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Benjamin Franklin was a preeminent proponent of the new colonial and Continental paper monetary system in 18th-century America. He established a network of printers, designing and printing money notes at the same time. Franklin recognized the necessity of paper money in breaking American dependence on the British trading system, and he helped print Continental money to finance the American War of Independence. We use a unique combination of nondistractive, microdestructive, and advanced atomic-level imaging methods, including Raman, Infrared, electron energy loss spectroscopy, X-ray diffraction, X-ray fluorescence, and aberration-corrected scanning transmission electron microscopy, to analyze pre-Federal American paper money from the Rare Books and Special Collections of the Hesburgh Library at the University of Notre Dame. We investigate and compare the chemical compositions of the paper fibers, the inks, and fillers made of special crystals in the bills printed by Franklin’s printing network, other colonial printers, and counterfeit money. Our results reveal previously unknown ways that Franklin developed to safeguard printed money notes against counterfeiting. Franklin used natural graphite pigments to print money and developed durable “money paper” with colored fibers and translucent muscovite fillers, along with his own unique designs of “nature-printed” patterns and paper watermarks. These features and inventions made pre-Federal American paper currency an archetype for developing paper money for centuries to come. Our multiscale analysis also provides essential information for the preservation of historical paper money. [ABSTRACT FROM AUTHOR]

The present study examines the assumptions, modeling structure, and results of DICE-2023, the revised Dynamic Integrated Model of Climate and the Economy (DICE), updated to 2023. The revision contains major changes in the treatment of risk, the carbon and climate modules, the treatment of nonindustrial greenhouse gases, discount rates, as well as updates on all the major components. Noteworthy changes are a significant reduction in the target for the optimal (cost-beneficial) temperature path, a lower cost of reaching the 2 °C target, an analysis of the impact of the Paris Accord, and a major increase in the estimated social cost of carbon. [ABSTRACT FROM AUTHOR]

Conjecture about the weak replicability in social sciences has made scholars eager to quantify the scale and scope of replication failure for a discipline. Yet small-scale manual replication methods alone are ill-suited to deal with this big data problem. Here, we conduct a discipline-wide replication census in science. Our sample (N= 14,126 papers) covers nearly all papers published in the six top-tier Psychology journals over the past 20 y. Using a validated machine learning model that estimates a paper's likelihood of replication, we found evidence that both supports and refutes speculations drawn from a relatively small sample of manual replications. First, we find that a single overall replication rate of Psychology poorly captures the varying degree of replicability among subfields. Second, we find that replication rates are strongly correlated with research methods in all subfields. Experiments replicate at a significantly lower rate than do non-experimental studies. Third, we find that authors' cumulative publication number and citation impact are positively related to the likelihood of replication, while other proxies of research quality and rigor, such as an author's university prestige and a paper's citations, are unrelated to replicability. Finally, contrary to the ideal that media attention should cover replicable research, we find that media attention is positively related to the likelihood of replication failure. Our assessments of the scale and scope of replicability are important next steps toward broadly resolving issues of replicability. [ABSTRACT FROM AUTHOR]

The large-scale implementation of renewable energy systems necessitates the development of energy storage solutions to effectively manage imbalances between energy supply and demand. Herein, we investigate such a scalable material solution for energy storage in supercapacitors constructed from readily available material precursors that can be locally sourced from virtually anywhere on the planet, namely cement, water, and carbon black. We characterize our carbon-cement electrodes by combining correlative EDS-Raman spectroscopy with capacitance measurements derived from cyclic voltammetry and galvanostatic charge-discharge experiments using integer and fractional derivatives to correct for rate and current intensity effects. Texture analysis reveals that the hydration reactions of cement in the presence of carbon generate a fractal-like electron-conducting carbon network that permeates the load-bearing cement-based matrix. The energy storage capacity of this space-filling carbon black network of the high specific surface area accessible to charge storage is shown to be an intensive quantity, whereas the high-rate capability of the carbon-cement electrodes exhibits self-similarity due to the hydration porosity available for charge transport. This intensive and self-similar nature of energy storage and rate capability represents an opportunity for mass scaling from electrode to structural scales. The availability, versatility, and scalability of these carbon-cement supercapacitors opens a horizon for the design of multifunctional structures that leverage high energy storage capacity, highrate charge/discharge capabilities, and structural strength for sustainable residential and industrial applications ranging from energy autarkic shelters and self-charging roads for electric vehicles, to intermittent energy storage for wind turbines and tidal power stations. [ABSTRACT FROM AUTHOR]

Sluggish CO2 reduction reaction (CO2RR) and evolution reaction (CO2ER) kinetics at cathodes seriously hamper the applications of Li-CO2 batteries, which have attracted vast attention as one kind of promising carbon-neutral technology. Two-dimensional transition metal dichalcogenides (TMDs) have shown great potential as the bidirectional catalysts for CO2 redox, but how to achieve a high exposure of dual active sites of TMDs with CO2RR/CO2ER activities remains a challenge. Herein, a bidirectional catalyst that vertically growing MoS2 on Co9S8 supported by carbon paper (V-MoS2/Co9S8@CP) has been designed with abundant edge as active sites for both CO2RR and CO2ER, improves the interfacial conductivity, and modulates the electron transportation pathway along the basal planes. As evidenced by the outstanding energy efficiency of 81.2% and ultra-small voltage gap of 0.68 V at 20 µA cm-2, Li-CO2 batteries with V-MoS2/Co9S8@CP show superior performance compared with horizontally growing MoS2 on Co9S8 (H-MoS2/Co9S8@CP), MoS2@CP, and Co9S8@CP. Density functional theory calculations help reveal the relationship between performance and structure and demonstrate the synergistic effect between MoS2 edge sites and Co9S8. This work provides an avenue to understand and realize rationally designed electronic contact of TMDs with specified crystal facets, but more importantly, provides a feasible guide for the design of high-performance cathodic catalyst materials in Li-CO2 batteries. [ABSTRACT FROM AUTHOR]

