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The assembly of the β-amyloid peptide (Aβ) to form oligomers and fibrils is closely associated with the pathogenesis and progression of Alzheimer's disease. Aβ is a shape-shifting peptide capable of adopting many conformations and folds within the multitude of oligomers and fibrils the peptide forms. These properties have precluded detailed structural elucidation and biological characterization of homogeneous, well-defined Aβ oligomers. In this paper, we compare the structural, biophysical, and biological characteristics of two different covalently stabilized isomorphic trimers derived from the central and C-terminal regions Aβ. X-ray crystallography reveals the structures of the trimers and shows that each trimer forms a ball-shaped dodecamer. Solution-phase and cell-based studies demonstrate that the two trimers exhibit markedly different assembly and biological properties. One trimer forms small soluble oligomers that enter cells through endocytosis and activate capase-3/7-mediated apoptosis, while the other trimer forms large insoluble aggregates that accumulate on the outer plasma membrane and elicit cellular toxicity through an apoptosis-independent mechanism. The two trimers also exhibit different effects on the aggregation, toxicity, and cellular interaction of full-length Aβ, with one trimer showing a greater propensity to interact with Aβ than the other. The studies described in this paper indicate that the two trimers share structural, biophysical, and biological characteristics with oligomers of full-length Aβ. The varying structural, assembly, and biological characteristics of the two trimers provide a working model for how different Aβ trimers can assemble and lead to different biological effects, which may help shed light on the differences among Aβ oligomers.

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout to assisting in university admissions and facilitating the rise of massive open online courses (MOOCs). Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand? We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Cryoelectron tomography of the cell nucleus using scanning transmission electron microscopy and deconvolution processing technology has highlighted a large-scale, 100- to 300-nm interphase chromosome structure, which is present throughout the nucleus. This study further documents and analyzes these chromosome structures. The paper is divided into four parts: 1) evidence (preliminary) for a unified interphase chromosome structure; 2) a proposed unified interphase chromosome architecture; 3) organization as chromosome territories (e.g., fitting the 46 human chromosomes into a 10-μm-diameter nucleus); and 4) structure unification into a polytene chromosome architecture and lampbrush chromosomes. Finally, the paper concludes with a living light microscopy cell study showing that the G1 nucleus contains very similar structures throughout. The main finding is that this chromosome structure appears to coil the 11-nm nucleosome fiber into a defined hollow structure, analogous to a Slinky helical spring [https://en.wikipedia.org/wiki/Slinky; motif used in Bowerman et al., eLife 10, e65587 (2021)]. This Slinky architecture can be used to build chromosome territories, extended to the polytene chromosome structure, as well as to the structure of lampbrush chromosomes.

Science is undergoing rapid change with the movement to improve science focused largely on reproducibility/replicability and open science practices. This moment of change-in which science turns inward to examine its methods and practices-provides an opportunity to address its historic lack of diversity and noninclusive culture. Through network modeling and semantic analysis, we provide an initial exploration of the structure, cultural frames, and women's participation in the open science and reproducibility literatures (n = 2,926 articles and conference proceedings). Network analyses suggest that the open science and reproducibility literatures are emerging relatively independently of each other, sharing few common papers or authors. We next examine whether the literatures differentially incorporate collaborative, prosocial ideals that are known to engage members of underrepresented groups more than independent, winner-takes-all approaches. We find that open science has a more connected, collaborative structure than does reproducibility. Semantic analyses of paper abstracts reveal that these literatures have adopted different cultural frames: open science includes more explicitly communal and prosocial language than does reproducibility. Finally, consistent with literature suggesting the diversity benefits of communal and prosocial purposes, we find that women publish more frequently in high-status author positions (first or last) within open science (vs. reproducibility). Furthermore, this finding is further patterned by team size and time. Women are more represented in larger teams within reproducibility, and women's participation is increasing in open science over time and decreasing in reproducibility. We conclude with actionable suggestions for cultivating a more prosocial and diverse culture of science.

Science is undergoing rapid change with the movement to improve science focused largely on reproducibility/replicability and open science practices. This moment of change-in which science turns inward to examine its methods and practices-provides an opportunity to address its historic lack of diversity and noninclusive culture. Through network modeling and semantic analysis, we provide an initial exploration of the structure, cultural frames, and women's participation in the open science and reproducibility literatures (n = 2,926 articles and conference proceedings). Network analyses suggest that the open science and reproducibility literatures are emerging relatively independently of each other, sharing few common papers or authors. We next examine whether the literatures differentially incorporate collaborative, prosocial ideals that are known to engage members of underrepresented groups more than independent, winner-takes-all approaches. We find that open science has a more connected, collaborative structure than does reproducibility. Semantic analyses of paper abstracts reveal that these literatures have adopted different cultural frames: open science includes more explicitly communal and prosocial language than does reproducibility. Finally, consistent with literature suggesting the diversity benefits of communal and prosocial purposes, we find that women publish more frequently in high-status author positions (first or last) within open science (vs. reproducibility). Furthermore, this finding is further patterned by team size and time. Women are more represented in larger teams within reproducibility, and women's participation is increasing in open science over time and decreasing in reproducibility. We conclude with actionable suggestions for cultivating a more prosocial and diverse culture of science.

