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The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

The relationship between social networks and health encompasses everything from the flow of pathogens and information to the diffusion of beliefs and behaviors. This review addresses the vast and multidisciplinary literature that studies social networks as a structural determinant of health. In particular, we report on the current state of knowledge on how social contagion dynamics influence individual and collective health outcomes. We pay specific attention to research that leverages large-scale online data and social network experiments to empirically identify three broad classes of contagion processes: pathogenic diffusion, informational and belief diffusion, and behavioral diffusion. We conclude by identifying the need for more research on ( a ) how multiple contagions interact within the same social network, ( b ) how online social networks impact offline health, and ( c ) the effectiveness of social network interventions for improving population health.

Despite an unprecedented global gain in knowledge since the emergence of SARS-CoV-2, almost all mechanistic knowledge related to the molecular and cellular details of viral replication, pathology and virulence has been generated using early prototypic isolates of SARS-CoV-2. Here, using atomic force microscopy and molecular dynamics, we investigated how these mutations quantitatively affected the kinetic, thermodynamic and structural properties of RBD—ACE2 complex formation. We observed for several variants of concern a significant increase in the RBD—ACE2 complex stability. While the N501Y and E484Q mutations are particularly important for the greater stability, the N501Y mutation is unlikely to significantly affect antibody neutralization. This work provides unprecedented atomistic detail on the binding of SARS-CoV-2 variants and provides insight into the impact of viral mutations on infection-induced immunity. Here, the authors combine single-molecule atomic force spectroscopy measurements and molecular dynamics simulations to investigate the binding of spike proteins from four SARS-CoV-2 variants of concern (VoC) to the human ACE2 receptor. They observe an increase in the RBD-ACE2 complex stability for several of the VoCs and derive how the mutations affect the kinetic, thermodynamic and structural properties of complex formation.

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales. An E(3)-equivariant deep learning interatomic potential is introduced for accelerating molecular dynamics simulations. The method obtains state-of-the-art accuracy, can faithfully describe dynamics of complex systems with remarkable sample efficiency.

It is no longer possible nor desirable to address the dual challenges of equity and sustainability separately. Instead, they require new thinking and approaches which recognize their interlinkages, as well as the multiple perspectives and dimensions involved. We illustrate how equity and sustainability are intertwined, and how a complex social–ecological systems lens brings together advances from across the social and natural sciences to show how (in)equity and (un)sustainability are produced by the interactions and dynamics of coupled social–ecological systems. This should help understand which possible pathways could lead to sustainable and fair futures. There is remarkably little work on the interlinkages between sustainability and equity. This paper proposes an interdisciplinary conceptual framework addressing these twin challenges in the context of the Anthropocene. It shows that both equity and sustainability need to be understood as multi-dimensional and from diverse perspectives, with acceptable standards in all defining a desirable and acceptable life support zone. It proposes a shift in focus from individual elements and interactions, to system level dynamics and behaviour, advancing a social–ecological systems perspective through which both equity and sustainability are understood as intertwined drivers and outcomes of coupled systems dynamics. Over time, such dynamics become part of pathways which may move outside, or potentially be steered within, a desirable zone of ‘equitable sustainability’. Ten sets of ‘interaction dynamics’, involving different dimensions of equity and sustainability, are illustrated, along with a provisional categorization of their interrelationships and potential intervention points. The paper discusses their roles in transformational pathways towards equitable sustainability, highlighting the importance of cross-scale change shaped by politics and power. Further conceptual, empirical and transdisciplinary effort is now needed to enrich this framework and address a range of implied research and practice questions critical to shaping fair and sustainable futures.

We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48-53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809-834], and provides a unique framework for the investigation of timing in complex systems.

Complex I is the first and the largest enzyme of respiratory chains in bacteria and mitochondria. The mechanism which couples spatially separated transfer of electrons to proton translocation in complex I is not known. Here we report five crystal structures of T. thermophilus enzyme in complex with NADH or quinone-like compounds. We also determined cryo-EM structures of major and minor native states of the complex, differing in the position of the peripheral arm. Crystal structures show that binding of quinone-like compounds (but not of NADH) leads to a related global conformational change, accompanied by local re-arrangements propagating from the quinone site to the nearest proton channel. Normal mode and molecular dynamics analyses indicate that these are likely to represent the first steps in the proton translocation mechanism. Our results suggest that quinone binding and chemistry play a key role in the coupling mechanism of complex I. Complex I (NADH:ubiquinone oxidoreductase) is the first enzyme of the respiratory chain in bacteria and mitochondria. Here, the authors present cryo-EM and crystal structures of T. thermophilus complex I in different conformational states and further analyse them by Normal Mode Analysis and molecular dynamics simulations and conclude that quinone redox reactions are important for the coupling mechanism of complex I.

The distinction between adaptive and transformative capacities is still not well understood, and in this study we aimed to build a transformative learning space to strengthen transformative capacities. We proposed that two capacities will be essential to transformation: the capacity to navigate emergence and cross-scale systems reflexivity. We outline our efforts to design and deliver a Global Fellowship program in social innovation, intended to strengthen these two capacities among practitioners already engaged in socially innovative work. Results indicated that the concepts, frameworks, and experiences introduced through the Fellowship led to four key insights about these capacities. Firstly, individual Fellows and their organizations were able to see some complex system dynamics that were previously invisible, which in turn, allowed Fellows to see the distribution of resources and agency across the system in new ways. Secondly, engaging with diversity is essential in social innovation and transformative change processes, and system reflexivity aided in doing this. Additionally, Fellows indicated they were able to identify different kinds of opportunities and the generative potential that can lie within social-ecological systems. Lastly, the findings demonstrate the challenging nature of crossing scales and how a transformative space, such as a Fellowship, helps to practice the experience of contestation, unpredictability, and the uncontrollable dynamics of transformation and social innovation.

The strength of weak ties is that they tend to be long--they connect socially distant locations, allowing information to diffuse rapidly. The authors test whether this \"strength of weak ties\" generalizes from simple to complex contagions. Complex contagions require social affirmation from multiple sources. Examples include the spread of high-risk social movements, avant garde fashions, and unproven technologies. Results show that as adoption thresholds increase, long ties can impede diffusion. Complex contagions depend primarily on the width of the bridges across a network, not just their length. Wide bridges are a characteristic feature of many spatial networks, which may account in part for the widely observed tendency for social movements to diffuse spatially. [PUBLICATION ABSTRACT]