Retracted papers often circulate widely on social media, digital news, and other websites before their official retraction. The spread of potentially inaccurate or misleading results from retracted papers can harm the scientific community and the public. Here, we quantify the amount and type of attention 3,851 retracted papers received over time in different online platforms. Comparingwith a set of nonretracted control papers fromthe same journals with similar publication year, number of coauthors, and author impact, we show that retracted papers receive more attention after publication not only on social media but also, on heavily curated platforms, such as news outlets and knowledge repositories, amplifying the negative impact on the public. At the same time, we find that posts on Twitter tend to express more criticism about retracted than about control papers, suggesting that criticism-expressing tweets could contain factual information about problematic papers. Most importantly, around the time they are retracted, papers generate discussions that are primarily about the retraction incident rather than about research findings, showing that by this point, papers have exhausted attention to their results and highlighting the limited effect of retractions. Our findings reveal the extent to which retracted papers are discussed on different online platforms and identify at scale audience criticism toward them. In this context, we show that retraction is not an effective tool to reduce online attention to problematic papers. [ABSTRACT FROM AUTHOR]

Hard carbon is regarded as the most promising anode material for sodium-ion (Na-ion) batteries, owing to its advantages of high abundance, low cost, and low operating potential. However, the rate capability and cycle life span of hard carbon anodes are far from satisfactory, severely hindering its industrial applications. Here, we demonstrate that the desolvation process defines the Na-ion diffusion kinetics and the formation of a solid electrolyte interface (SEI). The 3A zeolite molecular sieve film on the hard carbon is proposed to develop a step-by-step desolvation pathway that effectively reduces the high activation energy of the direct desolvation process. Moreover, step-by-step desolvation yields a thin and inorganic-dominated SEI with a lower activation energy for Na+ transport. As a result, it contributes to greatly improved power density and cycling stability for both ester and ether electrolytes. When the above insights are applied, the hard carbon anode achieves the longest life span and minimum capacity fading rate at all evaluated current densities. Moreover, with the increase in current densities, an improved plateau capacity ratio is observed. This step-by-step desolvation strategy comprehensively enhances various properties of hard carbon anodes, which provides the possibility of building practical Na-ion batteries with high power density, high energy density, and durability. [ABSTRACT FROM AUTHOR]

Porous carbon is a pivotal material for electrochemical applications. The manufacture of porous carbon has relied on chemical treatments (etching or template) that require processing in all areas of the carbon/carbon precursor. We present a unique approach to preparing porous carbon nanospheres by inhibiting the pyrolytic condensation of polymers. Specifically, the porous carbon nanospheres are obtained by coating a thin film of ZnO on polystyrene spheres. The porosity of the porous carbon nanospheres is controlled by the thickness of the ZnO shell, achieving a BET-specific area of 1,124 m²/g with a specific volume of 1.09 cm³/g. We confirm that under the support force by the ZnO shell, a hierarchical pore structure in which small mesopores are connected by large mesopores is formed and that the pore-associated sp³ defects are enriched. These features allow full utilization of the surface area of the carbon pores. The electrochemical capacitive performance of porous carbon nanospheres was evaluated, achieving a high capacitance of 389 F/g at 1 A/g, capacitance retention of 71% at a 20-fold increase in current density, and stability up to 30,000 cycles. In particular, we achieve a specific area-normalized capacitance of 34.6 µF/cm², which overcomes the limitations of conventional carbon materials. [ABSTRACT FROM AUTHOR]

This document is a compilation of various scientific papers and preprints covering a wide range of topics, including protein folding, cell migration, machine learning, deep neural networks, neural scaling laws, representations and generalization in artificial and brain neural networks, and the neuron as a direct data-driven controller. The papers discuss different aspects of these subjects, providing a comprehensive overview of the current research in these fields. This document can be a valuable resource for library patrons conducting research on these topics, particularly in the fields of neuroscience and artificial intelligence. [Extracted from the article]

In the mid-1950s, Arthur Kornberg elucidated the enzymatic synthesis of DNA by DNA polymerase, for which he was recognized with the 1959 Nobel Prize in Physiology or Medicine. He then identified many of the proteins that cooperate with DNA polymerase to replicate duplex DNA of small bacteriophages. However, one major unanswered problem was understanding the mechanism and control of the initiation of chromosome replication in bacteria. In a seminal paper in 1981, Fuller, Kaguni, and Kornberg reported the development of a cell-free enzyme system that could replicate DNA that was dependent on the bacterial origin of DNA replication, oriC. This advance opened the door to a flurry of discoveries and important papers that elucidated the process and control of initiation of chromosome replication in bacteria. [ABSTRACT FROM AUTHOR]

While scientific researchers often aim for high productivity, prioritizing the quantity of publications may come at the cost of time and effort dedicated to individual research. It is thus important to examine the relationship between productivity and disruption for individual researchers. Here, we show that with the increase in the number of published papers, the average citation per paper will be higher yet the mean disruption of papers will be lower. In addition, we find that the disruption of scientists' papers may decrease when they are highly productive in a given year. The disruption of papers in each year is not determined by the total number of papers published in the author's career, but rather by the productivity of that particular year. Besides, more productive authors also tend to give references to recent and high-impact research. Our findings highlight the potential risks of pursuing productivity and aim to encourage more thoughtful career planning among scientists. [ABSTRACT FROM AUTHOR]