Cognitive abilities can vary dramatically among species. The relative importance of social and ecological challenges in shaping cognitive evolution has been the subject of a long-running and recently renewed debate, but little work has sought to understand the selective dynamics underlying the evolution of cognitive abilities. Here, we investigate recent selection related to cognition in the paper wasp Polistes fuscatus-a wasp that has uniquely evolved visual individual recognition abilities. We generate high quality de novo genome assemblies and population genomic resources for multiple species of paper wasps and use a population genomic framework to interrogate the probable mode and tempo of cognitive evolution. Recent, strong, hard selective sweeps in P. fuscatus contain loci annotated with functions in long-term memory formation, mushroom body development, and visual processing, traits which have recently evolved in association with individual recognition. The homologous pathways are not under selection in closely related wasps that lack individual recognition. Indeed, the prevalence of candidate cognition loci within the strongest selective sweeps suggests that the evolution of cognitive abilities has been among the strongest selection pressures in P. fuscatus' recent evolutionary history. Detailed analyses of selective sweeps containing candidate cognition loci reveal multiple cases of hard selective sweeps within the last few thousand years on de novo mutations, mainly in noncoding regions. These data provide unprecedented insight into some of the processes by which cognition evolves.

This paper introduces the paradigm of in-context operator learning and the corresponding model In-Context Operator Networks to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update. Existing methods are limited to using a neural network to approximate a specific equation solution or a specific operator, requiring retraining when switching to a new problem with different equations. By training a single neural network as an operator learner, rather than a solution/operator approximator, we can not only get rid of retraining (even fine-tuning) the neural network for new problems but also leverage the commonalities shared across operators so that only a few examples in the prompt are needed when learning a new operator. Our numerical results show the capability of a single neural network as a few-shot operator learner for a diversified type of differential equation problems, including forward and inverse problems of ordinary differential equations, partial differential equations, and mean-field control problems, and also show that it can generalize its learning capability to operators beyond the training distribution.

Subsidies are widely criticized in fisheries management for promoting global fishing capacity growth and overharvesting. Scientists worldwide have thus called for a ban on "harmful" subsidies that artificially increase fishing profits, resulting in the recent agreement among members of the World Trade Organization to eliminate such subsidies. The argument for banning harmful subsidies relies on the assumption that fishing will be unprofitable after eliminating subsidies, incentivizing some fishermen to exit and others to refrain from entering. These arguments follow from open-access governance regimes where entry has driven profits to zero. Yet many modern-day fisheries are conducted under limited-access regimes that limit capacity and maintain economic profits, even without subsidies. In these settings, subsidy removal will reduce profits but perhaps without any discernable effect on capacity. Importantly, until now, there have been no empirical studies of subsidy reductions to inform us about their likely quantitative impacts. In this paper, we evaluate a policy reform that reduced fisheries subsidies in China. We find that China's subsidy reductions accelerated the rate at which fishermen retired their vessels, resulting in reduced fleet capacity, particularly among older and smaller vessels. Notably, the reduction of harmful subsidies was only partly responsible for reducing fleet capacity; an increase in vessel retirement subsidies was also a necessary driver of capacity reduction. Our study demonstrates that the efficacy of removing harmful subsidies depends on the policy environment in which removals occur.

In the hot dry spring of monsoon-driven environments, keeping an accurate calendar to regulate the annual planting of crops is of critical importance. Before the Spanish conquest, the Basin of Mexico had a highly productive farming system able to feed its very large population. However, how they managed to keep their farming dates in synchrony with the solar year is not known. In this paper, we show that the observation of sunrise against the Basin's eastern horizon could have provided an accurate solar calendar and that some important sunrise landmarks coincide well with the themes of seasonal festivities described in early codices. We also show that a long stone causeway in the summit of Mount Tlaloc aligns perfectly with the rising sun on February 23 to 24, in coincidence with the Basin's new year in the Mexica calendar. Third, we demonstrate that, when viewed from the sacred Mount Tepeyac in the bottom of the Basin, sunrise aligns with Mount Tlaloc also on February 24. The importance of Mount Tlaloc as a calendric landmark seems to be corroborated by illustrations and texts in ancient Mexica codices. Our findings demonstrate that by using carefully developed alignments with the rugged eastern horizon, the inhabitants of the Basin of Mexico were able to adjust their calendar to keep in synchrony with the solar year and successfully plan their corn harvests.

