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Motor competence (MC) is conceptually defined as a multidimensional latent construct that covers the proficient performance in motor skills and its underlying mechanisms This study aimed to statistically provide arguments that MC is a network of interconnected constructs, such as FMS, coordination, and its underlying mechanisms, which are responsible for preschoolers' proficiency in motor tasks. Participated 102 preschoolers (65 girls, M age = 4.22 ± 0.19) who were assessed for the Test of Gross Motor Development - 2nd edition, the Motor Competence Assessment, and the Supine-to-Stand. Data were explored using Exploratory Graph Analysis, using the EGAnet package in RStudio. A four-dimensional structure (61.2% of interactions) comprising tasks of the different protocols was underlined, in which all the nodes presented stable and adequate indexes (≥0.65; TEFI = -2.67). Four dimensions of MC were highlighted, namely Dimension 1, which combined movements for locomotor patterns; Dimension 2, comprising three process-oriented measures of object control skills to project objects; Dimension 3, which comprised of skills which require body coordination to displace body through space; and Dimension 4, composed by object control skills evaluated through product-oriented measures. For a better understanding of MC, the assessment of these different aspects that comprises MC should be considered.

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This study presents extended Immunity Agent-Based Model (IABM) simulations to evaluate vaccination strategies in controlling the spread of infectious diseases. The application of IABM in the analysis of vaccination configurations is innovative, as a vaccinated individual can be infected depending on how their immune system acts against the invading pathogen, without a pre-established infection rate. Analysis at the microscopic level demonstrates the impact of vaccination on individual immune responses and infection outcomes, providing a more realistic representation of how the humoral response caused by vaccination affects the individual's immune defense. At the macroscopic level, the effects of different population-wide vaccination strategies are explored, including random vaccination, targeted vaccination of specific demographic groups, and spatially focused vaccination. The results indicate that increased vaccination rates are correlated with decreased infection and mortality rates, highlighting the importance of achieving herd immunity. Furthermore, strategies focused on vulnerable populations or densely populated regions prove to be more effective in reducing disease transmission compared to randomly distributed vaccination. The results presented in this work show that vaccination strategies focused on highly crowded regions are more efficient in controlling epidemics and outbreaks. Results suggest that applying vaccination only in the densest region resulted in the suppression of infection in that region, with less intense viral spread in areas with lower population densities. Strategies focused on specific regions, in addition to being more efficient in reducing the number of infected and dead people, reduce costs related to transportation, storage, and distribution of doses compared to the random vaccination strategy. Considering that, despite scientific efforts to consolidate the use of mass vaccination, the accessibility, affordability, and acceptability of vaccines are problems that persist, investing in the study of strategies that mitigate such issues is crucial in the development and application of government policies that make immunization systems more efficient and robust.

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BACKGROUND: Transportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity. METHODS: The ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios. RESULTS: Our model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a 'free-fare' scenario, largely through reductions in walking trips. 'Free fare' policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups. CONCLUSIONS: Transport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

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Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

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Purpose: Our purpose was to investigate the interplay between runners and their environment using a network approach. Methods: This cross-sectional study sampled Brazilian runners of both sexes, from the five macro-regions of the country. An electronic questionnaire was used to obtain information regarding age, sex, training volume, socio-economic level, place of residence, and running pace. Environmental indicators (public illumination, pavement, sidewalk, and green areas) were collected from available public information. Descriptive statistics were presented in mean (SD), and frequency (%). A network analysis was performed to evaluate the association between individual and environmental characteristics. Statistical analyses were performed in the JASP, considering p < 0.05. Results: At North and Mid-West regions, public illumination presents the highest values for the expected influence (1.74 and 1.56), while in Northeast and Southeast, sidewalks present the highest values (2.13; 0.91). For betweenness centrality, in North, Northeast, and Mid-West regions, residency in the capital of a state presented a hub. In contrast, pavement, and training volume present higher values in the South and Southeast. Network topologies are different. Conclusion: Public illumination (North and Mid-West) and sidewalk (Northeast, Southeast) were the most important variables for runners. Continental size countries need specific approaches to improve physical activity levels and health outcomes that consider the cultural, historical, and environmental background.