This article provides an overview of the Flotillin cage, a protein complex found in various organisms, including humans. The Flotillin cage plays a role in several cellular processes, such as endocytosis, mitochondrial function, intracellular trafficking, and touch sensation. The study focuses on the structure of the Flotillin-1/Flotillin-2 complex, which forms a circular cage structure consisting of 22 heterodimers. The research paper cited, titled "In-situ architecture of the human prohibitin complex" by Lange et al., delves into the architecture and function of the human prohibitin complex, offering valuable insights. The citation provides links to access the full paper for further exploration. [Extracted from the article]

Electrochemical conversion of CO2 into formate is a promising strategy for mitigating the energy and environmental crisis, but simultaneously achieving high selectivity and activity of electrocatalysts remains challenging. Here, we report low-dimensional SnO2 quantum dots chemically coupled with ultrathin Ti3C2Tx MXene nanosheets (SnO2/ MXene) that boost the CO2 conversion. The coupling structure is well visualized and verified by high-resolution electron tomography together with nanoscale scanning transmission X-ray microscopy and ptychography imaging. The catalyst achieves a large partial current density of 257.8 mA cm-2 and high Faradaic efficiency of 94% for formate formation. Additionally, the SnO2/MXene cathode shows excellent Zn–CO2 battery performance, with a maximum power density of 4.28 mW cm-2, an open-circuit voltage of 0.83 V, and superior rechargeability of 60 h. In situ X-ray absorption spectroscopy analysis and first-principles calculations reveal that this remarkable performance is attributed to the unique and stable structure of the SnO2/MXene, which can significantly reduce the reaction energy of CO2 hydrogenation to formate by increasing the surface coverage of adsorbed hydrogen. [ABSTRACT FROM AUTHOR]

Carbon-based single-atom catalysts, a promising candidate in electrocatalysis, offer insights into electron-donating effects of metal center on adjacent atoms. Herein, we present a practical strategy to rationally design a model catalyst with a single zinc (Zn) atom coordinated with nitrogen and sulfur atoms in a multilevel carbon matrix. The Zn site exhibits an atomic interface configuration of ZnN4S1, where Zn's electron injection effect enables thermal-neutral hydrogen adsorption on neighboring atoms, pushing the activity boundaries of carbon electrocatalysts toward electrochemical hydrogen evolution to an unprecedented level. Experimental and theoretical analyses confirm the low-barrier Volmer-Tafel mechanism of proton reduction, while the multishell hollow structures facilitate the hydrogen evolution even at high current intensities. This work provides insights for understanding the actual active species during hydrogen evolution reaction and paves the way for designing high-performance electrocatalysts. [ABSTRACT FROM AUTHOR]

Creating opportunities for people to achieve socioeconomic mobility is a widely shared societal goal. Paradoxically, however, achieving this goal can pose a threat to high-socioeconomic-status (SES) people as they look to maintain their privileged positions in society for both them and their children. Two studies evaluate whether this threat manifests as "opportunity hoarding" in which high-SES parents adopt attitudes and behaviors aimed at shoring up their families' access to valuable educational and economic resources. The current paper provides converging evidence for this hypothesis across two studies conducted with 2,557 American parents. An initial correlational study demonstrated that believing that socioeconomic mobility is possible was associated with high-SES parents being more inclined to attempt to secure valuable educational and economic resources for their children, even when doing so came at the cost of low-SES families. Specifically, high-SES parents with stronger beliefs in socioeconomic mobility exhibited decreased support for redistributive policies and viewed engaging in discrete behaviors that would unfairly advantage their children (e.g., allowing them to misrepresent their identities on school and job applications) as more acceptable relative to both low-SES parents with similar beliefs and high-SES parents who were less optimistic about socioeconomic mobility. A subsequent experimental study established these relationships causally by comparing parents' responses to different types of socioeconomic mobility. Together, the current findings merge insights across psychology and economics to deepen understandings of the processes through which societal inequities emerge and persist, especially during times of apparently abundant opportunity. [ABSTRACT FROM AUTHOR]

Finding optimal bipartite matchings--e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review--is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons "compete" with each other to "win" muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems. [ABSTRACT FROM AUTHOR]

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures. [ABSTRACT FROM AUTHOR]

In recent years, self-assembly has emerged as a powerful tool for fabricating functional materials. Since self-assembly is fundamentally determined by the particle interactions in the system, if we can gain full control over these interactions, it would open the door for creating functional materials by design. In this paper, we exploit capillary interactions between colloidal particles at liquid interfaces to create two-dimensional (2D) materials where particle interactions and self-assembly can be fully programmed using particle shape alone. Specifically, we consider colloidal particles which are polygonal plates with homogeneous surface chemistry and undulating edges as this particle geometry gives us precise and independent control over both short-range hard-core repulsions and longer-range capillary interactions. To illustrate the immense potential provided by our system for programming self-assembly, we use minimum energy calculations and Monte Carlo simulations to show that polygonal plates with different in-plane shapes (hexagons, truncated triangles, triangles, squares) and edge undulations of different multipolar order (hexapolar, octopolar, dodecapolar) can be used to create a rich variety of 2D structures, including hexagonal close-packed, honeycomb, Kagome, and quasicrystal lattices. Since the required particle shapes can be readily fabricated experimentally, we can use our colloidal system to control the entire process chain for materials design, from initial design and fabrication of the building blocks, to final assembly of the emergent 2D material. [ABSTRACT FROM AUTHOR]

