Renli Wu, Christopher Esposito, and James Evans

PNAS 122 (44) e2414893122

China’s emergence as one of the world’s top producers of high-quality science raises critical questions about its trajectory toward achieving scientific leadership. Traditional methods for evaluating the power of national scientific ecosystems, however, often overlook the nuances of a country’s global influence. In this perspective, we introduce a framework that highlights shifting power dynamics in international scientific collaborations, focusing on whether leadership positions in international scientific teams are moving from one country to another. Using rich sociological data from nearly 6 million scientific publications, we document a marked shift in team leadership from Western countries to China. In particular, the share of team leaders involved in U.S-China scientific collaborations that were affiliated with Chinese institutions grew from 30% of the total in 2010 to 45% in 2023. We further explore the implications of China’s rise by forecasting when Chinese scientists are projected to achieve parity in leadership vis-à-vis the United States, including in 11 critical technology areas that are focal points of technological development, and by analyzing how a potential decoupling of U.S.-Chinese science might affect Chinese scientific leadership. We conclude by considering the impacts of China’s growing investments in the training of young scientists in countries participating in the Belt and Road Initiative.

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David Dahmen, Axel Hutt, Giacomo Indiveri, Ann Kennedy, Jeremie Lefebvre, Luca Mazzucato, Adilson E. Motter, Rishikesh Narayanan, Melika Payvand , Henrike Planert , Richard Gast

Much effort has been spent clustering neurons into transcriptomic or functional cell types and characterizing the differences between them. Beyond subdividing neurons into categories, we must recognize that no two neurons are identical and that graded physiological or transcriptomic properties exist within cells of a given type. This often overlooked “within-type” heterogeneity is a specific neuronal implementation of what statistical physics refers to as “disorder” and exhibits rich computational properties, the identification of which may shed crucial insights into theories of brain function. In this perspective article, we address this gap by highlighting theoretical frameworks for the study of neural tissue heterogeneity and discussing the benefits and implications of within-type heterogeneity for neural network dynamics, computation, and self-organization.

Read the full article at: inria.hal.science

Read the full article at: www.pnas.org

José Ignacio Arroyo, Amahury J. Lopez-Diaz, Alejandro Maass, Carlos Gershenson, Pablo Marquet, Geoffrey West, Christopher P. Kempes

Understanding the extent to which genetic correlations change in response to environmental factors, such as temperature, is a poorly explored question, despite the importance of understanding how different processes will change with climate warming. Despite correlations between thermal performance traits having been reported in the literature for a few taxa and performance tasks, such as population growth rate, a comprehensive global analysis of the entire tree of life and multiple performance tasks remains an open challenge. To advance in this open question, we compile a database of 1,300 thermal response curves, encompassing 38 variable types related to individuals’ performance (including per capita population growth rate, photosynthetic rate, among others) and 1,125 different species, ranging from viruses to mammals, encompassing all major lineages of the tree of life. Our analysis reveals that among all possible relationships between traits and optimal performance, four traits form a line with a high goodness-of-fit, while the remaining traits exhibit a polygonal pattern, either a triangle or a tetrahedron. We derive a thermodynamic framework that explains the relationships described by a curve or line (as opposed to a surface or polygon), highlighting the linear relationship between maximum and minimum temperatures, as well as between maximum and optimum temperatures. We also discuss other generic trait evolution models, which could account for the other significant sublinear relationships, as well as the more general model, Pareto optimality theory, which could account for relationships in the form of lines or polygons. Our theoretical framework and empirical evidence suggest that, based on a single data point (e.g., minimum temperature), all critical temperature limits and maximum performance boundaries can be predicted using the estimated parameter from this study. Our results reveal universal scaling relationships in thermal performance, which could be useful for predicting changes in performance under scenarios of climate warming.

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See Also: A database of biological thermal performances