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We propose a new agent-based model for studying wealth distribution. We show that a model that links wealth to information (interaction and trade among agents) and to trade advantage is able to qualitatively reproduce real wealth distributions, as well as their evolution over time and equilibrium distributions. These distributions are shown in four scenarios, with two different taxation schemes where, in each scenario, only one of the taxation schemes is applied. In general, the evolving end state is one of extreme wealth concentration, which can be counteracted with an appropriate wealth-based tax. Taxation on annual income alone cannot prevent the evolution towards extreme wealth concentration.

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Understanding the dynamics of complex systems defined in the sense of Caputo, such as fractional differences, is crucial for predicting their behavior and improving their functionality. In this paper, the emergence of chaos in complex dynamical networks with indirect coupling and discrete systems, both utilizing fractional order, is presented. The study employs indirect coupling to produce complex dynamics in the network, where the connection between the nodes occurs through intermediate fractional order nodes. The temporal series, phase planes, bifurcation diagrams, and Lyapunov exponent are considered to analyze the inherent dynamics of the network. Analyzing the spectral entropy of the chaotic series generated, the complexity of the network is quantified. As a final step, we demonstrate the feasibility of implementing the complex network. It is implemented on a field-programmable gate array (FPGA), which confirms its hardware realizability.

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The COVID-19 pandemic has caused unprecedented disruptions to urban systems worldwide, but the extent and nature of these disruptions are not yet fully understood when it comes to transportation. In this work, we aim to explore how social distancing policies have affected passenger demand in urban mass transportation systems with the goal of supporting informed decisions in policy planning. We propose an approach based on complex networks and clustering time series with similar behavior, investigating possible changes in similarity patterns during pandemics and how they reflect into a regional scale. The methods shown here proved useful in detecting that lines in central or peripheral regions present different dynamics, that bus lines have changed their behavior during pandemic so that similarity relations have changed significantly, and that when social distancing started, there was an abrupt shock in the properties of daily passenger time series, and the system did not return to its original behavior until the end of the evaluated period. The approach allows to track evolution of the community structure in different scenarios providing managers with tools to reinforce or destabilize similarities if needed.

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Non-extensive statistical mechanics (or q-statistics) is based on the so-called non-additive Tsallis entropy. Since its introduction by Tsallis, in 1988, as a generalization of the Boltzmann-Gibbs equilibrium statistical mechanics, it has steadily gained ground as a suitable theory for the description of the statistical properties of non-equilibrium complex systems. Therefore, it has been applied to numerous phenomena, including real seismicity. In particular, Tsallis entropy is expected to provide a guiding principle to reveal novel aspects of complex dynamical systems with catastrophes, such as seismic events. The exploration of the existing connections between Tsallis formalism and real seismicity has been the focus of extensive research activity in the last two decades. In particular, Tsallis q-statistics has provided a unified framework for the description of the collective properties of earthquakes and faults. Despite this progress, our present knowledge of the physical processes leading to the initiation of a rupture, and its subsequent growth through a fault system, remains quite limited. The aim of this paper was to provide an overview of the non-extensive interpretation of seismicity, along with the contributions of the Tsallis formalism to the statistical description of seismic events.

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Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study cancer therapies' effects, which are often designed to disrupt single-cell dynamics. In this work, we propose them as viable methods for studying the time evolution of cancer imaging biomarkers (IBM). We propose a cellular automata model for tumor growth and three different therapies: chemotherapy, radiotherapy, and immunotherapy, following well-established modeling procedures documented in the literature. The model generates a sequence of tumor images, from which a time series of two biomarkers: entropy and fractal dimension, is obtained. Our model shows that the fractal dimension increased faster at the onset of cancer cell dissemination. At the same time, entropy was more responsive to changes induced in the tumor by the different therapy modalities. These observations suggest that the prognostic value of the proposed biomarkers could vary considerably with time. Thus, it is essential to assess their use at different stages of cancer and for different imaging modalities. Another observation derived from the results was that both biomarkers varied slowly when the applied therapy attacked cancer cells scattered along the automatons' area, leaving multiple independent clusters of cells at the end of the treatment. Thus, patterns of change of simulated biomarkers time series could reflect on essential qualities of the spatial action of a given cancer intervention.