Desired fertility measures are routinely collected and used by researchers and policy makers, but their self-reported nature raises the possibility of reporting bias. In this paper, we test for the presence of such bias by comparing responses to direct survey questions with indirect questions offering a varying, randomized, degree of confidentiality to respondents in a socioeconomically diverse sample of Nigerian women (N = 6,256). We find that women report higher fertility preferences when asked indirectly, but only when their responses afford them complete confidentiality, not when their responses are simply blind to the enumerator. Our results suggest that there may be fewer unintended pregnancies than currently thought and that the effectiveness of family planning policy targeting may be weakened by the bias we uncover. We conclude with suggestions for future work on how to mitigate reporting bias. [ABSTRACT FROM AUTHOR]

Ethics standards reference the need for special consideration of vulnerable populations, such as pregnant women, incarcerated individuals, and minors. The concept of vulnerability is poorly conceptualized in the medical sciences where it originated, and its application to the social sciences is even more challenging. Social science researchers may unwittingly fail to appreciate preexisting vulnerabilities and indeed may be responsible for inducing new research-related vulnerability. In this paper, we present the first comprehensive coding of country-level vulnerability designations. Specifically, we coded all 355 official documents governing social/behavioral human subjects research for the 107 countries with such regulations and identified 68 distinct vulnerability categories. The data reveal substantial regional variation, overemphasis of categories derived from medical sciences, neglect of critical categories such as displacement, and likely heterogeneity within and across groups. The article provides a conceptual framework that shifts the problem away from static, enumerated categories toward emphasis on research-induced vulnerability. Based on our conceptualization and coding, we present a framework for assessing vulnerability and implementing appropriate protections. [ABSTRACT FROM AUTHOR]

An increasingly common viewpoint is that protein dynamics datasets reside in a nonlinear subspace of low conformational energy. Ideal data analysis tools should therefore account for such nonlinear geometry. The Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich mathematical structure to account for a wide range of geometries that can be modeled after an energy landscape. Second, many standard data analysis tools developed for data in Euclidean space can be generalized to Riemannian manifolds. In the context of protein dynamics, a conceptual challenge comes from the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium-sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop. [ABSTRACT FROM AUTHOR]

One of California's most pressing social and environmental challenges is the rapid expansion of the wildlands-urban interface (WUI). Multiple issues associated with WUI growth compared to more dense and compact urban form are of concern--including greatly increased fire risk, greenhouse gas emissions, and fragmentation of habitat. However, little is understood about the factors driving this growth in the first place and, specifically, its relationship to urban-regional housing dynamics. This paper connects work in urban social science, urban and regional planning, and natural sciences to highlight the potential role of housing crises in driving displacement from the urban core to relatively more affordable exurbs, and with this, WUI growth. We analyze this relationship in California, which leads the nation in lack of affordable housing, scale of WUI growth, and many associated WUI hazards, including wildfire. We offer three related arguments: first, that California's affordable housing crisis, with its effect of driving migration to exurban areas, should be recognized as a significant urban form-related sustainability challenge; second, that to understand this challenge scholars must expand the spatial scale and analytic toolkit of both urban and WUI analysis through relational, mixed methods research; and third, that political and programmatic efforts to address California's housing crisis should undergird efforts to address WUI growth and climate change. Ultimately, we argue that expanding access to affordable urban housing can produce a more sustainable and just urban form that mitigates WUI-related climate and environmental impacts and reduces the vulnerability of growing numbers of WUI residents living in harm's way. [ABSTRACT FROM AUTHOR]

We utilize a coupled economy–agroecology–hydrology modeling framework to capture the cascading impacts of climate change mitigation policy on agriculture and the resulting water quality cobenefits. We analyze a policy that assigns a range of United States government’s social cost of carbon estimates ($51, $76, and $152/ton of CO2-equivalents) to fossil fuel–based CO2 emissions. This policy raises energy costs and, importantly for agriculture, boosts the price of nitrogen fertilizer production. At the highest carbon price, US carbon emissions are reduced by about 50%, and nitrogen fertilizer prices rise by about 90%, leading to an approximate 15% reduction in fertilizer applications for corn production across the Mississippi River Basin. Corn and soybean production declines by about 7%, increasing crop prices by 6%, while nitrate leaching declines by about 10%. Simulated nitrate export to the Gulf of Mexico decreases by 8%, ultimately shrinking the average midsummer area of the Gulf of Mexico hypoxic area by 3% and hypoxic volume by 4%. We also consider the additional benefits of restored wetlands to mitigate nitrogen loading to reduce hypoxia in the Gulf of Mexico and find a targeted wetland restoration scenario approximately doubles the effect of a low to moderate social cost of carbon. Wetland restoration alone exhibited spillover effects that increased nitrate leaching in other parts of the basin which were mitigated with the inclusion of the carbon policy. We conclude that a national climate policy aimed at reducing greenhouse gas emissions in the United States would have important water quality cobenefits. [ABSTRACT FROM AUTHOR]

