Natural gas shortages during the “coal-to-gas” transition in China have caused a large redistribution of air pollution in winter 2017

The Chinese “coal-to-gas” and “coal-to-electricity” strategies aim at reducing dispersed coal consumption and related air pollution by promoting the use of clean and low-carbon fuels in northern China. Here, we show that on top of meteorological influences, the effective emission mitigation measures achieved an average decrease of fine particulate matter (PM_2.5) concentrations of ∼14% in Beijing and surrounding areas (the “2+26” pilot cities) in winter 2017 compared to the same period of 2016, where the dispersed coal control measures contributed ∼60% of the total PM_2.5 reductions. However, the localized air quality improvement was accompanied by a contemporaneous ∼15% upsurge of PM_2.5 concentrations over large areas in southern China. We find that the pollution transfer that resulted from a shift in emissions was of a high likelihood caused by a natural gas shortage in the south due to the coal-to-gas transition in the north. The overall shortage of natural gas greatly jeopardized the air quality benefits of the coal-to-gas strategy in winter 2017 and reflects structural challenges and potential threats in China’s clean-energy transition.

The “airpocalypse” in China stems from a multitude of air pollutants (1–3) that are associated with significant climate and health effects (4–8). Eliminating the severe fine particulate matter (PM_2.5; particulate matter with a diameter smaller than 2.5 µm) pollution smog has been perceived as a national priority, with the establishment of a coal-consumption cap that required the share of coal in the national primary energy mix to drop to below 65% in 2017 by a transition to cleaner natural gas and nonfossil energy sources (9). Coal reduction is, however, the crux of China’s air pollution control (10). Fig. 1 shows the coal-control roadmap of China from 2010 through 2030. As of 2017, over half of the world’s coal consumption has occurred in China, accounting for ∼60% of the country’s primary energy consumption (11). After effective coal reductions from the power sector and key energy-intensive industries (phase I), the further coal control in China has been focusing on the reduction of dispersed coal use in residential and small industrial facilities (phase II). The dispersed coal (the so-called “Sanmei” in Chinese) refers to raw coal, usually a high-polluting fuel with high ash residue, burned in noncentralized combustion facilities without end-of-pipe air pollutant treatment. The residential dispersed coal combustion in vast rural areas has been estimated to be a major contributor to high PM_2.5 exposure and premature mortality in China (7, 12–14).Open in a separate windowFig. 1.The roadmap of coal control in China from 2010 to 2030. The light orange shaded area shows the share of coal in the primary energy mix for 2010–2030 (11) with three-phased coal controls (phase I: in the power section and key energy-intensive industries; phase II: toward dispersed coal reductions; and phase III: pertaining to clean-energy development). The black bars indicate the dispersed coal consumption for 2016–2018 and 2020 with source decomposition for sectoral changes (17). The doughnut charts show the primary energy structures of China in 2010, 2017, and 2030, respectively (11, 37). Note: Energy data for Hong Kong, Macau, and Taiwan are not included here.Since early 2017, a series of clean-heating actions have been implemented in Beijing and its neighboring provinces, especially in the “2+26” cities located along the air pollution transport channel of the Beijing–Tianjin–Hebei (BTH) region (Beijing, Tianjin, and 26 other cities in Hebei, Shanxi, Shandong, and Henan provinces) (15, 16). The major dispersed coal control measures include replacing traditional household coal-fired stoves with wall-mounted natural gas heaters (“coal-to-gas”) or electric stoves (“coal-to-electricity”) and eliminating the small industrial coal-fired steam boilers and construction materials industrial kilns (brick, ceramic, and lime industries) (17). The changes in coal consumption related to individual measures are given in Fig. 1.Combining the ground-based measurements, localized emission estimates, and chemical transport model simulations, we show that, although air pollution in the northern 2+26 pilot cities has been greatly improved by the emission mitigation measures, the coal-to-gas action in winter 2017 has caused a severe natural gas shortage in the rest of China (18, 19 and SI Appendix, section S1), which necessitated the use of more polluting alternative energy sources and led to a deteriorated air quality in the gas-shortage regions.     

