Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power

Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. Both models had good prediction accuracy despite using less input variables than traditional models and using more challenging prediction conditions by enforcing rapid speed changes during cycling. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled.

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems

BACKGROUND

Disease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems.

METHODS

In this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state.

RESULTS

The proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called "dark genes" that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis.

CONCLUSIONS

The proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Emergent Coordination of Heading in Soccer: Of Two Players and a Single Ball

Purpose: In many sports situations, two or more players need to coordinate their actions to make sure that one of them intercepts a ball or opponent. We considered how two soccer players head back a thrown ball. Two accounts for the joint decision making by both players were considered. These two accounts not only differ in their theoretical basis but also have vastly different implications for training practice. In a first account, players know their areas of responsibility for interception, and combine this with their prediction of the ball's landing location. In a second account, the coordination emerges from the unfolding dynamics of the system of informationally connected players and ball. According to this second account, especially for balls aimed in between the two players, both of the players may start moving and one player sees that the ball will be interceptable for the other player, and subsequently yields the interception. Methods: We instrumented soccer players and the ball with Kinexon sensors and had pairs of players head back the thrown ball. Results: In line with the second account, the results showed a fair number of instances where the player who intercepted the ball had to move the longest distance. Furthermore, considerable movement by both players was not an exception. Conclusion: The results can be taken as a first step towards an understanding of joint coordination as an emergent phenomenon.

A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

Gesture-speech synchrony in schizophrenia: A pilot study using a kinematic-acoustic analysis

Severe impairment of social functioning is the core feature of schizophrenia that persists despite treatment, and contributes to chronic functional disability. Abnormal non-verbal behaviors have been reported during interpersonal interactions but the temporal coordination of co-speech gestures with language abilities have been poorly studied to date in this pathology. Using the dynamical systems framework, the goal of the current study was to investigate whether gestures and speech synchrony is impaired in schizophrenia, exploring a new approach to report communicational skill disorders. Performing the first continuous kinematic-acoustic analysis in individuals with schizophrenia, we examined gesture-speech synchrony in solo spontaneous speech and in sensorimotor synchronization task. The experimental group consisted of twenty-eight participants with a diagnosis of schizophrenia and the control group consisted of twenty-four healthy participants age-gender-education matched. The results showed that spontaneous gesture-speech synchrony was preserved while intentional finger tapping-speech synchrony was impaired. In sensorimotor synchronization task, the schizophrenia group displayed greater asynchronies between finger tapping and syllable uttering and lower stability of coordination patterns. These findings suggest a specific deficit in time delay of information circulation and processing, especially in explicit functions. Thus, investigating intrapersonal coordination in schizophrenia may constitute a promising window into brain/behavior dynamic relationship.

Stability and flexibility in psychotherapy process predict outcome

Ten good outcome and ten poor outcome psychotherapy cases were compared to investigate whether or not the temporal stability and flexibility of their process variables can predict their outcomes. Each participant was monitored daily using the Therapy Process Questionnaire (TPQ), which has 43 items and seven sub-scales, and responses over time were analyzed in terms of correlation robustness and correlation variability across the TPQ sub-scales. "Correlation robustness" and "correlation variability" are two basic characteristics of any correlation matrix: the first is calculated as the sum of the absolute values of Pearson correlation coefficients, the second as the standard deviation of Pearson correlation coefficients. The results demonstrated that the patients within the poor outcome group had lower values on both variables, suggesting lower stability and flexibility. Furthermore, a higher number of cycles of increase and decrease in correlation robustness and variability of the TPQ sub-scales was observed within good outcome psychotherapies, suggesting that, these cycles can be considered as process-markers of good-outcomes. These results provide support for the validity of these quantitative process-parameters, correlation robustness and variability, in predicting psychotherapeutic outcomes. Moreover, the results lend support to the common clinical experience of alternating periods of flexibility and integration being beneficial to good psychotherapeutic processes.

