After people have been vaccinated against COVID-19, the levels of infection-blocking antibodies in their blood are a strong indicator of how much protection they’ve gained against the disease, according to a modelling study1. The research showed that the presence of even small quantities of these potent ‘neutralizing antibodies’ indicates that a vaccine is effective at protecting against COVID-19.

The study is the best attempt yet to define features of the immune response that can act as a proxy for protection against COVID-19, known as a ‘correlate of protection’, says Daniel Altmann, an immunologist at Imperial College London. “Finding the correlate of protection has really been a holy grail for this disease, as for others. It’s surprisingly hard to do.”

If researchers have a well-defined correlate of protection, they can predict from early trial data how effective a vaccine will be, says James Triccas, a medical microbiologist at the University of Sydney in Australia and a co-author of the study. This “alleviates the need to do larger, more expensive and time-consuming phase III trials”.

Triccas and his colleagues examined neutralizing-antibody data from trials of seven widely used vaccines. The team found a strong link between participants’ antibody levels recorded in early-stage trials and vaccine-efficacy results from late-stage trials. The researchers estimate that a vaccine has an efficacy of 50% even if it induces antibody levels 80% lower than those found, on average, in a person who has recovered from COVID-19.

Vaccines that generated the strongest neutralizing-antibody responses, such as the mRNA-based vaccines made by Moderna and Pfizer–BioNTech, were the most protective. Vaccines that induced a weaker response, which included the Oxford–AstraZeneca jab, provided lower levels of protection.

The researchers predict that because antibody levels wane over time, booster shots might be needed in about a year, but protection against severe disease could last many years even without them.

The findings help to explain why, despite studies showing that some variants of the SARS-CoV-2 coronavirus reduce the ability of neutralizing antibodies to block infection, most people who have been vaccinated, even with just one dose, don’t fare too badly if infected with those variants, says Altmann...

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally over the past year, infecting an immunologically naive population and causing significant morbidity and mortality. Immunity to SARS-CoV-2 induced either through natural infection or vaccination has been shown to afford a degree of protection against reinfection and/or reduce the risk of clinically significant outcomes. Seropositive recovered subjects have been estimated to have 89% protection from reinfection1, and vaccine efficacies from 50 to 95% have been reported2. However, the duration of protective immunity is presently unclear, primary immune responses are inevitably waning3,4,5, and there is ongoing transmission of increasingly concerning viral variants that may escape control by both vaccine-induced and convalescent immune responses6.

A critical challenge at present is to identify the immune correlate(s) of protection from SARS-CoV-2 infection and thereby predict how changes in immunity will be reflected in clinical outcomes. A defined correlate of protection will permit both confidence in opening up economies and facilitate rapid improvements in vaccines and immunotherapies. In influenza infection, for example, a hemagglutination inhibition (HAI) titer of 1:40 is thought to provide 50% protection from influenza infection7 (although estimates range from 1:17 to 1:110, refs. 8,9). This level was established over many years using data from a standardized HAI assay10 applied to serological samples from human challenge and cohort studies. This assay is used to predict vaccine efficacy and to assist in the annual reformulation of seasonal influenza vaccines. At present, however, there are few standardized assays for assessing SARS-CoV-2 immunity, little data comparing immune levels in susceptible versus resistant individuals, and no human challenge model11.

The data currently available for SARS-CoV-2 infection include immunogenicity data from phase 1 and 2 studies of vaccines, and data on protection from preliminary reports from phase 3 studies and from seropositive convalescent individuals (Supplementary Tables 1 and 2). Although antiviral T and B cell memory certainly contribute some degree of protection, strong evidence of a protective role for neutralizing serum antibodies exists. For example, passive transfer of neutralizing antibodies can prevent severe SARS-CoV-2 infection in multiple animal models,12,13 and Regeneron has recently reported similar data in humans14...

a, Relationship between neutralization level and protection from SARS-CoV-2 infection. The reported mean neutralization level from phase 1 and 2 trials and the protective efficacy from phase 3 trials for seven vaccines, as well as the protection observed in a seropositive convalescent cohort, are shown (details of data sources are given in Supplementary Tables 1 and 2). The 95% CIs are indicated as vertical and as horizontal whiskers. The red solid line indicates the best fit of the logistic model and the red shading indicates the 95% predictive interval of the model. The mean neutralization level and protective efficacy of the Covaxin vaccine are indicated as a green circle (data from this study were available only after modeling was complete and did not contribute to fitting). b, Schematic illustration of the logistic approach to identifying the protective neutralization level. The data for each study include the distribution of the measured in vitro neutralization titer against SARS-CoV-2 in vaccinated or convalescent subjects (as a proportion of the mean titer in convalescent subjects (dashed line)) (blue/red bell curve), accompanied by a level of protective efficacy for the same regimen. The efficacy is illustrated by the proportions of the bell curve ‘protected’ (blue) and ‘susceptible’ (red) for individual studies. The modeling fits the optimal 50% protective neutralization level (blue solid line, the shaded area indicates the 95% CI) that best estimates the correct levels of protection observed across the different studies. c, Predictions of the leave-one-out analysis. Modeling was repeated multiple times using all potential sets of the seven vaccination studies and the convalescent study to predict the efficacy of the eighth study. The diagonal dashed line indicates the position of a 1:1 correlation (i.e., the relationship if the model were completely accurate). The horizontal whiskers indicate 95% CIs and the vertical whiskers indicate 95% predictive intervals.

