Search

Aim: The aim of the present study was to explore if children who spend more time on screen-based sedentary behaviors (i.e.TV viewing and computer use) drink more sugar-sweetened soft drinks. The study also assessed whether these associations were independent of individual and home environmental correlates of soft drink consumption and whether they were moderated by parental education. Methods: Data were collected from 7886 children participating in the EuropeaN Energy balance Research to prevent excessive weight Gain among Youth (ENERGY) survey conducted in eight European countries. Self-report questionnaires were used. Multilevel linear regression analyses with soft drink consumption as dependent variable, TV viewing and computer use as independent variables and age, gender, parental education, attitude towards soft drinks, self-efficacy, parental modelling, parental rules and home availability of soft drinks as covariates were conducted. Further interactions were tested to explore if these associations were moderated by parental education. Country-specific analyses were conducted. Results: In six of the eight included countries, a significant positive association was observed between TV viewing (min/day) and soft drink consumption (ml/day), independent of individual and home environmental correlates of soft drink consumption (B = 0.46 (0.26–0.66) in Greece, B = 0.77 (0.36–1.17) in Norway, B = 0.82 (0.12–1.51) in Hungary, B = 1.06 (0.67–1.46) in Spain, B = 1.21 (0.67–1.74) in Belgium and B = 1.49 (0.72–2.27) in Switzerland). There was no significant association between computer use and soft drink consumption in six of the eight included countries in the final models. Moderation effects of parental education in the association between TV viewing and soft drink consumption were found in Norway and Hungary, the association being stronger among those with low parental education. Conclusions: TV viewing appears to be independently associated with soft drink consumption and this association was moderated by parental education in two countries only. Reducing TV time might therefore favorably impact soft drink consumption. [ABSTRACT FROM AUTHOR]

We study secondary random access in multi-input multi-output cognitive radio networks, where a slotted ALOHA-type protocol and successive interference cancellation are used. We first introduce three types of transmit beamforming performed by secondary users, where multiple antennas are used to suppress the interference at the primary base station and/or to increase the received signal power at the secondary base station. Then, we show a simple decentralized power allocation along with the equivalent single-antenna conversion. To exploit the multiuser diversity gain, an opportunistic transmission protocol is proposed, where the secondary users generating less interference are opportunistically selected, resulting in a further reduction of the interference temperature. The proposed methods are validated via computer simulations. Numerical results show that increasing the number of transmit antennas can greatly reduce the interference temperature, while increasing the number of receive antennas leads to a reduction of the total transmit power. Optimal parameter values of the opportunistic transmission protocol are examined according to three types of beamforming and different antenna configurations, in terms of maximizing the cognitive transmission capacity. All the beamforming, decentralized power allocation, and opportunistic transmission protocol are performed by the secondary users in a decentralized manner, thus resulting in an easy implementation in practice. [ABSTRACT FROM AUTHOR]

Introduction: Knowledge about variables associated with context-specific sitting time in older adults is limited. Therefore, this study explored cross-sectional and longitudinal associations of socio-demographic, social-cognitive, physical-environmental and health-related variables with sitting during TV viewing, computer use and motorized transport in older adults. Methods: A sample of Belgian older adults completed structured interviews on context-specific sitting time and associated variables using a longitudinal study design. Objective measurements of grip strength and physical performance were also completed. Complete baseline data were available of 258 participants (73.98±6.16 years) of which 229 participants remained in the study at one year follow-up (retention rate: 91.60%). Cross-sectional correlates (baseline data) and longitudinal predictors (change-scores in relation with change in sitting time) were explored through multiple linear regression analyses. Results: Per context-specific sitting time, most of the cross-sectional correlates differed from the longitudinal predictors. Increases over time in enjoyment of watching TV (+one unit), encouragement of partner to watch less TV (+one unit) and TV time of partner (+30.0 min/day) were associated with respectively 9.1 min/day (p<0.001), 16.0 min/day (p<0.001) and 12.0 min/day (p<0.001) more sitting during TV viewing at follow-up. Increases over time in enjoyment of using a computer (+one unit), the number of smartphones and tablets (+1) and computer use of the partner (+30.0 min/day) were associated with respectively 5.5 min/day (p < .01), 10.4 min/day (p < .05) and 3.0 min/day (p < .05) more sitting during computer use at follow-up. An increase over time in self-efficacy regarding taking a bicycle or walking was associated with 2.9 min/day (p < .05) less sitting during motorized transport at follow-up. Conclusions: The results stressed the importance of looking at separate contexts of sitting. Further, the results highlighted the importance of longitudinal research in order to reveal which changes in particular variables predicted changes in context-specific sitting time. Variables at the social-cognitive level were most frequently related to context-specific sitting. [ABSTRACT FROM AUTHOR]

