Franziska L. Sendker, Yat Kei Lo, Thomas Heimerl, Stefan Bohn, Louise J. Persson, Christopher-Nils Mais, Wiktoria Sadowska, Nicole Paczia, Eva Nußbaum, María del Carmen Sánchez Olmos, Karl Forchhammer, Daniel Schindler, Tobias J. Erb, Justin L. P. Benesch, Erik G. Marklund, Gert Bange, Jan M. Schuller & Georg K. A. Hochberg 
Nature (2024)

Fractals are patterns that are self-similar across multiple length-scales. Macroscopic fractals are common in nature; however, so far, molecular assembly into fractals is restricted to synthetic systems. Here we report the discovery of a natural protein, citrate synthase from the cyanobacterium Synechococcus elongatus, which self-assembles into Sierpiński triangles. Using cryo-electron microscopy, we reveal how the fractal assembles from a hexameric building block. Although different stimuli modulate the formation of fractal complexes and these complexes can regulate the enzymatic activity of citrate synthase in vitro, the fractal may not serve a physiological function in vivo. We use ancestral sequence reconstruction to retrace how the citrate synthase fractal evolved from non-fractal precursors, and the results suggest it may have emerged as a harmless evolutionary accident. Our findings expand the space of possible protein complexes and demonstrate that intricate and regulatable assemblies can evolve in a single substitution.

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Mohsen Mosleh, Rocky Cole, David G Rand Author Notes
PNAS Nexus, Volume 3, Issue 3, March 2024, pgae111,

There is considerable concern about users posting misinformation and harmful language on social media. Substantial—yet largely distinct—bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from N = 6,832 Twitter (now called X) users, and a dataset of N = 14,617 true and false headlines from professional fact-checking websites. Our analyses reveal substantial positive associations between misinformation and harmful language. On average, Twitter posts containing links to lower-quality news outlets also contain more harmful language (β = 0.10); and false headlines contain more harmful language than true headlines (β = 0.19). Additionally, Twitter users who share links to lower-quality news sources also use more harmful language—even in non-news posts that are unrelated to (mis)information (β = 0.13). These consistent findings across different datasets and levels of analysis suggest that misinformation and harmful language are related in important ways, rather than being distinct phenomena. At the same, however, the strength of associations is not sufficiently high to make the presence of harmful language a useful diagnostic for information quality: most low-quality information does not contain harmful language, and a considerable fraction of high-quality information does contain harmful language. Overall, our results underscore important opportunities to integrate these largely disconnected strands of research and understand their psychological connections.

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Kaloyan Danovski, Miguel C. Soriano, Lucas Lacasa
The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network’s prediction, when confronted with a learning task. This iterative change can be naturally interpreted as a trajectory in network space — a time series of networks — and thus the training algorithm (e.g. gradient descent optimization of a suitable loss function) can be interpreted as a dynamical system in graph space. In order to illustrate this interpretation, here we study the dynamical properties of this process by analyzing through this lens the network trajectories of a shallow neural network, and its evolution through learning a simple classification task. We systematically consider different ranges of the learning rate and explore both the dynamical and orbital stability of the resulting network trajectories, finding hints of regular and chaotic behavior depending on the learning rate regime. Our findings are put in contrast to common wisdom on convergence properties of neural networks and dynamical systems theory. This work also contributes to the cross-fertilization of ideas between dynamical systems theory, network theory and machine learning

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Kishore Vasan, Marton Karsai, Albert-Laszlo Barabasi

The metaverse promises a shift in the way humans interact with each other, and with their digital and physical environments. The lack of geographical boundaries and travel costs in the metaverse prompts us to ask if the fundamental laws that govern human mobility in the physical world apply. We collected data on avatar movements, along with their network mobility extracted from NFT purchases. We find that despite the absence of commuting costs, an individuals inclination to explore new locations diminishes over time, limiting movement to a small fraction of the metaverse. We also find a lack of correlation between land prices and visitation, a deviation from the patterns characterizing the physical world. Finally, we identify the scaling laws that characterize meta mobility and show that we need to add preferential selection to the existing models to explain quantitative patterns of metaverse mobility. Our ability to predict the characteristics of the emerging meta mobility network implies that the laws governing human mobility are rooted in fundamental patterns of human dynamics, rather than the nature of space and cost of movement.

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