The Emotion Dynamics Conundrum in Developmental Psychopathology: Similarities, Distinctions, and Adaptiveness of Affective Variability and Socioaffective Flexibility

A recent emphasis in developmental psychopathology research has been on emotion dynamics, or how emotional experience changes over time in response to context, and how those emotion dynamics affect psychosocial functioning. Two prominent emotion dynamics constructs have emerged in the developmental psychopathology literature: affective variability and socioaffective flexibility. Affective variability is most often measured using momentary methods (e.g., EMA) and is theorized to reflect reactivity and regulation in response to context, whereas socioaffective flexibility is typically measured in the context of parent-child interactions and theorized as the ability to move effectively through a range of affective states. Notably, affective variability is considered broadly maladaptive; however, socioaffective flexibility is theorized to be fundamentally adaptive. Despite these diametric views on adaptability, these two constructs share an underlying dependency on non-effortful emotion change in response to context, which raises questions about whether these constructs are, at their core, more similar than dissimilar. This review examined the literatures on affective variability and socioaffective flexibility in child and adolescent samples, examining associations with psychosocial and clinical correlates, as well as conceptual and methodological similarities and distinctions. Findings indicate that despite considerable theoretical overlap, there are sufficient differences-albeit largely methodological-that justify continuing to treat these constructs as distinct, most notably the influence of parents in socioaffective flexibility. The review closes with several recommendations for future study targeted at further clarifying the distinctions (or lack thereof) between affective variability and socioaffective flexibility.

Intervening on psychopathology networks: Evaluating intervention targets through simulations

Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.

Comparison of two coupling methods regarding coupling patterns, sensitivity to footwear and potential future injury applications

Researchers have quantified the effect of footwear conditions on movement patterns and injury risk for runners using discrete kinematic variables and/or relative coupling variables. Coupling is typically assessed using the transfer coefficient (TC) or the vector coding (VC) approach. However, a thorough comparison of both methods regarding their interpretation, sensitivity to testing conditions and information regarding coupling strength as one overall coupling score is missing. This study aimed to compare both methods regarding a) their coupling patterns, b) their sensitivity to footwear conditions and c) to discuss both coupling approaches regarding an overall coupling score. 3D motion capture data was collected of 10 males running on a treadmill with and without shoes. Rearfoot frontal and tibia transverse plane motion was analyzed. Discrete kinematic variables and relative coupling variables were calculated via the TC and VC approach for the landing and push-off phase. A novel variable, the coupling score, was developed and calculated using both coupling methods. Coupling variables calculated with both methods showed differences in coupling patterns, especially for the landing phase (TC ≈ 0.5/in-phase towards rearfoot-phase, VC ≈ 300°/anti-phase). VC offers further details, such as coupling frequencies, compared to the TC. Moreover, both methods were unable to distinguish between footwear conditions regarding their coupling patterns or coupling scores. Strong correlations (r ≤ 0.7, p < 0.0125) between coupling scores assessed with either method suggest that both methods contain similar information regarding coupling strength. The coupling score represents a first attempt to quantify a weighted coupling pattern quantitatively. Future studies have to validate whether the coupling score might be linked to injury risks.

Peek-a-who? Exploring the dynamics of early communication with an interactive partner swap paradigm and state space grid visualization

Effective caregiver-infant communication occurs when interactive partners successfully coordinate multiple modalities (e.g., body movements, affect, eye gaze). The complex interplay of multiple modalities during caregiver-infant interactions is difficult to capture, which has made a comprehensive, evidence-based understanding of caregiver-infant communication difficult to achieve. We present a novel methodological approach to address this challenge by combining an Interactive Partner Swap (IPS) paradigm with a longitudinal design, detailed multimodal coding, and data visualization via state space grids (SSGs). We demonstrate the utility of our approach by presenting three sets of SSGs which reveal both dyadic flexibility and stability in caregiver-infant peek-a-boo interactions across three levels: micro (moment-to-moment), meso (interactive context), and macro (infant development). By using SSGs to explore the patterns that hold and others that differ systematically across interactive partner and infant development, our novel approach promises to offer critical first steps to creating a more detailed understanding of the dynamics of early multimodal communication.