Introduction: More knowledge is warranted about multilevel ecological variables associated with context-specific sitting time among adolescents. The present study explored cross-sectional and longitudinal associations of ecological domains of sedentary behaviour, including socio-demographic, social-cognitive, health-related and physical-environmental variables with sitting during TV viewing, computer use, electronic gaming and motorized transport among adolescents. Methods: For this longitudinal study, a sample of Belgian adolescents completed questionnaires at school on context-specific sitting time and associated ecological variables. At baseline, complete data were gathered from 513 adolescents (15.0±1.7 years). At one-year follow-up, complete data of 340 participants were available (retention rate: 66.3%). Multilevel linear regression analyses were conducted to explore cross-sectional correlates (baseline variables) and longitudinal predictors (change scores variables) of context-specific sitting time. Results: Social-cognitive correlates/predictors were most frequently associated with context-specific sitting time. Longitudinal analyses revealed that increases over time in considering it pleasant to watch TV (p < .001), in perceiving TV watching as a way to relax (p < .05), in TV time of parents/care givers (p < .01) and in TV time of siblings (p < .001) were associated with more sitting during TV viewing at follow-up. Increases over time in considering it pleasant to use a computer in leisure time (p < .01) and in the computer time of siblings (p < .001) were associated with more sitting during computer use at follow-up. None of the changes in potential predictors were significantly related to changes in sitting during motorized transport or during electronic gaming. Conclusions: Future intervention studies aiming to decrease TV viewing and computer use should acknowledge the importance of the behaviour of siblings and the pleasure adolescents experience during these screen-related behaviours. In addition, more time parents or care givers spent sitting may lead to more sitting during TV viewing of the adolescents, so that a family-based approach may be preferable for interventions. Experimental study designs are warranted to confirm the present findings. [ABSTRACT FROM AUTHOR]

Background: We previously reported that stable rotors were observed in in-silico human atrial fibrillation (AF) models, and were well represented by dominant frequency (DF). We explored the spatiotemporal stability of DF sites in 3D-AF models imported from patient CT images of the left atrium (LA). Methods: We integrated 3-D CT images of the LA obtained from ten patients with persistent AF (male 80%, 61.8 ± 13.5 years old) into an in-silico AF model. After induction, we obtained 6 seconds of AF simulation data for DF analyses in 30 second intervals (T1–T9). The LA was divided into ten sections. Spatiotemporal changes and variations in the temporal consistency of DF were evaluated at each section of the LA. The high DF area was defined as the area with the highest 10% DF. Results: 1. There was no spatial consistency in the high DF distribution at each LA section during T1–T9 except in one patient (p = 0.027). 2. Coefficients of variation for the high DF area were highly different among the ten LA sections (p < 0.001), and they were significantly higher in the four pulmonary vein (PV) areas, the LA appendage, and the peri-mitral area than in the other LA sections (p < 0.001). 3. When we conducted virtual ablation of 10%, 15%, and 20% of the highest DF areas (n = 270 cases), AF was changed to atrial tachycardia (AT) or terminated at a rate of 40%, 57%, and 76%, respectively. Conclusions: Spatiotemporal consistency of the DF area was observed in 10% of AF patients, and high DF areas were temporally variable. Virtual ablation of DF is moderately effective in AF termination and AF changing into AT. [ABSTRACT FROM AUTHOR]

The standard approach in accounting for hierarchical differentiation in biology and the social sciences considers a hierarchy as a static distribution of individuals possessing differing amounts of some valued commodity, assumes that the hierarchy is generated by micro-level processes involving individuals, and attempts to reverse engineer the processes that produced the hierarchy. However, sufficient experimental and analytical results are available to evaluate this standard approach in the case of animal dominance hierarchies (pecking orders). Our evaluation using evidence from hierarchy formation in small groups of both hens and cichlid fish reveals significant deficiencies in the three tenets of the standard approach in accounting for the organization of dominance hierarchies. In consequence, we suggest that a new approach is needed to explain the organization of pecking orders and, very possibly, by implication, for other kinds of social hierarchies. We develop an example of such an approach that considers dominance hierarchies to be dynamic networks, uses dynamic sequences of interaction (dynamic network motifs) to explain the organization of dominance hierarchies, and derives these dynamic sequences directly from observation of hierarchy formation. We test this dynamical explanation using computer simulation and find a good fit with actual dynamics of hierarchy formation in small groups of hens. We hypothesize that the same dynamic sequences are used in small groups of many other animal species forming pecking orders, and we discuss the data required to evaluate our hypothesis. Finally, we briefly consider how our dynamic approach may be generalized to other kinds of social hierarchies using the example of the distribution of empty gastropod (snail) shells occupied in populations of hermit crabs. [ABSTRACT FROM AUTHOR]

