Brain Injury?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

Attention and memory deficits observed in traumatic brain injury (TBI) are postulated to result from the shearing of white matter connections between the prefrontal cortex, parietal lobe, and cerebellum that are critical in the generation, maintenance, and precise timing of anticipatory neural activity. These fiber tracts are part of a neural network that generates predictions of future states and events, processes that are required for optimal performance on attention and working memory tasks. The authors discuss the role of this anticipatory neural system for understanding the varied symptoms and potential rehabilitation interventions for TBI. Preparatory neural activity normally allows the efficient integration of sensory information with goal-based representations. It is postulated that an impairment in the generation of this activity in traumatic brain injury (TBI) leads to performance variability as the brain shifts from a predictive to reactive mode. This dysfunction may constitute a fundamental defect in TBI as well as other attention disorders, causing working memory deficits, distractibility, a loss of goal-oriented behavior, and decreased awareness. “The future is not what is coming to meet us, but what we are moving forward to meet.” —Jean-Marie Guyau1 PMID:18460693

Have We Entered a 21st Century Prolonged Minimum of Solar Activity? Updated Implications of a 1987 Prediction

NASA Astrophysics Data System (ADS)

Shirley, James H.

2009-05-01

Fairbridge and Shirley (1987) predicted that a new prolonged minimum of solar activity would be underway by the year 2013 (Solar Physics 110, 191). While it is much too early to tell if this prediction will be fully realized, recent observations document a striking reduction in the Sun's general level of activity. While other forecasts of reduced future activity levels on decadal time scales have appeared, the Fairbridge-Shirley (FS) prediction is unique in pinpointing the current epoch. We are unaware of any forecast method that shows a better correspondence with the actual behavior of the Sun to this point. The FS prediction was based on the present-day recurrence of two physical indicators that were correlated in time with the occurrence of the Wolf, Sporer, and Maunder Minima. The amplitude of the inertial revolution of the axis of symmetry of the Sun's orbital motion about the solar system barycenter, and the direction in space of that axis, each bear a relationship to the occurrence of the prolonged minima of the historic record. The FS prediction appeared before the importance of solar meridional flows was generally appreciated, and before the existence and role of the tachocline was suspected. We will update and restate some of the physical implications of the FS results, along with those of some more recent investigations, particularly with reference to orbit-spin coupling hypotheses (Shirley, 2006: M.N.R.A.S. 368, 280). New investigations combining and integrating modern dynamo models with physical solutions describing key aspects of the variability of the solar motion may lead to significant advances in our ability to forecast future changes in the Sun. Acknowledgement: This work was supported by the resources of the author. No part of this work was performed at the Jet Propulsion Laboratory under a contract from NASA.

Students' tripartite efficacy beliefs in high school physical education: within- and cross-domain relations with motivational processes and leisure-time physical activity.

PubMed

Jackson, Ben; Whipp, Peter R; Chua, K L Peter; Dimmock, James A; Hagger, Martin S

2013-02-01

Within instructional settings, individuals form relational efficacy appraisals that complement their self-efficacy beliefs. In high school physical education (PE), for instance, students develop a level of confidence in their teacher's capabilities, as well as estimating how confident they think their teacher is in their (i.e., the students') ability. Grounded in existing transcontextual work, we examined the motivational pathways through which students' relational efficacy and self-efficacy beliefs in PE were predictive of their leisure-time physical activity. Singaporean students (N = 990; age M = 13.95, SD = 1.02) completed instruments assessing efficacy beliefs, perceptions of teacher relatedness support, and autonomous motivation toward PE, and 2 weeks later they reported their motivation toward, and engagement in, leisure-time physical activity. Structural equation modeling revealed that students reported stronger other-efficacy and RISE beliefs when they felt that their teacher created a highly relatedness-supportive environment. In turn, their relational efficacy beliefs (a) supported their confidence in their own ability, (b) directly and indirectly predicted more autonomous motives for participation in PE, and (c) displayed prospective transcontextual effects in relation to leisure-time variables. By emphasizing the adaptive motivational effects associated with the tripartite constructs, these findings highlight novel pathways linking students' efficacy perceptions with leisure-time outcomes.

Role of Amygdala Connectivity in the Persistence of Emotional Memories Over Time: An Event-Related fMRI Investigation

PubMed Central

Dolcos, Florin; Cabeza, Roberto

2008-01-01

According to the consolidation hypothesis, enhanced memory for emotional information reflects the modulatory effect of the amygdala on the medial temporal lobe (MTL) memory system during consolidation. Although there is evidence that amygdala–MTL connectivity enhances memory for emotional stimuli, it remains unclear whether this enhancement increases over time, as consolidation processes unfold. To investigate this, we used functional magnetic resonance imaging to measure encoding activity predicting memory for emotionally negative and neutral pictures after short (20-min) versus long (1-week) delays. Memory measures distinguished between vivid remembering (recollection) and feelings of knowing (familiarity). Consistent with the consolidation hypothesis, the persistence of recollection over time (long divided by short) was greater for emotional than neutral pictures. Activity in the amygdala predicted subsequent memory to a greater extent for emotional than neutral pictures. Although this advantage did not vary with delay, the contribution of amygdala–MTL connectivity to subsequent memory for emotional items increased over time. Moreover, both this increase in connectivity and amygdala activity itself were correlated with individual differences in recollection persistence for emotional but not neutral pictures. These results suggest that the amygdala and its connectivity with the MTL are critical to sustaining emotional memories over time, consistent with the consolidation hypothesis. PMID:18375529

The daily lives of adolescents with an autism spectrum disorder

PubMed Central

Orsmond, Gael I.; Kuo, Hsin-Yu

2013-01-01

This study explores the daily lives, particularly discretionary time, of adolescents with an autism spectrum disorder (ASD). We describe the activities and activity partners of adolescents, the factors associated with their discretionary time use, and the impact of time use on their autism symptoms. Mothers of 103 adolescents with an ASD completed two 24-hour time diaries to describe their adolescent’s activity participation during the third wave of a longitudinal study. Adolescents with an ASD spent considerable time in discretionary activities, with watching television and using a computer as the most frequent activities. They most frequently spent discretionary time alone or with their mothers. They spent little time engaged in conversations or doing activities with peers. Age, gender, the presence of intellectual disability, severity of autism symptoms and maladaptive behaviors, the number of siblings, maternal education, marital status, and family income were associated with adolescent time use. Notably, greater time spent in conversation and reading predicted future decreases in severity of social impairment. The way that adolescents with an ASD spend their free time may have implications for their development and the course of their autism symptoms. PMID:21697194

Time Use during First Year of College Predicts Participation in High-Impact Activities during Later Years

ERIC Educational Resources Information Center

Small, Meg L.; Waterman, Emily; Lender, Taylor

2017-01-01

To increase student engagement, many universities are adopting high-impact educational practices that include study abroad opportunities, faculty mentoring, internships, service learning, challenging coursework, and research experiences; these institutions are also intentionally promoting high-impact cocurricular activities such as community…

Analysis of Patent Activity in the Field of Quantum Information Processing

NASA Astrophysics Data System (ADS)

Winiarczyk, Ryszard; Gawron, Piotr; Miszczak, Jarosław Adam; Pawela, Łukasz; Puchała, Zbigniew

2013-03-01

This paper provides an analysis of patent activity in the field of quantum information processing. Data from the PatentScope database from the years 1993-2011 was used. In order to predict the future trends in the number of filed patents time series models were used.

The effect of concurrent hand movement on estimated time to contact in a prediction motion task.

PubMed

Zheng, Ran; Maraj, Brian K V

2018-04-27

In many activities, we need to predict the arrival of an occluded object. This action is called prediction motion or motion extrapolation. Previous researchers have found that both eye tracking and the internal clocking model are involved in the prediction motion task. Additionally, it is reported that concurrent hand movement facilitates the eye tracking of an externally generated target in a tracking task, even if the target is occluded. The present study examined the effect of concurrent hand movement on the estimated time to contact in a prediction motion task. We found different (accurate/inaccurate) concurrent hand movements had the opposite effect on the eye tracking accuracy and estimated TTC in the prediction motion task. That is, the accurate concurrent hand tracking enhanced eye tracking accuracy and had the trend to increase the precision of estimated TTC, but the inaccurate concurrent hand tracking decreased eye tracking accuracy and disrupted estimated TTC. However, eye tracking accuracy does not determine the precision of estimated TTC.

The costs and benefits of temporal predictability: impaired inhibition of prepotent responses accompanies increased activation of task-relevant responses.

PubMed

Korolczuk, Inga; Burle, Boris; Coull, Jennifer T

2018-06-20

While the benefit of temporal predictability on sensorimotor processing is well established, it is still unknown whether this is due to efficient execution of an appropriate response and/or inhibition of an inappropriate one. To answer this question, we examined the effects of temporal predictability in tasks that required selective (Simon task) or global (Stop-signal task) inhibitory control of prepotent responses. We manipulated temporal expectation by presenting cues that either predicted (temporal cues) or not (neutral cues) when the target would appear. In the Simon task, performance was better when target location (left/right) was compatible with the hand of response and performance was improved further still if targets were temporally cued. However, Conditional Accuracy Functions revealed that temporal predictability selectively increased the number of fast, impulsive errors. Temporal cueing had no effect on selective response inhibition, as measured by the dynamics of the interference effect (delta plots) in the Simon task. By contrast, in the Stop-signal task, Stop-signal reaction time, a covert measure of a more global form of response inhibition, was significantly longer in temporally predictive trials. Therefore, when the time of target onset could be predicted in advance, it was harder to stop the impulse to respond to the target. Collectively, our results indicate that temporal cueing compounded the interfering effects of a prepotent response on task performance. We suggest that although temporal predictability enhances activation of task-relevant responses, it impairs inhibition of prepotent responses. Copyright © 2018 Elsevier B.V. All rights reserved.

Four minutes of in-class high-intensity interval activity improves selective attention in 9- to 11-year olds.

PubMed

Ma, Jasmin K; Le Mare, Lucy; Gurd, Brendon J

2015-03-01

The amount of time allocated to physical activity in schools is declining. Time-efficient physical activity solutions that demonstrate their impact on academic achievement-related outcomes are needed to prioritize physical activity within the school curricula. "FUNtervals" are 4-min, high-intensity interval activities that use whole-body actions to complement a storyline. The purpose of this study was to (i) explore whether FUNtervals can improve selective attention, an executive function posited to be essential for learning and academic success; and (ii) examine whether this relationship is predicted by students' classroom off-task behaviour. Seven grade 3-5 classes (n = 88) were exposed to a single-group, repeated cross-over design where each student's selective attention was compared between no-activity and FUNtervals days. In week 1, students were familiarized with the d2 test of attention and FUNterval activities, and baseline off-task behaviour was observed. In both weeks 2 and 3 students completed the d2 test of attention following either a FUNterval break or a no-activity break. The order of these breaks was randomized and counterbalanced between weeks. Neither motor nor passive off-task behaviour predicted changes in selective attention following FUNtervals; however, a weak relationship was observed for verbal off-task behaviour and improvements in d2 test performance. More importantly, students made fewer errors during the d2 test following FUNtervals. In supporting the priority of physical activity inclusion within schools, FUNtervals, a time efficient and easily implemented physical activity break, can improve selective attention in 9- to 11-year olds.

Assessment of Predictive Capabilities of L1 Orbiters using Realtime Solar Wind Data

NASA Astrophysics Data System (ADS)

Holmes, J.; Kasper, J. C.; Welling, D. T.

2017-12-01

Realtime measurements of solar wind conditions at L1 point allow us to predict geomagnetic activity at Earth up to an hour in advance. These predictions are quantified in the form of geomagnetic indices such as Kp and Ap, allowing for a concise, standardized prediction and measurement system. For years, the Space Weather Prediction Center used ACE realtime solar wind data to develop its one and four-hour Kp forecasts, but has in the past year switched to using DSCOVR data as its source. In this study, the performance of both orbiters in predicting Kp over the course of one month was assessed in an attempt to determine whether or not switching to DSCOVR data has resulted in improved forecasts. The period of study was chosen to encompass a time when the satellites were close to each other, and when moderate to high activity was observed. Kp predictions were made using the Geospace Model, part of the Space Weather Modeling Framework, to simulate conditions based on observed solar wind parameters. The performance of each satellite was assessed by comparing the model output to observed data.

Predicting students' physical activity and health-related well-being: a prospective cross-domain investigation of motivation across school physical education and exercise settings.

PubMed

Standage, Martyn; Gillison, Fiona B; Ntoumanis, Nikos; Treasure, Darren C

2012-02-01

A three-wave prospective design was used to assess a model of motivation guided by self-determination theory (Ryan & Deci, 2008) spanning the contexts of school physical education (PE) and exercise. The outcome variables examined were health-related quality of life (HRQoL), physical self-concept (PSC), and 4 days of objectively assessed estimates of activity. Secondary school students (n = 494) completed questionnaires at three separate time points and were familiarized with how to use a sealed pedometer. Results of structural equation modeling supported a model in which perceptions of autonomy support from a PE teacher positively predicted PE-related need satisfaction (autonomy, competence, and relatedness). Competence predicted PSC, whereas relatedness predicted HRQoL. Autonomy and competence positively predicted autonomous motivation toward PE, which in turn positively predicted autonomous motivation toward exercise (i.e., 4-day pedometer step count). Autonomous motivation toward exercise positively predicted step count, HRQoL, and PSC. Results of multisample structural equation modeling supported gender invariance. Suggestions for future work are discussed.

Market Confidence Predicts Stock Price: Beyond Supply and Demand

PubMed Central

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. PMID:27391816

Neural Network of Predictive Motor Timing in the Context of Gender Differences

PubMed Central

Lošák, Jan; Kašpárek, Tomáš; Vaníček, Jiří; Bareš, Martin

2016-01-01

Time perception is an essential part of our everyday lives, in both the prospective and the retrospective domains. However, our knowledge of temporal processing is mainly limited to the networks responsible for comparing or maintaining specific intervals or frequencies. In the presented fMRI study, we sought to characterize the neural nodes engaged specifically in predictive temporal analysis, the estimation of the future position of an object with varying movement parameters, and the contingent neuroanatomical signature of differences in behavioral performance between genders. The established dominant cerebellar engagement offers novel evidence in favor of a pivotal role of this structure in predictive short-term timing, overshadowing the basal ganglia reported together with the frontal cortex as dominant in retrospective temporal processing in the subsecond spectrum. Furthermore, we discovered lower performance in this task and massively increased cerebellar activity in women compared to men, indicative of strategy differences between the genders. This promotes the view that predictive temporal computing utilizes comparable structures in the retrospective timing processes, but with a definite dominance of the cerebellum. PMID:27019753

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

PubMed Central

Prachayasittikul, Veda; Pingaew, Ratchanok; Worachartcheewan, Apilak; Sitthimonchai, Somkid; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

2017-01-01

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors. PMID:28827987

Hope-inspiring therapeutic relationships, professional expectations and social inclusion for young people with psychosis.

PubMed

Berry, Clio; Greenwood, Kathryn

2015-10-01

Personal recovery accounts suggest that a positive therapeutic relationship with an optimistic mental health professional may facilitate social inclusion. However, little empirical research has investigated the role of the therapeutic relationship in social outcomes or explored potential mechanisms of change within community psychosis care. This study investigated the direct predictive associations of the therapeutic relationship and professional expectancies for social inclusion and vocational activity for young people with psychosis, and indirect associations through hopefulness. Young people with psychosis and their main mental health professional (n=51 dyads) participated across two time points. Measures of therapeutic relationships, professional expectancies, and vocational activity were obtained at baseline. Measures of hopefulness, social inclusion and vocational activity were obtained at follow-up. Direct and indirect associations between variables were analysed using path modelling. Directed path models were consistent with a positive therapeutic relationship and positive professional expectancies predicting social inclusion and vocational activity through mediation by increased patient domain-specific hopefulness. The professional-rated therapeutic relationship more directly predicts change in vocational activity status. Change in vocational activity status predicts increased patient hopefulness. The therapeutic relationship between professionals and young people with psychosis appears hope-inspiring and important to patients' social inclusion and vocational outcomes. Vocational activity may produce reciprocal gains in hopefulness. Copyright © 2015 Elsevier B.V. All rights reserved.

Ongoing behavior predicts perceptual report of interval duration

PubMed Central

Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

2014-01-01

The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

Different types of Internet use, depression, and social anxiety: the role of perceived friendship quality.

PubMed

Selfhout, Maarten H W; Branje, Susan J T; Delsing, M; ter Bogt, Tom F M; Meeus, Wim H J

2009-08-01

The current study examined the longitudinal associations of time spent on Internet activities for communication purposes (i.e., IM-ing) versus time spent on Internet activities for non-communication purposes (i.e., surfing) with depression and social anxiety, as well as the moderating role of perceived friendship quality in these associations. Questionnaire data were gathered from 307 Dutch middle adolescents (average age 15 years) on two waves with a one-year interval. For adolescents who perceive low friendship quality, Internet use for communication purposes predicted less depression, whereas Internet use for non-communication purposes predicted more depression and more social anxiety. These results support social compensation effects of IM-ing on depression and poor-get-poorer effects of surfing on depression and social anxiety, respectively.

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

PubMed Central

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

Associations between objectively measured physical activity and academic attainment in adolescents from a UK cohort

PubMed Central

Booth, J N; Leary, S D; Joinson, C; Ness, A R; Tomporowski, P D; Boyle, J M; Reilly, J J

2014-01-01

Background To test for cross-sectional (at age 11) and longitudinal associations between objectively measured free-living physical activity (PA) and academic attainment in adolescents.Method Data from 4755 participants (45% male) with valid measurement of PA (total volume and intensity) by accelerometry at age 11 from the Avon Longitudinal Study of Parents and Children (ALSPAC) was examined. Data linkage was performed with nationally administered school assessments in English, Maths and Science at ages 11, 13 and 16. Results In unadjusted models, total volume of PA predicted decreased academic attainment. After controlling for total volume of PA, percentage of time spent in moderate-vigorous intensity PA (MVPA) predicted increased performance in English assessments in both sexes, taking into account confounding variables. In Maths at 16 years, percentage of time in MVPA predicted increased performance for males (standardised β=0.11, 95% CI 0.00 to 0.22) and females (β=0.08, 95% CI 0.00 to 0.16). For females the percentage of time spent in MVPA at 11 years predicted increased Science scores at 11 and 16 years (β=0.14 (95% CI 0.03 to 0.25) and 0.14 (0.07 to 0.21), respectively). The correction for regression dilution approximately doubled the standardised β coefficients. Conclusions Findings suggest a long-term positive impact of MVPA on academic attainment in adolescence. PMID:24149097

Predictive microbiology in a dynamic environment: a system theory approach.

PubMed

Van Impe, J F; Nicolaï, B M; Schellekens, M; Martens, T; De Baerdemaeker, J

1995-05-01

The main factors influencing the microbial stability of chilled prepared food products for which there is an increased consumer interest-are temperature, pH, and water activity. Unlike the pH and the water activity, the temperature may vary extensively throughout the complete production and distribution chain. The shelf life of this kind of foods is usually limited due to spoilage by common microorganisms, and the increased risk for food pathogens. In predicting the shelf life, mathematical models are a powerful tool to increase the insight in the different subprocesses and their interactions. However, the predictive value of the sigmoidal functions reported in the literature to describe a bacterial growth curve as an explicit function of time is only guaranteed at a constant temperature within the temperature range of microbial growth. As a result, they are less appropriate in optimization studies of a whole production and distribution chain. In this paper a more general modeling approach, inspired by system theory concepts, is presented if for instance time varying temperature profiles are to be taken into account. As a case study, we discuss a recently proposed dynamic model to predict microbial growth and inactivation under time varying temperature conditions from a system theory point of view. Further, the validity of this methodology is illustrated with experimental data of Brochothrix thermosphacta and Lactobacillus plantarum. Finally, we propose some possible refinements of this model inspired by experimental results.

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

PubMed

Mansfield, Theodore J; MacDonald Gibson, Jacqueline

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.

Sleep deprivation alters functioning within the neural network underlying the covert orienting of attention.

PubMed

Mander, Bryce A; Reid, Kathryn J; Davuluri, Vijay K; Small, Dana M; Parrish, Todd B; Mesulam, M-Marsel; Zee, Phyllis C; Gitelman, Darren R

2008-06-27

One function of spatial attention is to enable goal-directed interactions with the environment through the allocation of neural resources to motivationally relevant parts of space. Studies have shown that responses are enhanced when spatial attention is predictively biased towards locations where significant events are expected to occur. Previous studies suggest that the ability to bias attention predictively is related to posterior cingulate cortex (PCC) activation [Small, D.M., et al., 2003. The posterior cingulate and medial prefrontal cortex mediate the anticipatory allocation of spatial attention. Neuroimage 18, 633-41]. Sleep deprivation (SD) impairs selective attention and reduces PCC activity [Thomas, M., et al., 2000. Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. J. Sleep Res. 9, 335-352]. Based on these findings, we hypothesized that SD would affect PCC function and alter the ability to predictively allocate spatial attention. Seven healthy, young adults underwent functional magnetic resonance imaging (fMRI) following normal rest and 34-36 h of SD while performing a task in which attention was shifted in response to peripheral targets preceded by spatially informative (valid), misleading (invalid), or uninformative (neutral) cues. When rested, but not when sleep-deprived, subjects responded more quickly to targets that followed valid cues than those after neutral or invalid cues. Brain activity during validly cued trials with a reaction time benefit was compared to activity in trials with no benefit. PCC activation was greater during trials with a reaction time benefit following normal rest. In contrast, following SD, reaction time benefits were associated with activation in the left intraparietal sulcus, a region associated with receptivity to stimuli at unexpected locations. These changes may render sleep-deprived individuals less able to anticipate the locations of upcoming events, and more susceptible to distraction by stimuli at irrelevant locations.

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

NASA Astrophysics Data System (ADS)

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 Outlooks.

Vision and Vestibular System Dysfunction Predicts Prolonged Concussion Recovery in Children.

PubMed

Master, Christina L; Master, Stephen R; Wiebe, Douglas J; Storey, Eileen P; Lockyer, Julia E; Podolak, Olivia E; Grady, Matthew F

2018-03-01

Up to one-third of children with concussion have prolonged symptoms lasting beyond 4 weeks. Vision and vestibular dysfunction is common after concussion. It is unknown whether such dysfunction predicts prolonged recovery. We sought to determine which vision or vestibular problems predict prolonged recovery in children. A retrospective cohort of pediatric patients with concussion. A subspecialty pediatric concussion program. Four hundred thirty-two patient records were abstracted. Presence of vision or vestibular dysfunction upon presentation to the subspecialty concussion program. The main outcome of interest was time to clinical recovery, defined by discharge from clinical follow-up, including resolution of acute symptoms, resumption of normal physical and cognitive activity, and normalization of physical examination findings to functional levels. Study subjects were 5 to 18 years (median = 14). A total of 378 of 432 subjects (88%) presented with vision or vestibular problems. A history of motion sickness was associated with vestibular dysfunction. Younger age, public insurance, and presence of headache were associated with later presentation for subspecialty concussion care. Vision and vestibular problems were associated within distinct clusters. Provocable symptoms with vestibulo-ocular reflex (VOR) and smooth pursuits and abnormal balance and accommodative amplitude (AA) predicted prolonged recovery time. Vision and vestibular problems predict prolonged concussion recovery in children. A history of motion sickness may be an important premorbid factor. Public insurance status may represent problems with disparities in access to concussion care. Vision assessments in concussion must include smooth pursuits, saccades, near point of convergence (NPC), and accommodative amplitude (AA). A comprehensive, multidomain assessment is essential to predict prolonged recovery time and enable active intervention with specific school accommodations and targeted rehabilitation.