Superelastic oxide micropillars enabled by surface tension–modulated 90° domain switching with excellent fatigue resistance

Superelastic materials capable of recovering large nonlinear strains are ideal for a variety of applications in morphing structures, reconfigurable systems, and robots. However, making oxide materials superelastic has been a long-standing challenge due to their intrinsic brittleness. Here, we fabricate ferroelectric BaTiO₃ (BTO) micropillars that not only are superelastic but also possess excellent fatigue resistance, lasting over 1 million cycles without accumulating residual strains or noticeable variation in stress–strain curves. Phase field simulations reveal that the large recoverable strains of BTO micropillars arise from surface tension–modulated 90° domain switching and thus are size dependent, while the small energy barrier and ultralow energy dissipation are responsible for their unprecedented cyclic stability among superelastic materials. This work demonstrates a general strategy to realize superelastic and fatigue-resistant domain switching in ferroelectric oxides for many potential applications.

Superelastic materials are capable of recovering large amount of nonlinear “plastic” strains, way beyond their linear elastic regimes (1–4). They are ideal for a variety of applications from morphing structures, reconfigurable systems, to robots (5–8). The effects have traditionally been associated with macroscopically compliant/ductile rubbers (2) or microscopically phase-transforming shape memory alloys (SMAs) (7–11). The only macroscopically brittle oxide recently discovered to be superelastic is ZrO₂-based micropillars or particles (12–20), which is realized via austenite-martensite phase transformation similar to SMAs. Although ultimate strengths approaching the theoretical limit have been demonstrated in nanoscale samples (21, 22), long fatigue life is elusive, which is arguably more important for most applications. As a matter of fact, poor fatigue life has been a long-standing challenge for oxide ceramics in general (23, 24). Even for ductile SMAs that enjoy excellent fatigue life, irrecoverable residual strains gradually accumulate over cycling, leading to substantial variations in stress–strain curves at different cycles (9, 10, 25). We overcome these difficulties by reporting superelastic barium titanate (BaTiO₃ [BTO]) micropillars enabled by surface tension–modulated 90° domain switching, which exhibit excellent fatigue resistance, while bulk BTO crystals or ceramics are rather brittle. The demonstration of over one million cycles of loading and unloading without accumulating residual strains or noticeable variation in stress–strain curves is unprecedented among superelastic materials.BTO is a ferroelectric oxide exhibiting modest piezoelectric strains around 0.1 to 0.2% (26) and fracture toughness of ∼1 MPa ⋅ m^1/2, and thus it is quite brittle (27). Considerable research efforts have been devoted to enhancing its electric field–induced strain via 90° ferroelectric domain switching (28–30). However, the process is often irreversible, and external mechanisms such as restoring force (28, 29) and internal mechanisms such as defect pinning (30) have to be invoked to make the electrostrain recoverable. Nevertheless, it hints at the possibility of BTO being made superelastic by taking advantage of the stress-induced 90° domain switching (6). Earlier works suggest that surface tension induces an in-plane compressive stress that favors the axial polarization in one-dimensional ferroelectrics at small size (31, 32), which may provide the necessary restoring mechanism for the stress-switched domains. Thus, if a compressive axial force is applied, reversible domain switching may occur during unloading, leading to superelasticity. To verify this hypothesis, we fabricated single-crystalline BTO micropillars from [001]-oriented bulk crystals (SI Appendix, Fig. S1A) via focused ion beam (FIB), as detailed in Materials and Methods and SI Appendix, Fig. S1B. The diameters (Φ) of the micropillars range from 0.5 μm to 5 μm, with their height to diameter ratio fixed at 3. No visible defects can be seen from the scanning electron microscopy (SEM) images of these micropillars shown in Fig. 1 A–D, and their surfaces appear to be quite smooth, suggesting that no apparent damages are induced by FIB.Open in a separate windowFig. 1.Superelastic BTO micropillars below a critical size. (A–D) SEM images of the micropillars with Φ = 5, 3, 2, and 0.5 μm. (E–G) The first and second cycles of stress–strain curves for BTO micropillars with Φ = 5, 2, and 0.5 μm. (H) S_r/S_max and ΔW/W_max during the first cycle for BTO micropillars of different diameters. Here, S_r and S_max denote the residual strain and the maximum strain (SI Appendix, Fig. S6A), while ΔW and W_max are energy dissipated and stored in the first cycle, respectively (SI Appendix, Fig. S6F).     