The identification of modules or communities in sets of related variables is a key step in the analysis and modeling of biological systems. Procedures for this identification are usually designed to allow fast analyses of very large datasets and may produce suboptimal results when these sets are of a small to moderate size. This article introduces BoCluSt, a new, somewhat more computationally intensive, community detection procedure that is based on combining a clustering algorithm with a measure of stability under bootstrap resampling. Both computer simulation and analyses of experimental data showed that BoCluSt can outperform current procedures in the identification of multiple modules in data sets with a moderate number of variables. In addition, the procedure provides users with a null distribution of results to evaluate the support for the existence of community structure in the data. BoCluSt takes individual measures for a set of variables as input, and may be a valuable and robust exploratory tool of network analysis, as it provides 1) an estimation of the best partition of variables into modules, 2) a measure of the support for the existence of modular structures, and 3) an overall description of the whole structure, which may reveal hierarchical modular situations, in which modules are composed of smaller sub-modules. [ABSTRACT FROM AUTHOR]

In this study we have examined the effect of prolonged endurance training program on the pulmonary oxygen uptake (V’O2) kinetics during heavy-intensity cycling-exercise and its impact on maximal cycling and running performance. Twelve healthy, physically active men (mean±SD: age 22.33±1.44 years, V’O2peak 3198±458 mL ∙ min-1) performed an endurance training composed mainly of moderate-intensity cycling, lasting 20 weeks. Training resulted in a decrease (by ~5%, P = 0.027) in V’O2 during prior low-intensity exercise (20 W) and in shortening of τp of the V’O2 on-kinetics (30.1±5.9 s vs. 25.4±1.5 s, P = 0.007) during subsequent heavy-intensity cycling. This was accompanied by a decrease of the slow component of V’O2 on-kinetics by 49% (P = 0.001) and a decrease in the end-exercise V’O2 by ~5% (P = 0.005). An increase (P = 0.02) in the vascular endothelial growth factor receptor 2 mRNA level and a tendency (P = 0.06) to higher capillary-to-fiber ratio in the vastus lateralis muscle were found after training (n = 11). No significant effect of training on the V’O2peak was found (P = 0.12). However, the power output reached at the lactate threshold increased by 19% (P = 0.01). The power output obtained at the V’O2peak increased by 14% (P = 0.003) and the time of 1,500-m performance decreased by 5% (P = 0.001). Computer modeling of the skeletal muscle bioenergetic system suggests that the training-induced decrease in the slow component of V’O2 on-kinetics found in the present study is mainly caused by two factors: an intensification of the each-step activation (ESA) of oxidative phosphorylation (OXPHOS) complexes after training and decrease in the ‘‘additional” ATP usage rising gradually during heavy-intensity exercise. [ABSTRACT FROM AUTHOR]

Radiofrequency catheter ablation (RFCA) is a routine treatment for cardiac arrhythmias. During RFCA, the electrode-tissue interface temperature should be kept below 80°C to avoid thrombus formation. Open-irrigated electrodes facilitate power delivery while keeping low temperatures around the catheter. No computational model of an open-irrigated electrode in endocardial RFCA accounting for both the saline irrigation flow and the blood motion in the cardiac chamber has been proposed yet. We present the first computational model including both effects at once. The model has been validated against existing experimental results. Computational results showed that the surface lesion width and blood temperature are affected by both the electrode design and the irrigation flow rate. Smaller surface lesion widths and blood temperatures are obtained with higher irrigation flow rate, while the lesion depth is not affected by changing the irrigation flow rate. Larger lesions are obtained with increasing power and the electrode-tissue contact. Also, larger lesions are obtained when electrode is placed horizontally. Overall, the computational findings are in close agreement with previous experimental results providing an excellent tool for future catheter research. [ABSTRACT FROM AUTHOR]

Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data. [ABSTRACT FROM AUTHOR]