Perinatal foundations of personality pathology from a dynamical systems perspective

The development of personality pathology is an interactive process between biologically based susceptibilities, interpersonal patterns, and contextual factors across the lifespan. In this paper, we argue that these interactions begin before birth. We describe the perinatal period (i.e. pregnancy and up to one year postpartum) as a sensitive developmental window during which regulatory and stress response systems that confer risk for personality pathology begin forming. In addition, we present converging evidence for significant associations between perinatal factors and later life personality disorders. Finally, we present this perinatal perspective through the lens of dynamical systems theory and emphasize the promise of this framework for guiding future personality disorder research, prevention, and intervention.

On forecasting the spread of the COVID-19 in Iran: The second wave

One of the common misconceptions about COVID-19 disease is to assume that we will not see a recurrence after the first wave of the disease has subsided. This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. From the results, improving the recovery rate of people with weak immune systems via appropriate medical incentives is resulted as one of the most effective prescriptions to prevent the widespread unbridled outbreak of the second wave of COVID-19.

Chronic ankle instability patients exhibit higher variability in lower extremity joint-coupling variability during drop vertical jumps

Chronic ankle instability (CAI) has been associated with biomechanical alterations during landing tasks. While joint coupling differences have been reported during gait in patients with CAI, there is no known research assessing joint coupling during a drop-vertical jumping (DVJ). Joint coupling variability measure may provide information on the sensorimotor health of these patients. The purpose of this study was to compare lower extremity joint coupling variability during a DVJ between patients with CAI and controls. Twenty-eight young, active individuals (CAI:n = 14, Control:n = 14) participated in the study. A 3D motion capture system was used to collect kinematics during 15 drop-vertical jump trials. A vector coding analysis was used to assess the variability in the following joint couples: knee sagittal-ankle frontal, knee sagittal-ankle sagittal, hip frontal-ankle frontal, and hip frontal-ankle sagittal. The CAI group had higher joint coupling variability in hip frontal-ankle sagittal, knee sagittal-ankle frontal and knee sagittal-ankle sagittal planes both prior to and following ground contact during the drop vertical jumps. These changes indicate potential adaptations to the constraint of CAI and the task of the DVJ. Higher variability may reflect an attempt by the subjects to explore alternate movement strategies or reflect poor sensorimotor control strategies. Clinicians should consider the challenges of DVJ during rehabilitation as they create a unique task constraint.

The Link between Microdevelopment and Long-Term Learning Trajectories in Science Learning

Long-term learning trajectories evolve through microdevelopmental sequences (i.e., short-term processes of change during learning tasks) and depend on variability during and across learning tasks. The aim of this study is to examine the coupling between short-term teacher-student dynamics and students' long-term learning trajectories, thereby providing empirical support for the link between the short- and long-term time scale in cognitive development. For 31 students (ages 3-5 years) from regular and special education, five teacher-student interactions during science tasks were filmed and coded in real time with regard to the student's level of understanding and the teacher's support throughout the task. A hierarchical cluster analysis resulted in four different learning trajectories over the course of 1.5 years, labeled as a high-scoring, mid-scoring, fluctuating, and low-scoring group of students. When connecting these trajectories to microdevelopmental data, the interactions of the high-scoring students were characterized by more moment-to-moment variations in the teacher's support and student's level of understanding, while the low-scoring group had the least variability compared to the other groups. Students with emotional and behavioral disabilities were represented across all learning trajectories, despite frequent accounts in the literature on their significant academic delays.