“Soft” oxidative coupling of methane to ethylene: Mechanistic insights from combined experiment and theory

The oxidative coupling of methane to ethylene using gaseous disulfur (2CH₄ + S₂ → C₂H₄ + 2H₂S) as an oxidant (SOCM) proceeds with promising selectivity. Here, we report detailed experimental and theoretical studies that examine the mechanism for the conversion of CH₄ to C₂H₄ over an Fe₃O₄-derived FeS₂ catalyst achieving a promising ethylene selectivity of 33%. We compare and contrast these results with those for the highly exothermic oxidative coupling of methane (OCM) using O₂ (2CH₄ + O₂ → C₂H₄ + 2H₂O). SOCM kinetic/mechanistic analysis, along with density functional theory results, indicate that ethylene is produced as a primary product of methane activation, proceeding predominantly via CH₂ coupling over dimeric S–S moieties that bridge Fe surface sites, and to a lesser degree, on heavily sulfided mononuclear sites. In contrast to and unlike OCM, the overoxidized CS₂ by-product forms predominantly via CH₄ oxidation, rather than from C₂ products, through a series of C–H activation and S-addition steps at adsorbed sulfur sites on the FeS₂ surface. The experimental rates for methane conversion are first order in both CH₄ and S₂, consistent with the involvement of two S sites in the rate-determining methane C–H activation step, with a CD₄/CH₄ kinetic isotope effect of 1.78. The experimental apparent activation energy for methane conversion is 66 ± 8 kJ/mol, significantly lower than for CH₄ oxidative coupling with O₂. The computed methane activation barrier, rate orders, and kinetic isotope values are consistent with experiment. All evidence indicates that SOCM proceeds via a very different pathway than that of OCM.