Background: New technologies are diffusing into medical practice swiftly. Hand-held devices such as smartphones can record short-duration (e.g., 1-minute) ECGs, but their effectiveness in identifying patients with paroxysmal atrial fibrillation (AF) is unknown. Methods: We used data from the TRENDS study, which included 370 patients (mean age 71 years, 71% men, CHADS2 score≥1 point: mean 2.3 points) who had no documentation of atrial tachycardia (AT)/AF or antiarrhythmic or anticoagulant drug use at baseline. All were subsequently newly diagnosed with AT/AF by a cardiac implantable electronic device (CIED) over one year of follow-up. Using a computer simulation approach (5,000 repetitions), we estimated the detection rate for paroxysmal AT/AF via daily snapshot ECG monitoring over various periods, with the probability of detection equal to the percent AT/AF burden on each day. Results: The estimated AT/AF detection rates with snapshot monitoring periods of 14, 28, 56, 112, and 365 days were 10%, 15%, 21%, 28%, and 50% respectively. The detection rate over 365 days of monitoring was higher in those with CHADS2 scores ≥2 than in those with CHADS2 scores of 1 (53% vs. 38%), and was higher in those with AT/AF burden ≥0.044 hours/day compared to those with AT/AF burden <0.044 hours/day (91% vs. 14%; both P<0.05). Conclusions: Daily snapshot ECG monitoring over 365 days detects half of patients who developed AT/AF as detected by CIED, and shorter intervals of monitoring detected fewer AT/AF patients. The detection rate was associated with individual CHADS2 score and AT/AF burden. Trial Registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach. [ABSTRACT FROM AUTHOR]

How communication systems emerge is a topic of relevance to several academic disciplines. Numerous existing models, both mathematical and computational, study this emergence. However, with few exceptions, these models all build some form of communication into their initial specification. Consequently, what these models study is how communication systems transition from one form to another, and not how communication itself emerges in the first place. Here we present a new computational model of the emergence of communication which, unlike previous models, does not pre-specify the existence of communication. We conduct two experiments using this model, in order to derive general statements about how communication systems emerge. The two main routes to communication that we identify correspond with findings from the empirical literature on the evolution of animal signals. We use this finding to explain when and why we should expect communication to emerge in nature. We also compare our model to experimental research on the origins of human communication systems, and hence show that humans are an important exception to the general trends we observe. We argue that this is because humans, and probably only humans, are able to ‘signal signalhood’, i.e. to express communicative intentions. [ABSTRACT FROM AUTHOR]

Large populations of free-roaming cats (FRCs) generate ongoing concerns for welfare of both individual animals and populations, for human public health, for viability of native wildlife populations, and for local ecological damage. Managing FRC populations is a complex task, without universal agreement on best practices. Previous analyses that use simulation modeling tools to evaluate alternative management methods have focused on relative efficacy of removal (or trap-return, TR), typically involving euthanasia, and sterilization (or trap-neuter-return, TNR) in demographically isolated populations. We used a stochastic demographic simulation approach to evaluate removal, permanent sterilization, and two postulated methods of temporary contraception for FRC population management. Our models include demographic connectivity to neighboring untreated cat populations through natural dispersal in a metapopulation context across urban and rural landscapes, and also feature abandonment of owned animals. Within population type, a given implementation rate of the TR strategy results in the most rapid rate of population decline and (when populations are isolated) the highest probability of population elimination, followed in order of decreasing efficacy by equivalent rates of implementation of TNR and temporary contraception. Even low levels of demographic connectivity significantly reduce the effectiveness of any management intervention, and continued abandonment is similarly problematic. This is the first demographic simulation analysis to consider the use of temporary contraception and account for the realities of FRC dispersal and owned cat abandonment. [ABSTRACT FROM AUTHOR]

When we look at the world—or a graphical depiction of the world—we perceive surface materials (e.g. a ceramic black and white checkerboard) independently of variations in illumination (e.g. shading or shadow) and atmospheric media (e.g. clouds or smoke). Such percepts are partly based on the way physical surfaces and media reflect and transmit light and partly on the way the human visual system processes the complex patterns of light reaching the eye. One way to understand how these percepts arise is to assume that the visual system parses patterns of light into layered perceptual representations of surfaces, illumination and atmospheric media, one seen through another. Despite a great deal of previous experimental and modelling work on layered representation, however, a unified computational model of key perceptual demonstrations is still lacking. Here we present the first general computational model of perceptual layering and surface appearance—based on a boarder theoretical framework called gamut relativity—that is consistent with these demonstrations. The model (a) qualitatively explains striking effects of perceptual transparency, figure-ground separation and lightness, (b) quantitatively accounts for the role of stimulus- and task-driven constraints on perceptual matching performance, and (c) unifies two prominent theoretical frameworks for understanding surface appearance. The model thereby provides novel insights into the remarkable capacity of the human visual system to represent and identify surface materials, illumination and atmospheric media, which can be exploited in computer graphics applications. [ABSTRACT FROM AUTHOR]

Background: Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. Methods and Findings: A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. Conclusions: Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes. [ABSTRACT FROM AUTHOR]

Background: Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. Methodology: We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. Results: The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). Conclusions: According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process. [ABSTRACT FROM AUTHOR]