Soft and biological matter come in a variety of shapes and geometries. When soft surfaces that do not fit into each other due to a mismatch in Gaussian curvatures form an interface, beautiful geometry-induced patterns are known to emerge. In this paper, we study the effect of geometry on the dynamical response of soft surfaces moving relative to each other. Using a simple experimental scheme, we measure friction between a highly bendable thin polymer sheet and a hydrogel substrate. At this soft and low-friction interface, we find a strong dependence of friction on the relative geometry of the two surfaces--a flat sheet experiences significantly larger friction on a spherical substrate than on flat or cylindrical substrate. We show that the stress developed in the sheet due to its geometrically incompatible confinement is responsible for the enhanced friction. This mechanism also leads to a transition in the nature of friction as the sheet radius is increased beyond a critical value. Our finding reveals a hitherto unnoticed mechanism based on an interplay between geometry and elasticity that may influence friction significantly in soft, biological, and nanoscale systems. In particular, it provokes us to reexamine our understanding of phenomena such as the curvature dependence of biological cell mobility. [ABSTRACT FROM AUTHOR]

This document serves as an introduction to a special feature on modeling for sustainability. It showcases recent advances in sustainability science and the integration of modeling with theory and data-focused approaches. The papers in this feature highlight innovative modeling methods and their applications in fields such as air quality, water resources, and energy systems. The authors emphasize the importance of equitable distribution and consider distributional issues in modeling. They also discuss the different stages of modeling practice, including defining the purpose, selecting components, analyzing interactions, and assessing interventions. The document concludes by emphasizing the potential of modeling to contribute to long-term sustainability goals. Additionally, there is a list of references to various scientific articles and studies related to topics such as water management, climate change, energy transition, and sustainability. These articles cover a range of subjects including modeling and decision-making in watershed management, the nexus between water, land, food, and climate, the impact of climate oscillations on water supply planning, and the social aspects of the energy transition. These articles provide valuable insights and research findings for library patrons conducting research on these specific topics. [Extracted from the article]

Works of fiction play a crucial role in the production of cultural stereotypes. Concerning gender, a widely held presumption is that many such works ascribe agency to men and passivity to women. However, large-scale diachronic analyses of this notion have been lacking. This paper provides an assessment of agency attributions in 87,531 fiction works written between 1850 and 2010. It introduces a syntax-based approach for extracting networks of character interactions. Agency is then formalized as a dyadic property: Does a character primarily serve as an agent acting upon the other character or as recipient acted upon by the other character? Findings indicate that female characters are more likely to be passive in cross-gender relationships than their male counterparts. This difference, the gender agency gap, has declined since the 19th century but persists into the 21st. Male authors are especially likely to attribute less agency to female characters. Moreover, certain kinds of actions, especially physical and villainous ones, have more pronounced gender disparities. [ABSTRACT FROM AUTHOR]

Iron antimonide (FeSb2) has been investigated for decades due to its puzzling electronic properties. It undergoes the temperature-controlled transition from an insulator to an ill-defined metal, with a cross-over from diamagnetism to paramagnetism. Extensive efforts have been made to uncover the underlying mechanism, but a consensus has yet to be reached. While macroscopic transport and magnetic measurements can be explained by different theoretical proposals, the essential spectroscopic evidence required to distinguish the physical origin is missing. In this paper, through the use of X-ray absorption spectroscopy and atomic multiplet simulations, we have observed the mixed spin states of 3d6 configuration in FeSb2. Furthermore, we reveal that the enhancement of the conductivity, whether induced by temperature or doping, is characterized by populating the high-spin state from the low-spin state. Our work constitutes vital spectroscopic evidence that the electrical/magnetical transition in FeSb2 is directly associated with the spin-state excitation. [ABSTRACT FROM AUTHOR]

Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons--here termed "pixels"--rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by "external" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons. [ABSTRACT FROM AUTHOR]

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general. [ABSTRACT FROM AUTHOR]

In one of the first papers on the impact of early-life conditions on individuals' health in older age, Barker and Osmond [Lancet, 327, 1077-1081 (1986)] show a strong positive relationship between infant mortality rates in the 1920s and ischemic heart disease in the 1970s. We merge historical data on infant mortality rates to 370,000 individual records in the UK Biobank using information on local area and year of birth. We replicate the association between the early-life infant mortality rate and laterlife ischemic heart disease in our sample. We then go "beyond Barker," by showing considerable genetic heterogeneity in this association that is robust to within-area as well as within-family analyses. We find no association between the polygenic index and heart disease in areas with the lowest infant mortality rates, but a strong positive relationship in areas characterized by high infant mortality. These findings suggest that advantageous environments can cushion one's genetic disease risk. [ABSTRACT FROM AUTHOR]

Recent years witnessed the development of powerful generative models based on flows, diffusion, or autoregressive neural networks, achieving remarkable success in generating data from examples with applications in a broad range of areas. A theoretical analysis of the performance and understanding of the limitations of these methods remain, however, challenging. In this paper, we undertake a step in this direction by analyzing the efficiency of sampling by these methods on a class of problems with a known probability distribution and comparing it with the sampling performance of more traditional methods such as the Monte Carlo Markov chain and Langevin dynamics. We focus on a class of probability distribution widely studied in the statistical physics of disordered systems that relate to spin glasses, statistical inference, and constraint satisfaction problems. We leverage the fact that sampling via flow-based, diffusionbased, or autoregressive networks methods can be equivalently mapped to the analysis of a Bayes optimal denoising of a modified probability measure. Our findings demonstrate that these methods encounter difficulties in sampling stemming from the presence of a first-order phase transition along the algorithm's denoising path. Our conclusions go both ways: We identify regions of parameters where these methods are unable to sample efficiently, while that is possible using standard Monte Carlo or Langevin approaches. We also identify regions where the opposite happens: standard approaches are inefficient while the discussed generative methods work well. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "MDGA1 negatively regulates amyloid precursor protein-mediated synapse inhibition in the hippocampus." The authors discovered errors in the images included in previous publications, which were due to a lapse in attention during figure presentation. The corrected figures have been provided, and the authors state that these corrections do not change the conclusions of the paper. The document describes the methods and materials used in the study, including gel digestion, LC-MS/MS analysis, antibodies, expression vectors, and various assays and experiments. It also provides detailed information on the experimental procedures used to study the effects of a virus on synaptic changes, as well as the methods used to measure synaptic currents and conduct behavioral analyses in mice. The results of the experiments suggest that APP loss affects GABAergic synapse density, synaptic transmission, and excitability in specific types of interneurons in the hippocampus. [Extracted from the article]