Metamemory Judgments and the Benefits of Repeated Study: Improving Recall Predictions through the Activation of Appropriate Knowledge

ERIC Educational Resources Information Center

Tiede, Heather L.; Leboe, Jason P.

2009-01-01

Correspondence between judgments of learning (JOLs) and actual recall tends to be poor when the same items are studied and recalled multiple times (e.g., A. Koriat, L. Sheffer, & H. Ma'ayan, 2002). The authors investigated whether making relevant metamemory knowledge more salient would improve the association between actual and predicted recall as…

Modeling and prediction of relaxation of polar order in high-activity nonlinear optical polymers

NASA Astrophysics Data System (ADS)

Guenthner, Andrew J.; Lindsay, Geoffrey A.; Wright, Michael E.; Fallis, Stephen; Ashley, Paul R.; Sanghadasa, Mohan

2007-09-01

Mach-Zehnder optical modulators were fabricated using the CLD and FTC chromophores in polymer-on-silicon optical waveguides. Up to 17 months of oven-ageing stability are reported for the poled polymer films. Modulators containing an FTC-polyimide had the best over all aging performance. To model and extrapolate the ageing data, a relaxation correlation function attributed to A. K. Jonscher was compared to the well-established stretched exponential correlation function. Both models gave a good fit to the data. The Jonscher model predicted a slower relaxation rate in the out years. Analysis showed that collecting data for a longer period relative to the relaxation time was more important for generating useful predictions than the precision with which individual model parameters could be estimated. Thus from a practical standpoint, time-temperature superposition must be assumed in order to generate meaningful predictions. For this purpose, Arrhenius-type expressions were found to relate the model time constants to the ageing temperatures.

Total energy expenditure in burned children using the doubly labeled water technique

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

Goran, M.I.; Peters, E.J.; Herndon, D.N.

Total energy expenditure (TEE) was measured in 15 burned children with the doubly labeled water technique. Application of the technique in burned children required evaluation of potential errors resulting from nutritional intake altering background enrichments during studies and from the high rate of water turnover relative to CO2 production. Five studies were discarded because of these potential problems. TEE was 1.33 +/- 0.27 times predicted basal energy expenditure (BEE), and in studies where resting energy expenditure (REE) was simultaneously measured, TEE was 1.18 +/- 0.17 times REE, which in turn was 1.16 +/- 0.10 times predicted BEE. TEE was significantlymore » correlated with measured REE (r2 = 0.92) but not with predicted BEE. These studies substantiate the advantage of measuring REE to predict TEE in severely burned patients as opposed to relying on standardized equations. Therefore we recommend that optimal nutritional support will be achieved in convalescent burned children by multiplying REE by an activity factor of 1.2.« less

Predicting adult pulmonary ventilation volume and wearing complianceby on-board accelerometry during personal level exposure assessments

NASA Astrophysics Data System (ADS)

Rodes, C. E.; Chillrud, S. N.; Haskell, W. L.; Intille, S. S.; Albinali, F.; Rosenberger, M. E.

2012-09-01

BackgroundMetabolic functions typically increase with human activity, but optimal methods to characterize activity levels for real-time predictions of ventilation volume (l min-1) during exposure assessments have not been available. Could tiny, triaxial accelerometers be incorporated into personal level monitors to define periods of acceptable wearing compliance, and allow the exposures (μg m-3) to be extended to potential doses in μg min-1 kg-1 of body weight? ObjectivesIn a pilot effort, we tested: 1) whether appropriately-processed accelerometer data could be utilized to predict compliance and in linear regressions to predict ventilation volumes in real-time as an on-board component of personal level exposure sensor systems, and 2) whether locating the exposure monitors on the chest in the breathing zone, provided comparable accelerometric data to other locations more typically utilized (waist, thigh, wrist, etc.). MethodsPrototype exposure monitors from RTI International and Columbia University were worn on the chest by a pilot cohort of adults while conducting an array of scripted activities (all <10 METS), spanning common recumbent, sedentary, and ambulatory activity categories. Referee Wocket accelerometers that were placed at various body locations allowed comparison with the chest-located exposure sensor accelerometers. An Oxycon Mobile mask was used to measure oral-nasal ventilation volumes in-situ. For the subset of participants with complete data (n = 22), linear regressions were constructed (processed accelerometric variable versus ventilation rate) for each participant and exposure monitor type, and Pearson correlations computed to compare across scenarios. ResultsTriaxial accelerometer data were demonstrated to be adequately sensitive indicators for predicting exposure monitor wearing compliance. Strong linear correlations (R values from 0.77 to 0.99) were observed for all participants for both exposure sensor accelerometer variables against ventilation volume for recumbent, sedentary, and ambulatory activities with MET values ˜<6. The RTI monitors mean R value of 0.91 was slightly higher than the Columbia monitors mean of 0.86 due to utilizing a 20 Hz data rate instead of a slower 1 Hz rate. A nominal mean regression slope was computed for the RTI system across participants and showed a modest RSD of +/-36.6%. Comparison of the correlation values of the exposure monitors with the Wocket accelerometers at various body locations showed statistically identical regressions for all sensors at alternate hip, ankle, upper arm, thigh, and pocket locations, but not for the Wocket accelerometer located at the dominant side wrist location (R = 0.57; p = 0.016). ConclusionsEven with a modest number of adult volunteers, the consistency and linearity of regression slopes for all subjects were very good with excellent within-person Pearson correlations for the accelerometer versus ventilation volume data. Computing accelerometric standard deviations allowed good sensitivity for compliance assessments even for sedentary activities. These pilot findings supported the hypothesis that a common linear regression is likely to be usable for a wider range of adults to predict ventilation volumes from accelerometry data over a range of low to moderate energy level activities. The predicted volumes would then allow real-time estimates of potential dose, enabling more robust panel studies. The poorer correlation in predicting ventilation rate for an accelerometer located on the wrist suggested that this location should not be considered for predictions of ventilation volume.

Potential for thermal tolerance to mediate climate change effects on three members of a cool temperate lizard genus, Niveoscincus.

PubMed

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 consequences of a greater available activity period will depend on the extent to which changes in climate alters other related factors, such as the nature and level of competition between the respective species. Copyright © 2015 Elsevier Ltd. All rights reserved.

Does Sedentary Behavior Predict Academic Performance in Adolescents or the Other Way Round? A Longitudinal Path Analysis

PubMed Central

Lizandra, Jorge; Devís-Devís, José; Pérez-Gimeno, Esther; Valencia-Peris, Alexandra; Peiró-Velert, Carmen

2016-01-01

This study examined whether adolescents’ time spent on sedentary behaviors (academic, technological-based and social-based activities) was a better predictor of academic performance than the reverse. A cohort of 755 adolescents participated in a three-year period study. Structural Equation Modeling techniques were used to test plausible causal hypotheses. Four competing models were analyzed to determine which model best fitted the data. The Best Model was separately tested by gender. The Best Model showed that academic performance was a better predictor of sedentary behaviors than the other way round. It also indicated that students who obtained excellent academic results were more likely to succeed academically three years later. Moreover, adolescents who spent more time in the three different types of sedentary behaviors were more likely to engage longer in those sedentary behaviors after the three-year period. The better the adolescents performed academically, the less time they devoted to social-based activities and more to academic activities. An inverse relationship emerged between time dedicated to technological-based activities and academic sedentary activities. A moderating auto-regressive effect by gender indicated that boys were more likely to spend more time on technological-based activities three years later than girls. To conclude, previous academic performance predicts better sedentary behaviors three years later than the reverse. The positive longitudinal auto-regressive effects on the four variables under study reinforce the ‘success breeds success’ hypothesis, with academic performance and social-based activities emerging as the strongest ones. Technological-based activities showed a moderating effect by gender and a negative longitudinal association with academic activities that supports a displacement hypothesis. Other longitudinal and covariate effects reflect the complex relationships among sedentary behaviors and academic performance and the need to explore these relationships in depth. Theoretical and practical implications for school health are outlined. PMID:27055121

Does Sedentary Behavior Predict Academic Performance in Adolescents or the Other Way Round? A Longitudinal Path Analysis.

PubMed

Lizandra, Jorge; Devís-Devís, José; Pérez-Gimeno, Esther; Valencia-Peris, Alexandra; Peiró-Velert, Carmen

2016-01-01

This study examined whether adolescents' time spent on sedentary behaviors (academic, technological-based and social-based activities) was a better predictor of academic performance than the reverse. A cohort of 755 adolescents participated in a three-year period study. Structural Equation Modeling techniques were used to test plausible causal hypotheses. Four competing models were analyzed to determine which model best fitted the data. The Best Model was separately tested by gender. The Best Model showed that academic performance was a better predictor of sedentary behaviors than the other way round. It also indicated that students who obtained excellent academic results were more likely to succeed academically three years later. Moreover, adolescents who spent more time in the three different types of sedentary behaviors were more likely to engage longer in those sedentary behaviors after the three-year period. The better the adolescents performed academically, the less time they devoted to social-based activities and more to academic activities. An inverse relationship emerged between time dedicated to technological-based activities and academic sedentary activities. A moderating auto-regressive effect by gender indicated that boys were more likely to spend more time on technological-based activities three years later than girls. To conclude, previous academic performance predicts better sedentary behaviors three years later than the reverse. The positive longitudinal auto-regressive effects on the four variables under study reinforce the 'success breeds success' hypothesis, with academic performance and social-based activities emerging as the strongest ones. Technological-based activities showed a moderating effect by gender and a negative longitudinal association with academic activities that supports a displacement hypothesis. Other longitudinal and covariate effects reflect the complex relationships among sedentary behaviors and academic performance and the need to explore these relationships in depth. Theoretical and practical implications for school health are outlined.

Behavioral Timing without Clockwork: Photoperiod-Dependent Trade-Off between Predation Hazard and Energy Balance in an Arctic Ungulate.

PubMed

Tyler, Nicholas J C; Gregorini, Pablo; Forchhammer, Mads C; Stokkan, Karl-Arne; van Oort, Bob E H; Hazlerigg, David G

2016-10-01

Occurrence of 24-h rhythms in species apparently lacking functional molecular clockwork indicates that strong circadian mechanisms are not essential prerequisites of robust timing, and that rhythmical patterns may arise instead as passive responses to periodically changing environmental stimuli. Thus, in a new synthesis of grazing in a ruminant (MINDY), crepuscular peaks of activity emerge from interactions between internal and external stimuli that influence motivation to feed, and the influence of the light/dark cycle is mediated through the effect of low nocturnal levels of food intake on gastric function. Drawing on risk allocation theory, we hypothesized that the timing of behavior in ruminants is influenced by the independent effects of light on motivation to feed and perceived risk of predation. We predicted that the antithetical relationship between these 2 drivers would vary with photoperiod, resulting in a systematic shift in the phase of activity relative to the solar cycle across the year. This prediction was formalized in a model in which phase of activity emerges from a photoperiod-dependent trade-off between food and safety. We tested this model using data on the temporal pattern of activity in reindeer/caribou Rangifer tarandus free-living at natural mountain pasture in sub-Arctic Norway. The resulting nonlinear relationship between the phasing of crepuscular activity and photoperiod, consistent with the model, suggests a mechanism for behavioral timing that is independent of the core circadian system. We anticipate that such timing depends on integration of metabolic feedback from the digestive system and the activity of the glucocorticoid axis which modulates the behavioral responses of the animal to environmental hazard. The hypothalamus is the obvious neural substrate to achieve this integration. © 2016 The Author(s).

Real-time emissions from construction equipment compared with model predictions.

PubMed

Heidari, Bardia; Marr, Linsey C

2015-02-01

The construction industry is a large source of greenhouse gases and other air pollutants. Measuring and monitoring real-time emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. We employed a portable emission measurement system (PEMS) for real-time measurement of carbon dioxide (CO), nitrogen oxides (NOx), hydrocarbon, and carbon monoxide (CO) emissions from construction equipment to derive emission rates (mass of pollutant emitted per unit time) and emission factors (mass of pollutant emitted per unit volume of fuel consumed) under real-world operating conditions. Measurements were compared with emissions predicted by methodologies used in three models: NONROAD2008, OFFROAD2011, and a modal statistical model. Measured emission rates agreed with model predictions for some pieces of equipment but were up to 100 times lower for others. Much of the difference was driven by lower fuel consumption rates than predicted. Emission factors during idling and hauling were significantly different from each other and from those of other moving activities, such as digging and dumping. It appears that operating conditions introduce considerable variability in emission factors. Results of this research will aid researchers and practitioners in improving current emission estimation techniques, frameworks, and databases.

Solar flux forecasting using mutual information with an optimal delay

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Conway, D.; Rokni, M.; Sperling, R.; Roszman, L.; Cooley, J.

1993-01-01

Solar flux F(sub 10.7) directly affects the atmospheric density, thereby changing the lifetime and prediction of satellite orbits. For this reason, accurate forecasting of F(sub 10.7) is crucial for orbit determination of spacecraft. Our attempts to model and forecast F(sub 10.7) uncovered highly entangled dynamics. We concluded that the general lack of predictability in solar activity arises from its nonlinear nature. Nonlinear dynamics allow us to predict F(sub 10.7) more accurately than is possible using stochastic methods for time scales shorter than a characteristic horizon, and with about the same accuracy as using stochastic techniques when the forecasted data exceed this horizon. The forecast horizon is a function of two dynamical invariants: the attractor dimension and the Lyapunov exponent. In recent years, estimation of the attractor dimension reconstructed from a time series has become an important tool in data analysis. In calculating the invariants of the system, the first necessary step is the reconstruction of the attractor for the system from the time-delayed values of the time series. The choice of the time delay is critical for this reconstruction. For an infinite amount of noise-free data, the time delay can, in principle, be chosen almost arbitrarily. However, the quality of the phase portraits produced using the time-delay technique is determined by the value chosen for the delay time. Fraser and Swinney have shown that a good choice for this time delay is the one suggested by Shaw, which uses the first local minimum of the mutual information rather than the autocorrelation function to determine the time delay. This paper presents a refinement of this criterion and applies the refined technique to solar flux data to produce a forecast of the solar activity.

The use of multiple time point dynamic positron emission tomography/computed tomography in patients with oral/head and neck cancer does not predictably identify metastatic cervical lymph nodes.

PubMed

Carlson, Eric R; Schaefferkoetter, Josh; Townsend, David; McCoy, J Michael; Campbell, Paul D; Long, Misty

2013-01-01

To determine whether the time course of 18-fluorine fluorodeoxyglucose (18F-FDG) activity in multiple consecutively obtained 18F-FDG positron emission tomography (PET)/computed tomography (CT) scans predictably identifies metastatic cervical adenopathy in patients with oral/head and neck cancer. It is hypothesized that the activity will increase significantly over time only in those lymph nodes harboring metastatic cancer. A prospective cohort study was performed whereby patients with oral/head and neck cancer underwent consecutive imaging at 9 time points with PET/CT from 60 to 115 minutes after injection with (18)F-FDG. The primary predictor variable was the status of the lymph nodes based on dynamic PET/CT imaging. Metastatic lymph nodes were defined as those that showed an increase greater than or equal to 10% over the baseline standard uptake values. The primary outcome variable was the pathologic status of the lymph node. A total of 2,237 lymph nodes were evaluated histopathologically in the 83 neck dissections that were performed in 74 patients. A total of 119 lymph nodes were noted to have hypermetabolic activity on the 90-minute (static) portion of the study and were able to be assessed by time points. When we compared the PET/CT time point (dynamic) data with the histopathologic analysis of the lymph nodes, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 60.3%, 70.5%, 66.0%, 65.2%, and 65.5%, respectively. The use of dynamic PET/CT imaging does not permit the ablative surgeon to depend only on the results of the PET/CT study to determine which patients will benefit from neck dissection. As such, we maintain that surgeons should continue to rely on clinical judgment and maintain a low threshold for executing neck dissection in patients with oral/head and neck cancer, including those patients with N0 neck designations. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Childhood temperament predictors of adolescent physical activity.

PubMed

Janssen, James A; Kolacz, Jacek; Shanahan, Lilly; Gangel, Meghan J; Calkins, Susan D; Keane, Susan P; Wideman, Laurie

2017-01-05

Physical inactivity is a leading cause of mortality worldwide. Many patterns of physical activity involvement are established early in life. To date, the role of easily identifiable early-life individual predictors of PA, such as childhood temperament, remains relatively unexplored. Here, we tested whether childhood temperamental activity level, high intensity pleasure, low intensity pleasure, and surgency predicted engagement in physical activity (PA) patterns 11 years later in adolescence. Data came from a longitudinal community study (N = 206 participants, 53% females, 70% Caucasian). Parents reported their children's temperamental characteristics using the Child Behavior Questionnaire (CBQ) when children were 4 & 5 years old. Approximately 11 years later, adolescents completed self-reports of PA using the Godin Leisure Time Exercise Questionnaire and the Youth Risk Behavior Survey. Ordered logistic regression, ordinary least squares linear regression, and Zero-inflated Poisson regression models were used to predict adolescent PA from childhood temperament. Race, socioeconomic status, and adolescent body mass index were used as covariates. Males with greater childhood temperamental activity level engaged in greater adolescent PA volume (B = .42, SE = .13) and a 1 SD difference in childhood temperamental activity level predicted 29.7% more strenuous adolescent PA per week. Males' high intensity pleasure predicted higher adolescent PA volume (B = .28, SE = .12). Males' surgency positively predicted more frequent PA activity (B = .47, SE = .23, OR = 1.61, 95% CI: 1.02, 2.54) and PA volume (B = .31, SE = .12). No predictions from females' childhood temperament to later PA engagement were identified. Childhood temperament may influence the formation of later PA habits, particularly in males. Boys with high temperamental activity level, high intensity pleasure, and surgency may directly seek out pastimes that involve PA. Indirectly, temperament may also influence caregivers' perceptions of optimal activity choices for children. Understanding how temperament influences the development of PA patterns has the potential to inform efforts aimed at promoting long-term PA engagement and physical health.

Noise and diffusion of a vibrated self-propelled granular particle

NASA Astrophysics Data System (ADS)

Walsh, Lee; Wagner, Caleb G.; Schlossberg, Sarah; Olson, Christopher; Baskaran, Aparna; Menon, Narayanan

Granular materials are an important physical realization of active matter. In vibration-fluidized granular matter, both diffusion and self-propulsion derive from the same collisional forcing, unlike many other active systems where there is a clean separation between the origin of single-particle mobility and the coupling to noise. Here we present experimental studies of single-particle motion in a vibrated granular monolayer, along with theoretical analysis that compares grain motion at short and long time scales to the assumptions and predictions, respectively, of the active Brownian particle (ABP) model. The results demonstrate that despite the unique relation between noise and propulsion, granular media do show the generic features predicted by the ABP model and indicate that this is a valid framework to predict collective phenomena. Additionally, our scheme of analysis for validating the inputs and outputs of the model can be applied to other granular and non-granular systems.

Spatiotemporal property and predictability of large-scale human mobility

NASA Astrophysics Data System (ADS)

Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

2018-04-01

Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms.

PubMed

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.

Interactions between the nucleus accumbens and auditory cortices predict music reward value.

PubMed

Salimpoor, Valorie N; van den Bosch, Iris; Kovacevic, Natasa; McIntosh, Anthony Randal; Dagher, Alain; Zatorre, Robert J

2013-04-12

We used functional magnetic resonance imaging to investigate neural processes when music gains reward value the first time it is heard. The degree of activity in the mesolimbic striatal regions, especially the nucleus accumbens, during music listening was the best predictor of the amount listeners were willing to spend on previously unheard music in an auction paradigm. Importantly, the auditory cortices, amygdala, and ventromedial prefrontal regions showed increased activity during listening conditions requiring valuation, but did not predict reward value, which was instead predicted by increasing functional connectivity of these regions with the nucleus accumbens as the reward value increased. Thus, aesthetic rewards arise from the interaction between mesolimbic reward circuitry and cortical networks involved in perceptual analysis and valuation.

The Predictive Effects of Protection Motivation Theory on Intention and Behaviour of Physical Activity in Patients with Type 2 Diabetes

PubMed Central

Ali Morowatisharifabad, Mohammad; Abdolkarimi, Mahdi; Asadpour, Mohammad; Fathollahi, Mahmood Sheikh; Balaee, Parisa

2018-01-01

INTRODUCTION: Theory-based education tailored to target behaviour and group can be effective in promoting physical activity. AIM: The purpose of this study was to examine the predictive power of Protection Motivation Theory on intent and behaviour of Physical Activity in Patients with Type 2 Diabetes. METHODS: This descriptive study was conducted on 250 patients in Rafsanjan, Iran. To examine the scores of protection motivation theory structures, a researcher-made questionnaire was used. Its validity and reliability were confirmed. The level of physical activity was also measured by the International Short - form Physical Activity Inventory. Its validity and reliability were also approved. Data were analysed by statistical tests including correlation coefficient, chi-square, logistic regression and linear regression. RESULTS: The results revealed that there was a significant correlation between all the protection motivation theory constructs and the intention to do physical activity. The results showed that the Theory structures were able to predict 60% of the variance of physical activity intention. The results of logistic regression demonstrated that increase in the score of physical activity intent and self - efficacy increased the chance of higher level of physical activity by 3.4 and 1.5 times, respectively OR = (3.39, 1.54). CONCLUSION: Considering the ability of protection motivation theory structures to explain the physical activity behaviour, interventional designs are suggested based on the structures of this theory, especially to improve self -efficacy as the most powerful factor in predicting physical activity intention and behaviour. PMID:29731945

Single-digit Arabic numbers do not automatically activate magnitude representations in adults or in children: evidence from the symbolic same-different task.

PubMed

Wong, Becky; Szücs, Dénes

2013-11-01

We investigated whether the mere presentation of single-digit Arabic numbers activates their magnitude representations using a visually-presented symbolic same-different task for 20 adults and 15 children. Participants saw two single-digit Arabic numbers on a screen and judged whether the numbers were the same or different. We examined whether reaction time in this task was primarily driven by (objective or subjective) perceptual similarity, or by the numerical difference between the two digits. We reasoned that, if Arabic numbers automatically activate magnitude representations, a numerical function would best predict reaction time; but if Arabic numbers do not automatically activate magnitude representations, a perceptual function would best predict reaction time. Linear regressions revealed that a perceptual function, specifically, subjective visual similarity, was the best and only significant predictor of reaction time in adults and in children. These data strongly suggest that, in this task, single-digit Arabic numbers do not necessarily automatically activate magnitude representations in adults or in children. As the first study to date to explicitly study the developmental importance of perceptual factors in the symbolic same-different task, we found no significant differences between adults and children in their reliance on perceptual information in this task. Based on our findings, we propose that visual properties may play a key role in symbolic number judgements. © 2013. Published by Elsevier B.V. All rights reserved.

Time series analysis of monthly pulpwood use in the Northeast

Treesearch

James T. Bones

1980-01-01

Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.

Emerging effortful control in toddlerhood: the role of infant orienting/regulation, maternal effortful control, and maternal time spent in caregiving activities.