The oxidative coupling of methane (OCM) with O₂ would seem to be a concise, direct route to convert methane, one of the most Earth-abundant carbon sources (1), to ethylene (2CH₄ + O₂ → C₂H₄ + 2H₂O), a key chemical intermediate (2, 3), and this process has been extensively studied (1, 4–19) since 1982 (20). Nevertheless, the widespread use of OCM is challenged by methane overoxidation to CO₂ and other oxygenates. Furthermore, the severe reaction conditions of nonoxidative pathways (2, 21–28) typically risk carbon deposition and catalyst deactivation (2, 21–26). In preliminary studies, we reported a 2CH₄ + S₂ → C₂H₄ + 2H₂S coupling process that moderates the methane overoxidation driving force using gaseous disulfur (S₂) as a “soft” oxidant (SOCM; Fig. 1A) (29). S₂ is isoelectronic with O₂, the major sulfur vapor species at 700 to 925 °C (30–32), and is a less aggressive oxidant than O₂ (33). In this scenario, elemental sulfur is recovered from the H₂S coproduct via the known Claus process (Fig. 1B) (30), in a cycle where sulfur mediates/moderates the high nonselective O₂ reactivity. SOCM achieved promising ethylene selectivity, raising intriguing mechanistic questions and the possibility of higher selectivity. Methane + S_2(g) ethylene selectivities near ∼20% are achieved over a PdS/ZrO₂ catalyst (29), and oxide precatalysts give selectivities near 33% (34).Open in a separate windowFig. 1.Energetic comparison between the oxidative coupling of methane with O₂ (OCM) and with S₂ (SOCM) and the pathway to recover elemental sulfur from H₂S. (A) Gibbs free energy of desired and overoxidation processes in OCM and SOCM at 800 and 1,050 °C. (B) Industrialized catalytic Claus process used to recover elemental sulfur from H₂S.Nevertheless, in contrast to extensive OCM (17, 35–39) and nonoxidative CH₄ coupling studies (40), far less is known about the SOCM reaction pathway. Post-SOCM X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and elemental analysis (29, 34) indicate that the oxide precatalysts are predominantly sulfided. Density functional theory (DFT) analyses of molybdenum sulfide catalysts suggest that methane is activated at M–S or S–S sites to form surface-bound CH₃* species which dehydrogenate to form CH₂* (methylidene) species, which then couple to produce C₂H₄. It was proposed that CH₃* species can also desorb as methyl radicals which couple to form ethane (29). The overoxidation product, CS₂, was suggested to form via sulfur addition to methylidene surface intermediates (29).Kinetic, mechanistic, and theoretical analyses are needed to better understand the CH₄ conversion pathways to C₂H₄ and other products. In principle, there are two plausible pathways following methane activation: 1) H abstraction from adsorbed methyl species forms methylidene (CH₂*) and methylidyne (CH*) species then couple to C₂ products or undergo oxidation to CS₂ or 2) coupling of surface or gas phase methyl species form ethane, which then dehydrogenates to form ethylene or oxidizes to CS₂. For further SOCM optimization it is important to determine which pathways are operative, their relative rates, and the C₂ and CS₂ formation sites.Here we investigate SOCM pathways over a sulfided Fe₃O₄ precatalyst which affords C₂H₄ selectivities near 33%, complete oxide to sulfide conversion, minimal carbon deposition (coking), and 48-h SOCM stability at 950 °C (34). We first summarize SOCM phenomenology, followed by analysis of the Fe phases during sulfurization and SOCM. Next, kinetic/mechanistic studies focus on the methane and S₂ reaction orders, activation energetics, and isotope effects and probe the pathways governing C₂ vs. CS₂ formation. Complementary DFT calculations focus on reaction mechanisms, the active sites, and their role in product formation. The results are used in a microkinetic model to simulate reaction rates, apparent activation barriers, and reaction rate orders and to compare with experiment. Finally, SOCM and OCM are compared, revealing that they follow distinctly different pathways.     

Paleocene latitude of the Kohistan–Ladakh arc indicates multistage India–Eurasia collision

We report paleomagnetic data showing that an intraoceanic Trans-Tethyan subduction zone existed south of the Eurasian continent and north of the Indian subcontinent until at least Paleocene time. This system was active between 66 and 62 Ma at a paleolatitude of 8.1 ± 5.6 °N, placing it 600–2,300 km south of the contemporaneous Eurasian margin. The first ophiolite obductions onto the northern Indian margin also occurred at this time, demonstrating that collision was a multistage process involving at least two subduction systems. Collisional events began with collision of India and the Trans-Tethyan subduction zone in Late Cretaceous to Early Paleocene time, followed by the collision of India (plus Trans-Tethyan ophiolites) with Eurasia in mid-Eocene time. These data constrain the total postcollisional convergence across the India–Eurasia convergent zone to 1,350–2,150 km and limit the north–south extent of northwestern Greater India to <900 km. These results have broad implications for how collisional processes may affect plate reconfigurations, global climate, and biodiversity.