A fusion between the genes encoding the marine bacterium Cobetia marina alkaline phosphatase (CmAP) and Far Eastern holothurian Apostichopus japonicus mannan-binding C-type lectin (MBL-AJ) was performed. Expression of the fusion gene in E. coli cells resulted in yield of soluble recombinant chimeric protein CmAP/MBL-AJ with the high alkaline phosphatase activity and specificity of the lectin MBL-AJ. The bifunctional hybrid CmAP/MBL-AJ was produced as a dimer with the molecular mass of 200 kDa. The CmAP/MBL-AJ dimer model showed the two-subunit lectin part that is associated with two molecules of alkaline phosphatase functioning independently from each other. The highly active CmAP label genetically linked to MBL-AJ has advantaged the lectin-binding assay in its sensitivity and time. The double substitution A156N/F159K in the lectin domain of CmAP/MBL-AJ has enhanced its lectin activity by 25±5%. The bifunctional hybrid holothurian's lectin could be promising tool for developing non-invasive methods for biological markers assessment, particularly for improving the MBL-AJ-based method for early detection of a malignant condition in cervical specimens. [ABSTRACT FROM AUTHOR]

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur. [ABSTRACT FROM AUTHOR]

The aim of the study was to develop a three-dimensional (3D) anatomically-detailed model of the rabbit right atrium containing the sinoatrial and atrioventricular nodes to study the electrophysiology of the nodes. A model was generated based on 3D images of a rabbit heart (atria and part of ventricles), obtained using high-resolution magnetic resonance imaging. Segmentation was carried out semi-manually. A 3D right atrium array model (∼3.16 million elements), including eighteen objects, was constructed. For description of cellular electrophysiology, the Rogers-modified FitzHugh-Nagumo model was further modified to allow control of the major characteristics of the action potential with relatively low computational resource requirements. Model parameters were chosen to simulate the action potentials in the sinoatrial node, atrial muscle, inferior nodal extension and penetrating bundle. The block zone was simulated as passive tissue. The sinoatrial node, crista terminalis, main branch and roof bundle were considered as anisotropic. We have simulated normal and abnormal electrophysiology of the two nodes. In accordance with experimental findings: (i) during sinus rhythm, conduction occurs down the interatrial septum and into the atrioventricular node via the fast pathway (conduction down the crista terminalis and into the atrioventricular node via the slow pathway is slower); (ii) during atrial fibrillation, the sinoatrial node is protected from overdrive by its long refractory period; and (iii) during atrial fibrillation, the atrioventricular node reduces the frequency of action potentials reaching the ventricles. The model is able to simulate ventricular echo beats. In summary, a 3D anatomical model of the right atrium containing the cardiac conduction system is able to simulate a wide range of classical nodal behaviours. [ABSTRACT FROM AUTHOR]

Objective: To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet. Methods: We collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups. Results: A third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean  = 284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count. Conclusions: Some of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter. [ABSTRACT FROM AUTHOR]

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges. [ABSTRACT FROM AUTHOR]

Least-cost models are widely used to study the functional connectivity of habitat within a varied landscape matrix. A critical step in the process is identifying resistance values for each land cover based upon the facilitating or impeding impact on species movement. Ideally resistance values would be parameterised with empirical data, but due to a shortage of such information, expert-opinion is often used. However, the use of expert-opinion is seen as subjective, human-centric and unreliable. This study derived resistance values from grey squirrel habitat suitability models (HSM) in order to compare the utility and validity of this approach with more traditional, expert-led methods. Models were built and tested with MaxEnt, using squirrel presence records and a categorical land cover map for Cumbria, UK. Predictions on the likelihood of squirrel occurrence within each land cover type were inverted, providing resistance values which were used to parameterise a least-cost model. The resulting habitat networks were measured and compared to those derived from a least-cost model built with previously collated information from experts. The expert-derived and HSM-inferred least-cost networks differ in precision. The HSM-informed networks were smaller and more fragmented because of the higher resistance values attributed to most habitats. These results are discussed in relation to the applicability of both approaches for conservation and management objectives, providing guidance to researchers and practitioners attempting to apply and interpret a least-cost approach to mapping ecological networks. [ABSTRACT FROM AUTHOR]