In this paper, we systematically investigate three different routes of synthesizing 2% Na-doped PbTe after melting the elements: (i) quenching followed by hot-pressing (QH), (ii) annealing followed by hot-pressing, and (iii) quenching and annealing followed by hot-pressing. We found that the thermoelectric figure of merit, zT, strongly depends on the synthesis condition and that its value can be enhanced to ∼ 2.0 at 773 K by optimizing the size distribution of the nanostructures in the material. Based on our theoretical analysis on both electron and thermal transport, this zT enhancement is attributed to the reduction of both the lattice and electronic thermal conductivities; the smallest sizes (2 ∼ 6 nm) of nanostructures in the QH sample are responsible for effectively scattering the wide range of phonon wavelengths to minimize the lattice thermal conductivity to ∼ 0.5 W/m K. The reduced electronic thermal conductivity associated with the suppressed electrical conductivity by nanostructures also helped reduce the total thermal conductivity. In addition to the high zT of the QH sample, the mechanical hardness is higher than the other samples by a factor of around 2 due to the smaller grain sizes. Overall, this paper suggests a guideline on how to achieve high zT and mechanical strength of a thermoelectric material by controlling nano- and microstructures of the material.

Hundreds of millions of poor families receive some form of targeted social assistance. Many of these antipoverty programs involve some degree of geographic targeting, where aid is prioritized to the poorest regions of the country. However, policy makers in many low-resource settings lack the disaggregated poverty data required to make effective geographic targeting decisions. Using several independent datasets from Nigeria, this paper shows that high-resolution poverty maps, constructed by applying machine learning algorithms to satellite imagery and other nontraditional geospatial data, can improve the targeting of government cash transfers to poor families. Specifically, we find that geographic targeting relying on machine learning-based poverty maps can reduce errors of exclusion and inclusion relative to geographic targeting based on recent nationally representative survey data. This result holds for antipoverty programs that target both the poor and the extreme poor and for initiatives of varying sizes. We also find no evidence that machine learning-based maps increase targeting disparities by demographic groups, such as gender or religion. Based in part on these findings, the Government of Nigeria used this approach to geographically target emergency cash transfers in response to the COVID-19 pandemic.

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire-climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water supplies and runoff-related risks.

Observational studies reveal substantial variability in microbiome composition across individuals. Targeted studies in gnotobiotic animals underscore this variability by showing that some bacterial strains colonize deterministically, while others colonize stochastically. While some of this variability can be explained by external factors like environmental, dietary, and genetic differences between individuals, in this paper we show that for the model organism Drosophila melanogaster, interactions between bacteria can affect the microbiome assembly process, contributing to a baseline level of microbiome variability even among isogenic organisms that are identically reared, housed, and fed. In germ-free flies fed known combinations of bacterial species, we find that some species colonize more frequently than others even when fed at the same high concentration. We develop an ecological technique that infers the presence of interactions between bacterial species based on their colonization odds in different contexts, requiring only presence/absence data from two-species experiments. We use a progressive sequence of probabilistic models, in which the colonization of each bacterial species is treated as an independent stochastic process, to reproduce the empirical distributions of colonization outcomes across experiments. We find that incorporating context-dependent interactions substantially improves the performance of the models. Stochastic, context-dependent microbiome assembly underlies clinical therapies like fecal microbiota transplantation and probiotic administration and should inform the design of synthetic fecal transplants and dosing regimes.

The Earth BioGenome Project (EBP) is an audacious endeavor to obtain whole-genome sequences of representatives from all eukaryotic species on Earth. In addition to the project's technical and organizational challenges, it also faces complicated ethical, legal, and social issues. This paper, from members of the EBP's Ethical, Legal, and Social Issues (ELSI) Committee, catalogs these ELSI concerns arising from EBP. These include legal issues, such as sample collection and permitting; the applicability of international treaties, such as the Convention on Biological Diversity and the Nagoya Protocol; intellectual property; sample accessioning; and biosecurity and ethical issues, such as sampling from the territories of Indigenous peoples and local communities, the protection of endangered species, and cross-border collections, among several others. We also comment on the intersection of digital sequence information and data rights. More broadly, this list of ethical, legal, and social issues for large-scale genomic sequencing projects may be useful in the consideration of ethical frameworks for future projects. While we do not-and cannot-provide simple, overarching solutions for all the issues raised here, we conclude our perspective by beginning to chart a path forward for EBP's work.

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around [Formula: see text] However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

Genomic data are being produced and archived at a prodigious rate, and current studies could become historical baselines for future global genetic diversity analyses and monitoring programs. However, when we evaluated the potential utility of genomic data from wild and domesticated eukaryote species in the world's largest genomic data repository, we found that most archived genomic datasets (86%) lacked the spatiotemporal metadata necessary for genetic biodiversity surveillance. Labor-intensive scouring of a subset of published papers yielded geospatial coordinates and collection years for only 33% (39% if place names were considered) of these genomic datasets. Streamlined data input processes, updated metadata deposition policies, and enhanced scientific community awareness are urgently needed to preserve these irreplaceable records of today's genetic biodiversity and to plug the growing metadata gap.