PubMed

Bridgett, David J; Gartstein, Maria A; Putnam, Samuel P; Lance, Kate Oddi; Iddins, Erin; Waits, Robin; Vanvleet, Jessica; Lee, Lindsay

2011-02-01

Latent growth modeling (LGM) was used to examine the contribution of changes in infant orienting/regulation (O/R) to the emergence of toddler effortful control (EC), the contributions of maternal EC to the development of infant O/R and the emergence of toddler EC, the influence of maternal time spent in caregiving activities on toddler EC and the slope of infant O/R, and the contribution of maternal EC to subsequent maternal time spent in caregiving activities. Mothers from 158 families completed a self-report measure of EC when their infants were 4 months of age, a measure of infant O/R when their infants were 4, 6, 8, 10, and 12 months of age, and a measure of toddler EC when their children reached 18 months of age. Information concerning maternal time spent in various interactive caregiving activities was collected when infants were 6 months old. Results indicated higher maternal EC predicted interindividual differences in the intercept (i.e., higher intercepts), but not slope, of infant O/R and that higher maternal EC, higher infant O/R intercept, and higher infant O/R slope contributed to higher toddler EC. Furthermore, higher maternal EC predicted greater maternal time spent in interactive caregiving activities with their infants and greater maternal time in interactive caregiving with infants also contributed to higher toddler EC after controlling for maternal EC. These findings contribute to the understanding of the influence of maternal EC, directly and through caregiving, on toddler EC. Additional implications as they are related to early developing regulatory aspects of temperament are discussed. Copyright © 2010 Elsevier Inc. All rights reserved.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Development of measures from the theory of planned behavior applied to leisure-time physical activity.

PubMed

Kerner, Matthew S

2005-06-01

Using the theory of planned behavior as a conceptual framework, scales assessing Attitude to Leisure-time Physical Activity, Expectations of Others, Perceived Control, and Intention to Engage in Leisure-time Physical Activity were developed for use among middle-school students. The study sample included 349 boys and 400 girls, 10 to 14 years of age (M=11.9 yr., SD=.9). Unipolar and bipolar scales with seven response choices were developed, with each scale item phrased in a Likert-type format. Following revisions, 22 items were retained in the Attitude to Leisure-time Physical Activity Scale, 10 items in the Expectations of Others Scale, 3 items in the Perceived Control Scale, and 17 items in the Intention to Engage in Leisure-time Physical Activity Scale. Adequate internal consistency was indicated by standardized coefficients alpha ranging from .75 to .89. Current results must be extended to assess discriminant and predictive validities and to check various reliabilities with new samples, then evaluation of intervention techniques for promotion of positive attitudes about leisure-time physical activity, including perception of control and intentions to engage in leisure-time physical activity.

People's preference patterns for gains/losses in multiple time period situations.

PubMed

Chang, Shin-Shin; Chang, Jung-Hua

2013-10-01

Little research to date has been devoted to investigating whether people treat time differently from money when facing multiple gains or losses. This study tested the hypothesis that because time is characterized by perishability, fixed supply, and infungibility, people with strong motivation to obtain a long period of uninterrupted discretionary time would strive to trim the time needed for non-discretionary activities or to combine several non-discretionary activities. As a result, people prefer integration over segregation of multiple time losses or gains, which is not consistent with the prediction based on hedonic editing theory or the renewable resource model. This proposition is supported by results from four experiments.

Evaluation of geomagnetic field models using magnetometer measurements for satellite attitude determination system at low earth orbits: Case studies

NASA Astrophysics Data System (ADS)

Cilden-Guler, Demet; Kaymaz, Zerefsan; Hajiyev, Chingiz

2018-01-01

In this study, different geomagnetic field models are compared in order to study the errors resulting from the representation of magnetic fields that affect the satellite attitude system. For this purpose, we used magnetometer data from two Low Earth Orbit (LEO) spacecraft and the geomagnetic models IGRF-12 (Thébault et al., 2015) and T89 (Tsyganenko, 1989) models to study the differences between the magnetic field components, strength and the angle between the predicted and observed vector magnetic fields. The comparisons were made during geomagnetically active and quiet days to see the effects of the geomagnetic storms and sub-storms on the predicted and observed magnetic fields and angles. The angles, in turn, are used to estimate the spacecraft attitude and hence, the differences between model and observations as well as between two models become important to determine and reduce the errors associated with the models under different space environment conditions. We show that the models differ from the observations even during the geomagnetically quiet times but the associated errors during the geomagnetically active times increase. We find that the T89 model gives closer predictions to the observations, especially during active times and the errors are smaller compared to the IGRF-12 model. The magnitude of the error in the angle under both environmental conditions was found to be less than 1°. For the first time, the geomagnetic models were used to address the effects of the near Earth space environment on the satellite attitude.

The cerebellum predicts the temporal consequences of observed motor acts.

PubMed

Avanzino, Laura; Bove, Marco; Pelosin, Elisa; Ogliastro, Carla; Lagravinese, Giovanna; Martino, Davide

2015-01-01

It is increasingly clear that we extract patterns of temporal regularity between events to optimize information processing. The ability to extract temporal patterns and regularity of events is referred as temporal expectation. Temporal expectation activates the same cerebral network usually engaged in action selection, comprising cerebellum. However, it is unclear whether the cerebellum is directly involved in temporal expectation, when timing information is processed to make predictions on the outcome of a motor act. Healthy volunteers received one session of either active (inhibitory, 1 Hz) or sham repetitive transcranial magnetic stimulation covering the right lateral cerebellum prior the execution of a temporal expectation task. Subjects were asked to predict the end of a visually perceived human body motion (right hand handwriting) and of an inanimate object motion (a moving circle reaching a target). Videos representing movements were shown in full; the actual tasks consisted of watching the same videos, but interrupted after a variable interval from its onset by a dark interval of variable duration. During the 'dark' interval, subjects were asked to indicate when the movement represented in the video reached its end by clicking on the spacebar of the keyboard. Performance on the timing task was analyzed measuring the absolute value of timing error, the coefficient of variability and the percentage of anticipation responses. The active group exhibited greater absolute timing error compared with the sham group only in the human body motion task. Our findings suggest that the cerebellum is engaged in cognitive and perceptual domains that are strictly connected to motor control.

Predicting Growth in Word Level Reading Skills in Children With Developmental Dyslexia Using an Object Rhyming Functional Neuroimaging Task.

PubMed

Farris, Emily A; Ring, Jeremiah; Black, Jeffrey; Lyon, G Reid; Odegard, Timothy N

2016-04-01

An object rhyming task that does not require text reading and is suitable for younger children was used to predict gains in word level reading skills following an intensive 2-year reading intervention for children with developmental dyslexia. The task evoked activation in bilateral inferior frontal regions. Growth in untimed pseudoword reading was associated with increased pre-intervention activation of the left inferior frontal gyrus, and growth in timed word reading was associated with pre-intervention activation of the left and right inferior frontal gyri. These analyses help identify pre-intervention factors that facilitate reading skill improvements in children with developmental dyslexia.

Implementing unpredictability in feeding enrichment for Malayan sun bears (Helarctos malayanus).

PubMed

Schneider, Marion; Nogge, Gunther; Kolter, Lydia

2014-01-01

Bears in the wild spend large proportions of time in foraging activities. In zoos their time budgets differ markedly from those of their wild counterparts. Feeding enrichment has been documented to increase foraging behavior and to reduce stereotypies. But in general these procedures have no long-term effects and result in habituation. As can be expected by the predictions of the optimal foraging theory, foraging activities are restricted as long as the availability of food is predictable. To quantify the effect of spatial unpredictability, three feeding methods have been designed to stimulate functional foraging behavior in captive Malayan sun bears in the long-term. In order to examine if habituation occurs, the most effective method was tested for 12 consecutive days. Activities of four adult sun bears at the Cologne Zoo were recorded by focal animal recording of foraging behaviors and time sampling of activities for a total of 360 hr. Implementing unpredictability significantly increased the time the bears spent foraging and led to a higher diversity of foraging behaviors. The effects lasted throughout the entire day and no habituation occurred in the course of 12 consecutive days. The study shows how functional species typical behavior in captive Malayan sun bears can be stimulated in the long-term by simulating natural characteristics of food availability. © 2014 Wiley Periodicals, Inc.

Can changes in psychosocial factors and residency explain the decrease in physical activity during the transition from high school to college or university?

PubMed

Van Dyck, Delfien; De Bourdeaudhuij, Ilse; Deliens, Tom; Deforche, Benedicte

2015-04-01

When students make the transition from high school to college or university, their physical activity (PA) levels decrease strongly. Consequently, it is of crucial importance to identify the determinants of this decline in PA. The study aims were to (1) examine changes in psychosocial factors in students during the transition from high school to college/university, (2) examine if changes in psychosocial factors and residency can predict changes in PA, and (3) investigate the moderating effects of residency on the relationship between changes in psychosocial factors and changes in PA. Between March 2008 and October 2010, 291 Flemish students participated in a longitudinal study, with baseline measurements during the final year of high school and follow-up measurements at the start of second year of college/university. At both time points, participants completed a questionnaire assessing demographics, active transportation, leisure-time sports, psychosocial variables, and residency. Repeated measures MANOVA analyses and multiple moderated hierarchic regression analyses were conducted. Modeling, self-efficacy, competition-related benefits, and health-related, external and social barriers decreased, while health-related benefits and time-related barriers increased from baseline to follow-up. Decreases in modeling and time-related barriers were associated with a decrease in active transportation (adjusted R(2) = 3.2%); residency, decreases in self-efficacy, competition-related benefits, and increases in health- and time-related barriers predicted a decrease in leisure-time sports (adjusted R(2) = 29.3%). Residency only moderated two associations between psychosocial factors and changes in PA. Residency and changes in psychosocial factors were mainly important to explain the decrease in leisure-time sports. Other factors such as distance to college/university are likely more important to explain the decrease in active transportation; these are worth exploring in future studies. Because few interactions were found, similar interventions, focusing on self-efficacy, time management, and increasing perceived benefits may be effective to increase leisure-time sports in all students.

How many days of monitoring predict physical activity and sedentary behaviour in older adults?

PubMed Central

2011-01-01

Background The number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults. Methods Participants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour. Results Results of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed. Conclusions The number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the pedometer and ActiGraph, the magnitude of differences between days suggests that day of the week cannot be completely ignored in the design and analysis of PA studies that involve < 7-day monitoring protocols for these instruments. More days of accelerometer data were needed to determine typical sedentary behaviour than PA level in this population of older adults. PMID:21679426

How many days of monitoring predict physical activity and sedentary behaviour in older adults?

PubMed

Hart, Teresa L; Swartz, Ann M; Cashin, Susan E; Strath, Scott J

2011-06-16

The number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults. Participants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour. Results of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed. The number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the pedometer and ActiGraph, the magnitude of differences between days suggests that day of the week cannot be completely ignored in the design and analysis of PA studies that involve < 7-day monitoring protocols for these instruments. More days of accelerometer data were needed to determine typical sedentary behaviour than PA level in this population of older adults.

Prediction of iodine-131 biokinetics and radiation doses from therapy on the basis of tracer studies: an important question for therapy planning in nuclear medicine.

PubMed

Willegaignon, José; Pelissoni, Rogério A; Lima, Beatriz C G D; Sapienza, Marcelo T; Coura-Filho, George B; Buchpiguel, Carlos A

2016-05-01

This study aimed to present a comparison of iodine-131 (I) biokinetics and radiation doses to red-marrow (rm) and whole-body (wb), following the administration of tracer and therapeutic activities, as a means of confirming whether I clearance and radiation doses for therapy procedures can be predicted by tracer activities. Eleven differentiated thyroid cancer patients were followed after receiving tracer and therapeutic I activity. Whole-body I clearance was estimated using radiation detectors and OLINDA/EXM software was used to calculate radiation doses to rm and wb. Tracer I activity of 86 (±14) MBq and therapeutic activity of 8.04 (±1.18) GBq were administered to patients, thereby producing an average wb I effective half-time and residence time of, respectively, 13.51 (±4.05) and 23.13 (±5.98) h for tracer activities and 13.32 (±3.38) and 19.63 (±4.77) h for therapy. Radiation doses to rm and wb were, respectively, 0.0467 (±0.0208) and 0.0589 (±0.0207) mGy/MBq in tracer studies and 0.0396 (±0.0169) and 0.0500 (±0.0163) mGy/MBq in therapy. Although the differences were not considered statistically significant between averages, those between the values of effective half-times (P=0.906), residence times (P=0.145), and radiation doses to rm (P=0.393) and to wb (P=0.272), from tracer and therapy procedures, large differences of up to 80% in wb I clearance, and up to 50% in radiation doses were observed when patients were analyzed individually, thus impacting on the total amount of I activity calculated to be safe for application in individual therapy. I biokinetics and radiation doses to rm and wb in therapy procedures are well predicted by diagnostic activities when average values of a group of patients are compared. Nonetheless, when patients are analyzed individually, significant differences may be encountered, thus implying that nuclear medicine therapy-planning requires due consideration of changes in individual patient-body status from initial tracer to final therapy procedures to thus provide appropriate adjustments in therapeutic activities.

Attitudes and exercise adherence: test of the Theories of Reasoned Action and Planned Behaviour.

PubMed

Smith, R A; Biddle, S J

1999-04-01

Three studies of exercise adherence and attitudes are reported that tested the Theory of Reasoned Action and the Theory of Planned Behaviour. In a prospective study of adherence to a private fitness club, structural equation modelling path analysis showed that attitudinal and social normative components of the Theory of Reasoned Action accounted for 13.1% of the variance in adherence 4 months later, although only social norm significantly predicted intention. In a second study, the Theory of Planned Behaviour was used to predict both physical activity and sedentary behaviour. Path analyses showed that attitude and perceived control, but not social norm, predicted total physical activity. Physical activity was predicted from intentions and control over sedentary behaviour. Finally, an intervention study with previously sedentary adults showed that intentions to be active measured at the start and end of a 10-week intervention were associated with the planned behaviour variables. A multivariate analysis of variance revealed no significant multivariate effects for time on the planned behaviour variables measured before and after intervention. Qualitative data provided evidence that participants had a positive experience on the intervention programme and supported the role of social normative factors in the adherence process.

Advancing Atmospheric River Forecasts into Subseasonal-to-Seasonal Timescales

NASA Astrophysics Data System (ADS)

Barnes, E. A.; Baggett, C.; Mundhenk, B. D.; Nardi, K.; Maloney, E. D.

2017-12-01

Atmospheric rivers can cause considerable mayhem along the west coast of North America - delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal (S2S) timescales ( 2 to 6 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into S2S timescales through knowledge of two of the atmosphere's most prominent oscillations; the Madden-Julian oscillation (MJO) and the Quasi-biennial oscillation (QBO). The dynamical relationship between atmospheric rivers, the MJO and the QBO is hypothesized to occur through modulation of North Pacific blocking. We present an empirical prediction scheme for anomalous atmospheric river activity based solely on the MJO and QBO and demonstrate skillful subseasonal "forecasts of opportunity" 5+ weeks ahead. We conclude with a discussion of the ability of state-of-the-art NWP models to predict atmospheric river characteristics on S2S timescales. With the wide-ranging impacts associated with landfalling atmospheric rivers, even modest gains in the subseasonal prediction of anomalous atmospheric river activity may support early action decision making and benefit numerous sectors of society.

Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding.

PubMed

Hogendoorn, Hinze; Burkitt, Anthony N

2018-05-01

Due to the delays inherent in neuronal transmission, our awareness of sensory events necessarily lags behind the occurrence of those events in the world. If the visual system did not compensate for these delays, we would consistently mislocalize moving objects behind their actual position. Anticipatory mechanisms that might compensate for these delays have been reported in animals, and such mechanisms have also been hypothesized to underlie perceptual effects in humans such as the Flash-Lag Effect. However, to date no direct physiological evidence for anticipatory mechanisms has been found in humans. Here, we apply multivariate pattern classification to time-resolved EEG data to investigate anticipatory coding of object position in humans. By comparing the time-course of neural position representation for objects in both random and predictable apparent motion, we isolated anticipatory mechanisms that could compensate for neural delays when motion trajectories were predictable. As well as revealing an early neural position representation (lag 80-90 ms) that was unaffected by the predictability of the object's trajectory, we demonstrate a second neural position representation at 140-150 ms that was distinct from the first, and that was pre-activated ahead of the moving object when it moved on a predictable trajectory. The latency advantage for predictable motion was approximately 16 ± 2 ms. To our knowledge, this provides the first direct experimental neurophysiological evidence of anticipatory coding in human vision, revealing the time-course of predictive mechanisms without using a spatial proxy for time. The results are numerically consistent with earlier animal work, and suggest that current models of spatial predictive coding in visual cortex can be effectively extended into the temporal domain. Copyright © 2018 Elsevier Inc. All rights reserved.

Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA).

PubMed

Potter, Adam W; Blanchard, Laurie A; Friedl, Karl E; Cadarette, Bruce S; Hoyt, Reed W

2017-02-01

Physiological models provide useful summaries of complex interrelated regulatory functions. These can often be reduced to simple input requirements and simple predictions for pragmatic applications. This paper demonstrates this modeling efficiency by tracing the development of one such simple model, the Heat Strain Decision Aid (HSDA), originally developed to address Army needs. The HSDA, which derives from the Givoni-Goldman equilibrium body core temperature prediction model, uses 16 inputs from four elements: individual characteristics, physical activity, clothing biophysics, and environmental conditions. These inputs are used to mathematically predict core temperature (T c ) rise over time and can estimate water turnover from sweat loss. Based on a history of military applications such as derivation of training and mission planning tools, we conclude that the HSDA model is a robust integration of physiological rules that can guide a variety of useful predictions. The HSDA model is limited to generalized predictions of thermal strain and does not provide individualized predictions that could be obtained from physiological sensor data-driven predictive models. This fully transparent physiological model should be improved and extended with new findings and new challenging scenarios. Published by Elsevier Ltd.

Predicting Epileptic Seizures in Advance

PubMed Central

Moghim, Negin; Corne, David W.

2014-01-01

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance. PMID:24911316

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

PubMed

Basant, Nikita; Gupta, Shikha

2017-06-01

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Baseline predictors of maintenance of intervention-induced changes in physical activity and sitting time among diabetic and pre-diabetic patients: a descriptive case series.

PubMed

Helmink, Judith H M; Gubbels, Jessica S; van Brussel-Visser, Femke N; de Vries, Nanne K; Kremers, Stef P J

2013-05-08

The aim of this study was to explore the predictive value of baseline characteristics in relation to changes in physical activity (PA) and sedentary behaviour among diabetic and pre-diabetic patients participating in a primary care based exercise intervention. We used a descriptive case series among diabetic and pre-diabetic patients (n = 119, 50.8% male, mean age 65.5 (SD = 7.8)). Measurements took place with questionnaires at baseline and two years after the start of the intervention. Predictor variables included demographic factors, Body Mass Index, baseline PA and sitting time, and baseline socio-cognitive profile. At follow-up, respondents spent more time being physically active than at baseline. For the total group, the average sitting time remained almost unchanged between the two measurements. Further exploration showed that respondents who had relatively high levels of PA at the start of the intervention, increased their total sitting time, while respondents with relatively low levels of PA at the start decreased their sitting time. The socio-cognitive profile did not predict behaviour change. The intervention appeared to be suitable for people with a low-education level, but the results should be interpreted in view of the limitations of the study such as the non-controlled design, self-reported outcomes and selective drop-out of participants. Interventions for this specific target group may need to put more emphasis on the prevention of increased sitting time. The finding that the socio-cognitive profile did not predict behaviour change may underline the proposition that decisions to initiate and maintain PA behaviour change are to a large extend non-linear events. Acknowledging the possible non-linearity of the relationship between socio-cognitive determinants and behaviour change will help our understanding of this complex and dynamic process.

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

PubMed

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 (<9 years) predicted enhanced verbal working memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not general lifestyle activities, predicted variability across both verbal and visuospatial domains in aging. These findings are suggestive of different use-dependent adaptation periods depending on cognitive domain. Furthermore, they imply that early age of musical acquisition, sustained and maintained during advanced age, may enhance cognitive functions and buffer age and education influences.

Animal cognition: an insect's sense of time?

PubMed

Skorupski, Peter; Chittka, Lars

2006-10-10

For Immanuel Kant, time was the very form of the inner sense, the bedrock of our consciousness and also the origin of arithmetic ability. New research on bumblebees has shown that even an invertebrate with a brain the size of a pinhead can actively sense the passage of elapsed time, allowing it to predict when certain salient events will occur in the future.

Optimization of cellulase-assisted extraction process and antioxidant activities of polysaccharides from Tricholoma mongolicum Imai.

PubMed

Zhao, Yong-Ming; Song, Jin-Hui; Wang, Jin; Yang, Jian-Ming; Wang, Zhi-Bao; Liu, Ying-Hui

2016-10-01

Tricholoma mongolicum Imai is a well-known edible and medicinal mushroom which in recent years has attracted increasing attention because of its bioactivities. In this study, water-soluble polysaccharides were extracted from T. mongolicum Imai by cellulase-assisted extraction and their antioxidant activities were investigated. In order to improve the yield of polysaccharides, four variables, cellulase amount (X1 ), pH (X2 ), temperature (X3 ) and extraction time (X4 ), were investigated with a Box-Behnken design. The optimal conditions were predicted to be cellulase amount of 20 g kg(-1) , pH of 4.0, temperature of 50 °C and extraction time of 127 min, with a predicted polysaccharide yield of 190.1 g kg(-1) . The actual yield of polysaccharides under these conditions was 189.6 g kg(-1) , which matched the predicted value well. The crude polysaccharides were purified to obtain four fractions, and characterization of each was carried out. In addition, antioxidant properties of four polysaccharides assessed by 1,1-diphenyl-2-picryldydrazyl (DPPH) and hydroxyl radical-scavenging assays indicated that polysaccharides from T. mongolicum Imai (TMIPs) possessed antioxidant activity in a dose-dependent manner. TMIPs show moderate antioxidant activities in vitro. Therefore it is suggested that TMIPs are potential natural antioxidants for use in functional foods. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Profiling Physical Activity, Diet, Screen and Sleep Habits in Portuguese Children

PubMed Central

Pereira, Sara; Katzmarzyk, Peter T.; Gomes, Thayse Natacha; Borges, Alessandra; Santos, Daniel; Souza, Michele; dos Santos, Fernanda K.; Chaves, Raquel N.; Champagne, Catherine M.; Barreira, Tiago V.; Maia, José A.R.

2015-01-01

Obesity in children is partly due to unhealthy lifestyle behaviours, e.g., sedentary activity and poor dietary choices. This trend has been seen globally. To determine the extent of these behaviours in a Portuguese population of children, 686 children 9.5 to 10.5 years of age were studied. Our aims were to: (1) describe profiles of children’s lifestyle behaviours; (2) identify behaviour pattern classes; and (3) estimate combined effects of individual/socio-demographic characteristics in predicting class membership. Physical activity and sleep time were estimated by 24-h accelerometry. Nutritional habits, screen time and socio-demographics were obtained. Latent Class Analysis was used to determine unhealthy lifestyle behaviours. Logistic regression analysis predicted class membership. About 78% of children had three or more unhealthy lifestyle behaviours, while 0.2% presented no risk. Two classes were identified: Class 1-Sedentary, poorer diet quality; and Class 2-Insufficiently active, better diet quality, 35% and 65% of the population, respectively. More mature children (Odds Ratio (OR) = 6.75; 95%CI = 4.74–10.41), and boys (OR = 3.06; 95% CI = 1.98–4.72) were more likely to be overweight/obese. However, those belonging to Class 2 were less likely to be overweight/obese (OR = 0.60; 95% CI = 0.43–0.84). Maternal education level and household income did not significantly predict weight status (p ≥ 0.05). PMID:26043034

Prospective validation of a novel renal activity index of lupus nephritis.