Flexible Nets: a modeling formalism for dynamic systems with uncertain parameters

The modeling of dynamic systems is frequently hampered by a limited knowledge of the system to be modeled and by the difficulty of acquiring accurate data. This often results in a number of uncertain system parameters that are hard to incorporate into a mathematical model. Thus, there is a need for modeling formalisms that can accommodate all available data, even if uncertain, in order to employ them and build useful models. This paper shows how the Flexible Nets (FNs) formalism can be exploited to handle uncertain parameters while offering attractive analysis possibilities. FNs are composed of two nets, an event net and an intensity net, that model the relation between the state and the processes of the system. While the event net captures how the state of the system is updated by the processes in the system, the intensity net models how the speed of such processes is determined by the state of the system. Uncertain parameters are accounted for by sets of inequalities associated with both the event net and the intensity net. FNs are not only demonstrated to be a valuable formalism to cope with system uncertainties, but also to be capable of modeling different system features, such as resource allocation and control actions, in a facile manner.

Cardiac and gait rhythms in healthy younger and older adults during treadmill walking tasks

BACKGROUND

Aging and pathology result in changes in the dynamics of several physiological subsystems. Often, these changes are concurrent, altering the dynamics between subsystems. Cardiac and gait rhythms are one example in which patterns change during physical activity.

AIMS

The purpose of this research is to simultaneously monitor changes in cardiac and gait rhythms when participants complete various treadmill walking tasks-normal speed, fast speed, and while synchronizing steps with a blinking metronome.

METHODS

The cardiac and gait rhythms of younger and older healthy adults were examined in this study during treadmill walking tasks. Pre-test and post-test walking at a preferred walking speed were compared to fast walking and walking with a gait synchronization test. Cardiac and gait rhythms were observed to calculate the mean, standard deviation, coefficient of variation, detrended fluctuation analysis scaling exponent alpha (DFA α), and sample entropy from each 15-min trial. Separate MANOVAs were used to examine the two experimental conditions for cardiac and gait rhythm variability.

RESULTS

During the gait synchronization experiment, main effects for phase were exhibited for all gait variables, but none were shown during the fast walking task. Meanwhile, the cardiac rhythms demonstrated decreased mean and increased DFA α only during the synchronization condition.

DISCUSSION

Participants, regardless of age, exhibited similar patterns of change in their cardiac and locomotor rhythms during the treadmill walking tasks. Cardiac rhythms were only altered during the gait synchronization task, suggesting it may be possible to simultaneously influence the variability and structure of cardiac and gait rhythms.

The self-organization of ball bouncing

The hybrid rhythmic ball-bouncing task considered in this study requires a participant to hit a ball in a virtual environment by moving a paddle in the real environment. It allows for investigation of the online visual control of action in humans. Changes in gravity acceleration in the virtual environment affect the ball dynamics and modify the ball-paddle system limit cycle. These changes are shown to be accurately reproduced through simulation by a model integrating continuous information-movement couplings between the ball trajectory and the paddle trajectory, giving rise to a resonance-tuning phenomenon. On the contrary, the tested models integrating only intermittent sensorimotor couplings were unable to replicate the observed human behavior. Results suggest that the visual control of action is achieved online, in a prospective way. Human rhythmic motor control would benefit from the timing and phase control emerging from the low-level continuous coupling between the central pattern generator and the visual perception of the ball trajectory. This control strategy, which precludes the need for internal clock and explicit environmental representation, is also able to explain the empirical result that the bounces tend to converge toward a passive stability regime during human ball bouncing.

Learning pharmacokinetic models for in vivo glucocorticoid activation

To understand trends in individual responses to medication, one can take a purely data-driven machine learning approach, or alternatively apply pharmacokinetics combined with mixed-effects statistical modelling. To take advantage of the predictive power of machine learning and the explanatory power of pharmacokinetics, we propose a latent variable mixture model for learning clusters of pharmacokinetic models demonstrated on a clinical data set investigating 11β-hydroxysteroid dehydrogenase enzymes (11β-HSD) activity in healthy adults. The proposed strategy automatically constructs different population models that are not based on prior knowledge or experimental design, but result naturally as mixture component models of the global latent variable mixture model. We study the parameter of the underlying multi-compartment ordinary differential equation model via identifiability analysis on the observable measurements, which reveals the model is structurally locally identifiable. Further approximation with a perturbation technique enables efficient training of the proposed probabilistic latent variable mixture clustering technique using Estimation Maximization. The training on the clinical data results in 4 clusters reflecting the prednisone conversion rate over a period of 4 h based on venous blood samples taken at 20-min intervals. The learned clusters differ in prednisone absorption as well as prednisone/prednisolone conversion. In the discussion section we include a detailed investigation of the relationship of the pharmacokinetic parameters of the trained cluster models for possible or plausible physiological explanation and correlations analysis using additional phenotypic participant measurements.

