Sara Imari Walker: An Informational Theory of Life

Physicist and astrobiologist Sara Imari Walker proposes a new paradigm for using physics to deepen our understanding of what we recognize as life. Assembly theory is a framework that uses the physics of molecular complexity to open a new path to identify where the threshold lies for life to arise from non-life, drawing in insights from new discoveries on the nature of historical contingency and time itself. 

Watch/read at: longnow.org

Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media

Matthew R. DeVerna, Francesco Pierri, Yong-Yeol Ahn, Santo Fortunato, Alessandro Flammini & Filippo Menczer
npj Complexity volume 2, Article number: 11 (2025)

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation.

Read the full article at: www.nature.com

Gabriele Scheler: From Verbal Thought to Neuron Computation | Brain Inspired

Gabriele Scheler co-founded the Carl Correns Foundation for Mathematical Biology. Carl Correns was her great grandfather, one of the early pioneers in genetics. Gabriele is a computational neuroscientist, whose goal is to build models of cellular computation, and much of her focus is on neurons.
We discuss her theoretical work building a new kind of single neuron model. She, like Dmitri Chklovskii a few episodes ago, believes we’ve been stuck with essentially the same family of models for a neuron for a long time, despite minor variations on those models. The model Gabriele is working on, for example, respects the computations going on not only externally, via spiking, which has been the only game in town forever, but also the computations going on within the cell itself. Gabriele is in line with previous guests like Randy Gallistel, David Glanzman, and Hessam Akhlaghpour, who argue that we need to pay attention to how neurons are computing various things internally and how that affects our cognition. Gabriele also believes the new neuron model she’s developing will improve AI, drastically simplifying the models by providing them with smarter neurons, essentially.
We also discuss the importance of neuromodulation, her interest in wanting to understand how we think via our internal verbal monologue, her lifelong interest in language in general, what she thinks about LLMs, why she decided to start her own foundation to fund her science, what that experience has been like so far. Gabriele has been working on these topics for many years, and as you’ll hear in a moment, she was there when computational neuroscience was just starting to pop up in a few places, when it was a nascent field, unlike its current ubiquity in neuroscience.
Listen at: braininspired.co

Spatial econometrics to estimate traffic reduction by transforming office space into housing and other land uses: The case for Barcelona

Javier Argota Sánchez-Vaquerizo, Dirk Helbing

Cities Volume 162, July 2025, 105901

Origin-destination (OD) matrices are essential for the analysis, planning, and simulation of urban areas, infrastructure, and transportation systems. However, they are often costly and time-consuming to determine, which reduces their potential use for informed decision-making and planning in cities. This research introduces a novel spatial econometric method that considers spatial spillover effects of socio-demographic, land use, and topological variables to directly estimate traffic OD flows between zones of the Metropolitan Area of Barcelona. Employing a two-part Hurdle model with gradient boosting (XGBoost), our approach achieves low error rates (MAE = 6.109, RMSE = 98.774), comparable to other established models also analyzed, but the proposed method’s simplicity facilitates its practical application in urban planning and policy-making. This is illustrated by applying the proposed model to predict changes in vehicle flows resulting from the conversion of offices into other urban uses such as housing, commerce, education, or storage. Despite the related population increase, we expect a reduction in vehicle trips by up to 10 % even with limited spatial interventions. Our findings suggest the model’s power to assess urban trends and policies, particularly in considering teleworking expansion, housing shortages, and contemporary planning practices promoting alternative mobility modes and densification. This research underscores the dual benefits of methodological innovation and practical policy application, marking a significant advancement in urban planning.

Read the full article at: www.sciencedirect.com