PubMed

Gulati, G; Bennett, M R; Abulaban, K; Song, H; Zhang, X; Ma, Q; Brodsky, S V; Nadasdy, T; Haffner, C; Wiley, K; Ardoin, S P; Devarajan, P; Ying, J; Rovin, B H; Brunner, H I

2017-08-01

Objectives The renal activity index for lupus (RAIL) score was developed in children with lupus nephritis as a weighted sum of six urine biomarkers (UBMs) (neutrophil gelatinase-associated lipocalin, monocyte chemotactic protein 1, ceruloplasmin, adiponectin, hemopexin and kidney injury molecule 1) measured in a random urine sample. We aimed at prospectively validating the RAIL in adults with lupus nephritis. Methods Urine from 79 adults was collected at the time of kidney biopsy to assay the RAIL UBMs. Using receiver operating characteristic curve analysis, we evaluated the accuracy of the RAIL to discriminate high lupus nephritis activity status (National Institutes of Health activity index (NIH-AI) score >10), from low/moderate lupus nephritis activity status (NIH-AI score ≤10). Results In this mixed racial cohort, high lupus nephritis activity was present in 15 patients (19%), and 71% had proliferative lupus nephritis. Use of the identical RAIL algorithm developed in children resulted in only fair prediction of lupus nephritis activity status of adults (area under the receiver operating characteristic curve (AUC) 0.62). Alternative weightings of the six RAIL UBMs as suggested by logistic regression yielded excellent accuracy to predict lupus nephritis activity status (AUC 0.88). Accuracy of the model did not improve with adjustment of the UBMs for urine creatinine or albumin, and was little influenced by concurrent kidney damage. Conclusions The RAIL UBMs provide excellent prediction of lupus nephritis activity in adults. Age adaption of the RAIL is warranted to optimize its discriminative validity to predict high lupus nephritis activity status non-invasively.

Predicting the Intention to Use Condoms and Actual Condom Use Behaviour: A Three-Wave Longitudinal Study in Ghana

PubMed Central

Teye-Kwadjo, Enoch; Kagee, Ashraf; Swart, Hermann

2017-01-01

Background Growing cross-sectional research shows that the theory of planned behaviour (TPB) is robust in predicting intentions to use condoms and condom use behaviour. Yet, little is known about the TPB’s utility in explaining intentions to use condoms and condom use behaviour over time. Methods This study used a longitudinal design and latent variable structural equation modelling to test the longitudinal relationships postulated by the TPB. School-going youths in Ghana provided data on attitudes, subjective norms, perceived control, intentions, and behaviour regarding condom use at three-time points, spaced approximately three-months apart. Results As predicted by the TPB, the results showed that attitudes were significantly positively associated with intentions to use condoms over time. Contrary to the TPB, subjective norms were not significantly associated with intentions to use condoms over time. Perceived control did not predict intentions to use condoms over time. Moreover, intentions to use condoms were not significantly associated with self-reported condom use over time. Conclusion These results suggest that school-going youths in Ghana may benefit from sex education programmes that focus on within-subject attitude formation and activation. The theoretical and methodological implications of these results are discussed. PMID:27925435

Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

PubMed Central

2012-01-01

Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.” Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection. PMID:23046104

Predicting green: really radical (plant) predictive processing

PubMed Central

Friston, Karl

2017-01-01

In this article we account for the way plants respond to salient features of their environment under the free-energy principle for biological systems. Biological self-organization amounts to the minimization of surprise over time. We posit that any self-organizing system must embody a generative model whose predictions ensure that (expected) free energy is minimized through action. Plants respond in a fast, and yet coordinated manner, to environmental contingencies. They pro-actively sample their local environment to elicit information with an adaptive value. Our main thesis is that plant behaviour takes place by way of a process (active inference) that predicts the environmental sources of sensory stimulation. This principle, we argue, endows plants with a form of perception that underwrites purposeful, anticipatory behaviour. The aim of the article is to assess the prospects of a radical predictive processing story that would follow naturally from the free-energy principle for biological systems; an approach that may ultimately bear upon our understanding of life and cognition more broadly. PMID:28637913

Toward Skillful Subseasonal Prediction of North Atlantic Hurricanes with regionally-refined GFDL HiRAM

NASA Astrophysics Data System (ADS)

Gao, K.; Harris, L.; Chen, J. H.; Lin, S. J.

2017-12-01

Skillful subseasonal prediction of hurricane activity (from two weeks to less than a season) is important for early preparedness and reducing the hurricane damage in coastal regions. In this study, we will present evaluations of the performance of GFDL HiRAM (High-Resolution Atmospheric Model) for the simulation and prediction of the North Atlantic hurricane activity on the sub-seasonal time scale. A series of sub-seasonal (30-day duration) retrospective predictions were performed over the years 2000-2014 using two configurations of HiRAM: a) global uniform 25km-resolution grid and b) two-way nested grid with a 8km-resolution nest over North Atlantic. The analysis of hurricane structure from the two sets of simulations indicates the two-way-nesting method is an efficient way to improve the representation of hurricanes in global models: the two-way nested configuration produces realistic hurricane inner-core size and structure, which leads to improved lifetime maximum intensity distribution. Both configurations show very promising performance in the subseasonal hurricane genesis prediction, but the two-way nested configuration shows better performance in the prediction of major hurricane (Categories 3-5) activity because of the improved intensity simulation. We will also present the analysis of how the phase and magnitude of MJO, as well as the initial SST anomaly affect the model's prediction skill.

Charting the Eccles' expectancy-value model from mothers' beliefs in childhood to youths' activities in adolescence.

PubMed

Simpkins, Sandra D; Fredricks, Jennifer A; Eccles, Jacquelynne S

2012-07-01

The Eccles' expectancy-value model posits that a cascade of mechanisms explain associations between parents' beliefs and youths' achievement-related behaviors. Specifically, parents' beliefs predict parents' behaviors; in turn, parents' behaviors predict youths' motivational beliefs, and youths' motivational beliefs predict their behaviors. This investigation focused on testing this model with mothers in sports, music, math, and reading over a 12-year period. Data were drawn from mother, youth, and teacher questionnaires collected as part of Childhood and Beyond Study (92% European American; N = 723). Mothers' beliefs in sports, music, and math positively predicted their behaviors in these areas 1 year later, which predicted youths' self-concepts of ability and values (i.e., their motivational beliefs) in these domains 1 year later. Adolescents' motivational beliefs predicted time spent in organized sport activities, playing music, and reading after school measured 4 years later as well as the number of math courses taken in high school. Furthermore, except in reading, mothers' behaviors mediated the relations between mothers' and youths' beliefs, and youths' beliefs mediated the relations between mothers' behaviors and youths' behaviors. Although there were mean-level differences in several indicators based on child gender, in most cases the relations among these indicators did not significantly vary by child gender. This study highlights the processes by which mothers' beliefs during their children's childhood can predict children's activities in adolescence.

Family characteristics predicting favourable changes in 10 and 11-year-old children's lifestyle-related health behaviours during an 18-month follow-up.

PubMed

Ray, Carola; Roos, Eva

2012-02-01

Lifestyle-related health behaviours such as screen time, physical activity, sleep duration, and food intake tend to change into non-favourable directions when children become young adolescents. Cross-sectional studies show that family characteristics are important determinants for children's health behaviours. This study examined whether family characteristics such as parenting practices at meals and family involvement predict a more favourable change in children's lifestyle-related health behaviours during an 18-month follow-up. 745 children in school grades 4 and 5 (response rate 65%) filled in a baseline questionnaire in the autumn of 2006. A follow-up was conducted in the spring of 2008 (91%). Several health behaviours had changed in a non-favourable direction. Baseline parenting practices at meals and family involvement predicted some of the changes in the lifestyle-related health behaviours in 2008. Parenting practices at meals predicted a smaller increase in TV, DVD viewing time, and a smaller decrease in fruit intake. Amongst family involvement determinants, less time alone at home after school predicted a smaller increase in screen time, a smaller decrease in sleep duration, and a smaller increase in soft drink intake. For conclusion several family characteristics predicted favourable changes in children's lifestyle-related health behaviours. Copyright © 2011 Elsevier Ltd. All rights reserved.

A community computational challenge to predict the activity of pairs of compounds.

PubMed

Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P; Costello, James C; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea

2014-12-01

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

On Study Habits on an Introductory Course on Programming

ERIC Educational Resources Information Center

Willman, Salla; Lindén, Rolf; Kaila, Erkki; Rajala, Teemu; Laakso, Mikko-Jussi; Salakoski, Tapio

2015-01-01

Computer aided assessment systems enable the collection of exact time and date information on students' activity on a course. These activity patterns reflect students' study habits and these study habits further predict students' likelihood to pass or fail a course. By identifying such patterns, those who design the courses can enforce positive…

Understanding Children's Reading Activities: Reading Motivation, Skill and Child Characteristics as Predictors

ERIC Educational Resources Information Center

McGeown, Sarah P.; Osborne, Cara; Warhurst, Amy; Norgate, Roger; Duncan, Lynne G.

2016-01-01

This study examined the extent to which a range of child characteristics (sex, age, socioeconomic status, reading skill and intrinsic and extrinsic reading motivation) predicted engagement (i.e., time spent) in different reading activities (fiction books, factual books, school textbooks, comics, magazines and digital texts). In total, 791 children…

The Effects of Prior Knowledge Activation on Free Recall and Study Time Allocation.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

The effects of mobilizing prior knowledge on information processing were studied. Two hypotheses, the cognitive set-point hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. These hypotheses predict different recall patterns as a result of mobilizing prior knowledge. In…

Valve movement response of the freshwater clam Corbicula fluminea following exposure to waterborne arsenic.

PubMed

Liao, Chung-Min; Jau, Sheng-Feng; Lin, Chieh-Ming; Jou, Li-John; Liu, Chen-Wuing; Liao, Vivian Hsiu-Chuan; Chang, Fi-John

2009-07-01

We developed an inductance-based valvometry technique as a detection system to measure the valve daily activity in freshwater clam Corbicula fluminea in response to waterborne arsenic. Our findings reveal that C. fluminea experiences a valve opening in the absence of arsenic predominantly in the morning hours (03:00-08:00) with a mean daily opening/closing period of 21.32 (95% CI: 20.58-22.05) h. Amplification of daily activity occurred in the presence of arsenic. Behavioral toxicity assays revealed arsenic detection thresholds of 0.60 (95% CI: 0.53-0.66) mg l(-1) and 0.35 (95% CI: 0.30-0.40) mg l(-1) for response times of 60 and 300 min, respectively. The proposed valve daily activity model was linked with response time-specific Hill dose-response functions to predict valve opening/closing behavior in response to arsenic. The predictive capabilities were verified satisfactory with the measurements. Our results implicate a biomonitoring system by valve daily activity in C. fluminea to identify safe water uses in areas with elevated arsenic.

Perceived personal importance of exercise and fears of re-injury: a longitudinal study of psychological factors related to activity after anterior cruciate ligament reconstruction.

PubMed

Gignac, Monique Am; Cao, Xingshan; Ramanathan, Subha; White, Lawrence M; Hurtig, Mark; Kunz, Monica; Marks, Paul H

2015-01-01

Psychological perceptions are increasingly being recognized as important to recovery and rehabilitation post-surgery. This research longitudinally examined perceptions of the personal importance of exercise and fears of re-injury over a three-year period post anterior cruciate ligament (ACL) reconstruction. Stability and change in psychological perceptions was examined, as well as the association of perceptions with time spent in different types of physical activity, including walking, household activities, and lower and higher risk for knee injury activities. Participants were athletes, 18-40 years old, who underwent ACL reconstruction for first-time ACL injuries. They were recruited from a tertiary care centre in Toronto, Canada. Participants completed interviewer-administered questionnaires pre-surgery and at years one, two and three, postoperatively. Questions assessed demographics, pain, functional limitations, perceived personal importance of exercise, fear of re-injury and physical activities (i.e., walking; household activities; lower risk for knee injury activities; higher risk for knee injury activities). Analyses included fixed-effect longitudinal modeling to examine the association of a fear of re-injury and perceived personal importance of exercise and changes in these perceptions with the total hours spent in the different categories of physical activities, controlling for other factors. Baseline participants were 77 men and 44 women (mean age = 27.6 years; SD = 6.2). At year three, 78.5% of participants remained in the study with complete data. Fears of re-injury decreased over time while personal importance of exercise remained relatively stable. Time spent in walking and household activities did not significantly change with ACL injury or surgery. Time spent in lower and higher risk of knee injury physical activity did not return to pre-injury levels at three years, post-surgery. Greater time spent in higher risk of knee injury activities was predicted by decreases in fears of re-injury and by greater personal importance of exercise. This study highlights not only fears of re-injury, which has been documented in previous studies, but also the perceived personal importance of exercise in predicting activity levels following ACL reconstructive surgery. The findings can help in developing interventions to aid individuals make decisions about physical activities post knee injury and surgery.

Relations Between Autonomous Motivation and Leisure-Time Physical Activity Participation: The Mediating Role of Self-Regulation Techniques.

PubMed

Nurmi, Johanna; Hagger, Martin S; Haukkala, Ari; Araújo-Soares, Vera; Hankonen, Nelli

2016-04-01

This study tested the predictive validity of a multitheory process model in which the effect of autonomous motivation from self-determination theory on physical activity participation is mediated by the adoption of self-regulatory techniques based on control theory. Finnish adolescents (N = 411, aged 17-19) completed a prospective survey including validated measures of the predictors and physical activity, at baseline and after one month (N = 177). A subsample used an accelerometer to objectively measure physical activity and further validate the physical activity self-report assessment tool (n = 44). Autonomous motivation statistically significantly predicted action planning, coping planning, and self-monitoring. Coping planning and self-monitoring mediated the effect of autonomous motivation on physical activity, although self-monitoring was the most prominent. Controlled motivation had no effect on self-regulation techniques or physical activity. Developing interventions that support autonomous motivation for physical activity may foster increased engagement in self-regulation techniques and positively affect physical activity behavior.

Experience of implementing a National pre-hospital Code Red bleeding protocol in Scotland.

PubMed

Reed, Matthew J; Glover, Alison; Byrne, Lauren; Donald, Michael; McMahon, Niall; Hughes, Neil; Littlewood, Nicola K; Garrett, Justin; Innes, Catherine; McGarvey, Margaret; Hazra, Eleanor; Rawlinson, P Sam M

2017-01-01

The Scottish Transfusion and Laboratory Support in Trauma Group (TLSTG) have introduced a unified National pre-hospital Code Red protocol. This paper reports the results of a study aiming to establish whether current pre-hospital Code Red activation criteria for trauma patients successfully predict need for in hospital transfusion or haemorrhagic death, the current admission coagulation profile and Concentrated Red Cell (CRC): Fresh Frozen Plasma (FFP) ratio being used, and whether use of the protocol leads to increased blood component discards? Prospective cohort study. Clinical and transfusion leads for each of Scotland's pre-hospital services and their receiving hospitals agreed to enter data into the study for all trauma patients for whom a pre-hospital Code Red was activated. Outcome data collected included survival 24h after Code Red activation, survival to hospital discharge, death in the Emergency Department and death in hospital. Between June 1st 2013 and October 31st 2015 there were 53 pre-hospital Code Red activations. Median Injury Severity Score (ISS) was 24 (IQR 14-37) and mortality 38%. 16 patients received pre-hospital blood. The pre-hospital Code Red protocol was sensitive for predicting transfusion or haemorrhagic death (89%). Sensitivity, specificity, positive and negative predictive values of the pre-hospital SBP <90mmHg component were 63%, 33%, 86% and 12%. 19% had an admission prothrombin time >14s and 27% had a fibrinogen <1.5g/L. CRC: FFP ratios did not drop to below 2:1 until 150min after arrival in the ED. 16 red cell units, 33 FFP and 6 platelets were discarded. This was not significantly increased compared to historical data. A National pre-hospital Code Red protocol is sensitive for predicting transfusion requirement in bleeding trauma patients and does not lead to increased blood component discards. A significant number of patients are coagulopathic and there is a need to improve CRC: FFP ratios and time to transfusion support especially FFP provision. Training clinicians to activate pre-hospital Code Red earlier during the pre-hospital phase may give blood bank more time to thaw and prepare FFP and may improve FFP administration times and ratios so long as components are used upon their availability. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of calving time in dairy cattle.

PubMed

Mahmoud, Fadul; Christopher, Bogdahn; Maher, Alsaaod; Jürg, Hüsler; Alexander, Starke; Adrian, Steiner; Gaby, Hirsbrunner

2017-12-01

This prospective study was carried out to predict the calving time in primiparous (n=11) and multiparous (n=22) Holstein-Friesian cows using the combination of data obtained from the RumiWatch noseband-sensor and 3D-accelerometer. The animals included in the study were fitted with the RumiWatch noseband-sensor and 3D-accelerometer at least 10days before the expected calving day. The calving event was defined as the time of the first appearance of the calves' feet outside the vulva, and this moment was determined by farm staff and/or confirmed by video monitor. As primiparous and multiparous cows behaved differently, two models including data of noseband-sensors and 3D-accelerometers were used to predict the calving time in each group. Lying bouts (LB) increased and rumination chews (RC) decreased similarly in both groups; besides that, boluses (B) decreased and other activities (OA) increased significantly in multiparous and primiparous cows, respectively. The sensitivity (Se) and specificity (Sp) for prediction of the onset of calving within the next 3h were determined with the logistic regression and ROC analysis (Se=88.9%, 85% and Sp=93.3%, 74% for multiparous and primiparous cows, respectively). This pilot study revealed that the RumiWatch system is a useful tool to predict calving time under farm conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Stated Uptake of Physical Activity Rewards Programmes Among Active and Insufficiently Active Full-Time Employees.

PubMed

Ozdemir, Semra; Bilger, Marcel; Finkelstein, Eric A

2017-10-01

Employers are increasingly relying on rewards programmes in an effort to promote greater levels of activity among employees; however, if enrolment in these programmes is dominated by active employees, then they are unlikely to be a good use of resources. This study uses a stated-preference survey to better understand who participates in rewards-based physical activity programmes, and to quantify stated uptake by active and insufficiently active employees. The survey was fielded to a national sample of 950 full-time employees in Singapore between 2012 and 2013. Participants were asked to choose between hypothetical rewards programmes that varied along key dimensions and whether or not they would join their preferred programme if given the opportunity. A mixed logit model was used to analyse the data and estimate predicted uptake for specific programmes. We then simulated employer payments based on predictions for the percentage of each type of employee likely to meet the activity goal. Stated uptake ranged from 31 to 67% of employees, depending on programme features. For each programme, approximately two-thirds of those likely to enrol were insufficiently active. Results showed that insufficiently active employees, who represent the majority, are attracted to rewards-based physical activity programmes, and at approximately the same rate as active employees, even when enrolment fees are required. This suggests that a programme with generous rewards and a modest enrolment fee may have strong employee support and be within the range of what employers may be willing to spend.

Monitoring Influenza Epidemics in China with Search Query from Baidu

PubMed Central

Lv, Benfu; Peng, Geng; Chunara, Rumi; Brownstein, John S.

2013-01-01

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China. PMID:23750192

Flare Prediction Using Photospheric and Coronal Image Data

NASA Astrophysics Data System (ADS)

Jonas, Eric; Bobra, Monica; Shankar, Vaishaal; Todd Hoeksema, J.; Recht, Benjamin

2018-03-01

The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of T hours, where T = 2 and 24. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision-recall curve, all of the data are helpful. Our model performance yields a TSS of 0.84 ±0.03 and 0.81 ±0.03 in the T = 2- and 24-hour cases, respectively, and a value of 0.13 ±0.07 and 0.43 ±0.08 for the area under the precision-recall curve in the T=2- and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona, and it points the way towards greater data integration across diverse sources in future work.

Model and observation comparison of the universal time and IMF by dependence of the ionospheric polar hole

NASA Technical Reports Server (NTRS)

Sojka, J. J.; Schunk, R. W.; Hoegy, W. R.; Grebowsky, J. M.

1991-01-01

The polar ionospheric F-region often exhibits regions of marked density depletion. These depletions have been observed by a variety of polar orbiting ionospheric satellites over a full range of solar cycle, season, magnetic activity, and universal time (UT). An empirical model of these observations has recently been developed to describe the polar depletion dependence on these parameters. Specifically, the dependence has been defined as a function of F10.7 (solar), summer or winter, Kp (magnetic), and UT. Polar cap depletions have also been predicted /1, 2/ and are, hence, present in physical models of the high latitude ionosphere. Using the Utah State University Time Dependent Ionospheric Model (TDIM) the predicted polar depletion characteristics are compared with those described by the above empirical model. In addition, the TDIM is used to predict the IMF By dependence of the polar hole feature.

Probing the Magnetic Causes of CMEs: Free Magnetic Energy More Important Than Either Size Or Twist

NASA Technical Reports Server (NTRS)

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

2006-01-01

To probe the magnetic causes of CMEs, we have examined three types of magnetic measures: size, twist and total nonpotentiality (or total free magnetic energy) of an active region. Total nonpotentiality is roughly the product of size times twist. For predominately bipolar active regions, we have found that total nonpotentiality measures have the strongest correlation with future CME productivity (approx. 75% prediction success rate), while size and twist measures each have a weaker correlation with future CME productivity (approx. 65% prediction success rate) (Falconer, Moore, & Gary, ApJ, 644, 2006). For multipolar active regions, we find that the CME-prediction success rates for total nonpotentiality and size are about the same as for bipolar active regions. We also find that the size measure correlation with CME productivity is nearly all due to the contribution of size to total nonpotentiality. We have a total nonpotentiality measure that can be obtained from a line-of-sight magnetogram of the active region and that is as strongly correlated with CME productivity as are any of our total-nonpotentiality measures from deprojected vector magnetograms. We plan to further expand our sample by using MDI magnetograms of each active region in our sample to determine its total nonpotentiality and size on each day that the active region was within 30 deg. of disk center. The resulting increase in sample size will improve our statistics and allow us to investigate whether the nonpotentiality threshold for CME production is nearly the same or significantly different for multipolar regions than for bipolar regions. In addition, we will investigate the time rates of change of size and total nonpotentiality as additional causes of CME productivity.

78 FR 14099 - National Cancer Institute; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-04

...; Test to Predict Effectiveness of Docetaxel Treatment for Prostate Cancer. Date: March 28, 2013. Time: 8... Person: Michael B. Small, Ph.D., Scientific Review Officer, Division of Extramural Activities, National...

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

PubMed Central

Ritchie, J. Brendan; Tovar, David A.; Carlson, Thomas A.

2015-01-01

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition. PMID:26107634

The etiology of mathematical self-evaluation and mathematics achievement: understanding the relationship using a cross-lagged twin study from age 9 to 12

PubMed Central

Luo, Yu L.L.; Kovas, Yulia; Haworth, Claire M.A.; Plomin, Robert

2011-01-01

The genetic and environmental origins of individual differences in mathematical self-evaluation over time and its association with later mathematics achievement were investigated in a UK sample of 2138 twin pairs at ages 9 and 12. Self-evaluation indexed how good children think they are at mathematical activities and how much they like those activities. Mathematics achievement was assessed by teachers based on UK National Curriculum standards. At both ages self-evaluation was approximately 40% heritable, with the rest of the variance explained by non-shared environment. The results also suggested moderate reciprocal associations between self-evaluation and mathematics achievement across time, with earlier self-evaluation predicting later performance and earlier performance predicting later self-evaluation. These cross-lagged relationships were genetically rather than environmentally mediated. PMID:22102781

Has climatic warming altered spring flowering date of Sonoran Desert shrubs?

USGS Publications Warehouse

Bowers, Janice E.