Objective: Although plaque erosion causes approximately 40% of all coronary thrombi and disproportionally affects women more than men, its mechanism is not well understood. The role of tissue mechanics in plaque rupture and regulation of mechanosensitive inflammatory proteins is well established, but their role in plaque erosion is unknown. Given obvious differences in morphology between plaque erosion and rupture, we hypothesized that inflammation in general as well as the association between local mechanical strain and inflammation known to exist in plaque rupture may not occur in plaque erosion. Therefore, our objective was to determine if similar mechanisms underlie plaque rupture and plaque erosion. Methods and Results: We studied a total of 74 human coronary plaque specimens obtained at autopsy. Using lesion-specific computer modeling of solid mechanics, we calculated the stress and strain distribution for each plaque and determined if there were any relationships with markers of inflammation. Consistent with previous studies, inflammatory markers were positively associated with increasing strain in specimens with rupture and thin-cap fibroatheromas. Conversely, overall staining for inflammatory markers and apoptosis were significantly lower in erosion, and there was no relationship with mechanical strain. Samples with plaque erosion most closely resembled those with the stable phenotype of thick-cap fibroatheromas. Conclusions: In contrast to classic plaque rupture, plaque erosion was not associated with markers of inflammation and mechanical strain. These data suggest that plaque erosion is a distinct pathophysiological process with a different etiology and therefore raises the possibility that a different therapeutic approach may be required to prevent plaque erosion. [ABSTRACT FROM AUTHOR]

Lake cress, Rorippa aquatica (Brassicaceae), is a semi-aquatic plant that exhibits a variety of leaf shapes, from simple leaves to highly branched compound leaves, depending on the environment. Leaf shape can vary within a single plant, suggesting that the variation can be explained by a simple model. In order to simulate the branched structure in the compound leaves of R. aquatica, we implemented reaction-diffusion (RD) patterning onto a theoretical framework that had been developed for serration distribution in the leaves of Arabidopsis thaliana, with the modification of the one-dimensional reaction-diffusion domain being deformed with the spatial periodicity of the RD pattern while expanding. This simple method using an iterative pattern could create regular and nested branching patterns. Subsequently, we verified the plausibility of our theoretical model by comparing it with the experimentally observed branching patterns. The results suggested that our model successfully predicted both the qualitative and quantitative aspects of the timing and positioning of branching in growing R. aquatica leaves. [ABSTRACT FROM AUTHOR]

Variations in individual body mass and composition have long been a key focus in the health sciences, particularly now that overweight and obesity are considered as public health problems. We study a mathematical model that describes body mass variations which are determined by the energy balance between caloric intake and total energy expenditure. To calculate the change in caloric intake and energy expenditure over time, we proposed a relationship for each of these quantities, and we used measured values that are reported in the literature for the initial conditions. To account for small variations in the daily energy balance of an individual, we include social interactions as the multiplication of two terms: social proximity and social influence. We observe that social interactions have a considerable effect when the body mass of an individual is quite constant and social interactions take random values. However, when an individual's mass value changes (either increases or decreases), social interactions do not have a notable effect. In our simulation, we tested two different models that describe the body mass composition, and it resulted that one fits better the data. [ABSTRACT FROM AUTHOR]

Chronic Beryllium (Be) Disease (CBD) is a granulomatous disorder that predominantly affects the lung. The CBD is caused by Be exposure of individuals carrying the HLA-DP2 protein of the major histocompatibility complex class II (MHCII). While the involvement of Be in the development of CBD is obvious and the binding site and the sequence of Be and peptide binding were recently experimentally revealed , the interplay between induced conformational changes and the changes of the peptide binding affinity in presence of Be were not investigated. Here we carry out in silico modeling and predict the Be binding to be within the acidic pocket (Glu26, Glu68 and Glu69) present on the HLA-DP2 protein in accordance with the experimental work . In addition, the modeling indicates that the Be ion binds to the HLA-DP2 before the corresponding peptide is able to bind to it. Further analysis of the MD generated trajectories reveals that in the presence of the Be ion in the binding pocket of HLA-DP2, all the different types of peptides induce very similar conformational changes, but their binding affinities are quite different. Since these conformational changes are distinctly different from the changes caused by peptides normally found in the cell in the absence of Be, it can be speculated that CBD can be caused by any peptide in presence of Be ion. However, the affinities of peptides for Be loaded HLA-DP2 were found to depend of their amino acid composition and the peptides carrying acidic group at positions 4 and 7 are among the strongest binders. Thus, it is proposed that CBD is caused by the exposure of Be of an individual carrying the HLA-DP2*0201 allele and that the binding of Be to HLA-DP2 protein alters the conformational and ionization properties of HLA-DP2 such that the binding of a peptide triggers a wrong signaling cascade. [ABSTRACT FROM AUTHOR]

Background: Do peripersonal space for acting on objects and interpersonal space for interacting with con-specifics share common mechanisms and reflect the social valence of stimuli? To answer this question, we investigated whether these spaces refer to a similar or different physical distance. Methodology: Participants provided reachability-distance (for potential action) and comfort-distance (for social processing) judgments towards human and non-human virtual stimuli while standing still (passive) or walking toward stimuli (active). Principal Findings: Comfort-distance was larger than other conditions when participants were passive, but reachability and comfort distances were similar when participants were active. Both spaces were modulated by the social valence of stimuli (reduction with virtual females vs males, expansion with cylinder vs robot) and the gender of participants. Conclusions: These findings reveal that peripersonal reaching and interpersonal comfort spaces share a common motor nature and are sensitive, at different degrees, to social modulation. Therefore, social processing seems embodied and grounded in the body acting in space. [ABSTRACT FROM AUTHOR]