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En la investigación en salud es todavía poco frecuente el uso de la Teoría de la Complejidad y de la Fractalidad (más aún en tópicos no relacionados directamente con la biología molecular o con la clínica). La complejidad nos propone complementar con propuestas desde nuevas perspectivas el pensamiento lineal y cuantitativo predominante todavía en la metodología de producción del conocimiento científico. El estudio de los sistemas de salud necesita un enfoque que se aparte de la linealidad, lo rígido y lo direccional, dado que los mismos son sistemas complejos en los que el todo es más que la simple suma de sus partes. La crisis global generada ante la pandemia por COVID-19 nos puso frente a la oportunidad (y a la obligación) de repensar tanto nuestra praxis cotidiana como nuestra forma de producir conocimiento (AU)

In health research, the use of the Complexity and Fractality Theory is still infrequent (even more so in topics not directly related to molecular or clinical biology). The complexity proposes us to complement with proposals from new perspectives the linear and quantitative thinking still predominant in the methodology of production of scientific knowledge. The study of health systems needs an approach that moves away from linearity, rigidity and direction, since they are complex systems in which the whole is more than the simple sum of its parts. The global crisis generated by the COVID-19 pandemic presented us with the opportunity (and the obligation) to rethink both our daily praxis and our way of producing knowledge (AU)

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The present study examined gender differences in health, physical activity, physical fitness, real and perceived motor competence, and executive function indicators in three time points, and analyzed the dynamic and non-linear association between health, biological, behavioral, and cognitive variables in children followed over time. A total of 67 children (aged between six and 10 years) were followed during two years and split into two cohorts (six to eight years old: C1; eight to 10 years old: C2). Data regarding health, physical activity, real and perceived motor competence, physical fitness, and executive function indicators were obtained according to their respective protocols. Comparison tests and network analysis were estimated. Significant gender differences were found in both cohorts. The emerged networks indicated different topologies in both cohorts. No clusters were observed between the variables in C1, and there was a greater number of interactions at eight years of age. Sparse networks were observed in children aged eight and 10 years in C2, and greater connectivity was observed at nine years of age between health, physical fitness, motor competence, and physical activity indicators. This study showed that there are non-linear dynamic relationships between health, biological, behavioral, and cognitive variables over time during child development.

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The Covid-19 pandemic exacerbated pre-existing problems in Latin America and posed unprecedented challenges for Latin American universities (LAU). These challenges can be characterised as complex problems that cannot be understood through reductionist approaches. This paper aims (i) to provide a complex system perspective of the challenges confronting LAUs and (ii) to propose guidelines for managers of LAUs to address them in practice. A multidisciplinary group was formed and conducted an iterative process of research, brainstorming and debate of potential solutions to the following problems considered particularly important by their universities: mental health issues in the university environment, student learning gaps, brain drain, and anti-science movements. Complexity theory and E/HF concepts are integrated to demonstrate that understanding what LAUs are experiencing in a fragmented manner is impossible, and that the interactions between the challenges should be at the centre of the managers' actions plans. Practitioner summary: Managers of LAUs can benefit from the guidelines proposed to understand the pressing challenges confronting universities and develop systemic approaches to address them.