Can gender priming eliminate the effects of stereotype threat? The case of simple dynamic systems

Mathematics and mental rotation are classic fields where it has been shown that priming women with their gender identity impedes performance. Whereas past research focused mainly on stereotype threat effects in women in a narrowly defined context, this study broadened the research focus: We primed 264 women and men equally with a male, a neutral, or a female prime before they had to solve a simple dynamic system task. As expected, female-primed women subsequently performed worst of all six groups. Solution rates were almost 14% higher for the women in the male-primed condition. Men performed better than women in all three priming conditions. However, this difference was reduced in the male-primed condition as women's performance had increased as anticipated. Unexpected was a decline in the male performance in the same condition. The study showed that gender priming had a significant effect on women in tasks involving simple dynamic systems. However, mathematical knowledge and area of occupation clearly were stronger predictors for both men and women. Priming alone cannot eliminate the effects of stereotype threat.

All the Right Noises: Background Variability Helps Early Word Learning

Variability is prevalent in early language acquisition, but, whether it supports or hinders learning is unclear; while target variability has been shown to facilitate word learning, variability in competitor items has been shown to make the task harder. Here, we tested whether background variability could boost learning in a referent selection task. Two groups of 2-year-old children saw arrays of one novel and two known objects on a screen, and they heard a novel or known label. Stimuli were identical across conditions, with the exception that in the constant color condition objects appeared on a uniform white background, and in the variable color condition backgrounds were different, uniform colors. At test, only children in the variable condition showed evidence of retaining label-object associations. These data support findings from the adult memory literature, which suggest that variability supports learning by decontextualizing representations. We argue that these data are consistent with dynamic systems accounts of learning in which low-level entropy adds sufficient noise to the developmental system to precipitate a change in behavior.

Transformational change in parenting practices after child interpersonal trauma: A grounded theory examination of parental response

Child interpersonal trauma is associated with a host of negative outcomes, both concurrently and in adulthood. Parental responses following trauma can play an important role in modulating child responses, symptoms, and post-trauma functioning. However, parents themselves are also impacted after their child experiences trauma, reporting distress, psychopathology, concerns about the child's safety, changes in discipline and protectiveness, and feelings of blame. Most of this previous research, however, suffers from methodological limitations such as focusing on description and correlations, providing static "one shot" assessments of parenting after trauma, and relying mainly on results related to child sexual abuse. This project developed a comprehensive, explanatory theory of the dynamic process by which parenting changes in response to a range of child trauma, using a sample of parents whose children had experienced a range of interpersonal trauma types. Grounded theory analyses revealed a three-phase dynamic model of discontinuous transformation, in which parents experienced destabilization, recalibration, and re-stabilization of parenting practices in response to child trauma. Parents were focused on Protecting and Healing the child victim, often at the expense of their own needs. Most parents reached a phase of posttraumatic growth, labelled Thriving Recovery, but processes that hindered this recovery are also discussed. This study provides the first evidence that dynamic systems of change as well as vicarious posttraumatic growth can apply to parents of child trauma victims. Generating an explanatory theory provides important avenues for future research as well as interventions and services aimed at families who have experienced child trauma.

Dynamic Flux Balance Analysis Using DFBAlab

Bioprocesses are of critical importance in several industries such as the food and pharmaceutical industries. Despite their importance and widespread application, bioprocess models remain rather simplistic and based on unstructured models. These simple models have limitations, making it very difficult to model complex bioprocesses. With dynamic flux balance analysis (DFBA) more comprehensive bioprocess models can be obtained. DFBA simulations are difficult to carry out because they result in dynamic systems with linear programs embedded. Therefore, the use of DFBA as a modeling tool has been limited. With DFBAlab, a MATLAB code that performs efficient and reliable DFBA simulations, the use of DFBA as a modeling tool has become more accessible. Here, we illustrate with an example how to implement bioprocess models in DFBAlab.