a, Prediction of the effects of declining neutralization titer. Assuming that the observed relationship between neutralization level and protection is consistent over time, we estimate the decline in efficacy for vaccines with different levels of initial efficacy. The model assumes a half-life of the neutralization titer of 108 d over the first 250 d (as observed in a convalescent cohort5). b, Modeling of the time for efficacy to drop to 70% (red line) or 50% (blue line) for scenarios with different initial efficacy. For example, for a group starting with an initial protective efficacy of 90%, the model predicts that 70% efficacy will be reached after 201 d and 50% efficacy will not be reached before 250 d. c, Estimation of the impact of viral antigenic variation on vaccine efficacy. In vitro studies have shown that neutralization titers against some SARS-CoV-2 variants are reduced compared with titers against wild-type virus. If the relationship between neutralization and protection remains constant, we can predict the difference in protective efficacy against wild-type and variant viruses from the difference in neutralization level. The dashed line indicates equal protection against wild-type and variant strains. Details of the data and modeling are provided in the Methods.

a, The predicted relationship between efficacy against any symptomatic SARS-CoV-2 infection and the efficacy against severe infection. The black line indicates the best fit model for the relationship between protection against any versus severe SARS-CoV-2 infection. The shaded areas indicate the 95% CIs. Efficacy against severe infection was calculated using a threshold that was 0.15 times lower than that for mild infection (95% CI = 0.036–0.65) (see Methods and Supplementary Table 5). b, Extrapolation of the decay of neutralization titers over time. This model uses the estimated half-life of SARS-CoV-2 neutralization titer in convalescent subjects of 108 d over the first 250 d5, after which the decay decreases linearly until it achieves a 10-year half-life (consistent with the long-term stability of antibody responses seen after other vaccines47,48). We simulate three scenarios, with decay of neutralization taking 1 year (blue dashed line), 1.5 years (purple dashed line) or 2 years (red dashed line) to slow to a 10-year half-life. For different initial starting levels the model projects the decay in neutralization titer over the subsequent 1,000 d (the gray shaded area indicates projections beyond the currently available data). The purple shaded region indicates being below the 50% protective titer for any symptomatic SARS-CoV-2 infection, and the orange shaded region indicates being below the 50% protective titer for severe SARS-CoV-2 infection. The model illustrates that, depending on the initial neutralization level, individuals may maintain protection from severe infection while becoming susceptible to mild infection (that is, with neutralization levels remaining in the purple shaded region). c, Extrapolation of the trajectory of protection for groups with different starting levels of protection. The model uses the same assumptions for the rate of immune decay as discussed in b. The projections beyond 250 d (gray shaded region) rely on an assumption of how the decay in SARS-CoV-2 neutralization titer will slow over time. In addition, the modeling projects only how decay in neutralization is predicted to affect protection. Other mechanisms of immune protection may play important roles in providing long-term protection that are not captured in this simulation.

As discussed above, a major caveat of our estimate of the relative protective level of antibodies in SARS-CoV-2 infection is that it includes aggregation of data collected from diverse neutralization assays and clinical trial designs (Supplementary Tables 1 and 2). It is hoped that in the future a standardized neutralization assay may be developed and utilized, which will allow direct comparison of neutralization titers and further refinement of these analyses11. In addition, the development of standardized trials and case definitions is necessary, given that differences in classification (particularly of severe disease) may prove to be important (Supplementary Table 3). The association of neutralization with protection across these studies does not prove that neutralizing antibodies are necessarily mechanistic in mediating protection. It is possible that neutralization is correlated with other immune responses, leading to an apparent association (as has been suggested for the use of HAI titer in influenza50,51,52). Thus, it will be important to study other responses such as T cell responses or B cell memory responses as additional potential correlates of protection. Another important refinement of this approach would be to have standardized measures of other serological and cellular responses to infection, to identify if any of these provide a better predictive value than neutralization. However, despite these limitations, our work identifies neutralizing antibodies as an immune correlate of protection and provides a quantitative prediction of the link between neutralizing antibody levels and clinical protection

Demand for all fossil fuels is set to grow significantly in 2021. Coal demand alone is projected to increase by 60% more than all renewables combined, underpinning a rise in emissions of almost 5%, or 1 500 Mt. This expected increase would reverse 80% of the drop in 2020, with emissions ending up just 1.2% (or 400 Mt) below 2019 emissions levels.