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

Conventional methods of DNA sequence insertion into plants, using Agrobacterium-mediated transformation or microprojectile bombardment, result in the integration of the DNA at random sites in the genome. These plants may exhibit altered agronomic traits as a consequence of disruption or silencing of genes that serve a critical function. Also, genes of interest inserted at random sites are often not expressed at the desired level. For these reasons, targeted DNA insertion at suitable genomic sites in plants is a desirable alternative. In this paper we review approaches of targeted DNA insertion in plant genomes, discuss current technical challenges, and describe promising applications of targeted DNA insertion for crop genetic improvement.

The ethical debate about what is now called human gene editing (HGE) has gone on for more than 50 y. For nearly that entire time, there has been consensus that a moral divide exists between somatic and germline HGE. Conceptualizing this divide as a barrier on a slippery slope, in this paper, I first describe the slope, what makes it slippery, and describe strong barriers that arrest the slippage down to the dystopian bottom of pervasive eugenic enhancement. I then show how the somatic/germline barrier in the debate has been weakened to the level of ineffectiveness, with no replacement below. I examine a number of possible barriers on the slope below the somatic/germline barrier, most of which lack sufficient strength. With the exception of the minority of people in the HGE debate who see the eugenic society as utopia, the majority will need a barrier on the slope to stop the slide to dystopia.

Multimodal imaging-the ability to acquire images of an object through more than one imaging mode simultaneously-has opened additional perspectives in areas ranging from astronomy to medicine. In this paper, we report progress toward combining optical and magnetic resonance (MR) imaging in such a "dual" imaging mode. They are attractive in combination because they offer complementary advantages of resolution and speed, especially in the context of imaging in scattering environments. Our approach relies on a specific material platform, microdiamond particles hosting nitrogen vacancy (NV) defect centers that fluoresce brightly under optical excitation and simultaneously "hyperpolarize" lattice [Formula: see text] nuclei, making them bright under MR imaging. We highlight advantages of dual-mode optical and MR imaging in allowing background-free particle imaging and describe regimes in which either mode can enhance the other. Leveraging the fact that the two imaging modes proceed in Fourier-reciprocal domains (real and k-space), we propose a sampling protocol that accelerates image reconstruction in sparse-imaging scenarios. Our work suggests interesting possibilities for the simultaneous optical and low-field MR imaging of targeted diamond nanoparticles.

Multimodal imaging-the ability to acquire images of an object through more than one imaging mode simultaneously-has opened additional perspectives in areas ranging from astronomy to medicine. In this paper, we report progress toward combining optical and magnetic resonance (MR) imaging in such a "dual" imaging mode. They are attractive in combination because they offer complementary advantages of resolution and speed, especially in the context of imaging in scattering environments. Our approach relies on a specific material platform, microdiamond particles hosting nitrogen vacancy (NV) defect centers that fluoresce brightly under optical excitation and simultaneously "hyperpolarize" lattice [Formula: see text] nuclei, making them bright under MR imaging. We highlight advantages of dual-mode optical and MR imaging in allowing background-free particle imaging and describe regimes in which either mode can enhance the other. Leveraging the fact that the two imaging modes proceed in Fourier-reciprocal domains (real and k-space), we propose a sampling protocol that accelerates image reconstruction in sparse-imaging scenarios. Our work suggests interesting possibilities for the simultaneous optical and low-field MR imaging of targeted diamond nanoparticles.

India has set aggressive targets to install more than 400 GW of wind and solar electricity generation by 2030, with more than two-thirds of that capacity coming from solar. This paper examines the electricity and carbon mitigation costs to reliably operate India's grid in 2030 for a variety of wind and solar targets (200 GW to 600 GW) and the most promising options for reducing these costs. We find that systems where solar photovoltaic comprises only 25 to 50% of the total renewable target have the lowest carbon mitigation costs in most scenarios. This result invites a reexamination of India's proposed solar-majority targets. We also find that, compared to other regions and contrary to prevailing assumptions, meeting high renewable targets will avoid building very few new fossil fuel (coal and natural gas) power plants because of India's specific weather patterns and need to meet peak electricity demand. However, building 600 GW of renewable capacity, with the majority being wind plants, reduces how often fossil fuel power plants run, and this amount of capacity can hold India's 2030 emissions below 2018 levels for less than the social cost of carbon. With likely wind and solar cost declines and increases in coal energy costs, balanced or wind-majority high renewable energy systems (600 GW or ≈ 45% share by energy) could result in electricity costs similar to a fossil fuel-dominated system. As an alternative strategy for meeting peak electricity demand, battery storage can avert the need for new fossil fuel capacity but is cost effective only at low capital costs (≈ USD 150 per kWh).