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if R0 is less than a threshold set by the characteristic antigenic distances for crossprotection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strainspecific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification. [ABSTRACT FROM AUTHOR]

Renewable Portfolio Standards (RPSs) are one of the most prevalent and impactful clean energy policies implemented by states in the United States. This paper investigates the regional spillover effect of RPS policies using a directed dyad panel dataset of renewable electricity generation in US states from 1991 to 2021. Regional spillover effect is measured in two ways: by considering the influence of an RPS enacted in neighboring states and in states in the same regional transmission organization or independent system operator region. We use dyadic fixed effects estimation and conclude that the neighboring state's RPS stringency score is a strong determinant of a state's total renewable electricity generation. For states without an RPS, the positive influence of an RPS in a neighboring state is larger when the non-RPS state has more abundant renewable energy resources than the neighboring RPS state. Our findings suggest that past RPS policy evaluation research using a confined within-state focus may have underestimated the holistic impact of an RPS, as the impacts of an RPS policy can extend beyond the enacting state's borders. Overall, this study contributes to an improved understanding of the holistic impact of state RPS policies. [ABSTRACT FROM AUTHOR]

This paper exploits the potential of Global Positioning System datasets sourced from mobile phones to estimate the racial composition of road users, leveraging data from their respective Census block group. The racial composition data encompasses approximately 46 million trips in the Chicago metropolitan region. The research focuses on the relationship between camera tickets and racial composition of drivers vs. police stops for traffic citations and the racial composition in these locations. Black drivers exhibit a higher likelihood of being ticketed by automated speed cameras and of being stopped for moving violations on roads, irrespective of the proportion of White drivers present. The research observes that this correlation attenuates as the proportion of White drivers on the road increases. The citation rate measured by cameras better matches the racial composition of road users on the links with cameras than do stops by police officers. This study therefore presents an important contribution to understanding racial disparities in moving violation stops, with implications for policy interventions and social justice reforms. [ABSTRACT FROM AUTHOR]

Sensations of heat and touch produced by receptors in the skin are of essential importance for perceptions of the physical environment, with a particularly powerful role in interpersonal interactions. Advances in technologies for replicating these sensations in a programmable manner have the potential not only to enhance virtual/augmented reality environments but they also hold promise in medical applications for individuals with amputations or impaired sensory function. Engineering challenges are in achieving interfaces with precise spatial resolution, power-efficient operation, wide dynamic range, and fast temporal responses in both thermal and in physical modulation, with forms that can extend over large regions of the body. This paper introduces a wireless, skin-compatible interface for thermo-haptic modulation designed to address some of these challenges, with the ability to deliver programmable patterns of enhanced vibrational displacement and high-speed thermal stimulation. Experimental and computational investigations quantify the thermal and mechanical efficiency of a vertically stacked design layout in the thermo-haptic stimulators that also supports real-time, closed-loop control mechanisms. The platform is effective in conveying thermal and physical information through the skin, as demonstrated in the control of robotic prosthetics and in interactions with pressure/temperature-sensitive touch displays. [ABSTRACT FROM AUTHOR]

The Hill functions, Hh(x) = xh/(1 + xh), have been widely used in biology for over a century but, with the exception of H1, they have had no justification other than as a convenient fit to empirical data. Here, we show that they are the universal limit for the sharpness of any input-output response arising from a Markov process model at thermodynamic equilibrium. Models may represent arbitrary molecular complexity, with multiple ligands, internal states, conformations, coregulators, etc, under core assumptions that are detailed in the paper. The model output may be any linear combination of steady-state probabilities, with components other than the chosen input ligand held constant. This formulation generalizes most of the responses in the literature. We use a coarse-graining method in the graph-theoretic linear framework to show that two sharpness measures for input-output responses fall within an effectively bounded region of the positive quadrant, Ωm ⊂ (R+)², for any equilibrium model with m input binding sites. Ωm exhibits a cusp which approaches, but never exceeds, the sharpness of Hm, but the region and the cusp can be exceeded when models are taken away from thermodynamic equilibrium. Such fundamental thermodynamic limits are called Hopfield barriers, and our results provide a biophysical justification for the Hill functions as the universal Hopfield barriers for sharpness. Our results also introduce an object, Ωm, whose structure may be of mathematical interest, and suggest the importance of characterizing Hopfield barriers for other forms of cellular information processing. [ABSTRACT FROM AUTHOR]

Closing the achievement gap for minority students in higher education requires addressing the lack of belonging these students experience. This paper introduces a psychological intervention that strategically targets key elements within the learning environment to foster the success of minority students. The intervention sought to enhance Palestinian minority student's sense of belonging by increasing the presence of their native language. We tested the effectiveness of the intervention in two field experiments in Israel (n > 20,000), at the height of the COVID-19 pandemic when all classes were held via Zoom. Lecturers in the experimental condition added a transcript of their names in Arabic to their default display (English/Hebrew only). Our findings revealed a substantial and positive impact on Palestinian student's sense of belonging, class participation, and overall grades. In experiment 1, Palestinian student's average grade increased by 10 points. In experiment 2, there was an average increase of 4 points among Palestinian students' semester grade. Our intervention demonstrates that small institutional changes when carefully crafted can have a significant impact on minority populations. These results have significant implications for addressing educational disparities and fostering inclusive learning environment. [ABSTRACT FROM AUTHOR]