Under scenarios S1 and S2, the production of the electricity fueling the EVs and BEBs follows the 2016 average grid mix of Ontario, which is 9% natural gas, 61% nuclear, 24% hydro, and 6% wind.(22)

In Canada, in 2017, the transportation sector was the second largest source of greenhouse gas (GHG) emissions(1) and an important contributor to air pollutant emissions.(2) Since one-third of the Canadian population lives in close vicinity to major roads (i.e., within 250 m), air pollutant emissions from the transportation sector have considerable health impacts: several Canadian studies have associated increased exposure to air pollution with increased risks of cancers, circulatory and respiratory diseases, and mortality.(3−7)

Light- and heavy-duty vehicles and transit buses constitute the majority of the on-road vehicle fleet in Canada.(8) Different approaches can be adopted to tackle their impacts on population exposure and health. Traffic management strategies (TMSs), such as congestion pricing, low emission zones, truck/bus lanes, or transit improvement, can be implemented. However, the use of TMS for improving ambient air quality is not always efficient because they can be counterbalanced by indirect effects (e.g., increased traffic volumes induced by congestion mitigation strategies and traffic diversion to different areas as a result of tolling and restrictions).(9) Therefore, such initiatives should be complemented by tackling the emissions at the source through a replacement of the existing fleet by lower-emitting vehicles. For private passenger vehicles (i.e., cars and SUVs owned by private households) and transit buses, vehicle electrification is often seen as an opportunity to decrease GHG emissions, and since electric vehicles (EVs) and battery electric buses (BEBs) do not generate exhaust emissions, electrification has also been promoted to reduce traffic-related air pollution, especially in regions with relatively clean electricity production.(10−13) For commercial vehicles (i.e., light-duty and heavy-duty), Pan et al.(14) highlighted that eliminating high-emitting trucks would bring substantial improvements in population exposure and health.
There are only few studies comparing the co-benefits of GHG mitigation strategies targeting all three fleets of vehicles. In India, Dhar and Shukla(15) quantified the changes of air pollutant emissions resulting from different policies involving sustainable technologies, fuels, and logistics for private cars, transit buses, and commercial vehicles. However, they did not quantify the implications on air quality, population exposure, and health. In the U.K., Smith et al.(16) analyzed the co-benefits and conflicts associated with measures aiming for a reduction of the carbon budget of the country, but the strategies incorporated in the scenario studied were broad and encompassed measures that went beyond improvements of vehicle fleets.

In this study, we applied an integrated framework combining a traffic assignment model with an air quality model to evaluate the health implications of a series of transportation scenarios designed by a panel of sustainable transportation experts in the context of the Greater Toronto and Hamilton Area (GTHA), the largest metropolitan area of Canada...

...After designing different study cases and transportation scenarios (2.1), we developed emission inventories for private passenger vehicles, transit buses, and commercial vehicles, which we then modified to reflect the transportation scenarios outlined (2.2). Then, we set up a chemical transport model (CTM) over the GTHA to quantify the air quality impacts under the study cases and scenarios (2.2). Finally, we estimated the exposure of the population and conducted an assessment of the health outcomes and social benefits associated with the scenarios (2.3)...

...Scenario 1 (S1—100% EV) assumes an electrification of the private passenger vehicle fleet;

Scenario 2 (S2—100% BEB) assumes an electrification of the transit bus fleet;

Scenario 3 (S3—cleaner trucks) assumes that trucks older than 8 years have recently been renewed. The rationale behind this choice is that most scrappage programs are for vehicles of that age; few target trucks, but those for private passenger vehicles implemented in Europe are usually applicable for vehicles older than 8–10.(20,21)...

Figure 1. Comparison of the NOx, BC, and fuel-cycle GHG emissions from private passenger vehicles, commercial vehicles, transit buses, and electricity for EVs and BEBs under the base case and the different scenarios for a typical weekday: (a) total daily emissions and (b) percent contribution of each mode.

Figure 2. Comparison of the annual health outcomes associated with each vehicle category and each scenario (each outcome related to the three vehicle categories indicates a burden, while the outcomes related to the scenarios indicate benefits). The uncertainty bars represent the interquartile range resulting from the use of a range of odds ratios associated with each pollutant and health outcome (indicated in parentheses in the legend).

Figure 3. Distribution by region of (a) the social costs and fuel-cycle GHG emissions associated with each vehicle category and (b) the social benefits and fuel-cycle GHG emission savings obtained under each scenario. These figures are based on average numbers of premature deaths extracted from the uncertainty analysis of the health outcome assessment.

Figure 4. Spatial distribution of years of life saved per 100,000 capita under each scenario. These figures are based on the average number of years of life saved extracted from the uncertainty analysis of the health outcome assessment.

...Finally, GHG emissions do not account for upstream impacts from battery manufacturing. Ma et al.(56) estimated life-cycle GHG emissions of internal combustion engine vehicles and EVs to be about 38.1 and 54.5 g of CO2 equiv/km, respectively. Using these numbers, we estimated that GHG emission savings under the EV scenarios are decreased by about 8%.

To conclude, this analysis highlights the necessity to tackle the emissions from all categories of vehicles: private passenger vehicles because they are important sources of GHG emissions and responsible for substantial social costs related to air pollution exposure; commercial vehicles because they are responsible for more than half of the YLL and premature deaths attributed to traffic-related air pollution exposure in the GTHA; and transit buses because they are operating in densely populated areas and their emissions have therefore higher health impacts in proportion.