2007-01-01

With global warming, flowering at many locations has shifted toward earlier dates of bloom. A steady increase in average annual temperature since the late 1890s makes it likely that flowering also has advanced in the northern Sonoran Desert of the southwestern United States and northwestern Mexico. In this study, phenological models were used to predict annual date of spring bloom in the northern Sonoran Desert from 1894 to 2004; then, herbarium specimens were assessed for objective evidence of the predicted shift in flowering time. The phenological models were derived from known flowering requirements (triggers and heat sums) of Sonoran Desert shrubs. According to the models, flowering might have advanced by 20-41 d from 1894 to 2004. Analysis of herbarium specimens collected during the 20th century supported the model predictions. Over time, there was a significant increase in the proportion of shrub specimens collected in flower in March and a significant decrease in the proportion collected in May. Thus, the flowering curve - the proportion of individuals in flower in each spring month - shifted toward the start of the calendar year between 1900 and 1999. This shift could not be explained by collection activity: collectors showed no tendency to be active earlier in the year as time went on, nor did activity toward the end of spring decline in recent decades. Earlier bloom eventually could have substantial impacts on plant and animal communities in the Sonoran Desert, especially on migratory hummingbirds and population dynamics of shrubs.

Features in visual search combine linearly

PubMed Central

Pramod, R. T.; Arun, S. P.

2014-01-01

Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features (intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and co-activation models (based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features—in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search. PMID:24715328

Pubertal timing and sexual risk behaviors among rural African American male youth: testing a model based on life history theory.

PubMed

Kogan, Steven M; Cho, Junhan; Simons, Leslie Gordon; Allen, Kimberly A; Beach, Steven R H; Simons, Ronald L; Gibbons, Frederick X

2015-04-01

Life History Theory (LHT), a branch of evolutionary biology, describes how organisms maximize their reproductive success in response to environmental conditions. This theory suggests that challenging environmental conditions will lead to early pubertal maturation, which in turn predicts heightened risky sexual behavior. Although largely confirmed among female adolescents, results with male youth are inconsistent. We tested a set of predictions based on LHT with a sample of 375 African American male youth assessed three times from age 11 to age 16. Harsh, unpredictable community environments and harsh, inconsistent, or unregulated parenting at age 11 were hypothesized to predict pubertal maturation at age 13; pubertal maturation was hypothesized to forecast risky sexual behavior, including early onset of intercourse, substance use during sexual activity, and lifetime numbers of sexual partners. Results were consistent with our hypotheses. Among African American male youth, community environments were a modest but significant predictor of pubertal timing. Among those youth with high negative emotionality, both parenting and community factors predicted pubertal timing. Pubertal timing at age 13 forecast risky sexual behavior at age 16. Results of analyses conducted to determine whether environmental effects on sexual risk behavior were mediated by pubertal timing were not significant. This suggests that, although evolutionary mechanisms may affect pubertal development via contextual influences for sensitive youth, the factors that predict sexual risk behavior depend less on pubertal maturation than LHT suggests.

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

PubMed

Akseli, Ilgaz; Xie, Jingjin; Schultz, Leon; Ladyzhynsky, Nadia; Bramante, Tommasina; He, Xiaorong; Deanne, Rich; Horspool, Keith R; Schwabe, Robert

2017-01-01

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams

USGS Publications Warehouse

Balistrieri, Laurie S.; Nimick, David A.; Mebane, Christopher A.

2012-01-01

Evaluating water quality and the health of aquatic organisms is challenging in systems with systematic diel (24 hour) or less predictable runoff-induced changes in water composition. To advance our understanding of how to evaluate environmental health in these dynamic systems, field studies of diel cycling were conducted in two streams (Silver Bow Creek and High Ore Creek) affected by historical mining activities in southwestern Montana. A combination of sampling and modeling tools were used to assess the toxicity of metals in these systems. Diffusive Gradients in Thin Films (DGT) samplers were deployed at multiple time intervals during diel sampling to confirm that DGT integrates time-varying concentrations of dissolved metals. Thermodynamic speciation calculations using site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, and a competitive, multiple-metal biotic ligand model incorporated into the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the chemical speciation of dissolved metals and biotic ligands. The model results were combined with previously collected toxicity data on cutthroat trout to derive a relationship that predicts the relative survivability of these fish at a given site. This integrative approach may prove useful for assessing water quality and toxicity of metals to aquatic organisms in dynamic systems and evaluating whether potential changes in environmental health of aquatic systems are due to anthropogenic activities or natural variability.

Sustained anterior cingulate cortex activation during reward processing predicts response to psychotherapy in major depressive disorder.

PubMed

Carl, Hannah; Walsh, Erin; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Dichter, Gabriel S; Smoski, Moria J

2016-10-01

The purpose of the present investigation was to evaluate whether pre-treatment neural activation in response to rewards is a predictor of clinical response to Behavioral Activation Therapy for Depression (BATD), an empirically validated psychotherapy that decreases depressive symptoms by increasing engagement with rewarding stimuli and reducing avoidance behaviors. Participants were 33 outpatients with major depressive disorder (MDD) and 20 matched controls. We examined group differences in activation, and the capacity to sustain activation, across task runs using functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task. Hierarchical linear modeling was used to investigate whether pre-treatment neural responses predicted change in depressive symptoms over the course of BATD treatment. MDD and Control groups differed in sustained activation during reward outcomes in the right nucleus accumbens, such that the MDD group experienced a significant decrease in activation in this region from the first to second task run relative to controls. Pretreatment anhedonia severity and pretreatment task-related reaction times were predictive of response to treatment. Furthermore, sustained activation in the anterior cingulate cortex during reward outcomes predicted response to psychotherapy; patients with greater sustained activation in this region were more responsive to BATD treatment. The current study only included a single treatment condition, thus it unknown whether these predictors of treatment response are specific to BATD or psychotherapy in general. Findings add to the growing body of literature suggesting that the capacity to sustain neural responses to rewards may be a critical endophenotype of MDD. Copyright © 2016 Elsevier B.V. All rights reserved.

The Recalibrated Sunspot Number: Impact on Solar Cycle Predictions

NASA Astrophysics Data System (ADS)

Clette, F.; Lefevre, L.

2017-12-01

Recently and for the first time since their creation, the sunspot number and group number series were entirely revisited and a first fully recalibrated version was officially released in July 2015 by the World Data Center SILSO (Brussels). Those reference long-term series are widely used as input data or as a calibration reference by various solar cycle prediction methods. Therefore, past predictions may now need to be redone using the new sunspot series, and methods already used for predicting cycle 24 will require adaptations before attempting predictions of the next cycles.In order to clarify the nature of the applied changes, we describe the different corrections applied to the sunspot and group number series, which affect extended time periods and can reach up to 40%. While some changes simply involve constant scale factors, other corrections vary with time or follow the solar cycle modulation. Depending on the prediction method and on the selected time interval, this can lead to different responses and biases. Moreover, together with the new series, standard error estimates are also progressively added to the new sunspot numbers, which may help deriving more accurate uncertainties for predicted activity indices. We conclude on the new round of recalibration that is now undertaken in the framework of a broad multi-team collaboration articulated around upcoming ISSI workshops. We outline the future corrections that can still be expected in the future, as part of a permanent upgrading process and quality control. From now on, future sunspot-based predictive models should thus be made more adaptable, and regular updates of predictions should become common practice in order to track periodic upgrades of the sunspot number series, just like it is done when using other modern solar observational series.

Some Issues Related to Integrating Active Flow Control With Flight Control

NASA Technical Reports Server (NTRS)

Williams, David; Colonius, Tim; Tadmor, Gilead; Rowley, Clancy

2010-01-01

Time varying control of CL is necessary for integrating AFC and Flight Control (Biasing allows for +/- changes in lift) Time delays associated with actuation are long (APPROX.5.8 c/U) and must be included in controllers. Convolution of input signal with single pulse kernel gives reasonable prediction of lift response.

Looking Back to Move Ahead: How Students Learn Geologic Time by Predicting Future Environmental Impacts

ERIC Educational Resources Information Center

Zhu, Chen; Rehrey, George; Treadwell, Brooke; Johnson, Claudia C.

2012-01-01

This Scholarship of Teaching and Learning project discusses the effectiveness of using distance metaphor-building activities along with a case study exam to help undergraduate nonscience majors understand and apply geologic time. Using action research, we describe how a scholarly teacher integrated previously published and often-used teaching…

Strategic Control over Extent and Timing of Distractor-Based Response Activation

ERIC Educational Resources Information Center

Jost, Kerstin; Wendt, Mike; Luna-Rodriguez, Aquiles; Löw, Andreas; Jacobsen, Thomas

2017-01-01

In choice reaction time (RT) tasks, performance is often influenced by the presence of nominally irrelevant stimuli, referred to as distractors. Recent research provided evidence that distractor processing can be adjusted to the utility of the distractors: Distractors predictive of the upcoming target/response were more attended to and also…

Dissociation between Neural Signatures of Stimulus and Choice in Population Activity of Human V1 during Perceptual Decision-Making

PubMed Central

Choe, Kyoung Whan; Blake, Randolph

2014-01-01

Primary visual cortex (V1) forms the initial cortical representation of objects and events in our visual environment, and it distributes information about that representation to higher cortical areas within the visual hierarchy. Decades of work have established tight linkages between neural activity occurring in V1 and features comprising the retinal image, but it remains debatable how that activity relates to perceptual decisions. An actively debated question is the extent to which V1 responses determine, on a trial-by-trial basis, perceptual choices made by observers. By inspecting the population activity of V1 from human observers engaged in a difficult visual discrimination task, we tested one essential prediction of the deterministic view: choice-related activity, if it exists in V1, and stimulus-related activity should occur in the same neural ensemble of neurons at the same time. Our findings do not support this prediction: while cortical activity signifying the variability in choice behavior was indeed found in V1, that activity was dissociated from activity representing stimulus differences relevant to the task, being advanced in time and carried by a different neural ensemble. The spatiotemporal dynamics of population responses suggest that short-term priors, perhaps formed in higher cortical areas involved in perceptual inference, act to modulate V1 activity prior to stimulus onset without modifying subsequent activity that actually represents stimulus features within V1. PMID:24523561

Multiplexing of Motor Information in the Discharge of a Collision Detecting Neuron during Escape Behaviors

PubMed Central

Fotowat, Haleh; Harrison, Reid R; Gabbiani, Fabrizio

2010-01-01

Locusts possess an identified neuron, the descending contralateral movement detector (DCMD), conveying visual information about impending collision from the brain to thoracic motor centers. We built a telemetry system to simultaneously record, in freely behaving animals, the activity of the DCMD and of motoneurons involved in jump execution. Co-contraction of antagonistic leg muscles, a required preparatory phase, was triggered after the DCMD firing rate crossed a threshold. Thereafter, the number of DCMD spikes predicted precisely motoneuron activity and jump occurrence. Additionally, the time of DCMD peak firing rate predicted that of jump. Ablation experiments suggest that the DCMD, together with a nearly identical ipsilateral descending neuron, is responsible for the timely execution of the escape. Thus, three distinct features that are multiplexed in a single neuron’s sensory response to impending collision – firing rate threshold, peak firing time, and spike count – likely control three distinct motor aspects of escape behaviors. PMID:21220105

Visually cued motor synchronization: modulation of fMRI activation patterns by baseline condition.

PubMed

Cerasa, Antonio; Hagberg, Gisela E; Bianciardi, Marta; Sabatini, Umberto

2005-01-03

A well-known issue in functional neuroimaging studies, regarding motor synchronization, is to design suitable control tasks able to discriminate between the brain structures involved in primary time-keeper functions and those related to other processes such as attentional effort. The aim of this work was to investigate how the predictability of stimulus onsets in the baseline condition modulates the activity in brain structures related to processes involved in time-keeper functions during the performance of a visually cued motor synchronization task (VM). The rational behind this choice derives from the notion that using different stimulus predictability can vary the subject's attention and the consequently neural activity. For this purpose, baseline levels of BOLD activity were obtained from 12 subjects during a conventional-baseline condition: maintained fixation of the visual rhythmic stimuli presented in the VM task, and a random-baseline condition: maintained fixation of visual stimuli occurring randomly. fMRI analysis demonstrated that while brain areas with a documented role in basic time processing are detected independent of the baseline condition (right cerebellum, bilateral putamen, left thalamus, left superior temporal gyrus, left sensorimotor cortex, left dorsal premotor cortex and supplementary motor area), the ventral premotor cortex, caudate nucleus, insula and inferior frontal gyrus exhibited a baseline-dependent activation. We conclude that maintained fixation of unpredictable visual stimuli can be employed in order to reduce or eliminate neural activity related to attentional components present in the synchronization task.

The Associations of Youth Physical Activity and Screen Time with Fatness and Fitness: The 2012 NHANES National Youth Fitness Survey

PubMed Central

Laurson, Kelly R.; Kim, Youngwon; Saint-Maurice, Pedro F.; Welk, Gregory J.

2016-01-01

The purpose of the study is to examine the associations of youth physical activity and screen time with weight status and cardiorespiratory fitness in children and adolescents, separately, utilizing a nationally representative sample. A total of 1,113 participants (692 children aged 6–11 yrs; 422 adolescents aged 12–15 yrs) from the 2012 NHANES National Youth Fitness Survey. Participants completed physical activity and screen time questionnaires, and their body mass index and cardiorespiratory fitness (adolescents only) were assessed. Adolescents completed additional physical activity questions to estimate daily MET minutes. Children not meeting the screen time guideline had 1.69 times the odds of being overweight/obese compared to those meeting the screen time guideline, after adjusting for physical activity and other control variables. Among adolescent, screen time was significantly associated with being overweight/obese (odds ratio = 1.82, 95% confidence interval: 1.06–3.15), but the association attenuated toward the borderline of being significant after controlling for physical activity. Being physically active was positively associated with cardiorespiratory fitness, independent of screen time among adolescents. In joint association analysis, children who did not meet physical activity nor screen time guidelines had 2.52 times higher odds of being overweight/obese than children who met both guidelines. Adolescents who did not meet the screen time guideline had significantly higher odds ratio of being overweight/obese regardless of meeting the physical activity guideline. Meeting the physical activity guideline was also associated with cardiorespiratory fitness regardless of meeting the screen time guideline in adolescents. Screen time is a stronger factor than physical activity in predicting weight status in both children and adolescents, and only physical activity is strongly associated with cardiorespiratory fitness in adolescents. PMID:26820144

Determinants of leisure-time physical activity and future intention to practice in Spanish college students.

PubMed

Molina-García, Javier; Castillo, Isabel; Pablos, Carlos

2009-05-01

Few studies analyze determinants and patterns of physical activity among college students, so it has not been possible to carry out effective interventions to promote this practice. The aim of this study was to analyze the associations between some personal, social, and environmental determinants, practice of physical activity and future intention to practice in a sample of 639 university students (321 men and 318 women), mean age 21.43 years (+/- 2.78). Physical fitness self-perception, physical activity history, and coach's support to practice physical activity have a direct effect on the practice of physical activity and an indirect effect on future intention to practice, both in men and women. The practice of physical activity has also a direct effect on future intention to practice. Likewise, the participation in sport competitions predicts practice of physical activity and future intention in men, whereas being a member of a sports club predicts practice and future intention in women.

Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder.

PubMed

Shamshirband, Shahaboddin; Banjanovic-Mehmedovic, Lejla; Bosankic, Ivan; Kasapovic, Suad; Abdul Wahab, Ainuddin Wahid Bin

2016-01-01

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

Wang, Cheng; Xin, Shaoming; Liu, Xiaolu; Shi, Chuang; Fan, Lei

2018-02-01

In this contribution, an adaptive autoregressive model is proposed and developed to predict global ionospheric vertical total electron content maps (VTEC). Specifically, the spherical harmonic (SH) coefficients are predicted based on the autoregressive model, and the order of the autoregressive model is determined adaptively using the F-test method. To test our method, final CODE and IGS global ionospheric map (GIM) products, as well as altimeter TEC data during low and mid-to-high solar activity period collected by JASON, are used to evaluate the precision of our forecasting products. Results indicate that the predicted products derived from the model proposed in this paper have good consistency with the final GIMs in low solar activity, where the annual mean of the root-mean-square value is approximately 1.5 TECU. However, the performance of predicted vertical TEC in periods of mid-to-high solar activity has less accuracy than that during low solar activity periods, especially in the equatorial ionization anomaly region and the Southern Hemisphere. Additionally, in comparison with forecasting products, the final IGS GIMs have the best consistency with altimeter TEC data. Future work is needed to investigate the performance of forecasting products using the proposed method in an operational environment, rather than using the SH coefficients from the final CODE products, to understand the real-time applicability of the method.

PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION

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

Boucheron, Laura E.; Al-Ghraibah, Amani; McAteer, R. T. James

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite (GOES) class. When we additionally consider non-flaring regions, we find an increased average error of approximately three-fourths a GOES class. We also consider thresholding the regressed flare size for the experimentmore » containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.« less

Aqueous Binary Lanthanide(III) Nitrate Ln(NO3)3 Electrolytes Revisited: Extended Pitzer and Bromley Treatments

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

Chatterjee, Sayandev; Campbell, Emily L.; Neiner, Doinita

To date, only limited thermodynamic models describing activity coefficients of the aqueous solutions of lanthanide ions are available. This work expands the existing experimental osmotic coefficient data obtained by classical isopiestic technique for the aqueous binary trivalent lanthanide nitrate Ln(NO3)3 solutions using a combination of water activity and vapor pressure osmometry measurements. The combined osmotic coefficient database for each aqueous lanthanide nitrate at 25°C, consisting of literature available data as well as data obtained in this work, was used to test the validity of Pitzer and Bromley thermodynamic models for the accurate prediction of mean molal activity coefficients of themore » Ln(NO3)3 solutions in wide concentration ranges. The new and improved Pitzer and Bromley parameters were calculated. It was established that the Ln(NO3)3 activity coefficients in the solutions with ionic strength up to 12 mol kg-1 can be estimated by both Pitzer and single-parameter Bromley models, even though the latter provides for more accurate prediction, particularly in the lower ionic strength regime (up to 6 mol kg-1). On the other hand for the concentrated solutions, the extended three-parameter Bromley model can be employed to predict the Ln(NO3)3 activity coefficients with remarkable accuracy. The accuracy of the extended Bromley model in predicting the activity coefficients was greater than ~95% and ~90% for all solutions with the ionic strength up to 12 mol kg-1 and and 20 mol kg-1, respectively. This is the first time that the activity coefficients for concentrated lanthanide solutions have been predicted with such a remarkable accuracy.« less

Pseudochemotaxis in inhomogeneous active Brownian systems

NASA Astrophysics Data System (ADS)

Vuijk, Hidde D.; Sharma, Abhinav; Mondal, Debasish; Sommer, Jens-Uwe; Merlitz, Holger

2018-04-01

We study dynamical properties of confined, self-propelled Brownian particles in an inhomogeneous activity profile. Using Brownian dynamics simulations, we calculate the probability to reach a fixed target and the mean first passage time to the target of an active particle. We show that both these quantities are strongly influenced by the inhomogeneous activity. When the activity is distributed such that high-activity zone is located between the target and the starting location, the target finding probability is increased and the passage time is decreased in comparison to a uniformly active system. Moreover, for a continuously distributed profile, the activity gradient results in a drift of active particle up the gradient bearing resemblance to chemotaxis. Integrating out the orientational degrees of freedom, we derive an approximate Fokker-Planck equation and show that the theoretical predictions are in very good agreement with the Brownian dynamics simulations.

Presynaptic ionotropic receptors controlling and modulating the rules for spike timing-dependent plasticity.

PubMed

Verhoog, Matthijs B; Mansvelder, Huibert D

2011-01-01

Throughout life, activity-dependent changes in neuronal connection strength enable the brain to refine neural circuits and learn based on experience. In line with predictions made by Hebb, synapse strength can be modified depending on the millisecond timing of action potential firing (STDP). The sign of synaptic plasticity depends on the spike order of presynaptic and postsynaptic neurons. Ionotropic neurotransmitter receptors, such as NMDA receptors and nicotinic acetylcholine receptors, are intimately involved in setting the rules for synaptic strengthening and weakening. In addition, timing rules for STDP within synapses are not fixed. They can be altered by activation of ionotropic receptors located at, or close to, synapses. Here, we will highlight studies that uncovered how network actions control and modulate timing rules for STDP by activating presynaptic ionotropic receptors. Furthermore, we will discuss how interaction between different types of ionotropic receptors may create "timing" windows during which particular timing rules lead to synaptic changes.

Do unfavourable alcohol, smoking, nutrition and physical activity predict sustained leisure time sedentary behaviour? A population-based cohort study.

PubMed

Nooijen, Carla F J; Möller, Jette; Forsell, Yvonne; Ekblom, Maria; Galanti, Maria R; Engström, Karin

2017-08-01

Comparing lifestyle of people remaining sedentary during longer periods of their life with those favourably changing their behaviour can provide cues to optimize interventions targeting sedentary behaviour. The objective of this study was to determine lifestyle predictors of sustained leisure time sedentary behaviour and assess whether these predictors were dependent on gender, age, socioeconomic position and occupational sedentary behaviour. Data from a large longitudinal population-based cohort of adults (aged 18-97years) in Stockholm responding to public health surveys in 2010 and 2014 were analysed (n=49,133). Leisure time sedentary behaviour was defined as >3h per day of leisure sitting time e.g. watching TV, reading or using tablet. Individuals classified as sedentary at baseline (n=9562) were subsequently categorized as remaining sedentary (n=6357) or reduced sedentary behaviour (n=3205) at follow-up. Lifestyle predictors were unfavourable alcohol consumption, smoking, nutrition, and physical activity. Odds ratios (OR) and corresponding 95% Confidence Intervals (CI) were calculated, adjusting for potential confounders. Unfavourable alcohol consumption (OR=1.22, CI:1.11-1.34), unfavourable candy- or cake consumption (OR=1.15, CI:1.05-1.25), and unfavourable physical activity in different contexts were found to predict sustained sedentary behaviour, with negligible differences according to gender, age, socioeconomic position and occupational sedentary behaviour. People with unfavourable lifestyle profiles regarding alcohol, sweets, or physical activity are more likely to remain sedentary compared to sedentary persons with healthier lifestyle. The impact of combining interventions to reduce leisure time sedentary behaviour with reducing alcohol drinking, sweet consumption and increasing physical activity should be tested as a promising strategy for behavioural modification. Copyright © 2017 Elsevier Inc. All rights reserved.

Variations in and predictors of the occurrence of depressive symptoms and mood symptoms in multiple sclerosis: a longitudinal two-year study.

PubMed

Johansson, Sverker; Gottberg, Kristina; Kierkegaard, Marie; Ytterberg, Charlotte

2016-03-05

There is limited knowledge regarding how depressive symptoms and a cluster of specific mood symptoms in people with multiple sclerosis (MS) vary over time and how they are influenced by contributing factors. Therefore, the aims of this study were a) to describe variations over 2 years in the occurrence of depressive symptoms and mood symptoms in a sample of people with MS, and b) to investigate the predictive value of sex, age, coping capacity, work status, disease severity, disease course, fatigue, cognition, frequency of social/lifestyle activities, and perceived impact of MS on health, on the occurrence of depressive symptoms and mood symptoms. Through using a protocol of measures of functioning and perceived impact of MS on health, comprising of the Beck Depression Inventory, 219 people with MS were assessed at 0, 12 and 24 months. Predictive values were explored with Generalised Estimating Equations. Proportions with depressive symptoms varied significantly (p < 0.001) from 21 to 30% between the three time points. Proportions with mood symptoms varied significantly (p < 0.001) from 14 to 17% between the three time points. Weak coping capacity and reduced frequency of social/lifestyle activities predicted the occurrence of depressive symptoms and mood symptoms, as did the psychological impact of MS on health in interaction with time. For people with MS of working age, not working predicted the occurrence of depressive symptoms and mood symptoms, as did the physical impact of MS on health on the occurrence of mood symptoms. The occurrence of depressive symptoms and mood symptoms in people with MS vary over a 2-year time period; almost half have depressive symptoms at least once. Health care services should develop strategies aimed at identifying people with MS who are depressed or who develop depressive symptoms. Interventions for alleviating depressive symptoms should consider the individual's coping capacity and perceived impact of MS on health, and facilitate their ability to maintain participation in valued everyday activities.