This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making. [ABSTRACT FROM AUTHOR]

As part of our drug discovery program for anti-filarial agents from Indian medicinal plants, leaves of Eucalyptus tereticornis were chemically investigated, which resulted in the isolation and characterization of an anti-filarial agent, ursolic acid (UA) as a major constituent. Antifilarial activity of UA against the human lymphatic filarial parasite Brugia malayi using in vitro and in vivo assays, and in silico docking search on glutathione-s-transferase (GST) parasitic enzyme were carried out. The UA was lethal to microfilariae (mf; LC100: 50; IC50: 8.84 µM) and female adult worms (LC100: 100; IC50: 35.36 µM) as observed by motility assay; it exerted 86% inhibition in MTT reduction potential of the adult parasites. The selectivity index (SI) of UA for the parasites was found safe. This was supported by the molecular docking studies, which showed adequate docking (LibDock) scores for UA (−8.6) with respect to the standard antifilarial drugs, ivermectin (IVM −8.4) and diethylcarbamazine (DEC-C −4.6) on glutathione-s-transferase enzyme. Further, in silico pharmacokinetic and drug-likeness studies showed that UA possesses drug-like properties. Furthermore, UA was evaluated in vivo in B. malayi-M. coucha model (natural infection), which showed 54% macrofilaricidal activity, 56% female worm sterility and almost unchanged microfilaraemia maintained throughout observation period with no adverse effect on the host. Thus, in conclusion in vitro, in silico and in vivo results indicate that UA is a promising, inexpensive, widely available natural lead, which can be designed and developed into a macrofilaricidal drug. To the best of our knowledge this is the first ever report on the anti-filarial potential of UA from E. tereticornis, which is in full agreement with the Thomson Reuter's ‘Metadrug’ tool screening predictions. [ABSTRACT FROM AUTHOR]

The production of reactive oxygen species (ROS) from the inner mitochondrial membrane is one of many fundamental processes governing the balance between health and disease. It is well known that ROS are necessary signaling molecules in gene expression, yet when expressed at high levels, ROS may cause oxidative stress and cell damage. Both hypoxia and hyperoxia may alter ROS production by changing mitochondrial Po2 (). Because depends on the balance between O2 transport and utilization, we formulated an integrative mathematical model of O2 transport and utilization in skeletal muscle to predict conditions to cause abnormally high ROS generation. Simulations using data from healthy subjects during maximal exercise at sea level reveal little mitochondrial ROS production. However, altitude triggers high mitochondrial ROS production in muscle regions with high metabolic capacity but limited O2 delivery. This altitude roughly coincides with the highest location of permanent human habitation. Above 25,000 ft., more than 90% of exercising muscle is predicted to produce abnormally high levels of ROS, corresponding to the “death zone” in mountaineering. [ABSTRACT FROM AUTHOR]

Roads can experience runoff problems due to the intense rain discharge associated to severe storms. Two advanced tools are combined to analyse the interaction of complex water flows with real terrains. UAV (Unmanned Aerial Vehicle) photogrammetry is employed to obtain accurate topographic information on small areas, typically on the order of a few hectares. The Smoothed Particle Hydrodynamics (SPH) technique is applied by means of the DualSPHysics model to compute the trajectory of the water flow during extreme rain events. The use of engineering solutions to palliate flood events is also analysed. The study case simulates how the collected water can flow into a close road and how precautionary measures can be effective to drain water under extreme conditions. The amount of water arriving at the road is calculated under different protection scenarios and the efficiency of a ditch is observed to decrease when sedimentation reduces its depth. [ABSTRACT FROM AUTHOR]

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making. [ABSTRACT FROM AUTHOR]

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments. [ABSTRACT FROM AUTHOR]

Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at . [ABSTRACT FROM AUTHOR]

Wildfires can pose a significant risk to people and property. Billions of dollars are spent investing in fire management actions in an attempt to reduce the risk of loss. One of the key areas where money is spent is through fuel treatment – either fuel reduction (prescribed fire) or fuel removal (fuel breaks). Individual treatments can influence fire size and the maximum distance travelled from the ignition and presumably risk, but few studies have examined the landscape level effectiveness of these treatments. Here we use a Bayesian Network model to examine the relative influence of the built and natural environment, weather, fuel and fuel treatments in determining the risk posed from wildfire to the wildland-urban interface. Fire size and distance travelled was influenced most strongly by weather, with exposure to fires most sensitive to changes in the built environment and fire parameters. Natural environment variables and fuel load all had minor influences on fire size, distance travelled and exposure of assets. These results suggest that management of fuels provided minimal reductions in risk to assets and adequate planning of the changes in the built environment to cope with the expansion of human populations is going to be vital for managing risk from fire under future climates. [ABSTRACT FROM AUTHOR]