Bioreactors and Microfluidics for Osteochondral Interface Maturation

The cell culture techniques are in the base of any biology-based science. The standard techniques are commonly static platforms as Petri dishes, tissue culture well plates, T-flasks, or well plates designed for spheroids formation. These systems faced a paradigm change from 2D to 3D over the current decade driven by the tissue engineering (TE) field. However, 3D static culture approaches usually suffer from several issues as poor homogenization of the formed tissues and development of a necrotic center which limits the size of in vitro tissues to hundreds of micrometers. Furthermore, for complex tissues as osteochondral (OC), more than recovering a 3D environment, an interface needs to be replicated. Although 3D cell culture is already the reality adopted by a newborn market, a technological revolution on cell culture devices needs a further step from static to dynamic already considering 3D interfaces with dramatic importance for broad fields such as biomedical, TE, and drug development. In this book chapter, we revised the existing approaches for dynamic 3D cell culture, focusing on bioreactors and microfluidic systems, and the future directions and challenges to be faced were discussed. Basic principles, advantages, and challenges of each technology were described. The reported systems for OC 3D TE were focused herein.

Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential

As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.

Moving Word Learning to a Novel Space: A Dynamic Systems View of Referent Selection and Retention

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child's active engagement with objects and people supports referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children's knowledge of words and objects grows. Together this work suggests that understanding systems for perception, action, attention, and memory, and their complex interaction, is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work.

Reasoning With Causal Cycles

This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks (DBNs) represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links that model feedback relations between variables. Unfolded chain graphs are chain graphs that unfold over time. An existing model of causal cycles (alpha centrality) is also evaluated. Four experiments in which subjects reason about categories with cyclically related features provided evidence against DBNs and alpha centrality and for the two types of chain graphs. Chain graphs-a mechanism for representing the equilibrium distribution of a dynamic system-may thus be good candidates for modeling how people reason causally with complex systems. Applications of chain graphs to areas of cognition other than category-based judgments are discussed.

From hands to minds: Gestures promote understanding

Gestures serve many roles in communication, learning and understanding both for those who view them and those who create them. Gestures are especially effective when they bear resemblance to the thought they represent, an advantage they have over words. Here, we examine the role of conceptually congruent gestures in deepening understanding of dynamic systems. Understanding the structure of dynamic systems is relatively easy, but understanding the actions of dynamic systems can be challenging. We found that seeing gestures representing actions enhanced understanding of the dynamics of a complex system as revealed in invented language, gestures and visual explanations. Gestures can map many meanings more directly than language, representing many concepts congruently. Designing and using gestures congruent with meaning can augment comprehension and learning.

Multi-thresholds for fault isolation in the presence of uncertainties

Monitoring of the faults is an important task in mechatronics. It involves the detection and isolation of faults which are performed by using the residuals. These residuals represent numerical values that define certain intervals called thresholds. In fact, the fault is detected if the residuals exceed the thresholds. In addition, each considered fault must activate a unique set of residuals to be isolated. However, in the presence of uncertainties, false decisions can occur due to the low sensitivity of certain residuals towards faults. In this paper, an efficient approach to make decision on fault isolation in the presence of uncertainties is proposed. Based on the bond graph tool, the approach is developed in order to generate systematically the relations between residuals and faults. The generated relations allow the estimation of the minimum detectable and isolable fault values. The latter is used to calculate the thresholds of isolation for each residual.