Extensive spatial and temporal distribution of high-quality data are essential for understanding regional and global behaviors of the geomagnetic field. We carried out chronological and archaeomagnetic studies at the Angkor-era iron-smelting site of Tonle Bak in Cambodia in Southeast Asia, an area with no data available to date. We recovered high-fidelity full-vector geomagnetic information from the 11th to 14th century for this region, which fill gaps in the global distribution of data and will significantly improve the global models. These results reveal a sharp directional change of the geomagnetic field between 1200 and 1300 CE, accompanied by an intensity dip between 1100 and 1300 CE. The fast geomagnetic variation recorded by our data provides evidence for the possible existence of low-latitude flux expulsion. Related discussions in this paper will inspire a new focus on detailed geomagnetic research in low-latitude areas around the equator, and exploration of related dynamic processes.

India has set aggressive targets to install more than 400 GW of wind and solar electricity generation by 2030, with more than two-thirds of that capacity coming from solar. This paper examines the electricity and carbon mitigation costs to reliably operate India's grid in 2030 for a variety of wind and solar targets (200 GW to 600 GW) and the most promising options for reducing these costs. We find that systems where solar photovoltaic comprises only 25 to 50% of the total renewable target have the lowest carbon mitigation costs in most scenarios. This result invites a reexamination of India's proposed solar-majority targets. We also find that, compared to other regions and contrary to prevailing assumptions, meeting high renewable targets will avoid building very few new fossil fuel (coal and natural gas) power plants because of India's specific weather patterns and need to meet peak electricity demand. However, building 600 GW of renewable capacity, with the majority being wind plants, reduces how often fossil fuel power plants run, and this amount of capacity can hold India's 2030 emissions below 2018 levels for less than the social cost of carbon. With likely wind and solar cost declines and increases in coal energy costs, balanced or wind-majority high renewable energy systems (600 GW or ≈ 45% share by energy) could result in electricity costs similar to a fossil fuel-dominated system. As an alternative strategy for meeting peak electricity demand, battery storage can avert the need for new fossil fuel capacity but is cost effective only at low capital costs (≈ USD 150 per kWh).

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip-flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip-flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

The recently developed new genome-editing technologies, such as the CRISPR/Cas system, have opened the door for generating genetically modified nonhuman primate (NHP) models for basic neuroscience and brain disorders research. The complex circuit formation and experience-dependent refinement of the human brain are very difficult to model in vitro, and thus require use of in vivo whole-animal models. For many neurodevelopmental and psychiatric disorders, abnormal circuit formation and refinement might be at the center of their pathophysiology. Importantly, many of the critical circuits and regional cell populations implicated in higher human cognitive function and in many psychiatric disorders are not present in lower mammalian brains, while these analogous areas are replicated in NHP brains. Indeed, neuropsychiatric disorders represent a tremendous health and economic burden globally. The emerging field of genetically modified NHP models has the potential to transform our study of higher brain function and dramatically facilitate the development of effective treatment for human brain disorders. In this paper, we discuss the importance of developing such models, the infrastructure and training needed to maximize the impact of such models, and ethical standards required for using these models.

The recently developed new genome-editing technologies, such as the CRISPR/Cas system, have opened the door for generating genetically modified nonhuman primate (NHP) models for basic neuroscience and brain disorders research. The complex circuit formation and experience-dependent refinement of the human brain are very difficult to model in vitro, and thus require use of in vivo whole-animal models. For many neurodevelopmental and psychiatric disorders, abnormal circuit formation and refinement might be at the center of their pathophysiology. Importantly, many of the critical circuits and regional cell populations implicated in higher human cognitive function and in many psychiatric disorders are not present in lower mammalian brains, while these analogous areas are replicated in NHP brains. Indeed, neuropsychiatric disorders represent a tremendous health and economic burden globally. The emerging field of genetically modified NHP models has the potential to transform our study of higher brain function and dramatically facilitate the development of effective treatment for human brain disorders. In this paper, we discuss the importance of developing such models, the infrastructure and training needed to maximize the impact of such models, and ethical standards required for using these models.

Ancient DNA (aDNA) analysis provides a powerful means of investigating human migration, social organization, and a plethora of other crucial questions about humanity's past. Recently, specialists have suggested that the ideal research design involving aDNA would include multiple independent lines of evidence. In this paper, we adopt a transdisciplinary approach integrating aDNA with archaeological, biogeochemical, and historical data to investigate six individuals found in two cemeteries that date to the Late Horizon (1400 to 1532 CE) and Colonial (1532 to 1825 CE) periods in the Chincha Valley of southern Peru. Genomic analyses indicate that these individuals are genetically most similar to ancient and present-day populations from the north Peruvian coast located several hundred kilometers away. These genomic data are consistent with 16th century written records as well as ceramic, textile, and isotopic data. These results provide some of the strongest evidence yet of state-sponsored resettlement in the pre-Colonial Andes. This study highlights the power of transdisciplinary research designs when using aDNA data and sets a methodological standard for investigating ancient mobility in complex societies.