Nanoparticles tethered with vasculature-binding epitopes have been used to deliver the drug into injured or diseased tissues via the bloodstream. However, the extent that blood flow dynamics affects nanoparticle retention at the target site after adhesion needs to be better understood. This knowledge gap potentially underlies significantly different therapeutic efficacies between animal models and humans. An experimentally validated mathematical model that accurately simulates the effects of blood flow on nanoparticle adhesion and retention, thus circumventing the limitations of conventional trial-and-error-based drug design in animal models, is lacking. This paper addresses this technical bottleneck and presents an integrated mathematical method that derives heavily from a unique combination of a mechanics-based dispersion model for nanoparticle transport and diffusion in the boundary layers, an asperity model to account for surface roughness of endothelium, and an experimentally calibrated stochastic nanoparticle-cell adhesion model to describe nanoparticle adhesion and subsequent retention at the target site under external flow. PLGA-b-HA nanoparticles tethered with VHSPNKK peptides that specifically bind to vascular cell adhesion molecules on the inflamed vascular wall were investigated. The computational model revealed that larger particles perform better in adhesion and retention at the endothelium for the particle sizes suitable for drug delivery applications and within physiologically relevant shear rates. The computational model corresponded closely to the in vitro experiments which demonstrates the impact that model-based simulations can have on optimizing nanocarriers in vascular microenvironments, thereby substantially reducing in vivo experimentation as well as the development costs. [ABSTRACT FROM AUTHOR]

This document is a correction to the supporting information for a medical sciences article titled "MEK1 mutations confer resistance to MEK and B-RAF inhibition." The authors acknowledge an error in one of the images in the supporting information, but assure that it does not affect the experiment's results or the overall paper. The corrected version of the supporting information is available online. The article provides detailed information on various aspects of the study, including cell lines, MEK1 expression constructs, mutagenesis screen, sequencing, growth inhibition assays, Western blot analysis, kinase assays, and colony formation assays. The document includes figures and tables presenting the experimental findings. [Extracted from the article]

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers. [ABSTRACT FROM AUTHOR]

The reaction kinetics of photocatalytic CO2 reduction is highly dependent on the transfer rate of electrons and protons to the CO2 molecules adsorbed on catalytic centers. Studies on uncovering the proton effect in catalysts on photocatalytic activity of CO2 reduction are significant but rarely reported. In this paper, we, from the molecular level, revealed that the photocatalytic activity of CO2 reduction is closely related to the proton availability in catalysts. Specifically, four dinuclear Co(II) complexes based on Robson-type ligands with different number of carboxylic groups (-nCOOH; n = 0, 2, 4, 6) were designed and synthesized. All these complexes show photocatalytic activity for CO2 reduction to CO in a water-containing system upon visible-light illumination. Interestingly, the CO yields increase positively with the increase of the carboxylic-group number in dinuclear Co(II) complexes. The one containing -6COOH shows the best photocatalytic activity for CO2 reduction to CO, with the TON value reaching as high as 10,294. The value is 1.8, 3.4, and 7.8 times higher than those containing -4COOH, -2COOH, and -0COOH, respectively. The high TON value also makes the dinuclear Co(II) complex with -6COOH outstanding among reported homogeneous molecular catalysts for photocatalytic CO2 reduction. Control experiments and density functional theory calculation indicated that more carboxylic groups in the catalyst endow the catalyst with more proton relays, thus accelerating the proton transfer and boosting the photocatalytic CO2 reduction. This study, at a molecular level, elucidates that more carboxylic groups in catalysts are beneficial for boosting the reaction kinetics of photocatalytic CO2 reduction. [ABSTRACT FROM AUTHOR]

Molecular chaperones assist in protein refolding by selectively binding to proteins in their nonnative states. Despite progress in creating artificial chaperones, these designs often have a limited range of substrates they can work with. In this paper, we present molecularly imprinted flexible polymer nanoparticles (nanoMIPs) designed as customizable biomimetic chaperones. We used model proteins such as cytochrome c, laccase, and lipase to screen polymeric monomers and identify the most effective formulations, offering tunable charge and hydrophobic properties. Utilizing a dispersed phase imprinting approach, we employed magnetic beads modified with destabilized whole-protein as solid-phase templates. This process involves medium exchange facilitated by magnetic pulldowns, resulting in the synthesis of nanoMIPs featuring imprinted sites that effectively mimic chaperone cavities. These nanoMIPs were able to selectively refold denatured enzymes, achieving up to 86.7% recovery of their activity, significantly outperforming control samples. Mechanistic studies confirmed that nanoMIPs preferentially bind denatured rather than native enzymes, mimicking natural chaperone interactions. Multifaceted analyses support the functionality of nanoMIPs, which emulate the protective roles of chaperones by selectively engaging with denatured proteins to inhibit aggregation and facilitate refolding. This approach shows promise for widespread use in protein recovery within biocatalysis and biomedicine. [ABSTRACT FROM AUTHOR]