This sample of red trinitite was found to contain a previously unknown type of quasicrystal.Credit: Luca Bindi, Paul J. Steinhardt

Scientists searching for quasicrystals — so-called ‘impossible’ materials with unusual, non-repeating structures — have identified one in remnants of the world’s first nuclear bomb test.

The previously unknown structure, made of iron, silicon, copper and calcium, probably formed from the fusion of vapourized desert sand and copper cables. Similar materials have been synthesized in the laboratory and identified in meteorites, but this one, described in Proceedings of the National Academy of Sciences on 17 May, is the first example of a quasicrystal with this combination of elements1.

Impossible symmetries

Quasicrystals contain building blocks of atoms that — unlike those in ordinary crystals — do not repeat in a regular, brickwork-like pattern. Whereas ordinary crystal structures look identical after being translated in certain directions, quasicrystals have symmetries that were once considered impossible: for example, some have pentagonal symmetry, and so look the same if rotated by one-fifth of a full twist.

Materials scientist Daniel Shechtman, now at the Technion Israel Institute of Technology in Haifa, first discovered such an impossible symmetry in a synthetic alloy in 1982. It had pentagonal symmetry when rotated in each of various possible directions, something that would occur if its building blocks were icosahedral — that is, had a regular shape with 20 faces. Many researchers initially questioned Shechtman’s findings, because it is mathematically impossible to fill space using only icosahedrons. Shechtman ultimately won the 2011 Nobel Prize in Chemistry for the discovery.

At around the same time, Paul Steinhardt, a theoretical physicist now at Princeton University in New Jersey, and his collaborators had begun to theorize the possible existence of non-repeating 3D structures. These had the same symmetry as an icosahedron, but were assembled from building blocks of several different types, which never repeated in the same pattern — thus explaining why the mathematics of symmetrical crystals had missed them...

...‘Slicing and dicing’

In the aftermath of the Trinity test — the first ever detonation of a nuclear bomb, which took place on 16 July 1945 at New Mexico’s Alamogordo Bombing Range — researchers found a vast field of greenish glassy material that had formed from the liquefaction of desert sand. They dubbed this trinitite.

The plutonium bomb had been detonated on top of a 30-metre-high tower, which was laden with sensors and their cables. As a result, some of the trinitite that formed had reddish inclusions, says Steinhardt. “It was a fusion of natural material with copper from the transmission lines.” Quasicrystals often form from elements that would not normally combine, so Steinhardt and his colleagues thought samples of the red trinitite would be a good place to look for quasicrystals .

“Over the course of ten months, we were slicing and dicing, looking at all sorts of minerals,” Steinhardt says. “Finally, we found a tiny grain.” The quasicrystal has the same kind of icosahedral symmetry as the one in Shechtman’s original discovery.

Abstract

Background:

Here, we generated human cardiac muscle patches (hCMPs) of clinically relevant dimensions (4 cm × 2 cm × 1.25 mm) by suspending cardiomyocytes, smooth muscle cells, and endothelial cells that had been differentiated from human induced-pluripotent stem cells in a fibrin scaffold and then culturing the construct on a dynamic (rocking) platform.

Methods:

In vitro assessments of hCMPs suggest maturation in response to dynamic culture stimulation. In vivo assessments were conducted in a porcine model of myocardial infarction (MI). Animal groups included: MI hearts treated with 2 hCMPs (MI+hCMP, n=13), MI hearts treated with 2 cell-free open fibrin patches (n=14), or MI hearts with neither experimental patch (n=15); a fourth group of animals underwent sham surgery (Sham, n=8). Cardiac function and infarct size were evaluated by MRI, arrhythmia incidence by implanted loop recorders, and the engraftment rate by calculation of quantitative polymerase chain reaction measurements of expression of the human Y chromosome. Additional studies examined the myocardial protein expression profile changes and potential mechanisms of action that related to exosomes from the cell patch.

Results:

The hCMPs began to beat synchronously within 1 day of fabrication, and after 7 days of dynamic culture stimulation, in vitro assessments indicated the mechanisms related to the improvements in electronic mechanical coupling, calcium-handling, and force generation, suggesting a maturation process during the dynamic culture. The engraftment rate was 10.9±1.8% at 4 weeks after the transplantation. The hCMP transplantation was associated with significant improvements in left ventricular function, infarct size, myocardial wall stress, myocardial hypertrophy, and reduced apoptosis in the periscar boarder zone myocardium. hCMP transplantation also reversed some MI-associated changes in sarcomeric regulatory protein phosphorylation. The exosomes released from the hCMP appeared to have cytoprotective properties that improved cardiomyocyte survival.

Conclusions:
We have fabricated a clinically relevant size of hCMP with trilineage cardiac cells derived from human induced-pluripotent stem cells. The hCMP matures in vitro during 7 days of dynamic culture. Transplantation of this type of hCMP results in significantly reduced infarct size and improvements in cardiac function that are associated with reduction in left ventricular wall stress. The hCMP treatment is not associated with significant changes in arrhythmogenicity.

