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The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

General circulation models (GCMs), unlike other lines of evidence, indicate that anthropogenic aerosols cause a global-mean increase in cloud liquid water path (ℒ) and thus a negative adjustment to radiative forcing of the climate by aerosol–cloud interactions. In part 1 of this series of papers, we showed that this is true even in models that reproduce the negative correlation observed in present-day internal variability in ℒ and cloud droplet number concentration (Nd). We studied several possible confounding mechanisms that could explain the noncausal cloud–aerosol correlations in GCMs and that possibly contaminate observational estimates of radiative adjustments. Here, we perform single-column and full-atmosphere GCM experiments to investigate the causal model-physics mechanisms underlying the model radiative adjustment estimate. We find that both aerosol–cloud interaction mechanisms thought to be operating in real clouds – precipitation suppression and entrainment evaporation enhancement – are active in GCMs and behave qualitatively in agreement with physical process understanding. However, the modeled entrainment enhancement has a negligible global-mean effect. This raises the question of whether the GCM estimate is incorrect due to parametric or base-state representation errors or whether the process understanding gleaned from a limited set of canonical cloud cases is insufficiently representative of the diversity of clouds in the real climate. Regardless, even at limited resolution, the GCM physics appears able to parameterize the small-scale microphysics–turbulence interplay responsible for the entrainment enhancement mechanism. We suggest ways to resolve tension between current and future (storm-resolving) global modeling systems and other lines of evidence in synthesis climate projections.

Enhanced protection is needed for freshwater bodies in the United States — in particular impermanent streams and wetlands outside floodplains — according to an assessment of their value and vulnerability. Governments worldwide do not adequately protect their limited freshwater systems and therefore place freshwater functions and attendant ecosystem services at risk. The best available scientific evidence compels enhanced protections for freshwater systems, especially for impermanent streams and wetlands outside of floodplains that are particularly vulnerable to alteration or destruction. New approaches to freshwater sustainability — implemented through scientifically informed adaptive management — are required to protect freshwater systems through periods of changing societal needs. One such approach introduced in the US in 2015 is the Clean Water Rule, which clarified the jurisdictional scope for federally protected waters. However, within hours of its implementation litigants convinced the US Court of Appeals for the Sixth Circuit to stay the rule, and the subsequently elected administration has now placed it under review for potential revision or rescission. Regardless of its outcome at the federal level, policy and management discussions initiated by the propagation of this rare rulemaking event have potential far-reaching implications at all levels of government across the US and worldwide. At this timely juncture, we provide a scientific rationale and three policy options for all levels of government to meaningfully enhance protection of these vulnerable waters. A fourth option, a 'do-nothing' approach, is wholly inconsistent with the well-established scientific evidence of the importance of these vulnerable waters.

General circulation models' (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs' inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between the present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in the simulated liquid water path due to the parameterized precipitation suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree.

Aerosol effective radiative forcing critically influences climate projections but remains poorly constrained. Using the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) v1 configuration, we assess the sensitivity of the radiative forcing due to anthropogenic aerosol changes using a simplified prescribed aerosol scheme (SPA) derived from E3SM v3. Nudged simulations at 3 and 12 km horizontal grid spacings reveal a more negative aerosol forcing than the reference 100 km E3SM v3 whence the SPA properties are derived. The resulting globally averaged aerosol forcing signal is largely due to aerosol–cloud interactions and exhibits little overall resolution sensitivity, while hints of resolution sensitivity appear regionally between the 3 and 12 km runs. While the default SPA scheme overestimates cloud droplet dependence on aerosols, parameterization adjustments in the activation process reconcile forcing estimates with the reference model. Our results demonstrate the ability to use a prescribed aerosol scheme to hold aerosol forcing to a desired strength across resolutions.

General circulation models (GCMs), unlike other lines of evidence, indicate that anthropogenic aerosols cause a global-mean increase in cloud liquid water path ($\\mathscr{L}$) and thus a negative adjustment to radiative forcing of the climate by aerosol–cloud interactions. In part 1 of this series of papers, we showed that this is true even in models that reproduce the negative correlation observed in present-day internal variability in $\\mathscr{L}$ and cloud droplet number concentration (Nd). We studied several possible confounding mechanisms that could explain the noncausal cloud–aerosol correlations in GCMs and that possibly contaminate observational estimates of radiative adjustments. Here, we perform single-column and full-atmosphere GCM experiments to investigate the causal model-physics mechanisms underlying the model radiative adjustment estimate. We find that both aerosol–cloud interaction mechanisms thought to be operating in real clouds – precipitation suppression and entrainment evaporation enhancement – are active in GCMs and behave qualitatively in agreement with physical process understanding. However, the modeled entrainment enhancement has a negligible global-mean effect. This raises the question of whether the GCM estimate is incorrect due to parametric or base-state representation errors or whether the process understanding gleaned from a limited set of canonical cloud cases is insufficiently representative of the diversity of clouds in the real climate. Regardless, even at limited resolution, the GCM physics appears able to parameterize the small-scale microphysics–turbulence interplay responsible for the entrainment enhancement mechanism. We suggest ways to resolve tension between current and future (storm-resolving) global modeling systems and other lines of evidence in synthesis climate projections.