There is growing recognition of the potential of migration to contribute to climate-change adaptation. Yet, there is limited evidence to what degree, under what conditions, for whom, and with which limitations this is effectively the case. We argue that this results from a lack of recognition and systematic incorporation of sociospatiality--the nested, networked, and intersectional nature of migration-as-adaptation. Our central objective is to utilize the translocal social-resilience approach to overcome these gaps, to identify processes and structures that shape the social resilience of translocal livelihood systems, and to illustrate the mechanisms behind the multiplicity of possible resilience outcomes. Translocal livelihood constellations anchored in rural Thailand as well as in domestic and international destinations of Thai migrants serve as illustrative empirical cases. Data were gathered through a multisited and mixed-methods research design. This paper highlights the role of the distinct but interlinked situations and operational logics at places of origin and destination, as well as the different positionalities and resulting vulnerabilities, roles, commitments, and practices of individuals and households with regard to resilience. Based on the empirical results, the paper distills a generalized typology of five broad categories of resilience outcomes, which explicitly considers sociospatiality. Our approach helps to grasp the complexity of migration-as-adaptation and to avoid simplistic conclusions about the benefits and costs of migration for adaptation--both of which are necessary for sound, evidence-based, migration-as-adaptation policymaking. [ABSTRACT FROM AUTHOR]

The impact of a scientific publication is often measured by the number of citations it receives from the scientific community. However, citation count is susceptible to well-documented variations in citation practices across time and discipline, limiting our ability to compare different scientific achievements. Previous efforts to account for citation variations often rely on a priori discipline labels of papers, assuming that all papers in a discipline are identical in their subject matter. Here, we propose a network-based methodology to quantify the impact of an article by comparing it with locally comparable research, thereby eliminating the discipline label requirement. We show that the developed measure is not susceptible to discipline bias and follows a universal distribution for all articles published in different years, offering an unbiased indicator for impact across time and discipline. We then use the indicator to identify science-wide high impact research in the past half century and quantify its temporal production dynamics across disciplines, helping us identifying breakthroughs from diverse, smaller disciplines, such as geosciences, radiology, and optics, as opposed to citation-rich biomedical sciences. Our work provides insights into the evolution of science and paves a way for fair comparisons of the impact of diverse contributions across many fields. [ABSTRACT FROM AUTHOR]

The need for faster and deeper transitions toward more sustainable development pathways is now widely recognized. How to meet that need has been at the center of a growing body of academic research and real-world policy implementation. This paper presents our perspective on some of the most powerful insights that have emerged from this ongoing work. In particular, we highlight insights on how sustainability transitions can be usefully conceptualized, how they come about and evolve, and how they can be shaped and guided through deliberate policy interventions. Throughout the paper, we also highlight some of the many how questions that remain unresolved and on which progress would be especially helpful for the pursuit of sustainable development. Our approach to these "how" questions on sustainability transitions draws on two strands of solution-driven research and policy advice: one emerging from studies of how human societies interact with nature and the other emerging from studies of how those societies interact with their technologies. Consumption-production systems have been a focus of extensive work in both strands. To help build bridges between them, we recently brought together a cross-section of relevant scholars for a PNAS Special Feature on "Sustainability transitions in consumption-production systems." Their contributions are summarized in a companion paper we have written to introduce the Special Feature [F. W. Geels, F. Kern, W. C. Clark, Proc. Natl. Acad. Sci. U.S.A. (2023)]. We draw on that work in the Perspective we present here as well as our reading of the relevant literatures. [ABSTRACT FROM AUTHOR]

Addressing sustainability challenges requires fundamental transformations in electricity, heat, mobility, and agri-food systems. To do so, research and policy efforts tend to emphasise the importance of fostering new, more sustainable systems through innovation. Instead, this paper focuses explicitly on the "flipside" of innovation: The fate of established systems faced with their potential destabilisation and decline. It is argued that any transition in consumption-production systems involves a combination of innovation (something new emerges) and destabilisation (something old is being challenged). To examine the role of destabilisation, decline, and phase-out for policy and practice efforts, this paper advances conceptual and empirical contributions. Conceptually, it elaborates a framework based on three interacting destabilisation mechanisms: The build-up of pressures, strategic responses by central system actors, and changing commitments to reproductive activities. Empirically, it draws on the historic dismantling of electric tramways in France. The decline of the tramway in France followed a gradual erosion pattern resulting from the long-term degradation of technical, political, and economic conditions, which was accelerated by a relatively rapid phase-out programme. A discussion section offers insights on the temporality of destabilisation, the context of phase-out decisions, and the interaction of destabilisation and innovation processes. [ABSTRACT FROM AUTHOR]

Implementing electromobility is a central component in the de-carbonization of personal mobility. In recent years, the absolute number of electric vehicles (EVs) and their market share has increased sharply in many countries. This paper focuses on Norway, a pioneer market for EVs that other countries can learn from. The analysis highlights how a combination of local and national policies over a 30-y period, which targeted both industry development and vehicle demand, were important drivers of this development. It also highlights the importance of advocacy groups and strong networks in promoting EVs, as well as changes in user preferences. The paper demonstrates how the EV diffusion has been driven by alignments of multiple processes across different levels, involving interactions between multiple actors and social groups with different interests and views about desirable futures as described by the multi-level perspective (MLP). Building on the MLP, the study of EV diffusion in Norway illustrates how niches are often sustained through demonstrations, experimentation, strategic alliances, and actors securing favorable political and economic conditions. Further, it shows how local or national niches may depend on international regime actors, such as the car manufacturing industry and policies developed abroad. The paper also explores how the introduction of EVs has opened for wider effects, including innovation within production-consumption systems beyond mobility. Based on this analysis, we argue for a nuanced perspective on the relationship between incremental, regime-internal innovation, and wider transformative changes, where the merits of societal learning and experience with battery electricity for transportation are highlighted. [ABSTRACT FROM AUTHOR]