Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension.

Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective.

Complex interlinkages, key objectives, and nexuses among the Sustainable Development Goals and climate change: a network analysis.

Background: Global sustainability is an enmeshed system of complex socioeconomic, climatological, and ecological interactions. The numerous objectives of the UN's Sustainable Development Goals (SDGs) and the Paris Agreement have various levels of interdependence, making it difficult to ascertain the influence of changes to particular indicators across the whole system. In this analysis, we aimed to detect and rank the complex interlinkages between objectives of sustainability agendas.

Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects.

We point out an instantiation of Simpson's paradox in COVID-19 case fatality rates (cfrs): comparing a large-scale study from China (February 17) with early reports from Italy (March 9), we find that cfrs are lower in Italy for every age group, but higher overall. This phenomenon is explained by a stark difference in case demographic between the two countries. Using this as a motivating example, we introduce basic concepts from mediation analysis and show how these can be used to quantify different direct and indirect effects when assuming a coarse-grained causal graph involving country, age, and case fatality.

A bacterial size law revealed by a coarse-grained model of cell physiology.

Universal observations in Biology are sometimes described as "laws". In E. coli, experimental studies performed over the past six decades have revealed major growth laws relating ribosomal mass fraction and cell size to the growth rate.

Correction to: A Mathematical Model for the Kinetics of the MalFGK Maltose Transporter.

The original version of this article unfortunately contained a mistake. The co-author Dr. Franck Duong Van Hoa first name and last name were misinterpreted in the original publication.

A Mathematical Model for the Kinetics of the MalFGK[Formula: see text] Maltose Transporter.

The MalFGK[Formula: see text] transporter regulates the movement of maltose across the inner membrane of E. coli and serves as a model system for bacterial ATP binding cassette (ABC) importers. Despite the wealth of biochemical and structural data available, a general model describing the various translocation pathways is still lacking.