Ancient DNA (aDNA) analysis provides a powerful means of investigating human migration, social organization, and a plethora of other crucial questions about humanity's past. Recently, specialists have suggested that the ideal research design involving aDNA would include multiple independent lines of evidence. In this paper, we adopt a transdisciplinary approach integrating aDNA with archaeological, biogeochemical, and historical data to investigate six individuals found in two cemeteries that date to the Late Horizon (1400 to 1532 CE) and Colonial (1532 to 1825 CE) periods in the Chincha Valley of southern Peru. Genomic analyses indicate that these individuals are genetically most similar to ancient and present-day populations from the north Peruvian coast located several hundred kilometers away. These genomic data are consistent with 16th century written records as well as ceramic, textile, and isotopic data. These results provide some of the strongest evidence yet of state-sponsored resettlement in the pre-Colonial Andes. This study highlights the power of transdisciplinary research designs when using aDNA data and sets a methodological standard for investigating ancient mobility in complex societies.

Mean field games (MFG) and mean field control (MFC) are critical classes of multiagent models for the efficient analysis of massive populations of interacting agents. Their areas of application span topics in economics, finance, game theory, industrial engineering, crowd motion, and more. In this paper, we provide a flexible machine learning framework for the numerical solution of potential MFG and MFC models. State-of-the-art numerical methods for solving such problems utilize spatial discretization that leads to a curse of dimensionality. We approximately solve high-dimensional problems by combining Lagrangian and Eulerian viewpoints and leveraging recent advances from machine learning. More precisely, we work with a Lagrangian formulation of the problem and enforce the underlying Hamilton-Jacobi-Bellman (HJB) equation that is derived from the Eulerian formulation. Finally, a tailored neural network parameterization of the MFG/MFC solution helps us avoid any spatial discretization. Our numerical results include the approximate solution of 100-dimensional instances of optimal transport and crowd motion problems on a standard work station and a validation using a Eulerian solver in two dimensions. These results open the door to much-anticipated applications of MFG and MFC models that are beyond reach with existing numerical methods.

We consider two aspects of the human enterprise that profoundly affect the global environment: population and consumption. We show that fertility and consumption behavior harbor a class of externalities that have not been much noted in the literature. Both are driven in part by attitudes and preferences that are not egoistic but socially embedded; that is, each household's decisions are influenced by the decisions made by others. In a famous paper, Garrett Hardin [G. Hardin, Science 162, 1243-1248 (1968)] drew attention to overpopulation and concluded that the solution lay in people "abandoning the freedom to breed." That human attitudes and practices are socially embedded suggests that it is possible for people to reduce their fertility rates and consumption demands without experiencing a loss in wellbeing. We focus on fertility in sub-Saharan Africa and consumption in the rich world and argue that bottom-up social mechanisms rather than top-down government interventions are better placed to bring about those ecologically desirable changes.

We consider two aspects of the human enterprise that profoundly affect the global environment: population and consumption. We show that fertility and consumption behavior harbor a class of externalities that have not been much noted in the literature. Both are driven in part by attitudes and preferences that are not egoistic but socially embedded; that is, each household's decisions are influenced by the decisions made by others. In a famous paper, Garrett Hardin [G. Hardin, Science 162, 1243-1248 (1968)] drew attention to overpopulation and concluded that the solution lay in people "abandoning the freedom to breed." That human attitudes and practices are socially embedded suggests that it is possible for people to reduce their fertility rates and consumption demands without experiencing a loss in wellbeing. We focus on fertility in sub-Saharan Africa and consumption in the rich world and argue that bottom-up social mechanisms rather than top-down government interventions are better placed to bring about those ecologically desirable changes.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

Magnetic flux ropes are structures that are common in the corona of the sun and presumably all stars. They can be thought of as the building blocks of solar structures. They have been observed in Earth's magnetotail and near Mars and Venus. When multiple flux ropes are present magnetic field line reconnection, which converts magnetic energy to other forms, can occur when they collide. The structure of multiple magnetic ropes, the interactions between multiple ropes, and their topological properties such as helicity and writhing have been studied theoretically and in laboratory experiments. Here, we report on spiky potential and magnetic fields associated with the ropes. We show that the potential structures are chaotic for a range of their temporal half-widths and the probability density function (PDF) of their widths resembles the statistical distribution of crumpled paper. The spatial structure of the magnetic spikes is revealed using a correlation counting method. Computer simulation suggests that the potential structures are the nonlinear end result of an instability involving relative drift between ions and electrons.