Prediction Methods in Solar Sunspots Cycles

PubMed Central

Ng, Kim Kwee

2016-01-01

An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269

Constraining slip rates and spacings for active normal faults

NASA Astrophysics Data System (ADS)

Cowie, Patience A.; Roberts, Gerald P.

2001-12-01

Numerous observations of extensional provinces indicate that neighbouring faults commonly slip at different rates and, moreover, may be active over different time intervals. These published observations include variations in slip rate measured along-strike of a fault array or fault zone, as well as significant across-strike differences in the timing and rates of movement on faults that have a similar orientation with respect to the regional stress field. Here we review published examples from the western USA, the North Sea, and central Greece, and present new data from the Italian Apennines that support the idea that such variations are systematic and thus to some extent predictable. The basis for the prediction is that: (1) the way in which a fault grows is fundamentally controlled by the ratio of maximum displacement to length, and (2) the regional strain rate must remain approximately constant through time. We show how data on fault lengths and displacements can be used to model the observed patterns of long-term slip rate where measured values are sparse. Specifically, we estimate the magnitude of spatial variation in slip rate along-strike and relate it to the across-strike spacing between active faults.

Does patient time spent viewing computer-tailored colorectal cancer screening materials predict patient-reported discussion of screening with providers?

PubMed

Sanders, Mechelle; Fiscella, Kevin; Veazie, Peter; Dolan, James G; Jerant, Anthony

2016-08-01

The main aim is to examine whether patients' viewing time on information about colorectal cancer (CRC) screening before a primary care physician (PCP) visit is associated with discussion of screening options during the visit. We analyzed data from a multi-center randomized controlled trial of a tailored interactive multimedia computer program (IMCP) to activate patients to undergo CRC screening, deployed in primary care offices immediately before a visit. We employed usage time information stored in the IMCP to examine the association of patient time spent using the program with patient-reported discussion of screening during the visit, adjusting for previous CRC screening recommendation and reading speed.On average, patients spent 33 minutes on the program. In adjusted analyses, 30 minutes spent using the program was associated with a 41% increase in the odds of the patient having a discussion with their PCP (1.04, 1.59, 95% CI). In a separate analysis of the tailoring modules; the modules encouraging adherence to the tailored screening recommendation and discussion with the patient's PCP yielded significant results. Other predictors of screening discussion included better self-reported physical health and increased patient activation. Time spent on the program predicted greater patient-physician discussion of screening during a linked visit.Usage time information gathered automatically by IMCPs offers promise for objectively assessing patient engagement around a topic and predicting likelihood of discussion between patients and their clinician. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Cycle-time determination and process control of sequencing batch membrane bioreactors.

PubMed

Krampe, J

2013-01-01

In this paper a method to determine the cycle time for sequencing batch membrane bioreactors (SBMBRs) is introduced. One of the advantages of SBMBRs is the simplicity of adapting them to varying wastewater composition. The benefit of this flexibility can only be fully utilised if the cycle times are optimised for the specific inlet load conditions. This requires either proactive and ongoing operator adjustment or active predictive instrument-based control. Determination of the cycle times for conventional sequencing batch reactor (SBR) plants is usually based on experience. Due to the higher mixed liquor suspended solids concentrations in SBMBRs and the limited experience with their application, a new approach to calculate the cycle time had to be developed. Based on results from a semi-technical pilot plant, the paper presents an approach for calculating the cycle time in relation to the influent concentration according to the Activated Sludge Model No. 1 and the German HSG (Hochschulgruppe) Approach. The approach presented in this paper considers the increased solid contents in the reactor and the resultant shortened reaction times. This allows for an exact calculation of the nitrification and denitrification cycles with a tolerance of only a few minutes. Ultimately the same approach can be used for a predictive control strategy and for conventional SBR plants.

A real-time architecture for time-aware agents.

PubMed

Prouskas, Konstantinos-Vassileios; Pitt, Jeremy V

2004-06-01

This paper describes the specification and implementation of a new three-layer time-aware agent architecture. This architecture is designed for applications and environments where societies of humans and agents play equally active roles, but interact and operate in completely different time frames. The architecture consists of three layers: the April real-time run-time (ART) layer, the time aware layer (TAL), and the application agents layer (AAL). The ART layer forms the underlying real-time agent platform. An original online, real-time, dynamic priority-based scheduling algorithm is described for scheduling the computation time of agent processes, and it is shown that the algorithm's O(n) complexity and scalable performance are sufficient for application in real-time domains. The TAL layer forms an abstraction layer through which human and agent interactions are temporally unified, that is, handled in a common way irrespective of their temporal representation and scale. A novel O(n2) interaction scheduling algorithm is described for predicting and guaranteeing interactions' initiation and completion times. The time-aware predicting component of a workflow management system is also presented as an instance of the AAL layer. The described time-aware architecture addresses two key challenges in enabling agents to be effectively configured and applied in environments where humans and agents play equally active roles. It provides flexibility and adaptability in its real-time mechanisms while placing them under direct agent control, and it temporally unifies human and agent interactions.

Environment-dependence of behavioural consistency in adult male European green lizards (Lacerta viridis).

PubMed

Horváth, Gergely; Mészáros, Boglárka; Urszán, Tamás János; Bajer, Katalin; Molnár, Orsolya; Garamszegi, László Zsolt; Herczeg, Gábor

2017-01-01

Understanding the background mechanisms affecting the emergence and maintenance of consistent between-individual variation within population in single (animal personality) or across multiple (behavioural syndrome) behaviours has key importance. State-dependence theory suggests that behaviour is 'anchored' to individual state (e.g. body condition, gender, age) and behavioural consistency emerges through behavioural-state feedbacks. A number of relevant state variables are labile (e.g. body condition, physiological performance) and expected to be affected by short-term environmental change. Yet, whether short-term environmental shifts affect behavioural consistency during adulthood remains questionable. Here, by employing a full-factorial laboratory experiment, we explored if quantity of food (low vs. high) and time available for thermoregulation (3h vs. 10h per day) had an effect on activity and risk-taking of reproductive adult male European green lizards (Lacerta viridis). We focussed on different components of behavioural variation: (i) strength of behavioural consistency (repeatability for animal personality; between-individual correlation for behavioural syndrome), (ii) behavioural type (individual mean behaviour) and (iii) behavioural predictability (within-individual behavioural variation). Activity was repeatable in all treatments. Risk-taking was repeatable only in the low basking treatments. We found significant between-individual correlation only in the low food × long basking time group. The treatments did not affect behavioural type, but affected behavioural predictability. Activity predictability was higher in the short basking treatment, where it also decreased with size (≈ age). Risk-taking predictability in the short basking treatment increased with size under food limitation, but decreased when food supply was high. We conclude that short-term environmental change can alter various components of behavioural consistency. The effect could be detected in the presence/absence patterns of animal personality and behavioural syndrome and the level of individual behavioural predictability, but not in behavioural type.

Latino children's body mass index at 2-3.5 years predicts sympathetic nervous system activity at 5 years.

PubMed

Alkon, Abbey; Harley, Kim G; Neilands, Torsten B; Tambellini, Katelyn; Lustig, Robert H; Boyce, W Thomas; Eskenazi, Brenda

2014-06-01

To understand whether the relationship between young children's autonomic nervous system (ANS) responses predicted their BMI, or vice versa, the association between standardized BMI (zBMI) at 2, 3.5, and 5 years of age and ANS reactivity at 3.5-5 years of age, and whether zBMI predicts later ANS reactivity or whether early ANS reactivity predicts later zBMI, was studied. Low-income, primarily Latino children (n=112) were part of a larger cohort study of mothers recruited during early pregnancy. Study measures included maternal prenatal weight, children's health behaviors (i.e., time watching television, fast food consumption, and time playing outdoors), children's height and weight at 2, 3.5, and 5 years, and children's ANS reactivity at 3.5 and 5 years. ANS measures of sympathetic nervous system (i.e., pre-ejection period) and parasympathetic nervous system (i.e., respiratory sinus arrhythmia) activity were monitored during rest and four challenges. Reactivity was calculated as the difference between mean challenge response and rest. Structural equation models analyzed the relationship between children's zBMI at 2, 3.5, and 5 years and ANS reactivity at 3.5 and 5 years, adjusting for mother's BMI, children's behaviors, and changes in height. There was no association between zBMI and ANS cross-sectionally. Children with high zBMI at 2 or 3.5 years or large zBMI increases from 2 to 3.5 years of age had decreased sympathetic activity at 5 years. Neither sympathetic nor parasympathetic reactivity at 3.5 years predicted later zBMI. Increased zBMI early in childhood may dampen young children's SNS responses later in life.

Reinforcing value of smoking relative to physical activity and the effects of physical activity on smoking abstinence symptoms among young adults.

PubMed

Audrain-McGovern, Janet; Strasser, Andrew A; Ashare, Rebecca; Wileyto, E Paul

2015-12-01

This study sought to evaluate whether individual differences in the reinforcing value of smoking relative to physical activity (RRVS) moderated the effects of physical activity on smoking abstinence symptoms in young adult smokers. The repeated-measures within-subjects design included daily smokers (N = 79) 18-26 years old. RRVS was measured with a validated behavioral choice task. On 2 subsequent visits, participants completed self-report measures of craving, withdrawal, mood, and affective valence before and after they engaged in passive sitting or a bout of physical activity. RRVS did not moderate any effects of physical activity (ps > .05). Physical activity compared with passive sitting predicted decreased withdrawal symptoms, β = -5.23, 95% confidence interval (CI) [-6.93, -3.52] (p < .001), negative mood, β = -2.92, 95% CI [-4.13, -1.72] (p < .001), and urge to smoke. β = -7.13, 95% CI [-9.39, -4.86] (p < .001). Also, physical activity compared with passive sitting predicted increased positive affect, β = 3.08, 95% CI [1.87, 4.28] (p < .001) and pleasurable feelings, β = 1.07, 95% CI [0.58, 1.55] (p < .001), and greater time to first cigarette during the ad libitum smoking period, β = 211.76, 95% CI [32.54, 390.98] (p = .02). RRVS predicted higher levels of pleasurable feelings, β = 0.22, 95% CI [0.01, 0.43] (p = .045), increased odds of smoking versus remaining abstinent during the ad libitum smoking period, β = 0.04, 95% CI [0.01, 0.08] (p = .02), and reduced time to first cigarette, β = -163.00, 95% CI [-323.50, -2.49] (p = .047). Regardless of the RRVS, physical activity produced effects that may aid smoking cessation in young adult smokers. However, young adult smokers who have a higher RRVS will be less likely to choose to engage physical activity, especially when smoking is an alternative. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems.

PubMed

Zhang, Yingying; Wang, Juncheng; Vorontsov, A M; Hou, Guangli; Nikanorova, M N; Wang, Hongliang

2014-01-01

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.

Optimization of process variables by response surface methodology for malachite green dye removal using lime peel activated carbon

NASA Astrophysics Data System (ADS)

Ahmad, Mohd Azmier; Afandi, Nur Syahidah; Bello, Olugbenga Solomon

2017-05-01

This study investigates the adsorptive removal of malachite green (MG) dye from aqueous solutions using chemically modified lime-peel-based activated carbon (LPAC). The adsorbent prepared was characterized using FTIR, SEM, Proximate analysis and BET techniques, respectively. Central composite design (CCD) in response surface methodology (RSM) was used to optimize the adsorption process. The effects of three variables: activation temperature, activation time and chemical impregnation ratio (IR) using KOH and their effects on percentage of dye removal and LPAC yield were investigated. Based on CCD design, quadratic models and two factor interactions (2FI) were developed correlating the adsorption variables to the two responses. Analysis of variance (ANOVA) was used to judge the adequacy of the model. The optimum conditions of MG dye removal using LPAC are: activation temperature (796 °C), activation time (1.0 h) and impregnation ratio (2.6), respectively. The percentage of MG dye removal obtained was 94.68 % resulting in 17.88 % LPAC yield. The percentage of error between predicted and experimental results for the removal of MG dye is 0.4 %. Model prediction was in good agreement with experimental results and LPAC was found to be effective in removing MG dye from aqueous solution.

Predictive value of early magnetic resonance imaging measures is differentially affected by the dose of interferon beta-1a given subcutaneously three times a week: an exploratory analysis of the PRISMS study.

PubMed

Traboulsee, Anthony; Li, David K B; Cascione, Mark; Fang, Juanzhi; Dangond, Fernando; Miller, Aaron

2018-05-11

On-treatment magnetic resonance imaging lesions may predict long-term clinical outcomes in patients receiving interferon β-1a. This study aimed to assess the effect of active T2 and T1 gadolinium-enhancing (Gd+) lesions on relapses and 3-month confirmed Expanded Disability Status Scale (EDSS) progression in the PRISMS clinical trial. Exploratory analyses assessed whether active T2 and T1 Gd + lesions at Month 6, or active T2 lesions at Month 12, predicted clinical outcomes over 4 years in PRISMS. Mean active T2 lesion number at Month 6 was significantly lower with interferon beta-1a given subcutaneously (IFN β-1a SC) 44 μg and 22 μg 3×/week (tiw) than with placebo (p < 0.0001). The presence of ≥4 versus 0 active T2 lesions predicted disability progression at Years 3-4 in the IFN β-1a SC 22 μg group only (p < 0.05), whereas the presence of ≥2 versus 0-1 active T2 lesions predicted disability progression in the placebo/delayed treatment (DTx) (Years 2-4; p < 0.05) and IFN β-1a SC 22 μg groups (Years 3-4; p < 0.05). Greater active T2 lesion number at 6 months predicted relapses in the placebo/DTx group only (≥4 vs. 0, Years 1-4; ≥2 vs. 0-1, Years 2-4; p < 0.05), and the presence of T1 Gd + lesions at 6 months predicted disability progression in the IFN β-1a SC 44 μg group only (Year 1; p < 0.05). The presence of ≥2 versus 0-1 active T2 lesions at 12 months predicted disability progression over 3 and 4 years in the IFN β-1a SC 44 μg group. Active T2 lesions at 6 months predicted clinical outcomes in patients receiving placebo or IFN β-1a SC 22 μg, but not in those receiving IFN β-1a SC 44 μg. Active T2 lesions at 12 months may predict outcomes in those receiving IFN β-1a SC 44 μg and are possibly more suggestive of poor response to therapy than T2 results at 6 months.

Coagulation effect on the activity size distributions of long lived radon progeny aerosols and its application to atmospheric residence time estimation techniques.

PubMed

Anand, S; Mayya, Y S

2015-03-01

The long lived naturally occurring radon progeny species in the atmosphere, namely (210)Pb, (210)Bi and (210)Po, have been used as important tracers for understanding the atmospheric mixing processes and estimating aerosol residence times. Several observations in the past have shown that the activity size distribution of these species peaks at larger particle sizes as compared to the short lived radon progeny species - an effect that has been attributed to the process of coagulation of the background aerosols to which they are attached. To address this issue, a mathematical equation is derived for the activity-size distribution of tracer species by formulating a generalized distribution function for the number of tracer atoms present in coagulating background particles in the presence of radioactive decay and removal. A set of these equations is numerically solved for the progeny chain using Fuchs coagulation kernel combined with a realistic steady-state aerosol size spectrum that includes nucleation, accumulation and coarse mode components. The important findings are: (i) larger shifts in the modal sizes of (210)Pb and (210)Po at higher aerosol concentrations such as that found in certain Asian urban regions (ii) enrichment of tracer specific activity on particles as compared to that predicted by pure attachment laws (iii) sharp decline of daughter-to-parent activity ratios for decreasing particle sizes. The implication of the results to size-fractionated residence time estimation techniques is highlighted. A coagulation corrected graphical approach is presented for estimating the residence times from the size-segregated activity ratios of (210)Bi and (210)Po with respect to (210)Pb. The discrepancy between the residence times predicted by conventional formula and the coagulation corrected approach for specified activity ratios increases at higher atmospheric aerosol number concentrations (>10(10) #/m(3)) for smaller sizes (<1 μm). The results are further discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Dynamic Variation in Pleasure in Children Predicts Nonlinear Change in Lateral Frontal Brain Electrical Activity

ERIC Educational Resources Information Center

Light, Sharee N.; Coan, James A.; Frye, Corrina; Goldsmith, H. Hill; Davidson, Richard J.

2009-01-01

Individual variation in the experience and expression of pleasure may relate to differential patterns of lateral frontal activity. Brain electrical measures have been used to study the asymmetric involvement of lateral frontal cortex in positive emotion, but the excellent time resolution of these measures has not been used to capture…

Temporal clustering of tropical cyclones and its ecosystem impacts

PubMed Central

Mumby, Peter J.; Vitolo, Renato; Stephenson, David B.

2011-01-01

Tropical cyclones have massive economic, social, and ecological impacts, and models of their occurrence influence many planning activities from setting insurance premiums to conservation planning. Most impact models allow for geographically varying cyclone rates but assume that individual storm events occur randomly with constant rate in time. This study analyzes the statistical properties of Atlantic tropical cyclones and shows that local cyclone counts vary in time, with periods of elevated activity followed by relative quiescence. Such temporal clustering is particularly strong in the Caribbean Sea, along the coasts of Belize, Honduras, Costa Rica, Jamaica, the southwest of Haiti, and in the main hurricane development region in the North Atlantic between Africa and the Caribbean. Failing to recognize this natural nonstationarity in cyclone rates can give inaccurate impact predictions. We demonstrate this by exploring cyclone impacts on coral reefs. For a given cyclone rate, we find that clustered events have a less detrimental impact than independent random events. Predictions using a standard random hurricane model were overly pessimistic, predicting reef degradation more than a decade earlier than that expected under clustered disturbance. The presence of clustering allows coral reefs more time to recover to healthier states, but the impacts of clustering will vary from one ecosystem to another. PMID:22006300

Stereo Reconstruction Study

DTIC Science & Technology

1983-06-01

be registered on the agenda. At each step or analysis, the action with the highest score is executed and the database is changed. The agenda controls...activation of production rules according to changes in the database . The agenda is updated whenever the database is changed. Each time, the number of...views of an object. Total prediction has combinatorial complexity. For a polyhedron with n distinct faces, there are 2" views. Instead, ACRONYM predicts

Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection.

PubMed

Pollett, Simon; Boscardin, W John; Azziz-Baumgartner, Eduardo; Tinoco, Yeny O; Soto, Giselle; Romero, Candice; Kok, Jen; Biggerstaff, Matthew; Viboud, Cecile; Rutherford, George W

2017-01-01

 Latin America has a substantial burden of influenza and rising Internet access and could benefit from real-time influenza epidemic prediction web tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most Latin American countries or in any low- to middle-income country. Our aim was to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay.  Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011-December 2014. Concurrent weekly Google-predicted influenza activity in the same countries was abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.  There were frequent GFT prediction errors, with correlation ranging from r = -0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific for influenza virus activity.  We note substantial inaccuracies with GFT-predicted influenza activity compared with FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future Internet-based biosurveillance tools. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Predicting complex syntactic structure in real time: Processing of negative sentences in Russian.

PubMed

Kazanina, Nina

2017-11-01

In Russian negative sentences the verb's direct object may appear either in the accusative case, which is licensed by the verb (as is common cross-linguistically), or in the genitive case, which is licensed by the negation (Russian-specific "genitive-of-negation" phenomenon). Such sentences were used to investigate whether case marking is employed for anticipating syntactic structure, and whether lexical heads other than the verb can be predicted on the basis of a case-marked noun phrase. Experiment 1, a completion task, confirmed that genitive-of-negation is part of Russian speakers' active grammatical repertoire. In Experiments 2 and 3, the genitive/accusative case manipulation on the preverbal object led to shorter reading times at the negation and verb in the genitive versus accusative condition. Furthermore, Experiment 3 manipulated linear order of the direct object and the negated verb in order to distinguish whether the abovementioned facilitatory effect was predictive or integrative in nature, and concluded that the parser actively predicts a verb and (otherwise optional) negation on the basis of a preceding genitive-marked object. Similarly to a head-final language, case-marking information on preverbal noun phrases (NPs) is used by the parser to enable incremental structure building in a free-word-order language such as Russian.

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

PubMed Central

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel. PMID:24795875

10-year cumulative and bidirectional associations of domain-specific physical activity and sedentary behaviour with health-related quality of life in French adults: Results from the SU.VI.MAX studies.

PubMed

Omorou, Abdou Y; Vuillemin, Anne; Menai, Medhi; Latarche, Clotilde; Kesse-Guyot, Emmanuelle; Galan, Pilar; Hercberg, Serge; Oppert, Jean-Michel; Briançon, Serge

2016-07-01

The directionality of the associations of domain-specific physical activity (PA) and sedentary behaviour (SB) with health-related quality of life (HRQoL) in adults remain insufficiently known. This study investigated the longitudinal associations of 10-year cumulative levels of PA and SB with HRQoL and the reverse associations. A sample of 2093 (47.8% men) participants from a cohort of French adult (SU.VI.MAX) was included. Data were collected at 3 time points (1998, 2001 and 2007) using the Modifiable Activity Questionnaire (MAQ) for PA (leisure-time and occupational) and SB (screen-viewing, reading and total sitting time) and the DUKE Health Profile for HRQoL. The cumulative level (from 0 to 3) referred to the number of time points where a high PA level, high SB or good HRQoL was reported. Regression models examined the 10-year cumulative level of PA, SB as predictors of HRQoL and reverse associations. The 10-year cumulative level of high PA, both leisure-time and occupational, predicted a higher HRQoL while the 10-year cumulative level of high screen-viewing time and high total sitting time was associated with lower HRQoL. For the reverse association, cumulative level of good HRQoL predicted more leisure-time PA, less screen-viewing time and less total sitting time but was not related to occupational PA. Relationships between PA, SB and HRQoL are complex and should not be oversimplified in one or the other direction. Taking into account domain-specific PA and SB in health promotion programs appears of prime importance to design interventions aiming at improving HRQoL. Copyright © 2016 Elsevier Inc. All rights reserved.

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

PubMed Central

Ihlen, Espen A. F.; van Schooten, Kimberley S.; Bruijn, Sjoerd M.; van Dieën, Jaap H.; Vereijken, Beatrix; Helbostad, Jorunn L.; Pijnappels, Mirjam

2018-01-01

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers. PMID:29556188

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking.

PubMed

Ihlen, Espen A F; van Schooten, Kimberley S; Bruijn, Sjoerd M; van Dieën, Jaap H; Vereijken, Beatrix; Helbostad, Jorunn L; Pijnappels, Mirjam

2018-01-01

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME ( p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

Evidence that displacement activities facilitate behavioural transitions in ring-tailed lemurs.