Classically, the India–Eurasia collision has been considered to be a single-stage event that occurred at 50–55 million years ago (Ma) (1, 2). However, plate reconstructions show thousands of kilometers of separation between India and Eurasia at the inferred time of collision (3, 4). Accordingly, the northern extent of Greater India was thought to have protruded up to 2,000 km relative to present-day India (5, 6) (Fig. 1). Others have suggested that the India–Eurasia collision was a multistage process that involved an east–west trending Trans-Tethyan subduction zone (TTSZ) situated south of the Eurasian margin (7–9) (Fig. 1). Jagoutz et al. (9) concluded that collision between India and the TTSZ occurred at 50–55 Ma, and the final continental collision occurred between the TTSZ and Eurasia at 40 Ma (9, 10). This model reconciles the amount of convergence between India and Eurasia with the observed shortening across the India–Eurasia collision system with the addition of the Kshiroda oceanic plate. Additionally, the presence of two subduction systems can explain the rapid India–Eurasia convergence rates (up to 16 mm a⁻¹) that existed between 135 and 50 Ma (9), as well as variations in global climate in the Cenozoic (11).Open in a separate windowFig. 1.The first panel is an overview map of tectonic structure of the Karakoram–Himalaya–Tibet orogenic system. Blue represents India, red represents Eurasia, and the Kohistan–Ladakh arc (KLA) is shown in gray. The different shades of blue highlight the deformed margin of the Indian plate that has been uplifted to form the Himalayan belt, and the zones of darker red within the Eurasian plate highlight the Eurasian continental arc batholith. Thick black lines denote the suture zones which separate Indian and Eurasian terranes. The tectonic summary panels illustrate the two conflicting collision models and their differing predictions of the location of the Kohistan–Ladakh arc. India is shown in blue, Eurasia is shown in red, and the other nearby continents are shown in gray. Active plate boundaries are shown with black lines, and recently extinct boundaries are shown with gray lines. Subduction zones are shown with triangular tick marks.While the existence of the TTSZ in the Cretaceous is not disputed, the two conflicting collision models make distinct predictions about its paleolatitude in Late Cretaceous to Paleocene time; these can be tested using paleomagnetism. In the single-stage collision model, the TTSZ amalgamated with the Eurasian margin prior to ∼80 Ma (12) at a latitude of ≥20 °N (13, 14). In contrast, in the multistage model, the TTSZ remained near the equator at ≤10 °N, significantly south of Eurasia, until collision with India (9) (Fig. 1).No undisputed paleomagnetic constraints on the location of the TTSZ are available in the central Himalaya (15–17). Westerweel et al. (18) showed that the Burma Terrane, in the eastern Himalaya, was part of the TTSZ and was located near the equator at ∼95 Ma, but they do not constrain the location of the TTSZ in the time period between 50 and 80 Ma, which is required to test the two collision hypotheses. In the western Himalaya, India and Eurasia are separated by the Bela, Khost, and Muslimbagh ophiolites and the 60,000 km² intraoceanic Kohistan Ladakh arc (19, 20) (Fig. 1). These were obducted onto India in the Late Cretaceous to Early Paleocene (19), prior to the closure of the Eocene to Oligocene Katawaz sedimentary basin (20) (Fig. 1). The Kohistan–Ladakh arc contacts the Eurasian Karakoram terrane in the north along the Shyok suture and the Indian plate in the south along the Indus suture (21) (Fig. 1). Previous paleomagnetic studies suggest that the Kohistan–Ladakh arc formed as part of the TTSZ near the equator in the early Cretaceous but provide no information on its location after 80 Ma (22–25). While pioneering, these studies lack robust age constraints, do not appropriately average paleosecular variation of the geodynamo, and do not demonstrate that the measured magnetizations have not been reset during a subsequent metamorphic episode.     

The number of catalytic cycles in an enzyme’s lifetime and why it matters to metabolic engineering