Modeling Sustainable Food Systems

The processes underlying environmental, economic, and social unsustainability derive in part from the food system. Building sustainable food systems has become a predominating endeavor aiming to redirect our food systems and policies towards better-adjusted goals and improved societal welfare. Food systems are complex social-ecological systems involving multiple interactions between human and natural components. Policy needs to encourage public perception of humanity and nature as interdependent and interacting. The systemic nature of these interdependencies and interactions calls for systems approaches and integrated assessment tools. Identifying and modeling the intrinsic properties of the food system that will ensure its essential outcomes are maintained or enhanced over time and across generations, will help organizations and governmental institutions to track progress towards sustainability, and set policies that encourage positive transformations. This paper proposes a conceptual model that articulates crucial vulnerability and resilience factors to global environmental and socio-economic changes, postulating specific food and nutrition security issues as priority outcomes of food systems. By acknowledging the systemic nature of sustainability, this approach allows consideration of causal factor dynamics. In a stepwise approach, a logical application is schematized for three Mediterranean countries, namely Spain, France, and Italy.

FGF/MAPK signaling sets the switching threshold of a bistable circuit controlling cell fate decisions in embryonic stem cells

Intracellular transcriptional regulators and extracellular signaling pathways together regulate the allocation of cell fates during development, but how their molecular activities are integrated to establish the correct proportions of cells with particular fates is not known. Here we study this question in the context of the decision between the epiblast (Epi) and the primitive endoderm (PrE) fate that occurs in the mammalian preimplantation embryo. Using an embryonic stem cell (ESC) model, we discover two successive functions of FGF/MAPK signaling in this decision. First, the pathway needs to be inhibited to make the PrE-like gene expression program accessible for activation by GATA transcription factors in ESCs. In a second step, MAPK signaling levels determine the threshold concentration of GATA factors required for PrE-like differentiation, and thereby control the proportion of cells differentiating along this lineage. Our findings can be explained by a simple mutual repression circuit modulated by FGF/MAPK signaling. This might be a general network architecture to integrate the activity of signal transduction pathways and transcriptional regulators, and serve to balance proportions of cell fates in several contexts.

Is cognition enough to explain cognitive development?

Traditional views separate cognitive processes from sensory-motor processes, seeing cognition as amodal, propositional, and compositional, and thus fundamentally different from the processes that underlie perceiving and acting. These were the ideas on which cognitive science was founded 30 years ago. However, advancing discoveries in neuroscience, cognitive neuroscience, and psychology suggests that cognition may be inseparable from processes of perceiving and acting. From this perspective, this study considers the future of cognitive science with respect to the study of cognitive development.

Grounding cognitive-level processes in behavior: the view from dynamic systems theory

Marr's seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because dynamic systems theory (DST) focuses on time and interdependence of components, DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: dynamic field theory (DFT). We review research applying DFT to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding of behavior and cognition that results from a DST perspective. These point to a central challenge for cognitive science research as defined by Marr-emergence. We argue that appreciating emergence raises questions about the utility of computational-level analyses and opens the door to insights concerning the origin of novel forms of behavior and thought (e.g., a new chess strategy). We contend this is one of the most fundamental questions about cognition and behavior.

Inter-foot coordination dynamics of quiet standing postures

It has long been held that the net center of pressure (COP(NET)) is the controlling variable to human stance that indirectly represents postural sway. The formation of the COP(NET) trajectory emerges from an active control of transporting the body weight from one foot to the other and the self-organized coordination of the COP of each individual foot—properties that cannot be determined from the typical single force platform protocol. The findings of recent studies, with the application of the two-force platform paradigm, have revealed the coordination properties of the lower limbs in regulating COP(NET). In this article, we review these new findings and insights into the control of postural stability within the framework of a dynamic systems approach. The issues include: (1) the active asymmetrical body weight distribution and transportation process during both short- and long-term stances; (2) the spatial and temporal characteristics of the inter- and intra-foot COP coupling dynamics; (3) the influence of mechanical constraints (e.g., foot position, foot orientation, etc.) on the inter-foot and intra-foot COP coordination dynamics; and (4) the role of the specificity of task context to the functional asymmetry of the feet and its relation to footedness. The findings from foot coordination dynamics reveal subtle regulation of stability and instability in postural control that needs to be mapped to the coordination dynamics of the multi-link postural control system.