PubMed

Buckley, Victoria; Semple, Stuart

2012-07-01

Displacement activities are behavioural patterns defined by their apparent irrelevance to an animal's ongoing actions. Despite being identified in diverse taxa, their function remains poorly understood. One hypothesis posits that displacement activities facilitate transitions between different behaviours by mediating changes in animals' motivational state. Under this hypothesis, it is predicted that displacement activities will occur more frequently around changes in behaviour than at other times, and also that rates of displacement activities will be higher before than after such behavioural transitions. We tested these two predictions in wild ring-tailed lemurs (Lemur catta). During focal observations, animals' behavioural state was continuously recorded, as were all occurrences of self-scratching, a common displacement activity in this species. Self-scratching rates were found to be significantly elevated both before and after behavioural transitions. Furthermore, self-scratching rates were significantly higher before behavioural transitions occurred than after. These results, therefore, provide support for the hypothesis that displacement activities facilitate behavioural transitions in L. catta. Copyright © 2012 Elsevier B.V. All rights reserved.

Gauging climate change effects at local scales: weather-based indices to monitor insect harassment in caribou.

PubMed

Witter, Leslie A; Johnson, Chris J; Croft, Bruno; Gunn, Anne; Poirier, Lisa M

2012-09-01

Climate change is occurring at an accelerated rate in the Arctic. Insect harassment may be an important link between increased summer temperature and reduced body condition in caribou and reindeer (both Rangifer tarandus). To examine the effects of climate change at a scale relevant to Rangifer herds, we developed monitoring indices using weather to predict activity of parasitic insects across the central Arctic. During 2007-2009, we recorded weather conditions and used carbon dioxide baited traps to monitor activity of mosquitoes (Culicidae), black flies (Simuliidae), and oestrid flies (Oestridae) on the post-calving and summer range of the Bathurst barren-ground caribou (Rangifer tarandus groenlandicus) herd in Northwest Territories and Nunavut, Canada. We developed statistical models representing hypotheses about effects of weather, habitat, location, and temporal variables on insect activity. We used multinomial logistic regression to model mosquito and black fly activity, and logistic regression to model oestrid fly presence. We used information theory to select models to predict activity levels of insects. Using historical weather data, we used hindcasting to develop a chronology of insect activity on the Bathurst range from 1957 to 2008. Oestrid presence and mosquito and black fly activity levels were explained by temperature. Wind speed, light intensity, barometric pressure, relative humidity, vegetation, topography, location, time of day, and growing degree-days also affected mosquito and black fly levels. High predictive ability of all models justified the use of weather to index insect activity. Retrospective analyses indicated conditions favoring mosquito activity declined since the late 1950s, while predicted black fly and oestrid activity increased. Our indices can be used as monitoring tools to gauge potential changes in insect harassment due to climate change at scales relevant to caribou herds.

Behavioral response races, predator-prey shell games, ecology of fear, and patch use of pumas and their ungulate prey.

PubMed

Laundré, John W

2010-10-01

The predator-prey shell game predicts random movement of prey across the landscape, whereas the behavioral response race and landscape of fear models predict that there should be a negative relationship between the spatial distribution of a predator and its behaviorally active prey. Additionally, prey have imperfect information on the whereabouts of their predator, which the predator should incorporate in its patch use strategy. I used a one-predator-one-prey system, puma (Puma concolor)-mule deer (Odocoileus hemionus) to test the following predictions regarding predator-prey distribution and patch use by the predator. (1) Pumas will spend more time in high prey risk/low prey use habitat types, while deer will spend their time in low-risk habitats. Pumas should (2) select large forage patches more often, (3) remain in large patches longer, and (4) revisit individual large patches more often than individual smaller ones. I tested these predictions with an extensive telemetry data set collected over 16 years in a study area of patchy forested habitat. When active, pumas spent significantly less time in open areas of low intrinsic predation risk than did deer. Pumas used large patches more than expected, revisited individual large patches significantly more often than smaller ones, and stayed significantly longer in larger patches than in smaller ones. The results supported the prediction of a negative relationship in the spatial distribution of a predator and its prey and indicated that the predator is incorporating the prey's imperfect information about its presence. These results indicate a behavioral complexity on the landscape scale that can have far-reaching impacts on predator-prey interactions.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Development and validation of NIR-chemometric methods for chemical and pharmaceutical characterization of meloxicam tablets.

PubMed

Tomuta, Ioan; Iovanov, Rares; Bodoki, Ede; Vonica, Loredana

2014-04-01

Near-Infrared (NIR) spectroscopy is an important component of a Process Analytical Technology (PAT) toolbox and is a key technology for enabling the rapid analysis of pharmaceutical tablets. The aim of this research work was to develop and validate NIR-chemometric methods not only for the determination of active pharmaceutical ingredients content but also pharmaceutical properties (crushing strength, disintegration time) of meloxicam tablets. The development of the method for active content assay was performed on samples corresponding to 80%, 90%, 100%, 110% and 120% of meloxicam content and the development of the methods for pharmaceutical characterization was performed on samples prepared at seven different compression forces (ranging from 7 to 45 kN) using NIR transmission spectra of intact tablets and PLS as a regression method. The results show that the developed methods have good trueness, precision and accuracy and are appropriate for direct active content assay in tablets (ranging from 12 to 18 mg/tablet) and also for predicting crushing strength and disintegration time of intact meloxicam tablets. The comparative data show that the proposed methods are in good agreement with the reference methods currently used for the characterization of meloxicam tablets (HPLC-UV methods for the assay and European Pharmacopeia methods for determining the crushing strength and disintegration time). The results show the possibility to predict both chemical properties (active content) and physical/pharmaceutical properties (crushing strength and disintegration time) directly, without any sample preparation, from the same NIR transmission spectrum of meloxicam tablets.

Internal dynamics of semiflexible polymers with active noise

NASA Astrophysics Data System (ADS)

Eisenstecken, Thomas; Gompper, Gerhard; Winkler, Roland G.

2017-04-01

The intramolecular dynamics of flexible and semiflexible polymers in response to active noise is studied theoretically. The active noise may either originate from interactions of a passive polymer with a bath of active Brownian particles or the polymer itself is comprised of active Brownian particles. We describe the polymer by the continuous Gaussian semiflexible-polymer model, taking into account the finite polymer extensibility. Our analytical calculations predict a strong dependence of the polymer dynamics on the activity. In particular, active semiflexible polymers exhibit a crossover from a bending elasticity-dominated dynamics at weak activity to that of flexible polymers at strong activity. The end-to-end vector correlation function decays exponentially for times longer than the longest polymer relaxation time. Thereby, the polymer relaxation determines the decay of the correlation function for long and flexible polymers. For shorter and stiffer polymers, the relaxation behavior of individual active Brownian particles dominates the decay above a certain activity. The diffusive dynamics of a polymer is substantially enhanced by the activity. Three regimes can be identified in the mean square displacement for sufficiently strong activities: an activity-induced ballistic regime at short times, followed by a Rouse-type polymer-specific regime for any polymer stiffness, and free diffusion at long times, again determined by the activity.

Predictors of physical activity and sedentary behaviours among 11-16 year olds: Multilevel analysis of the 2013 Health Behaviour in School-aged Children (HBSC) study in Wales.

PubMed

Morgan, Kelly; Hallingberg, Britt; Littlecott, Hannah; Murphy, Simon; Fletcher, Adam; Roberts, Chris; Moore, Graham

2016-07-15

The present study investigated associations between individual- and school-level predictors and young people's self-reported physical activity (total activity and moderate-to-vigorous activity) and sedentary behaviours. Individual-level data provided by the 2013/14 cross-sectional survey 'Health Behaviour in School-aged Children (HBSC) study in Wales' were linked to school-level data within the 'HBSC School Environment Questionnaire'. The final sample comprised 7,376 young people aged 11-16 years across 67 schools. Multilevel modelling was used to examine predictors of total physical activity, moderate-to-vigorous physical activity (MVPA) and sedentary behaviours (screen-based behaviours). Taking more physical activity (less than 5 days vs. 5 or more days per week), engaging in higher levels of MVPA (less than 4 hours vs. 4 or more hours per week) and reporting 2 or less hours of sedentary time were predicted by several individual level variables. Active travel to school positively predicted high levels of physical activity, however, gender stratified models revealed active travel as a predictor amongst girls only (OR:1.25 (95 % CI:1.05 - 1.49)). No school-level factors were shown to predict physical activity levels, however, a lower school socio-economic status was associated with a higher level of MVPA (OR:1.02 (95 % CI:1.01 - 1.03)) and a lower risk of sedentary behaviour (OR:0.97 (95 % CI:0.96 - 0.99)). A shorter lunch break (OR:1.33 (95 % CI:1.11 - 1.49)) and greater provision of facilities (OR:1.02 (95 % CI:1.00 - 1.05)) were associated with increased sedentary activity. Gender stratified models revealed that PE lesson duration (OR:1.18 (95 % CI:1.01 - 1.37)) and the provision of sports facilities (OR:1.03 (95 % CI:1.00 - 1.06)) were predictors of boy's sedentary behaviours only. Shorter lunch breaks were associated with increased sedentary time. Therefore, while further research is needed to better understand the causal nature of this association, extending lunch breaks could have a positive impact on sedentary behaviour through the provision of more time for physical activity. The findings also suggest that active travel could offer a mechanism for increasing physical activity levels particularly amongst girls. Particularly, the design and evaluation of interventions to promote physical activity during school hours should employ a comprehensive approach, including a focus on school policies and behaviours both in and out of school hours.

Catastrophic subsidence: An environmental hazard, shelby county, Alabama

NASA Astrophysics Data System (ADS)

Lamoreaux, Philip E.; Newton, J. G.

1986-03-01

Induced sinkholes (catastrophic subsidence) are those caused or accelerated by human activities These sinkholes commonly result from a water level decline due to pumpage Construction activities in a cone of depression greatly increases the likelihood of sinkhole occurrence Almost all occur where cavities develop in unconsolidated deposits overlying solution openings in carbonate rocks. Triggering mechanisms resulting from water level declines are (1) loss of buoyant support of the water, (2) increased gradient and water velocity, (3) water-level fluctuations, and (4) induced recharge Construction activities triggering sinkhole development include ditching, removing overburden, drilling, movement of heavy equipment, blasting and the diversion and impoundment of drainage Triggering mechanisms include piping, saturation, and loading Induced sinkholes resulting from human water development/management activities are most predictable in a youthful karst area impacted by groundwater withdrawals Shape, depth, and timing of catastrophic subsidence can be predicted in general terms Remote sensing techniques are used in prediction of locations of catastrophic subsidence. This provides a basis for design and relocation of structures such as a gas pipeline, dam, or building Utilization of techniques and a case history of the relocation of a pipeline are described

Activity in the human brain predicting differential heart rate responses to emotional facial expressions.

PubMed

Critchley, Hugo D; Rotshtein, Pia; Nagai, Yoko; O'Doherty, John; Mathias, Christopher J; Dolan, Raymond J

2005-02-01

The James-Lange theory of emotion proposes that automatically generated bodily reactions not only color subjective emotional experience of stimuli, but also necessitate a mechanism by which these bodily reactions are differentially generated to reflect stimulus quality. To examine this putative mechanism, we simultaneously measured brain activity and heart rate to identify regions where neural activity predicted the magnitude of heart rate responses to emotional facial expressions. Using a forewarned reaction time task, we showed that orienting heart rate acceleration to emotional face stimuli was modulated as a function of the emotion depicted. The magnitude of evoked heart rate increase, both across the stimulus set and within each emotion category, was predicted by level of activity within a matrix of interconnected brain regions, including amygdala, insula, anterior cingulate, and brainstem. We suggest that these regions provide a substrate for translating visual perception of emotional facial expression into differential cardiac responses and thereby represent an interface for selective generation of visceral reactions that contribute to the embodied component of emotional reaction.

Modeling behavioral thermoregulation in a climate change sentinel.

PubMed

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, mechanistic predictions and empirical observations were congruent. This research is unique in providing both an empirical and mechanistic description of the effects of temperature on a mammalian sentinel of climate change, the American pika. Our results suggest that previously underinvestigated characteristics, specifically fur properties and body size, may play critical roles in pika populations' response to climate change. We also demonstrate the potential importance of considering behavioral thermoregulation and microclimate variability when predicting animal responses to climate change.

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

PubMed

Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

2016-08-01

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

Substantia nigra activity level predicts trial-to-trial adjustments in cognitive control

PubMed Central

Boehler, C.N.; Bunzeck, N.; Krebs, R.M.; Noesselt, T.; Schoenfeld, M.A.; Heinze, H.-J.; Münte, T.F.; Woldorff, M.G.; Hopf, J.-M.

2011-01-01

Effective adaptation to the demands of a changing environment requires flexible cognitive control. The medial and lateral frontal cortices are involved in such control processes, putatively in close interplay with the basal ganglia. In particular, dopaminergic projections from the midbrain (i.e., from the substantia nigra (SN) and the ventral tegmental area (VTA)) have been proposed to play a pivotal role in modulating the activity in these areas for cognitive control purposes. In that dopaminergic involvement has been strongly implicated in reinforcement learning, these ideas suggest functional links between reinforcement learning, where the outcome of actions shapes behavior over time, and cognitive control in a more general context, where no direct reward is involved. Here, we provide evidence from functional MRI in humans that activity in the SN predicts systematic subsequent trial-to-trial response time (RT) prolongations that are thought to reflect cognitive control in a Stop-signal paradigm. In particular, variations in the activity level of the SN in one trial predicted the degree of RT prolongation on the subsequent trial, consistent with a modulating output signal from the SN being involved in enhancing cognitive control. This link between SN activity and subsequent behavioral adjustments lends support to theoretical accounts that propose dopaminergic control signals that shape behavior both in the presence and absence of direct reward. This SN-based modulatory mechanism is presumably mediated via a wider network that determines response speed in this task, including frontal and parietal control regions, along with the basal ganglia and the associated subthalamic nucleus. PMID:20465358

National Institutes of Health Stroke Scale-Time Score Predicts Outcome after Endovascular Therapy in Acute Ischemic Stroke: A Retrospective Single-Center Study.

PubMed

Todo, Kenichi; Sakai, Nobuyuki; Kono, Tomoyuki; Hoshi, Taku; Imamura, Hirotoshi; Adachi, Hidemitsu; Kohara, Nobuo

2016-05-01

Outcomes after successful endovascular therapy in acute ischemic stroke are associated with onset-to-reperfusion time (ORT) and the National Institutes of Health Stroke Scale (NIHSS) score. In intravenous recombinant tissue plasminogen activator therapy, the NIHSS-time score, calculated by multiplying onset-to-treatment time with the NIHSS score, has been shown to predict clinical outcomes. In this study, we assessed whether a similar combination of the ORT and the NIHSS score can be applied to predict the outcomes after endovascular therapy. We retrospectively reviewed the charts of 128 consecutive ischemic stroke patients with successful reperfusion after endovascular therapy. We analyzed the association of the ORT, the NIHSS score, and the NIHSS-time score with good outcome (modified Rankin Scale score ≤ 2 at 3 months). Good outcome rates for patients with NIHSS-time scores of 84.7 or lower, scores higher than 84.7 up to 127.5 or lower, and scores higher than 127.5 were 72.1%, 44.2%, and 14.3%, respectively (P < .01). Multivariate logistic regression analysis revealed that the NIHSS-time score was an independent predictor of good outcomes (odds ratio, .372; 95% confidence interval, .175-.789) after adjusting for age, sex, internal carotid artery occlusion, plasma glucose level, ORT, and NIHSS score. The NIHSS-time score can predict good clinical outcomes after endovascular treatment. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

PubMed Central

Biewener, Andrew A.; Wakeling, James M.

2017-01-01

ABSTRACT Hill-type models are ubiquitous in the field of biomechanics, providing estimates of a muscle's force as a function of its activation state and its assumed force–length and force–velocity properties. However, despite their routine use, the accuracy with which Hill-type models predict the forces generated by muscles during submaximal, dynamic tasks remains largely unknown. This study compared human gastrocnemius forces predicted by Hill-type models with the forces estimated from ultrasound-based measures of tendon length changes and stiffness during cycling, over a range of loads and cadences. We tested both a traditional model, with one contractile element, and a differential model, with two contractile elements that accounted for independent contributions of slow and fast muscle fibres. Both models were driven by subject-specific, ultrasound-based measures of fascicle lengths, velocities and pennation angles and by activation patterns of slow and fast muscle fibres derived from surface electromyographic recordings. The models predicted, on average, 54% of the time-varying gastrocnemius forces estimated from the ultrasound-based methods. However, differences between predicted and estimated forces were smaller under low speed–high activation conditions, with models able to predict nearly 80% of the gastrocnemius force over a complete pedal cycle. Additionally, the predictions from the Hill-type muscle models tested here showed that a similar pattern of force production could be achieved for most conditions with and without accounting for the independent contributions of different muscle fibre types. PMID:28202584

Primary Visual Cortex as a Saliency Map: A Parameter-Free Prediction and Its Test by Behavioral Data

PubMed Central

Zhaoping, Li; Zhe, Li

2015-01-01

It has been hypothesized that neural activities in the primary visual cortex (V1) represent a saliency map of the visual field to exogenously guide attention. This hypothesis has so far provided only qualitative predictions and their confirmations. We report this hypothesis’ first quantitative prediction, derived without free parameters, and its confirmation by human behavioral data. The hypothesis provides a direct link between V1 neural responses to a visual location and the saliency of that location to guide attention exogenously. In a visual input containing many bars, one of them saliently different from all the other bars which are identical to each other, saliency at the singleton’s location can be measured by the shortness of the reaction time in a visual search for singletons. The hypothesis predicts quantitatively the whole distribution of the reaction times to find a singleton unique in color, orientation, and motion direction from the reaction times to find other types of singletons. The prediction matches human reaction time data. A requirement for this successful prediction is a data-motivated assumption that V1 lacks neurons tuned simultaneously to color, orientation, and motion direction of visual inputs. Since evidence suggests that extrastriate cortices do have such neurons, we discuss the possibility that the extrastriate cortices play no role in guiding exogenous attention so that they can be devoted to other functions like visual decoding and endogenous attention. PMID:26441341

New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

2009-12-01

Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product was developed for routine near-real time verification of these predictions. The footprint of the predicted smoke from identified fires is verified with satellite observations of the spatial extent of smoke aerosols (5km resolution). Based on geostationary aerosol optical depth measurements that provide good time resolution of the horizontal spatial extent of the plumes, these observations do not yield quantitative concentrations of smoke particles at the surface. Predicted surface smoke concentrations are consistent with the limited number of in situ observations of total fine particle mass from all sources; however they are much higher than predicted for most CONUS fires. To assess uncertainty associated with fire emissions estimates, sensitivity analyses are in progress.

Probability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus.

PubMed

Aldars-García, Laila; Berman, María; Ortiz, Jordi; Ramos, Antonio J; Marín, Sonia

2018-06-01

The probability of growth and aflatoxin B 1 (AFB 1 ) production of 20 isolates of Aspergillus flavus were studied using a full factorial design with eight water activity levels (0.84-0.98 a w ) and six temperature levels (15-40 °C). Binary data obtained from growth studies were modelled using linear logistic regression analysis as a function of temperature, water activity and time for each isolate. In parallel, AFB 1 was extracted at different times from newly formed colonies (up to 20 mm in diameter). Although a total of 950 AFB 1 values over time for all conditions studied were recorded, they were not considered to be enough to build probability models over time, and therefore, only models at 30 days were built. The confidence intervals of the regression coefficients of the probability of growth models showed some differences among the 20 growth models. Further, to assess the growth/no growth and AFB 1 /no- AFB 1 production boundaries, 0.05 and 0.5 probabilities were plotted at 30 days for all of the isolates. The boundaries for growth and AFB 1 showed that, in general, the conditions for growth were wider than those for AFB 1 production. The probability of growth and AFB 1 production seemed to be less variable among isolates than AFB 1 accumulation. Apart from the AFB 1 production probability models, using growth probability models for AFB 1 probability predictions could be, although conservative, a suitable alternative. Predictive mycology should include a number of isolates to generate data to build predictive models and take into account the genetic diversity of the species and thus make predictions as similar as possible to real fungal food contamination. Copyright © 2017 Elsevier Ltd. All rights reserved.

Explaining the Relationship Between Sexually Explicit Internet Material and Casual Sex: A Two-Step Mediation Model.

PubMed

Vandenbosch, Laura; van Oosten, Johanna M F

2018-07-01

Despite increasing interest in the implications of adolescents' use of sexually explicit Internet material (SEIM), we still know little about the relationship between SEIM use and adolescents' casual sexual activities. Based on a three-wave online panel survey study among Dutch adolescents (N = 1079; 53.1% boys; 93.5% with an exclusively heterosexual orientation; M age  = 15.11; SD = 1.39), we found that watching SEIM predicted engagement in casual sex over time. In turn, casual sexual activities partially predicted adolescents' use of SEIM. A two-step mediation model was tested to explain the relationship between watching SEIM and casual sex. It was partially confirmed. First, watching SEIM predicted adolescents' perceptions of SEIM as a relevant information source from Wave 2 to Wave 3, but not from Wave 1 to Wave 2. Next, such perceived utility of SEIM was positively related to stronger instrumental attitudes toward sex and thus their views about sex as a core instrument for sexual gratification. Lastly, adolescents' instrumental attitudes toward sex predicted adolescents' engagement in casual sex activities consistently across waves. Partial support emerged for a reciprocal relationship between watching SEIM and perceived utility. We did not find a reverse relationship between casual sex activities and instrumental attitudes toward sex. No significant gender differences emerged.

Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J; Le, Duc V; Meratnia, Nirvana

2018-05-24

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.

Predictability of weather and climate in a coupled ocean-atmosphere model: A dynamical systems approach. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Nese, Jon M.

1989-01-01

A dynamical systems approach is used to quantify the instantaneous and time-averaged predictability of a low-order moist general circulation model. Specifically, the effects on predictability of incorporating an active ocean circulation, implementing annual solar forcing, and asynchronously coupling the ocean and atmosphere are evaluated. The predictability and structure of the model attractors is compared using the Lyapunov exponents, the local divergence rates, and the correlation, fractal, and Lyapunov dimensions. The Lyapunov exponents measure the average rate of growth of small perturbations on an attractor, while the local divergence rates quantify phase-spatial variations of predictability. These local rates are exploited to efficiently identify and distinguish subtle differences in predictability among attractors. In addition, the predictability of monthly averaged and yearly averaged states is investigated by using attractor reconstruction techniques.