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Kleiber's empirical law, which describes that metabolism increases as the mass to the power 3/4, has arguably remained life sciences' enigma since its formal uncovering in 1930. Why is this behavior sustained over many orders of magnitude? There have been quantitative rationalizations put forward for both plants and animals based on realistic mechanisms. However, universality in scaling laws of this kind, like in critical phenomena, has not yet received substantiation. Here, we provide an account, with quantitative reproduction of the available data, of the metabolism for these two biology kingdoms by means of broad arguments based on statistical mechanics and nonlinear dynamics. We consider iterated renormalization group (RG) fixed-point maps that are associated with an extensive generalized (Tsallis) entropy. We find two unique universality classes that satisfy the 3/4 power law. One corresponds to preferential attachment processes-rich gets richer-and the other to critical processes that suppress the effort for motion. We discuss and generalize our findings to other empirical laws that exhibit similar situations, using data based on general but different concepts that form a conjugate pair that gives rise to the same power-law exponents.

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The limit of validity of ordinary statistical mechanics and the pertinence of Tsallis statistics beyond it is explained considering the most probable evolution of complex systems processes. To this purpose we employ a dissipative Landau-Ginzburg kinetic equation that becomes a generic one-dimensional nonlinear iteration map for discrete time. We focus on the Renormalization Group (RG) fixed-point maps for the three routes to chaos. We show that all fixed-point maps and their trajectories have analytic closed-form expressions, not only (as known) for the intermittency route to chaos but also for the period-doubling and the quasiperiodic routes. These expressions have the form of q-exponentials, while the kinetic equation's Lyapunov function becomes the Tsallis entropy. That is, all processes described by the evolution of the fixed-point trajectories are accompanied by the monotonic progress of the Tsallis entropy. In all cases the action of the fixed-point map attractor imposes a severe impediment to access the system's built-in configurations, leaving only a subset of vanishing measure available. Only those attractors that remain chaotic have ineffective configuration set reduction and display ordinary statistical mechanics. Finally, we provide a brief description of complex system research subjects that illustrates the applicability of our approach.

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Self-organized spatial patterns are ubiquitous in ecological systems and allow populations to adopt non-trivial spatial distributions starting from disordered configurations. These patterns form due to diverse nonlinear interactions among organisms and between organisms and their environment, and lead to the emergence of new (eco)system-level properties unique to self-organized systems. Such pattern consequences include higher resilience and resistance to environmental changes, abrupt ecosystem collapse, hysteresis loops, and reversal of competitive exclusion. Here, we review ecological systems exhibiting self-organized patterns. We establish two broad pattern categories depending on whether the self-organizing process is primarily driven by nonlinear density-dependent demographic rates or by nonlinear density-dependent movement. Using this organization, we examine a wide range of observational scales, from microbial colonies to whole ecosystems, and discuss the mechanisms hypothesized to underlie observed patterns and their system-level consequences. For each example, we review both the empirical evidence and the existing theoretical frameworks developed to identify the causes and consequences of patterning. Finally, we trace qualitative similarities across systems and propose possible ways of developing a more quantitative understanding of how self-organization operates across systems and observational scales in ecology.

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Theliteratureassociatesoxidativestresswiththeproductionoffreeradicals,whichleadtoneurodegeneration.Theypresentinnumerablehypotheses,amongwhichareabnormalitiesinthefunctioningofthehypothalamic-pituitary-adrenalaxis,neurotoxiceffectsandneuronaloxidativedamage.ClinicalobservationhasshownthatinneurodegenerativediseasessuchasMultipleSclerosis(MS)andAmyotrophicLateralSclerosis(ALS)thereisareportofprolonged or violent emotionalstressprecedingthesymptoms.Aims:UsingtheCarilloComplexSystemsModel,presentsomepossibilitiesonhowstresscancontributetoneurodegeneration.Methodology:NinecasesofALSandsixcasesofMSwereevaluated,pathologiesalreadyclassifiedasbelongingtosyphilinism.Literaturereviewonstressandneurotoxicitycarriedout.Resultsanddiscussion:Syphilinism is instability with a predominantly intrinsicorigin to the system with a chronic caracter.This diathesis is characterized by a dissipative deficiency, predominantly hepatic, to the processing of certain elements or potentially toxic substances with exogenous origin or endogenous Such non-processed substances are unstable factors in the system, with greater affinity for certain tissues,like the nervous system. Among the toxins, we find alcohol, esters, formaldehyde, aloe, ketones, aldehydes, etc. The final hepatic metabolism of cortisol results in cortic acids and cortol, which use the same enzymatic system as alcohol, and can be considered syphilinic toxins. Ethanol can act directly at the circadian rhythm, disrupting it and generating stressful substances such as cortisol, regardless of an external event, increasing the toxin level. The inflammatory process generated by the production of free radicals and metabolic abnormalities, including the reduction of neuropeptide Y that modulates inflammatory activity in the nervous system, leads to changes that can result in neurodegeneration. Conclusion: Inflammation caused by toxins from prolonged/violent emotional stress can lead to several changes in syphilinic individuals, due to failure in the dissipative process, including neurodegeneration.