All naturally occurring nuclides heavier than iron are produced in stellar environments, almost exclusively by nuclear processes involving the successive captures of neutrons to build up heavier masses. About half of these nuclides are synthesized slowly as a by-product of steady stellar fusion. The other half, including all actinide elements, require a very short but intense flux of neutrons, resulting in a rapid neutron capture process (r-process). The sites and yields of the r-process remain a topic of debate (1–6). It is expected to occur in explosive stellar environments such as certain types of supernovae (SNe) or neutron-star mergers (NSMs), the latter of which has been supported by observations of the gravitational-wave event GW170817 (7). The abundance patterns of r-process nuclides can be used to constrain the production site. Radioactive isotopes (radionuclides) provide additional time information resulting from their decay over time following their synthesis. Such radionuclides should be scattered through the interstellar medium (ISM) and could be deposited on Earth.

The Solar System (SS) is located inside a large ISM structure [the Local Superbubble (LB)] that was shaped by supernova (SN) explosions during the last ~12 million years (Myr) (8). Earth has therefore been exposed to both ejecta from the SNe and swept-up interstellar material that traversed the SS during this time period (9, 10). Dust particles from the ISM pass through the SS (11) and contain nucleosynthetic products of stellar events (e.g., stellar winds and SNe) (10, 12, 13). Earth’s initial abundance of the 60Fe radionuclide [half-life (t1/2) = 2.6 Myr (14, 15)] has decayed to extinction over the 4.6 billion years (Gyr) since the SS’s formation. 60Fe, however, is produced in massive stars and ejected in SN explosions. Evidence for the deposition of extraterrestrial 60Fe on Earth has been found in deep-sea geological archives dated to between 1.7 and 3.2 million years ago (Ma) (16–20), at recent times (21, 22), and possibly also around 7 Ma (19). 60Fe has also been detected in lunar samples (23), in astronomical observations of gamma rays associated with its radioactive decay (24), and in galactic cosmic rays (25). SN activity in the last ~2 Myr is suggested by an excess in the local cosmic-ray spectrum (26). Other radionuclides are also produced and ejected in such explosions (9, 27–30). If substantial r-process nuclei are produced in SNe this would also have enriched the local ISM with actinides, such as 244Pu. With a half-life of 80.6 Myr, 244Pu is much longer lived than 60Fe, so it can be contributed by older r-process events, not limited to those that formed the LB. Either as part of the SN direct ejecta or as continuous ISM influx, we expect dust particles containing 244Pu to enter the SS, similarly to 60Fe, but probing different nucleosynthetic processes. Previous measurements in terrestrial or lunar archives have provided only upper limits on actinide influx (12, 31–33).

We searched for extraterrestrial 60Fe and 244Pu incorporated into a deep-sea sample on Earth—a ferromanganese crust (which we refer to as Crust-3) that spans the last 10 Myr, sampled at ~1500 m below sea level in the Pacific Ocean, with 115-cm2 cross-sectional area and ~25-mm thickness (27). The radionuclides were identified and counted using accelerator mass spectrometry (AMS) (27). For 60Fe, a time-resolved depth profile of ~1-cm2 area was analyzed, subdivided into 24 layers, each ~1-mm thick, corresponding to a time resolution of ~0.4 Myr per layer [crust growth-rate of ~2.4 mm Myr−1, dated with terrestrial 10Be (27)]. The remaining part of Crust-3 (114-cm2 area), after separating the aliquots used for 60Fe analysis, was split into three thick, horizontal layers designated 3/A (extending from 0 to 3 mm, equivalent to 0 to 1.3 Ma, with a mass of ~20 g), 3/B (3 to 10 mm, 1.3 to 4.6 Ma, 179 g), and 3/C (10 to 20 mm, 4.6 to 9.0 Ma, 208 g), given the anticipated low abundance of 244Pu. We expected the top layer to contain anthropogenic Pu from atmospheric nuclear weapons tests performed during the 20th century...

ble 1 60Fe/Fe ratios from AMS measurements of layered samples of Crust-3.
Twenty-four individual samples, distributed equally across 10 Myr, were analyzed and combined into four sections (individual data are listed in table S1). The stable Fe content was measured separately through inductively coupled plasma mass spectrometry (ICP-MS) (table S4). Ages are derived from 10Be measurements (table S5) and have an uncertainty of ±0.3 Myr (27).The number of 60Fe background events expected was calculated by scaling (in measurement time and beam intensity) measurements of terrestrial Fe blank samples. 60Fe/Fed.c. denotes background- and decay-corrected data (27). Dashes in otherwise empty cells indicate not applicable.

We observed an anthropogenic Pu signal in the top layer of Crust-3, which has 240Pu/239Pu and 241Pu/239Pu isotope ratios that are consistent with the expected values for nuclear weapons fallout (supplementary text). 239Pu, 240Pu, and 244Pu events were observed in all three crust layers, and 241Pu was observed in the first two layers. The 240Pu/239Pu and 241Pu/239Pu ratios are constant over these layers. The 244Pu/239Pu ratio shows an excess over anthropogenic levels in the two deeper layers (Fig. 2 and fig. S4). Assuming that the 244Pu abundance is dominated by the anthropogenic contribution in the top layer, we correct the deeper layers for this contribution and attribute the additional 244Pu to extraterrestrial deposition. The amount of short-lived anthropogenic Pu in the deeper layers indicates some anthropogenic Pu penetrated below the top layer (with concentrations relative to the top layer A of 1.5 and 0.1% in layers B and C, respectively), possibly as a result of pore water–induced redistribution into the deeper layers (27). If 60Fe also migrates in crusts, our observed time profile (Fig. 1A) could have been slightly broadened by this effect.