Metabolic engineering uses enzymes as parts to build biosystems for specified tasks. Although a part’s working life and failure modes are key engineering performance indicators, this is not yet so in metabolic engineering because it is not known how long enzymes remain functional in vivo or whether cumulative deterioration (wear-out), sudden random failure, or other causes drive replacement. Consequently, enzymes cannot be engineered to extend life and cut the high energy costs of replacement. Guided by catalyst engineering, we adopted catalytic cycles until replacement (CCR) as a metric for enzyme functional life span in vivo. CCR is the number of catalytic cycles that an enzyme mediates in vivo before failure or replacement, i.e., metabolic flux rate/protein turnover rate. We used estimated fluxes and measured protein turnover rates to calculate CCRs for ∼100–200 enzymes each from Lactococcus lactis, yeast, and Arabidopsis. CCRs in these organisms had similar ranges (<10³ to >10⁷) but different median values (3–4 × 10⁴ in L. lactis and yeast versus 4 × 10⁵ in Arabidopsis). In all organisms, enzymes whose substrates, products, or mechanisms can attack reactive amino acid residues had significantly lower median CCR values than other enzymes. Taken with literature on mechanism-based inactivation, the latter finding supports the proposal that 1) random active-site damage by reaction chemistry is an important cause of enzyme failure, and 2) reactive noncatalytic residues in the active-site region are likely contributors to damage susceptibility. Enzyme engineering to raise CCRs and lower replacement costs may thus be both beneficial and feasible.

As the synthetic biology revolution brings engineering principles and practices into the life sciences, biomolecules are being rethought as component parts that are used to build new biosystems and improve existing ones (1–3). Enzymes—the working parts of metabolic systems—are targets for this rethinking and are increasingly being repurposed by rational design and directed evolution (4).Substrate specificity, catalytic efficiency, and expression level are common performance specifications for enzyme parts in metabolic engineering, but life span is not, despite its centrality in other engineering fields. Knowing an engineering component’s life span (how long it lasts in service) is critical to preventing system failures and optimizing maintenance schedules (5). Failure metrics such as “mean time to failure” (6) are consequently used widely in engineering, which distinguishes three types of failures: early, wear-out, and random or stochastic. All three have counterparts in enzymes operating in vivo (Fig. 1A) (7–18), but wear-out and random failures (Fig. 1A, red font) are most relevant to length of working life.Open in a separate windowFig. 1.The engineering concept of component failure and its application to enzymes in vivo. (A) The types of failure in manufactured components and their counterparts in enzymes operating in vivo. (B) Schematic representation of the time dependence of the hazard rate and the cumulative probability (increasing color density) that an individual component will have failed.In manufactured systems, wear-out failures are caused by cumulative deterioration processes or by use-dependent wear (Fig. 1A). Like all proteins, enzymes are subject to cumulative deterioration from oxidation, racemization, or other chemical events (“protein fatigue”) that can affect any part of the molecule and degrade its function (9–11). However, use-dependent wear-out has no equivalent in enzymes, i.e., enzyme performance is not progressively degraded by operation of the catalytic cycle in the way a bearing is worn down a little each time it turns (Fig. 1A). Rather, a random catalytic misfire or a chemical attack by a substrate or product on a vulnerable residue in the active-site region can instantly inactivate an enzyme, whatever its age (14–18). Such failures thus have a constant hazard rate and