Frequency of vocalization before and after cochlear implantation: dynamic effect of auditory feedback on infant behavior

The motivation for infants' non-word vocalizations in the second half of the first year of life and later is unclear. This study of hearing infants and infants with profound hearing loss with and without cochlear implants addressed the hypothesis that vocalizations are primarily motivated by auditory feedback. Early access to cochlear implants has created unique conditions of auditory manipulation that permit empirical tests of relations between auditory perception and infant behavior. Evidence from two separate tests of the research hypothesis showed that, before cochlear implantation, infants with profound hearing loss vocalized significantly less often than hearing infants; however, soon after cochlear implantation, they vocalized at levels commensurate with hearing peers. In contrast, vocal behaviors that are typically considered reflexive or emotion-based signals (e.g., crying) were infrequent overall and did not vary with auditory access. These results support the hypothesis that auditory feedback is a critical component motivating early vocalization frequency.

Body load and the postural precursors of motion sickness

Physical properties of the body affect the control of standing body sway. One example occurs when loads are added to the body, such as occurs when wearing a backpack. Other research has shown that subjective symptoms of motion sickness are preceded by differences in body sway between individuals who later report motion sickness and those who do not. In the present study we asked whether loads worn on the body would affect relations between body sway and motion sickness. We measured standing body sway without load and then with loads worn at the shoulders or thighs. Then participants were exposed to potentially nauseogenic visual motion stimulation while wearing shoulder or thigh loads. We measured body sway continuously during exposure to visual motion. Thirteen of 36 participants (36%) reported motion sickness. Body sway was affected by loads and by load position, consistent with previous research. Also consistent with previous research, sway differed between well and sick participants both before and during exposure to visual motion stimuli. In addition, during room motion the well and sick participants responded differently to loads. The results are consistent with the hypothesis that physical properties of the body can affect relations between body sway and motion sickness.

Primitive mechanisms of trauma response: an evolutionary perspective on trauma-related disorders

The symptoms we identify and the behaviors we recognize as defenses define which symptoms we see as trauma-related. Early conceptions of trauma-related disorders focused on physical signs of distress while current ones emphasize mental symptoms, but traumatizing experiences evoke psychobiological reactions. An evolutionary perspective presumes that psychophysical reactions to traumatizing events evolved to ensure survival. This theoretical review examines several primitive mechanisms (e.g., sensitization and dissolution) associated with responses to diverse stressors, from danger to life-threat. Some rapidly acquired symptoms form without conscious awareness because severe stresses can dysregulate mental and physical components within systems ensuring survival. Varied defensive options engage specialized and enduring psychophysical reactions; this allows for more adaptive responses to diverse threats. Thus, parasympathetically mediated defense states such as freeze or collapse increase trauma-related symptom variability. Comorbidity and symptom variability confuse those expecting mental rather than psychophysical responses to trauma, and active (sympathetically mediated flight and fight) rather than immobility defenses. Healthcare implications for stress research, clinical practice and diagnostic nosology stem from the broader evolutionary view.

Lose and gain: impacts of ERK5 and JNK cascades on each other

Kinase cascades in ERK5 (Extracellular signal-regulated kinases) and JNK (c-Jun N-terminal kinases) signaling pathways mediate the sensing and processing of stimuli. Cross-talks between signaling cascades is a likely phenomenon that can cause apparently different biological responses from a single pathway, on its activation. Feedback loops have the potential to greatly alter the properties of a pathway and its response to stimuli. Based on enzyme kinetic reactions, mathematical models have been developed to predict and analyze the impacts of cross-talks and feedback loops in ERK5 and JNK cascades. It has been observed that, there is no significant impact on neither ERK5 activation nor JNKs' activation due to cross-talks between them. But it is due to cross-talks and feedback loops in ERK5 and JNK cascade, ERK5 gets activated in a transient manner in the absence of input signals. Planning to obtain the parameter values from the experimentalist and the result should be validated by experimental verification.

ELECTRONIC SUPPLEMENTARY MATERIAL

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