Fig. 1 Influx of interstellar 60Fe and 244Pu.
(A) 60Fe incorporation rates for Crust-3. The data (red points) have been decay corrected, and each layer is equivalent to 400 thousand years. The absolute ages have an uncertainty of ~0.3 to 0.5 Myr (27). (B) 244Pu incorporation rates for the three layers after subtraction of the anthropogenic 244Pu fraction (27). (C) 244PuISM/60Fe number ratio in the crust sample with layers 1 and 2 combined (horizontal solid lines with shaded error bars). All error bars show 1σ Poisson statistics.

Fig. 2 Measured Pu isotope ratios and comparison with global fallout values.
(A and B) Variations of the measured 240Pu/239Pu ratio (A) and the 244Pu/239Pu ratio (B) across the three layers (solid red lines). The dashed red lines and gray shading indicate 1σ uncertainties. The blue shaded area and solid line represent the expected ratios for Pu from nuclear weapons fallout (27). 240Pu/239Pu remains constant across the three layers, whereas 244Pu/239Pu is enhanced in the deeper (older) layers. We attribute the excess above anthropogenic (anthr) levels to extraterrestrial 244Pu. Equivalent data for 241Pu/239Pu are shown in fig.

Table 2 60Fe and 244Pu data for three time periods during the last 10 Myr from three crust layers.
Crust-3 data are from this work, and Crust-0 data are from (12, 19). For details on the ISM (extraterrestrial) flux and fluence calculations, see (27). The FeMn-crust sample was split into three layers A, B, and C for 244Pu (18, 179, and 208 g, respectively). The individual 60Fe data (background-corrected) from table S1 were combined to cover the same time periods as 244Pu. All uncertainties are 1σ [we converted the 2σ values for Crust-0 (12) to 1σ]. The bolded lines (Crust-30-4.6 and Crust-00.5-5.0) cover the time periods of the younger 60Fe influx (0 to 4.6 Ma and 0.5 to 5.0 Ma, respectively). The italicized lines (Crust-30-9.0 and Crust-00.5-12) represent the time integral over the 9 Myr covered by Crust-3 as well as the similar time span of 0.5 to 12 Ma investigated for Crust-0 (12). Dashes in otherwise empty cells indicate not applicable.

...Our measurements of extraterrestrial 244Pu and 60Fe confirm an influx of interstellar material into the inner SS through two or more local and transient SN events over the last ~10 Myr, and they are compatible with some production of actinides in core-collapse SNe, possibly of common type. Combining these data with previous results of 244Pu influx over the past 25 Myr (12) (extending prior to the formation of the LB) indicates that SN actinide yields seem insufficient to account for the overall abundance of r-process nuclides in the Galaxy (fig. S8). These yields can be compared with the total r-process inventory calculated by SN actinide nucleosynthesis simulations, which, however, is limited by model uncertainties. The present data are compatible with the LB being a local disturbance of a large-scale Galactic steady-state (from SN enrichment of the ISM occurring more frequently than the radioactive half-life), with less-frequent injections from rarer r-process sources that nevertheless dominate the production of r-process elements, such as NSMs. The data are also consistent with the hypothesis of a nearby rare event before the time of SS formation that supplied the majority of the SS known inventory of the primordial actinides Th, U, and Pu (12, 38) (fig. S9 and supplementary text).

Limitations of the study

The correlates of protection from SARS-CoV-2 infection are yet to be established. The in vitro neutralization assays reported here do not convey the contributions to in vivo protection provided by T cells nor the contributions of Fcγ receptor interactions and complement activation. Convalescent plasma and vaccine serum were taken relatively soon after acute illness or following vaccination; it is possible that titers will drop over time to a point where they are no longer high enough to provide protection. It will be interesting to understand the antibody response made by people infected by B.1.1.7, particularly how antibodies adapt to the N501Y change, but also the deletions occurring in the NTD. It will also be instructive to look at how well convalescent or vaccine serum can neutralize the other recently described variants B.1.351 and P.1 and, conversely, how well serum from patients infected with these variants can neutralize B.1.1.7 and the original Wuhan strains.