A Study of the Solar Wind-Magnetosphere Coupling Using Neural Networks

NASA Astrophysics Data System (ADS)

Wu, Jian-Guo; Lundstedt, Henrik

1996-12-01

The interaction between solar wind plasma and interplanetary magnetic field (IMF) and Earth's magnetosphere induces geomagnetic activity. Geomagnetic storms can cause many adverse effects on technical systems in space and on the Earth. It is therefore of great significance to accurately predict geomagnetic activity so as to minimize the amount of disruption to these operational systems and to allow them to work as efficiently as possible. Dynamic neural networks are powerful in modeling the dynamics encoded in time series of data. In this study, we use partially recurrent neural networks to study the solar wind-magnetosphere coupling by predicting geomagnetic storms (as measured by the Dstindex) from solar wind measurements. The solar wind, the IMF and the geomagnetic index Dst data are hourly averaged and read from the National Space Science Data Center's OMNI database. We selected these data from the period 1963 to 1992, which cover 10552h and contain storm time periods 9552h and quiet time periods 1000h. The data are then categorized into three data sets: a training set (6634h), across-validation set (1962h), and a test set (1956h). The validation set is used to determine where the training should be stopped whereas the test set is used for neural networks to get the generalization capability (the out-of-sample performance). Based on the correlation analysis between the Dst index and various solar wind parameters (including various combinations of solar wind parameters), the best coupling functions can be found from the out-of-sample performance of trained neural networks. The coupling functions found are then used to forecast geomagnetic storms one to several hours in advance. The comparisons are made on iterating the single-step prediction several times and on making a non iterated, direct prediction. Thus, we will present the best solar wind-magnetosphere coupling functions and the corresponding prediction results. Interesting Links: Lund Space Weather and AI Center

Corticostriatal Divergent Function in Determining the Temporal and Spatial Properties of Motor Tics

PubMed Central

Israelashvili, Michal

2015-01-01

Striatal disinhibition leads to the formation of motor tics resembling those expressed during Tourette syndrome and other tic disorders. The spatial properties of these tics are dependent on the location of the focal disinhibition within the striatum; however, the factors affecting the temporal properties of tic expression are still unknown. Here, we used microstimulation within the motor cortex of freely behaving rats before and after striatal disinhibition to explore the factors underlying the timing of individual tics. Cortical activation determined the timing of individual tics via an accumulation process of inputs that was dependent on the frequency and amplitude of the inputs. The resulting tics and their neuronal representation within the striatum were highly stereotypic and independent of the cortical activity properties. The generation of tics was limited by absolute and relative tic refractory periods that were derived from an internal striatal state. Thus, the precise time of the tic expression depends on the interaction between the summation of incoming excitatory inputs to the striatum and the timing of the previous tic. A data-driven computational model of corticostriatal function closely replicated the temporal properties of tic generation and enabled the prediction of tic timing based on incoming cortical activity and tic history. These converging experimental and computational findings suggest a clear functional dichotomy within the corticostriatal network, pointing to disparate temporal (cortical) versus spatial (striatal) encoding. Thus, the abnormal striatal inhibition typical of Tourette syndrome and other tic disorders results in tics due to cortical activation of the abnormal striatal network. SIGNIFICANCE STATEMENT The factors underlying the temporal properties of tics expressed in Tourette syndrome and other tic disorders have eluded clinicians and scientists for decades. In this study, we highlight the key role of corticostriatal activity in determining the timing of individual tics. We found that cortical activation determined the timing of tics but did not determine their form. A data-driven computational model of the corticostriatal network closely replicated the temporal properties of tic generation and enabled the prediction of tic timing based on incoming cortical activity and tic history. This study thus shows that, although tics originate in the striatum, their timing depends on the interplay between incoming excitatory corticostriatal inputs and the internal striatal state. PMID:26674861

Corticostriatal Divergent Function in Determining the Temporal and Spatial Properties of Motor Tics.

PubMed

Israelashvili, Michal; Bar-Gad, Izhar

2015-12-16

Striatal disinhibition leads to the formation of motor tics resembling those expressed during Tourette syndrome and other tic disorders. The spatial properties of these tics are dependent on the location of the focal disinhibition within the striatum; however, the factors affecting the temporal properties of tic expression are still unknown. Here, we used microstimulation within the motor cortex of freely behaving rats before and after striatal disinhibition to explore the factors underlying the timing of individual tics. Cortical activation determined the timing of individual tics via an accumulation process of inputs that was dependent on the frequency and amplitude of the inputs. The resulting tics and their neuronal representation within the striatum were highly stereotypic and independent of the cortical activity properties. The generation of tics was limited by absolute and relative tic refractory periods that were derived from an internal striatal state. Thus, the precise time of the tic expression depends on the interaction between the summation of incoming excitatory inputs to the striatum and the timing of the previous tic. A data-driven computational model of corticostriatal function closely replicated the temporal properties of tic generation and enabled the prediction of tic timing based on incoming cortical activity and tic history. These converging experimental and computational findings suggest a clear functional dichotomy within the corticostriatal network, pointing to disparate temporal (cortical) versus spatial (striatal) encoding. Thus, the abnormal striatal inhibition typical of Tourette syndrome and other tic disorders results in tics due to cortical activation of the abnormal striatal network. The factors underlying the temporal properties of tics expressed in Tourette syndrome and other tic disorders have eluded clinicians and scientists for decades. In this study, we highlight the key role of corticostriatal activity in determining the timing of individual tics. We found that cortical activation determined the timing of tics but did not determine their form. A data-driven computational model of the corticostriatal network closely replicated the temporal properties of tic generation and enabled the prediction of tic timing based on incoming cortical activity and tic history. This study thus shows that, although tics originate in the striatum, their timing depends on the interplay between incoming excitatory corticostriatal inputs and the internal striatal state. Copyright © 2015 the authors 0270-6474/15/3516340-12$15.00/0.

Response surface methodology applied to the generation of casein hydrolysates with antioxidant and dipeptidyl peptidase IV inhibitory properties.

PubMed

Nongonierma, Alice B; Maux, Solène Le; Esteveny, Claire; FitzGerald, Richard J

2017-03-01

Hydrolysis parameters affecting the release of dipeptidyl peptidase IV (DPP-IV) inhibitory and antioxidant peptides from milk proteins have not been extensively studied. Therefore, a multifactorial (i.e. pH, temperature and hydrolysis time) composite design was used to optimise the release of bioactive peptides (BAPs) with DPP-IV inhibitory and antioxidant [oxygen radical absorbance capacity (ORAC)] properties from sodium caseinate. Fifteen sodium caseinate hydrolysates (H1-H15) were generated with Protamex TM , a bacillus proteinase activity. Hydrolysis time (1 to 5 h) had the highest influence on both DPP-IV inhibitory properties and ORAC activity (P < 0.05). Alteration of incubation temperature (40 to 60 °C) and pH (6.5 to 8.0) had an effect on the DPP-IV inhibitory properties but not the ORAC activity of the Protamex sodium caseinate hydrolysates. A multi-functional hydrolysate, H12, was identified having DPP-IV inhibitory (actual: 0.82 ± 0.24 vs. predicted optimum: 0.68 mg mL -1 ) and ORAC (actual: 639 ± 66 vs. predicted optimum: 639 µmol TE g -1 ) activity of the same order (P > 0.05) as the response surface methodology (RSM) predicted optimum bioactivities. Generation of milk protein hydrolysates through multifactorial design approaches may aid in the optimal enzymatic release of BAPs with serum glucose lowering and antioxidant properties. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Integrating discrete stochastic models and single-cell experiments to infer predictive models of MAPK-induced transcription dynamics

NASA Astrophysics Data System (ADS)

Munsky, Brian

2015-03-01

MAPK signal-activated transcription plays central roles in myriad biological processes including stress adaptation responses and cell fate decisions. Recent single-cell and single-molecule experiments have advanced our ability to quantify the spatial, temporal, and stochastic fluctuations for such signals and their downstream effects on transcription regulation. This talk explores how integrating such experiments with discrete stochastic computational analyses can yield quantitative and predictive understanding of transcription regulation in both space and time. We use single-molecule mRNA fluorescence in situ hybridization (smFISH) experiments to reveal locations and numbers of multiple endogenous mRNA species in 100,000's of individual cells, at different times and under different genetic and environmental perturbations. We use finite state projection methods to precisely and efficiently compute the full joint probability distributions of these mRNA, which capture measured spatial, temporal and correlative fluctuations. By combining these experimental and computational tools with uncertainty quantification, we systematically compare models of varying complexity and select those which give optimally precise and accurate predictions in new situations. We use these tools to explore two MAPK-activated gene regulation pathways. In yeast adaptation to osmotic shock, we analyze Hog1 kinase activation of transcription for three different genes STL1 (osmotic stress), CTT1 (oxidative stress) and HSP12 (heat shock). In human osteosarcoma cells under serum induction, we analyze ERK activation of c-Fos transcription.

Seasonal forecasting of fire over Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Spessa, A. C.; Field, R. D.; Pappenberger, F.; Langner, A.; Englhart, S.; Weber, U.; Stockdale, T.; Siegert, F.; Kaiser, J. W.; Moore, J.

2015-03-01

Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

Automatic prediction of solar flares and super geomagnetic storms

NASA Astrophysics Data System (ADS)

Song, Hui

Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth's magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the D st index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle [straight theta], determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (D= st <=-200nT). The results show that those active regions associated with | [straight theta]| < 90° are more likely to cause a super geomagnetic storm.

Novel enzymatic assay predicts minoxidil response in the treatment of androgenetic alopecia.

PubMed

Goren, Andy; Castano, Juan Antonio; McCoy, John; Bermudez, Fernando; Lotti, Torello

2014-01-01

Topical minoxidil is the most common drug used for the treatment of androgenetic alopecia (AGA) in men and women. Although topical minoxidil exhibits a good safety profile, the efficacy in the overall population remains relatively low at 30-40%. To observe significant improvement in hair growth, minoxidil is typically used daily for a period of at least 3-4 months. Due to the significant time commitment and low response rate, a biomarker for predicting patient response prior to therapy would be advantageous. Minoxidil is converted in the scalp to its active form, minoxidil sulfate, by the sulfotransferase enzyme SULT1A1. We hypothesized that SULT1A1 enzyme activity in the hair follicle correlates with minoxidil response for the treatment of AGA. Our preliminary retrospective study of a SULT1A1 activity assay demonstrates 95% sensitivity and 73% specificity in predicting minoxidil treatment response for AGA. A larger prospective study is now under way to further validate this novel assay. © 2013 Wiley Periodicals, Inc.

Predicting induced radioactivity for the accelerator operations at the Taiwan Photon Source.

PubMed

Sheu, R J; Jiang, S H

2010-12-01

This study investigates the characteristics of induced radioactivity due to the operations of a 3-GeV electron accelerator at the Taiwan Photon Source (TPS). According to the beam loss analysis, the authors set two representative irradiation conditions for the activation analysis. The FLUKA Monte Carlo code has been used to predict the isotope inventories, residual activities, and remanent dose rates as a function of time. The calculation model itself is simple but conservative for the evaluation of induced radioactivity in a light source facility. This study highlights the importance of beam loss scenarios and demonstrates the great advantage of using FLUKA in comparing the predicted radioactivity with corresponding regulatory limits. The calculated results lead to the conclusion that, due to fairly low electron consumption, the radioactivity induced in the accelerator components and surrounding concrete walls of the TPS is rather moderate and manageable, while the possible activation of air and cooling water in the tunnel and their environmental releases are negligible.

Active control strategy for the running attitude of high-speed train under strong crosswind condition

NASA Astrophysics Data System (ADS)

Li, Decang; Meng, Jianjun; Bai, Huan; Xu, Ruxun

2018-07-01

This paper focuses on the safety of high-speed trains under strong crosswind conditions. A new active control strategy is proposed based on the adaptive predictive control theory. The new control strategy aims at adjusting the attitudes of a train by controlling the new-type intelligent giant magnetostrictive actuator (GMA). It combined adaptive control with dynamic matrix control; parameters of predictive controller was real-time adjusted by online distinguishing to enhance the robustness of the control algorithm. On this basis, a correction control algorithm is also designed to regulate the parameters of predictive controller based on the step response of a controlled objective. Finally, the simulation results show that the proposed control strategy can adjust the running attitudes of high-speed trains under strong crosswind conditions; they also indicate that the new active control strategy is effective and applicable in improving the safety performance of a train based on a host-target computer technology provided by Matlab/Simulink.

Task network models in the prediction of workload imposed by extravehicular activities during the Hubble Space Telescope servicing mission

NASA Technical Reports Server (NTRS)

Diaz, Manuel F.; Takamoto, Neal; Woolford, Barbara

1994-01-01

In a joint effort with Brooks AFB, Texas, the Flight Crew Support Division at JSC has begun a computer simulation and performance modeling program directed at establishing the predictive validity of software tools for modeling human performance during spaceflight. This paper addresses the utility of task network modeling for predicting the workload that astronauts are likely to encounter in extravehicular activities (EVA) during the Hubble Space Telescope (HST) repair mission. The intent of the study was to determine whether two EVA crewmembers and one intravehicular activity (IVA) crewmember could reasonably be expected to complete HST Wide Field/Planetary Camera (WFPC) replacement in the allotted time. Ultimately, examination of the points during HST servicing that may result in excessive workload will lead to recommendations to the HST Flight Systems and Servicing Project concerning (1) expectation of degraded performance, (2) the need to change task allocation across crewmembers, (3) the need to expand the timeline, and (4) the need to increase the number of EVA's.

Parallel effects of memory set activation and search on timing and working memory capacity.

PubMed

Schweickert, Richard; Fortin, Claudette; Xi, Zhuangzhuang; Viau-Quesnel, Charles

2014-01-01

Accurately estimating a time interval is required in everyday activities such as driving or cooking. Estimating time is relatively easy, provided a person attends to it. But a brief shift of attention to another task usually interferes with timing. Most processes carried out concurrently with timing interfere with it. Curiously, some do not. Literature on a few processes suggests a general proposition, the Timing and Complex-Span Hypothesis: A process interferes with concurrent timing if and only if process performance is related to complex span. Complex-span is the number of items correctly recalled in order, when each item presented for study is followed by a brief activity. Literature on task switching, visual search, memory search, word generation and mental time travel supports the hypothesis. Previous work found that another process, activation of a memory set in long term memory, is not related to complex-span. If the Timing and Complex-Span Hypothesis is true, activation should not interfere with concurrent timing in dual-task conditions. We tested such activation in single-task memory search task conditions and in dual-task conditions where memory search was executed with concurrent timing. In Experiment 1, activating a memory set increased reaction time, with no significant effect on time production. In Experiment 2, set size and memory set activation were manipulated. Activation and set size had a puzzling interaction for time productions, perhaps due to difficult conditions, leading us to use a related but easier task in Experiment 3. In Experiment 3 increasing set size lengthened time production, but memory activation had no significant effect. Results here and in previous literature on the whole support the Timing and Complex-Span Hypotheses. Results also support a sequential organization of activation and search of memory. This organization predicts activation and set size have additive effects on reaction time and multiplicative effects on percent correct, which was found.

Vicarious reinforcement learning signals when instructing others.

PubMed

Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

2015-02-18

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

Managing PV Power on Mars - MER Rovers

NASA Technical Reports Server (NTRS)

Stella, Paul M.; Chin, Keith; Wood, Eric; Herman, Jennifer; Ewell, Richard

2009-01-01

The MER Rovers have recently completed over 5 years of operation! This is a remarkable demonstration of the capabilities of PV power on the Martian surface. The extended mission required the development of an efficient process to predict the power available to the rovers on a day-to-day basis. The performance of the MER solar arrays is quite unlike that of any other Space array and perhaps more akin to Terrestrial PV operation, although even severe by that comparison. The impact of unpredictable factors, such as atmospheric conditions and dust accumulation (and removal) on the panels limits the accurate prediction of array power to short time spans. Based on the above, it is clear that long term power predictions are not sufficiently accurate to allow for detailed long term planning. Instead, the power assessment is essentially a daily activity, effectively resetting the boundary points for the overall predictive power model. A typical analysis begins with the importing of the telemetry from each rover's previous day's power subsystem activities. This includes the array power generated, battery state-of-charge, rover power loads, and rover orientation, all as functions of time. The predicted performance for that day is compared to the actual performance to identify the extent of any differences. The model is then corrected for these changes. Details of JPL's MER power analysis procedure are presented, including the description of steps needed to provide the final prediction for the mission planners. A dust cleaning event of the solar array is also highlighted to illustrate the impact of Martian weather on solar array performance

Interest level in 2-year-olds with autism spectrum disorder predicts rate of verbal, nonverbal, and adaptive skill acquisition.

PubMed

Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

2015-11-01

Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2 years predicts subsequent rates of verbal, nonverbal, and adaptive skill acquisition to the age of 3 years. A total of 70 toddlers with autism spectrum disorder, mean age of 21.9 months, were scored using Interest Level Scoring for Autism, quantifying toddlers' interest in toys, social routines, and activities that could serve as reinforcers in an intervention. Adaptive level and mental age were measured concurrently (Time 1) and again after a mean of 16.3 months of treatment (Time 2). Interest Level Scoring for Autism score, Autism Diagnostic Observation Schedule score, adaptive age equivalent, verbal and nonverbal mental age, and intensity of intervention were entered into regression models to predict rates of skill acquisition. Interest level at Time 1 predicted subsequent acquisition rate of adaptive skills (R(2) = 0.36) and verbal mental age (R(2) = 0.30), above and beyond the effects of Time 1 verbal and nonverbal mental ages and Autism Diagnostic Observation Schedule scores. Interest level at Time 1 also contributed (R(2) = 0.30), with treatment intensity, to variance in development of nonverbal mental age. © The Author(s) 2014.

Momentary assessment of affect, physical feeling states, and physical activity in children.

PubMed

Dunton, Genevieve F; Huh, Jimi; Leventhal, Adam M; Riggs, Nathaniel; Hedeker, Donald; Spruijt-Metz, Donna; Pentz, Mary Ann

2014-03-01

Most research on the interplay of affective and physical feelings states with physical activity in children has been conducted under laboratory conditions and fails to capture intraindividual covariation. The current study used Ecological Momentary Assessment (EMA) to bidirectionally examine how affective and physical feeling states are related to objectively measured physical activity taking place in naturalistic settings during the course of children's everyday lives. Children (N = 119, ages 9-13 years, 52% male, 32% Hispanic) completed 8 days of EMA monitoring, which measured positive affect (PA), negative affect (NA), feeling tired, and feeling energetic up to 7 times per day. EMA responses were time-matched to accelerometer assessed moderate-to-vigorous physical activity (MVPA) in the 30 min before and after each EMA survey. Higher ratings of feeling energetic and lower ratings of feeling tired were associated with more MVPA in the 30 min after the EMA prompt. More MVPA in the 30 min before the EMA prompt was associated with higher ratings of PA and feeling energetic and lower ratings of NA. Between-subjects analyses indicated that mean hourly leisure-time MVPA was associated with less intraindividual variability in PA and NA. Physical feeling states predict subsequent physical activity levels, which in turn, predict subsequent affective states in children. Active children demonstrated higher positive and negative emotional stability. Although the strength of these associations were of modest magnitude and their clinical relevance is unclear, understanding the antecedents to and consequences of physical activity may have theoretical and practical implications for the maintenance and promotion of physical activity and psychological well-being in children. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Influence of stress systems and physical activity on different dimensions of fatigue in female fibromyalgia patients.

PubMed

Doerr, Johanna M; Fischer, Susanne; Nater, Urs M; Strahler, Jana

2017-02-01

Fatigue is a defining characteristic and one of the most debilitating features of fibromyalgia syndrome (FMS). The mechanisms underlying different dimensions of fatigue in FMS remain unclear. The aim of the current study was to test whether stress-related biological processes and physical activity modulate fatigue experience. Using an ambulatory assessment design, 26 female FMS patients reported general, mental, and physical fatigue levels at six time points per day for 14 consecutive days. Salivary cortisol and alpha-amylase were analyzed as markers of neuroendocrine functioning. Participants wore wrist actigraphs for the assessment of physical activity. Lower increases in cortisol after awakening predicted higher mean daily general and physical fatigue levels. Additionally, mean daily physical activity positively predicted next-day mean general fatigue. Levels of physical fatigue at a specific time point were positively associated with momentary cortisol levels. The increase in cortisol after awakening did not mediate the physical activity - fatigue relationship. There were no associations between alpha-amylase and fatigue. Our findings imply that both changes in hypothalamic-pituitary-adrenal axis activity and physical activity contribute to variance in fatigue in the daily lives of patients with FMS. This study helps to paint a clearer picture of the biological and behavioral underpinnings of fatigue in FMS and highlight the necessity of interdisciplinary treatment approaches targeting biological, behavioral and psychological aspects of FMS. Copyright © 2016 Elsevier Inc. All rights reserved.

COMPARISON OF CHAOTIC AND FRACTAL PROPERTIES OF POLAR FACULAE WITH SUNSPOT ACTIVITY

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

Deng, L. H.; Xiang, Y. Y.; Dun, G. T.

The solar magnetic activity is governed by a complex dynamo mechanism and exhibits a nonlinear dissipation behavior in nature. The chaotic and fractal properties of solar time series are of great importance to understanding the solar dynamo actions, especially with regard to the nonlinear dynamo theories. In the present work, several nonlinear analysis approaches are proposed to investigate the nonlinear dynamical behavior of the polar faculae and sunspot activity for the time interval from 1951 August to 1998 December. The following prominent results are found: (1) both the high- and the low-latitude solar activity are governed by a three-dimensional chaoticmore » attractor, and the chaotic behavior of polar faculae is the most complex, followed by that of the sunspot areas, and then the sunspot numbers; (2) both the high- and low-latitude solar activity exhibit a high degree of persistent behavior, and their fractal nature is due to such long-range correlation; (3) the solar magnetic activity cycle is predictable in nature, but the high-accuracy prediction should only be done for short- to mid-term due to its intrinsically dynamical complexity. With the help of the Babcock–Leighton dynamo model, we suggest that the nonlinear coupling of the polar magnetic fields with strong active-region fields exhibits a complex manner, causing the statistical similarities and differences between the polar faculae and the sunspot-related indicators.« less

10-Month-Old Infants Are Sensitive to the Time Course of Perceived Actions: Eye-Tracking and EEG Evidence.

PubMed

Bache, Cathleen; Springer, Anne; Noack, Hannes; Stadler, Waltraud; Kopp, Franziska; Lindenberger, Ulman; Werkle-Bergner, Markus

2017-01-01

Research has shown that infants are able to track a moving target efficiently - even if it is transiently occluded from sight. This basic ability allows prediction of when and where events happen in everyday life. Yet, it is unclear whether, and how, infants internally represent the time course of ongoing movements to derive predictions. In this study, 10-month-old crawlers observed the video of a same-aged crawling baby that was transiently occluded and reappeared in either a temporally continuous or non-continuous manner (i.e., delayed by 500 ms vs. forwarded by 500 ms relative to the real-time movement). Eye movement and rhythmic neural brain activity (EEG) were measured simultaneously. Eye movement analyses showed that infants were sensitive to slight temporal shifts in movement continuation after occlusion. Furthermore, brain activity associated with sensorimotor processing differed between observation of continuous and non-continuous movements. Early sensitivity to an action's timing may hence be explained within the internal real-time simulation account of action observation. Overall, the results support the hypothesis that 10-month-old infants are well prepared for internal representation of the time course of observed movements that are within the infants' current motor repertoire.

Clinical predictors of time to return to competition following hamstring injuries.

PubMed

Guillodo, Yannick; Here-Dorignac, Caroline; Thoribé, Bertrand; Madouas, Gwénaelle; Dauty, Marc; Tassery, Francois; Saraux, Alain

2014-07-01

hamstring strain injuries are the most common sports-related muscle injuries and one of the main causes of missed sporting events. clinical findings reflecting hamstring injury severity at presentation predict time to sports resumption. cohort study (prognosis); Level of evidence, 2. five sports medicine specialists at four sports medicine centers prospectively evaluated 120 athletes within 5 days of acute hamstring injury. Patients were interviewed and asked to evaluate their worst pain on a visual analog scale (VAS). Four physical criteria were assessed at baseline: bruising, tenderness to palpation, pain upon isometric contraction, and pain upon passive straightening. The same standardized rehabilitation protocol was used in all patients. A standardized telephone interview was conducted 45 days after the injury to determine the time to-full recovery (≤40 days or >40 days). by univariate analysis, clinical criteria associated with a full recovery time >40 days were VAS pain score greater than 6, popping sound injury, pain during everyday activities for more than 3 days, bruising, and greater than 15° motion-range limitation. By multivariate analysis, only VAS pain score and pain during everyday activities were significantly associated with time to recovery >40 days (53% sensitivity, 95% specificity). the initial examination provides valuable information that can be used to predict the time to full recovery after acute hamstring injuries in athletes.