are random or stochastic, like the abrupt failure of a transistor due to a current surge (Fig. 1A).Although the hazard of random failure does not depend on a part’s age, the cumulative probability that any individual part will experience a random failure increases with time (Fig. 1B). Given long enough, certain types of enzyme molecule may thus be doomed to have a terminal, catalysis-related accident. Such self-inflicted inactivation processes are important considerations for industrial enzymes (i.e., enzymes used ex vivo as reagents) and the number of catalytic cycles that each enzyme molecule carries out in its lifetime—often called “total turnover number”—is a key industrial performance criterion (19–21).The number of catalytic cycles mediated before self-inactivation could also be key to in vivo enzyme performance. Recent proteomic evidence points to damage from the reaction catalyzed as a major mode of enzyme failure and to the possibility that some reactions do more damage than others. Thus, in the bacterium Lactococcus lactis, a fivefold increase in growth rate was accompanied by a sevenfold increase in protein turnover rate (22). This near proportionality implies that L. lactis enzymes catalyze a similar number of reactions in their lifetimes, whatever the growth rate. This fits with reaction-related damage as a cause of failure: The faster the growth, the more flux through reactions, the more damage to enzymes, and the sooner enzymes fail. Similarly, protein turnover in yeast was faster when enzymes were in active use (23). Furthermore, in L. lactis, yeast, and Arabidopsis, the fastest turning-over metabolic enzymes include many with reactive substrates, products, or intermediates (SI Appendix, Table S1) (22–24), i.e., with a high risk of spontaneous chemical damage to the active site.The rates at which enzyme proteins are degraded and resynthesized are critical to the cellular energy economy because such turnover can consume about half the maintenance energy budget in microbes and plants (22, 25–27). High enzyme protein turnover rates therefore potentially reduce the productivity of biosystems ranging from microbial fermentations to crops (26, 28, 29). Consistent with such reduction, fast protein turnover is associated with low biomass yield in yeast (27) and with low growth rate in Arabidopsis (30). Also, slowing the turnover of abundant, fast-turnover enzymes is predicted to substantially increase growth rate and biomass yield in plants (26, 31) and other organisms (32).Rational design or directed evolution can now be used to tune protein turnover rates (33–35). However, before setting out to reduce enzyme turnover it is essential to define target enzymes and to understand why they turn over fast in the first place. Accordingly, here we calculate and compare the life spans of enzymes from three kingdoms using the criterion of “catalytic cycles until replacement” (CCR) (33), defined as the moles of substrate converted per mole of enzyme before the enzyme is replaced, i.e., the following:CCR=Metabolic flux rateEnzyme replacement rate.[1]CCR is the in vivo equivalent of the ex vivo “total turnover number” mentioned above but is a preferable term as it avoids confusion with the term “turnover number,” a synonym in enzymology for k_cat (20). CCR is envisioned as a potential constant, with reaction wear-and-tear being matched with degradation rates to maintain CCR as a factor hardwired to the structural and (bio)chemical stability of a given enzyme (33). We then compare each enzyme’s CCR to its reaction chemistry and across kingdoms to find shared attributes underlying CCR values. Our findings imply that CCRs are commonly influenced by random collateral damage from the reaction catalyzed and that enzymes could be engineered to reduce this damage and its attendant enzyme replacement costs. More generally, the findings point to catalysis-related accidents as a sizeable but underrecognized cause of enzyme failure and replacement.     