Hepatitis C Virus (HCV) infects 200 million individuals worldwide. Although several FDA approved drugs targeting the HCV serine protease and polymerase have shown promising results, there is a need for better drugs that are effective in treating a broader range of HCV genotypes and subtypes without being used in combination with interferon and/or ribavirin. Recently, two crystal structures of the core of the HCV E2 protein (E2c) have been determined, providing structural information that can now be used to target the E2 protein and develop drugs that disrupt the early stages of HCV infection by blocking E2’s interaction with different host factors. Using the E2c structure as a template, we have created a structural model of the E2 protein core (residues 421–645) that contains the three amino acid segments that are not present in either structure. Computational docking of a diverse library of 1,715 small molecules to this model led to the identification of a set of 34 ligands predicted to bind near conserved amino acid residues involved in the HCV E2: CD81 interaction. Surface plasmon resonance detection was used to screen the ligand set for binding to recombinant E2 protein, and the best binders were subsequently tested to identify compounds that inhibit the infection of Huh-7 cells by HCV. One compound, 281816, blocked E2 binding to CD81 and inhibited HCV infection in a genotype-independent manner with IC50’s ranging from 2.2 µM to 4.6 µM. 281816 blocked the early and late steps of cell-free HCV entry and also abrogated the cell-to-cell transmission of HCV. Collectively the results obtained with this new structural model of E2c suggest the development of small molecule inhibitors such as 281816 that target E2 and disrupt its interaction with CD81 may provide a new paradigm for HCV treatment. [ABSTRACT FROM AUTHOR]

Caspases are enzymes belonging to a conserved family of ysteine-dependent artic-specific prote that are involved in vital cellular processes and play a prominent role in apoptosis and inflammation. Determining all relevant protein substrates of caspases remains a challenging task. Over 1500 caspase substrates have been discovered in the human proteome according to published data and new substrates are discovered on a daily basis. To aid the discovery process we developed a caspase cleavage prediction method using the recently published curated MerCASBA database of experimentally determined caspase substrates and a Random Forest classification method. On both internal and external test sets, the ranking of predicted cleavage positions is superior to all previously developed prediction methods. The in silico predicted caspase cleavage positions in human proteins are available from a relational database: CaspDB. Our database provides information about potential cleavage sites in a verified set of all human proteins collected in Uniprot and their orthologs, allowing for tracing of cleavage motif conservation. It also provides information about the positions of disease-annotated single nucleotide polymorphisms, and posttranslational modifications that may modulate the caspase cleaving efficiency. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer. [ABSTRACT FROM AUTHOR]

Breathing is a vital process providing the exchange of gases between the lungs and atmosphere. During quiet breathing, pumping air from the lungs is mostly performed by contraction of the diaphragm during inspiration, and muscle contraction during expiration does not play a significant role in ventilation. In contrast, during intense exercise or severe hypercapnia forced or active expiration occurs in which the abdominal “expiratory” muscles become actively involved in breathing. The mechanisms of this transition remain unknown. To study these mechanisms, we developed a computational model of the closed-loop respiratory system that describes the brainstem respiratory network controlling the pulmonary subsystem representing lung biomechanics and gas (O2 and CO2) exchange and transport. The lung subsystem provides two types of feedback to the neural subsystem: a mechanical one from pulmonary stretch receptors and a chemical one from central chemoreceptors. The neural component of the model simulates the respiratory network that includes several interacting respiratory neuron types within the Bötzinger and pre-Bötzinger complexes, as well as the retrotrapezoid nucleus/parafacial respiratory group (RTN/pFRG) representing the central chemoreception module targeted by chemical feedback. The RTN/pFRG compartment contains an independent neural generator that is activated at an increased CO2 level and controls the abdominal motor output. The lung volume is controlled by two pumps, a major one driven by the diaphragm and an additional one activated by abdominal muscles and involved in active expiration. The model represents the first attempt to model the transition from quiet breathing to breathing with active expiration. The model suggests that the closed-loop respiratory control system switches to active expiration via a quantal acceleration of expiratory activity, when increases in breathing rate and phrenic amplitude no longer provide sufficient ventilation. The model can be used for simulation of closed-loop control of breathing under different conditions including respiratory disorders. [ABSTRACT FROM AUTHOR]