Since its first appearance in Wuhan in December 2019, SARS-CoV-2 rapidly spread around the world leading the WHO to declare a pandemic on March 11, 2020. Since then, drastic public health measures, including draconian lockdowns with severe economic cost, have been enacted to contain virus spread. Although initially successful at containing disease, many countries are now experiencing further waves of infection, coinciding with winter in the northern hemisphere, with infections in some countries outpacing those seen during the first wave (Kröger and Schlickeiser, 2021).
Huge strides have been made in the understanding of SARS-CoV-2 over the last year, which are exemplified by the licensing of several vaccines (in the UK those made by Pfizer-BioNtech, Moderna, and Oxford-AstraZeneca), which are being rolled out in massive global vaccination programs, with the aim to reach billions of individuals in 2021. Furthermore, Janssen and Novavax have recently reported results showing good efficacy and also report efficacy against the UK B.1.1.7 strain (https://blogs.sciencemag.org/pipeline/archives/2021/01/29/jj-and-novavax-data). In parallel, a number of potently neutralizing monoclonal antibodies (mAbs) have been developed that are in late-stage trials to be used prophylactically or therapeutically (Baum et al., 2020, Yang et al., 2020).

SARS-CoV-2 is a large positive-stranded RNA virus; the major virion surface glycoprotein is the trimeric spike that attaches the virus to host cells via the ACE2 receptor and, through a series of conformational changes, allows fusion of host and virion membranes releasing the virus RNA into the cell to start the infection cycle (Hoffmann et al., 2020; Ou et al., 2020). Spike is the target of RNA (Polack et al., 2020; Baden et al., 2021), viral vectored (Voysey et al., 2021), and inactivated virus and recombinant protein-based vaccines (Yadav et al., 2020).

Because of the huge number of genome replications that occur in infected populations and error-prone replication, viral mutations do and will continue to occur (Robson et al., 2020). Although the vast majority will be inconsequential or detrimental to viral fitness, a few may give the virus a competitive advantage and be the subject of rapid natural selection relating to transmission advantage, including enhanced replication and immune evasion. This leads to the emergence of dominant new variant viruses. Coronaviruses, as we are seeing with COVID-19, have the potential to alter their proteins with dramatic effect (Denison et al., 2011).

In recent months, a number of mutations in the spike protein have been exemplified by viruses that have grown in alternative hosts such as mink and transmitted back to humans or in immunocompromised chronically infected individuals (Kemp et al., 2020; Oude Munnink et al., 2021; Hayashi et al., 2020). While most of these mutations currently show little evidence of a selective advantage in humans, variants have been identified with multiple mutations in spike, which appear to have distinct selective advantages and have rapidly expanded in prevalence, notably that first identified in Kent in the UK (lineage B.1.1.7) and unrelated variants detected in South Africa (501Y.V2 also known as B.1.351) and Manaus in Brazil (P.1). All of these contain mutations in the ACE2 receptor binding footprint of the receptor binding domain (RBD), one in B.1.1.7, three in 501Y.V2, and three in P.1, with the N501Y mutation being common to all.

(A) The SARS-CoV-2 spike trimer is depicted as a gray surface with mutations highlighted in yellow-green or with symbols. The RBD N501Y and the NTD 144 and 69–70 deletions are highlighted with green stars and red triangles, respectively. On the right, a protomer is highlighted as a colored ribbon within the transparent gray spike surface, illustrating its topology and marking key domains.

(B) The RBD “torso” analogy. The RBD is represented as a gray surface with the ACE2 receptor binding site in dark green. Binding sites for the panel of antibodies (Dejnirattisai et al., 2021) on which this study draws are represented by spheres. The spheres represent the point at which placing spherical antibodies would optimally predict the BLI competition data and are colored according to their neutralization, from red (potent) to blue (non-neutralizing). The position of the B.1.1.7 N501Y mutation in the RBD is highlighted in light green toward the right shoulder.

(C) Proximity of ACE2 to N501Y. The RBD is depicted as in (B) with ACE2 bound (in yellow cartoon format) with glycosylation drawn as sticks.

(D) Left panel: interactions of N501 of WT RBD with residues Y41 and K353. The structure shown is the complex of N501 RBD with ACE2 determined by X-ray crystallography (PDB ID 6M0J, Lan et al., 2020). When the 501 is mutated to a tyrosine with the conformation seen in the N501Y RBD-269 Fab complex (right panel), Y501 makes T-shaped ring stacking interactions with Y41 and more hydrophobic contacts with K353 of ACE2 (note there are minor clashes of the side chain of Y501 to the end of the K353 side chain, which has ample room to adjust to optimize interactions).

(E) BLI plots for WT (left) and N501Y (right) RBDs binding to ACE2. A titration series is shown for each (see STAR Methods). Note the much slower off rate for the mutant.

Characterizing the N501Y mutation in the RBD

The RBD may be likened to a classic human torso; in this analogy, the shoulders and neck are involved in interactions with the ACE2 receptor (Figures 1B and 1C) (Dejnirattisai et al., 2021). In this context, residue 501 lies within the footprint of the receptor on the right shoulder and is involved in hydrophobic interactions, especially with the side chains of residues Y41 and K353 of ACE2 with the 501 mutation from N to Y offering the opportunity for enhanced interactions (Figures 1C and 1D).
Effect on ACE2 affinity

It has been reported that mutations at 501 can increase affinity for ACE2 (Starr et al., 2020; Gu et al., 2020), although these data are not for the mutation to Y. In contrast, Zahradník et al., 2021 report direct selection of N501Y when evolving the RBD to enhance affinity. We therefore investigated the effect of this mutation on ACE2 binding by RBD using biolayer interferometry (BLI) (Figure 1E). The results indicate a marked (7-fold) increase in binding affinity due to a slower off rate: WT RBD(501N)-ACE2: KD 75.1 nM (Kon 3.88E4 /Ms, Koff 2.92E-3 /s), RBD(501Y)-ACE2: KD 10.7 nM (Kon 6.38E4 /Ms, Koff 6.85E-4/s). This is in-line with enhanced interactions of the tyrosine side chain with the side chains of residues Y41 and K353 of ACE2 (Figure 1D). In the context of a multivalent interaction at the cell surface, this effect would be amplified. This alone might account for the selection of the N501Y mutation and an increase in transmission.