Great uncertainty exists about the costs associated with whole-genome sequencing (WGS). One hundred cardiology patients with cardiomyopathy diagnoses and 100 ostensibly healthy primary care patients were randomized to receive a family-history report alone or with a WGS report. Cardiology patients also reviewed prior genetic test results. WGS costs were estimated by tracking resource use and staff time. Downstream costs were estimated by identifying services in administrative data, medical records, and patient surveys for 6 months. The incremental cost per patient of WGS testing was $5,098 in cardiology settings and $5,073 in primary care settings compared with family history alone. Mean 6-month downstream costs did not differ statistically between the control and WGS arms in either setting (cardiology: difference = −$1,560, 95% confidence interval −$7,558 to $3,866, p = 0.36; primary care: difference = $681, 95% confidence interval −$884 to $2,171, p = 0.70). Scenario analyses showed the cost reduction of omitting or limiting the types of secondary findings was less than $69 and $182 per patient in cardiology and primary care, respectively. Short-term costs of WGS were driven by the costs of sequencing and interpretation rather than downstream health care. Disclosing additional types of secondary findings has a limited cost impact following disclosure.

All-perovskite–based polycrystalline thin-film tandem solar cells have the potential to deliver efficiencies of >30%. However, the performance of all-perovskite–based tandem devices has been limited by the lack of high-efficiency, low–band gap tin-lead (Sn-Pb) mixed-perovskite solar cells (PSCs). We found that the addition of guanidinium thiocyanate (GuaSCN) resulted in marked improvements in the structural and optoelectronic properties of Sn-Pb mixed, low–band gap (~1.25 electron volt) perovskite films. The films have defect densities that are lower by a factor of 10, leading to carrier lifetimes of greater than 1 microsecond and diffusion lengths of 2.5 micrometers. These improved properties enable our demonstration of >20% efficient low–band gap PSCs. When combined with wider–band gap PSCs, we achieve 25% efficient four-terminal and 23.1% efficient two-terminal all-perovskite–based polycrystalline thin-film tandem solar cells.

All-perovskite–based polycrystalline thin-film tandem solar cells have the potential to deliver efficiencies of >30%. However, the performance of all-perovskite–based tandem devices has been limited by the lack of high-efficiency, low–band gap tin-lead (Sn-Pb) mixed-perovskite solar cells (PSCs). We found that the addition of guanidinium thiocyanate (GuaSCN) resulted in marked improvements in the structural and optoelectronic properties of Sn-Pb mixed, low–band gap ( 1.25 electron volt) perovskite films. The films have defect densities that are lower by a factor of 10, leading to carrier lifetimes of greater than 1 microsecond and diffusion lengths of 2.5 micrometers. These improved properties enable our demonstration of >20% efficient low–band gap PSCs. When combined with wider–band gap PSCs, we achieve 25% efficient four-terminal and 23.1% efficient two-terminal all-perovskite–based polycrystalline thin-film tandem solar cells.

We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap encompasses four generations of devices: a single-qubit device that enables a measurement-based qubit benchmarking protocol; a two-qubit device that uses measurement-based braiding to perform single-qubit Clifford operations; an eight-qubit device that can be used to show an improvement of a two-qubit operation when performed on logical qubits rather than directly on physical qubits; and a topological qubit array supporting lattice surgery demonstrations on two logical qubits. Devices that enable this path require a superconductor-semiconductor heterostructure that supports a topological phase, quantum dots and coupling between those quantum dots that can create the appropriate loops for interferometric measurements, and a microwave readout system that can perform fast, low-error single-shot measurements. We describe the key design components of these qubit devices, along with the associated protocols for demonstrations of single-qubit benchmarking, Clifford gate execution, quantum error detection, and quantum error correction, which differ greatly from those in more conventional qubits. Finally, we comment on implications and advantages of this architecture for utility-scale quantum computation.