Numerous long-standing questions in origins-of-life research center on the history of biopolymers. For example, how and why did nature select the polypeptide backbone and proteinaceous side chains? Depsipeptides, containing both ester and amide linkages, have been proposed as ancestors of polypeptides. In this paper, we investigate cationic depsipeptides that form under mild dry-down reactions. We compare the oligomerization of various cationic amino acids, including the cationic proteinaceous amino acids (lysine, Lys; arginine, Arg; and histidine, His), along with nonproteinaceous analogs of Lys harboring fewer methylene groups in their side chains. These analogs, which have been discussed as potential prebiotic alternatives to Lys, are ornithine, 2,4-diaminobutyric acid, and 2,3-diaminopropionic acid (Orn, Dab, and Dpr). We observe that the proteinaceous amino acids condense more extensively than these nonproteinaceous amino acids. Orn and Dab readily cyclize into lactams, while Dab and Dpr condense less efficiently. Furthermore, the proteinaceous amino acids exhibit more selective oligomerization through their α-amines relative to their side-chain groups. This selectivity results in predominantly linear depsipeptides in which the amino acids are α-amine-linked, analogous to today's proteins. These results suggest a chemical basis for the selection of Lys, Arg, and His over other cationic amino acids for incorporation into proto-proteins on the early Earth. Given that electrostatics are key elements of protein-RNA and protein-DNA interactions in extant life, we hypothesize that cationic side chains incorporated into proto-peptides, as reported in this study, served in a variety of functions with ancestral nucleic acid polymers in the early stages of life.

Numerous long-standing questions in origins-of-life research center on the history of biopolymers. For example, how and why did nature select the polypeptide backbone and proteinaceous side chains? Depsipeptides, containing both ester and amide linkages, have been proposed as ancestors of polypeptides. In this paper, we investigate cationic depsipeptides that form under mild dry-down reactions. We compare the oligomerization of various cationic amino acids, including the cationic proteinaceous amino acids (lysine, Lys; arginine, Arg; and histidine, His), along with nonproteinaceous analogs of Lys harboring fewer methylene groups in their side chains. These analogs, which have been discussed as potential prebiotic alternatives to Lys, are ornithine, 2,4-diaminobutyric acid, and 2,3-diaminopropionic acid (Orn, Dab, and Dpr). We observe that the proteinaceous amino acids condense more extensively than these nonproteinaceous amino acids. Orn and Dab readily cyclize into lactams, while Dab and Dpr condense less efficiently. Furthermore, the proteinaceous amino acids exhibit more selective oligomerization through their α-amines relative to their side-chain groups. This selectivity results in predominantly linear depsipeptides in which the amino acids are α-amine-linked, analogous to today's proteins. These results suggest a chemical basis for the selection of Lys, Arg, and His over other cationic amino acids for incorporation into proto-proteins on the early Earth. Given that electrostatics are key elements of protein-RNA and protein-DNA interactions in extant life, we hypothesize that cationic side chains incorporated into proto-peptides, as reported in this study, served in a variety of functions with ancestral nucleic acid polymers in the early stages of life.

Techniques for forming sophisticated, 3D mesostructures in advanced, functional materials are of rapidly growing interest, owing to their potential uses across a broad range of fundamental and applied areas of application. Recently developed approaches to 3D assembly that rely on controlled buckling mechanics serve as versatile routes to 3D mesostructures in a diverse range of high-quality materials and length scales of relevance for 3D microsystems with unusual function and/or enhanced performance. Nonlinear buckling and delamination behaviors in materials that combine both weak and strong interfaces are foundational to the assembly process, but they can be difficult to control, especially for complex geometries. This paper presents theoretical and experimental studies of the fundamental aspects of adhesion and delamination in this context. By quantifying the effects of various essential parameters on these processes, we establish general design diagrams for different material systems, taking into account 4 dominant delamination states (wrinkling, partial delamination of the weak interface, full delamination of the weak interface, and partial delamination of the strong interface). These diagrams provide guidelines for the selection of engineering parameters that avoid interface-related failure, as demonstrated by a series of examples in 3D helical mesostructures and mesostructures that are reconfigurable based on the control of loading-path trajectories. Three-dimensional micromechanical resonators with frequencies that can be selected between 2 distinct values serve as demonstrative examples.

Designers and funders of payments for ecosystem services (PES) programs have long worried that payments flow to landholders who would have conserved forests even without the program, undermining the environmental benefits ("additionality") and cost-effectiveness of PES. If landholders self-select into PES programs based on how much conservation they were going to undertake anyway, then those who were planning to conserve should always enroll. This paper discusses the less-appreciated fact that enrollment is often based on other factors too. The hassle of signing up or financial costs of enrollment (e.g., purchasing seedlings) can affect who participates in a PES program. These enrollment costs reduce overall take-up, and, importantly, they can also influence the composition of landholders who select into the program-and thereby the program's environmental benefits per enrollee. Enrollment costs can increase a program's benefits per enrollee if they are systematically higher for (and thus deter enrollment by) landholders who would have conserved anyway. Alternatively, enrollment costs can dampen per-enrollee benefits if their correlation with status-quo conservation is in the opposite direction. We illustrate these points with evidence from two studies of randomized trials of PES programs aimed at increasing forest cover in Uganda and Malawi. We also discuss how in other sectors, such as social welfare, policy designers have purposefully adjusted the costs of program enrollment to influence the composition of participants and improve cost-effectiveness. We propose that these ideas for targeting could be incorporated into the design of PES programs.

Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various nonideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: (i) access to large-scale perceptual data of the kind that humans experience, (ii) flexible representations of objects, and (iii) an efficient unsupervised learning algorithm. The Internet fortunately provides unprecedented access to vast amounts of visual data. This paper leverages the availability of such data to develop a scalable framework for unsupervised learning of object prototypes-brain-inspired flexible, scale, and shift invariant representations of deformable objects (e.g., humans, motorcycles, cars, airplanes) comprised of parts, their different configurations and views, and their spatial relationships. Computationally, the object prototypes are represented as geometric associative networks using probabilistic constructs such as Markov random fields. We apply our framework to various datasets and show that our approach is computationally scalable and can construct accurate and operational part-aware object models much more efficiently than in much of the recent computer vision literature. We also present efficient algorithms for detection and localization in new scenes of objects and their partial views.

In light of calls for improving people's skill in collaboration, this paper examines strengths in processes of collaboration of Mexican immigrant children. Sibling pairs (6-10 years old) in California were asked to collaborate in planning the shortest route through a model grocery store. On average, 14 sibling pairs with Mexican Indigenous-heritage backgrounds engaged together collaboratively as an ensemble, making decisions in common and fluidly building on each other's ideas, more often than 16 middle-class European American sibling pairs, who on average more often divided decision making into a solo activity (often ignoring the other or simply bossing the other). Siblings who spent more time collaborating fluidly as an ensemble in the shared planning task were also more likely to collaborate with initiative at home, according to their mothers, which suggests that family socialization practices may contribute to cultural differences in collaboration.

Dynamic nuclear polarization (DNP) has enabled enormous gains in magnetic resonance signals and led to vastly accelerated NMR/MRI imaging and spectroscopy. Unlike conventional cw-techniques, DNP methods that exploit the full electron spectrum are appealing since they allow direct participation of all electrons in the hyperpolarization process. Such methods typically entail sweeps of microwave radiation over the broad electron linewidth to excite DNP but are often inefficient because the sweeps, constrained by adiabaticity requirements, are slow. In this paper, we develop a technique to overcome the DNP bottlenecks set by the slow sweeps, using a swept microwave frequency comb that increases the effective number of polarization transfer events while respecting adiabaticity constraints. This allows a multiplicative gain in DNP enhancement, scaling with the number of comb frequencies and limited only by the hyperfine-mediated electron linewidth. We demonstrate the technique for the optical hyperpolarization of 13C nuclei in powdered microdiamonds at low fields, increasing the DNP enhancement from 30 to 100 measured with respect to the thermal signal at 7T. For low concentrations of broad linewidth electron radicals [e.g., TEMPO ((2,2,6,6-tetramethylpiperidin-1-yl)oxyl)], these multiplicative gains could exceed an order of magnitude.

Dynamic nuclear polarization (DNP) has enabled enormous gains in magnetic resonance signals and led to vastly accelerated NMR/MRI imaging and spectroscopy. Unlike conventional cw-techniques, DNP methods that exploit the full electron spectrum are appealing since they allow direct participation of all electrons in the hyperpolarization process. Such methods typically entail sweeps of microwave radiation over the broad electron linewidth to excite DNP but are often inefficient because the sweeps, constrained by adiabaticity requirements, are slow. In this paper, we develop a technique to overcome the DNP bottlenecks set by the slow sweeps, using a swept microwave frequency comb that increases the effective number of polarization transfer events while respecting adiabaticity constraints. This allows a multiplicative gain in DNP enhancement, scaling with the number of comb frequencies and limited only by the hyperfine-mediated electron linewidth. We demonstrate the technique for the optical hyperpolarization of 13C nuclei in powdered microdiamonds at low fields, increasing the DNP enhancement from 30 to 100 measured with respect to the thermal signal at 7T. For low concentrations of broad linewidth electron radicals [e.g., TEMPO ((2,2,6,6-tetramethylpiperidin-1-yl)oxyl)], these multiplicative gains could exceed an order of magnitude.

In this paper, we report that notions of topological protection can be applied to stationary configurations that are driven far from equilibrium by active, dissipative processes. We consider two physically disparate systems: stochastic networks governed by microscopic single-particle dynamics, and collections of driven interacting particles described by coarse-grained hydrodynamic theory. We derive our results by mapping to well-known electronic models and exploiting the resulting correspondence between a bulk topological number and the spectrum of dissipative modes localized at the boundary. For the Markov networks, we report a general procedure to uncover the topological properties in terms of the transition rates. For the active fluid on a substrate, we introduce a topological interpretation of fluid dissipative modes at the edge. In both cases, the presence of dissipative couplings to the environment that break time-reversal symmetry are crucial to ensuring topological protection. These examples constitute proof of principle that notions of topological protection do indeed extend to dissipative processes operating out of equilibrium. Such topologically robust boundary modes have implications for both biological and synthetic systems.