These resources exhibit variability across different time scales, including subhourly resource fluctuations, larger ramps across multiple hours, sustained periods of high or low output for multiple days (the German term dunkelflaute or “dark doldrums” refers to extended periods with limited sunshine or wind), seasonal effects, and even variability across years and decades (e.g., interannual variation in hydro and wind resources).

Das heißt: In vier Jahren werden nur noch rund 67.000 Megawatt gesicherte Kapazität zur Verfügung stehen, obwohl Deutschland in Spitzenzeiten 81.000 Megawatt verbraucht. Kommt es zu einer „kalten Dunkelflaute“ ohne nennenswerten Wind- und Solarstrom, wäre die deutsche Stromversorgung in diesen Stunden, Tagen oder Wochen auf ausländische Lieferungen zwingend angewiesen.

Die Zeiten, in denen solche Knappheiten am Markt neue Investitionsanreize auslösten, sind vorbei. „Stattdessen sind wir schon heute auf eine Reihe von Reparaturmaßnahmen angewiesen: Netzreserve, Kapazitätsreserve oder netztechnische Betriebsmittel kaschieren mehr schlecht als recht die Defizite der deutschen Energiepolitik und Marktkonditionen“, schimpft Kapferer: „Auf Dauer wird das nicht funktionieren.“

Unlike fossil fuel‐based energy sources, some of the renewable energy sources (especially, wind and solar) strongly rely on meteorological conditions. As such sources of energy play a larger role in electricity networks, this presents an increasing challenge in terms of balancing supply and demand. Therefore, it is important to increase our understanding and forecasting capability of certain weather phenomena which can result in adverse renewable energy production from a system operator perspective. Such advanced knowledge and tools will further support the continuing growth of renewables in the foreseeable future.

In this paper, we focus on one such weather phenomenon called “Dunkelflaute” as it is rapidly becoming a major concern for the renewable energy community.6 The word Dunkelflaute was coined by combining two German words “Dunkelheit” (darkness) and “Windflaute” (little wind) to describe heavy overcast skies and weak wind conditions. These meteorological events can last from a few hours to a few consecutive days. It is needless to say that under the influence of such a meteorological condition, little or no wind and solar energy can be produced.

On the 30th April 2018, an unexpected Dunkelflaute event occurred over the southern part of the North Sea and caused a large imbalance in renewable power generation and overall consumption. Given the acuteness of the situation, TenneT—the main transmission system operator for Germany and the Netherlands—had to issue an emergency alert in the Netherlands.7, 8 The crisis could not be avoided by simple load management or by making use of reserve power; instead, a substantial amount of electricity had to be imported from neighboring countries at high market price.

This Dunkelflaute event was not an isolated episode. As a matter of fact, over the past few years, several Dunkelflaute events occurred in Belgium,9-12 Germany,6, 13-15 and other neighboring countries. Some of them caused significant impacts on the power grids and electricity markets. There is no reason to believe that the occurrences of Dunkelflaute will subside in the future. Instead, with the ever increasing penetration of renewables in the power grid, the (negative) impacts of Dunkelflaute events will likely become more and more detrimental.

Focusing only on renewables in power and not addressing thermal power will not automatically lead to reaching the decarbonisation target and will jeopardise reliability of supply for Germany and its neighbours. In the end, the resulting upcoming shortfall of dispatchable power may even throw into question the nuclear phaseout as fixed by law. With the closing of dispatchable capacity (nuclear – by law, conventional – due to market drivers) Germany will face an increasing gap between available dispatchable power and peak load, according to the NEP 2030. This gap cannot be filled with whatever extra capacity there is in wind and PV. Turning to neighbours for reliable power is not an option, as these countries are facing similar problems, as well as other country-specific problems.

Dunkelflaute is a term used in the energy sector for a period of time in which little to no energy can be generated with the use of wind and solar power. The term is German in origin and a blend word of the german ‘Dunkelheit’ (darkness) and ‘Flaute’ (little wind). The periods called Dunkelflaute are a big issue in energy infrastructure in which a significant amount of electricity is generated by wind and solar power. To ensure power during such periods alternative energy sources must be present in a sufficient capacity. When that happens countries use either fossil fuels (e.g. oil, methane gas, coal) or hydroelectricity, nuclear power and, less often, energy storage to prevent a power outage.[1][2][3][4][5]

The first use of the term in an academic paper is in 2014.[6]