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OBJECTIVES: To develop a simulation framework for assessing how combinations of taxes, nutrition warning labels and advertising levels could affect purchasing of ultra-processed foods (UPF) in Latin American countries and to understand whether policies reinforce or reduce pre-existing social disparities in UPF consumption. DESIGN: We developed an agent-based simulation model using international evidence regarding the effect of price, nutrition warning labels and advertising on UPF purchasing. SETTING: We estimated policy effects in scenarios representing two stages of the 'social transition' in UPF purchasing: (1) a pre-transition scenario, where UPF purchasing is higher among high-income households, similar to patterns in Mexico; and (2) a post-transition scenario where UPF purchasing is highest among low-income households, similar to patterns in Chile. PARTICIPANTS: A population of 1000 individual agents with levels of age, income, educational attainment and UPF purchasing similar to adult women in Mexico. RESULTS: A 20 % tax would decrease purchasing by 24 % relative to baseline in both the pre- and post-transition scenarios, an effect that is similar in magnitude to that of a nutrition warning label policy. A 50 % advertising increase or decrease had a comparatively small effect. Nutrition warning labels were most effective among those with higher levels of educational attainment. Labelling reduced inequities in the pre-transition scenario (i.e. highest UPF purchasing among the highest socio-economic group) but widened inequities in the post-transition scenario. CONCLUSIONS: Effective policy levers are available to reduce UPF purchasing, but policymakers should anticipate that equity impacts will differ depending on existing social patterns in UPF purchasing.

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Complex systems theory has become one of the main frameworks to understand, model and explain interactional phenomena such as interpersonal coordination. In her paper, Butler (this issue) applies this approach to theorise about coordination at large, including human interactions. We argue that the all-encompassing language of complex systems theory leads to overemphasising the physical aspects that human interactions share with other coordinated systems in nature. This emphasis ultimately disregards the meaningful dimension implied in any human movement, understanding it as mechanical motion, rather than expressive actions.

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Soil fauna plays a key role in organic matter decomposition. Litter decomposition depends on the relationships of soil fauna and microorganisms as well as climate and litter quality. The decomposer community is sensitive to land use. Thus, physical-chemical disturbances, like soil tillage, can exercise important control on the soil fauna. In order to study the effect of land use and its impact on litter decomposition by soil fauna, a litter-bag experiment was conducted in the Pampa Serrana region, Azul district, Argentina. Litter-bags were made in three different mesh-sizes, allowing the access of micro, micro + meso and micro + meso + macrofauna. Four different treatments were defined: naturalized grassland and three agricultural agroecosystems under different tillage systems, i.e., conservation tillage, conventional-conservation tillage and conventional tillage. Decomposition rate and remaining litter were measured across three different seasons. We found that naturalized grassland obtained the highest decomposition rates and the least remaining litter compared to conservation and conventional tillage systems. No difference in litter decomposition was identified among agricultural agroecosystems. Micro + meso + macrofauna presented the highest decomposition rate and the lowest remaining litter of soil fauna groups, in all agroecosystems. In contrast, microfauna decomposition rate was the lowest and produced the highest remaining litter. Micro + mesofauna presented values of decomposition rate and remaining litter that differed significantly from the rest of the groups in some seasons. These results highlight the importance of soil fauna in litter decomposition and the negative effects of different land use systems on litter decomposition by soil fauna.