“You” speaks to me: Effects of generic-you in creating resonance between people and ideas

Creating resonance between people and ideas is a central goal of communication. Historically, attempts to understand the factors that promote resonance have focused on altering the content of a message. Here we identify an additional route to evoking resonance that is embedded in the structure of language: the generic use of the word “you” (e.g., “You can’t understand someone until you’ve walked a mile in their shoes”). Using crowd-sourced data from the Amazon Kindle application, we demonstrate that passages that people highlighted—collectively, over a quarter of a million times—were substantially more likely to contain generic-you compared to yoked passages that they did not highlight. We also demonstrate in four experiments (n = 1,900) that ideas expressed with generic-you increased resonance. These findings illustrate how a subtle shift in language establishes a powerful sense of connection between people and ideas.

Consider the feeling evoked by watching a gripping scene in a film, hearing a moving song, or coming across a quotation that seems to be written just for you. Experiencing resonance, a sense of connection, is a pervasive human experience. Prior research examining the processes that promote this experience suggests that altering a message to evoke emotion (1–7), highlighting its applicability to a person’s life (2, 6, 8–10), or appealing to a person’s beliefs (4, 8, 11) can all contribute to an idea’s resonance. Here we examine an additional route to cultivating this experience, which is grounded in a message’s form rather than its content: the use of a linguistic device that frames an idea as applying broadly.The ability to frame an idea as general rather than specific is a universal feature of language (12–15). One frequently used device is the generic usage of the pronoun “you” (15–17). Although “you” is often used to refer to a specific person or persons (e.g., “How did you get to work today?”), in many languages, it can also be used to refer to people in general (e.g., “You avoid rush hour if you can.”). This general use of “you” is comparable to the more formal “one,” but is used much more frequently (18).Research indicates that people often use “you” in this way to generalize from their own experiences. For example, a person reflecting on getting fired from their job might say, “It makes you feel betrayed” (18). Here, we propose that using “you” to refer to people in general has additional social implications, affecting whether an idea evokes resonance.Two features of the general usage of “you” (hereafter, “generic-you”) motivate this hypothesis. First, generic-you conveys that ideas are generalizable. Rather than expressing information that applies to a particular situation (e.g., “Leo broke your heart”), generic-you expresses information that is timeless and applies across contexts (e.g., “Eventually, you recover from heartbreak”; 18–23). Second, generic-you is expressed with the same word ("you") that is used in nongeneric contexts to refer to the addressee. Thus, even when “you” is used generically, the association to its specific meaning may further pull in the addressee, heightening resonance. Together, these features suggest that generic-you should promote the resonance of an idea. We tested this hypothesis across five preregistered studies (24–28), using a combination of crowd-sourced data and online experimental paradigms. Data, code, and materials are publicly available via the Open Science Framework (https://osf.io/6J2ZC/) (29). Study 1 used publicly available data from the Amazon Kindle application. Studies 2–5 were approved by the University of Michigan Health Sciences and Behavioral Sciences institutional review board (IRB) under HUM00172473 and deemed exempt from ongoing IRB review. All participants who participated in studies 2–5 provided informed consent via a checkbox presented through the online survey platform, Qualtrics.     

On and off controls within dynein–dynactin on native cargoes

The dynein–dynactin nanomachine transports cargoes along microtubules in cells. Why dynactin interacts separately with the dynein motor and also with microtubules is hotly debated. Here we disrupted these interactions in a targeted manner on phagosomes extracted from cells, followed by optical trapping to interrogate native dynein–dynactin teams on single phagosomes. Perturbing the dynactin–dynein interaction reduced dynein’s on rate to microtubules. In contrast, perturbing the dynactin–microtubule interaction increased dynein’s off rate markedly when dynein was generating force against the optical trap. The dynactin–microtubule link is therefore required for persistence against load, a finding of importance because disease-relevant mutations in dynein–dynactin are known to interfere with “high-load” functions of dynein in cells. Our findings call attention to a less studied property of dynein–dynactin, namely, its detachment against load, in understanding dynein dysfunction.

Transport of organelles inside cells is driven by motors of the kinesin and dynein families that generate force, respectively, toward the plus and minus ends of microtubules (MTs). Cytoplasmic dynein, aided by many regulators such as dynactin (Fig. 1A), executes bewilderingly diverse cellular functions (1). Dynactin’s largest subunit P150 has a coiled-coil (CC) region that contains a cytoskeleton-associated protein glycine-rich (CAPGly) domain along with a stretch of basic residues (2). Dynactin binds dynein through its CC1 domain, and dynactin also binds MTs through its CAPGly and basic domains (Fig. 1A). Additional interactions between dynein heavy chain and dynactin’s Arp domain may stabilize the complex (3, 4). How all these linkages help dynein–dynactin to function is hotly debated (5, 6). Of particular interest is the recruitment of dynein–dynactin to cargoes by different adaptor proteins (1), as also revealed by the cryogenic electron microscopy (cryo-EM) structure of dynein–dynactin–adaptor complexes wherein two dimeric dynein motors were found to be recruited as a pair (3, 4). Dynein undergoes significant conformational changes in this process, allowing both motor domains to align along the MT for effective force generation. Notably, recruitment of dynein in pairs to phagosomes was suggested by us on the basis of force measurements inside cells (7).Open in a separate windowFig. 1.Effects of DIC-WT on dynein-membrane and dynein–MT binding. (A) (Left) Schematic of a single phagosome driven by a team of dyneins and a kinesin held in an optical trap over a MT. (Right) Magnified schematic of one dynein–dynactin complex. Treatments to target the DIC–CC1 interaction (with recombinant DIC protein) and CAPGly–MT interaction (with anti-CAPGly antibody) are shown. DIC, dynein intermediate chain; DHC, dynein heavy chain; ARP, actin-related protein. (B) Western blot showing levels of dynein (DIC) and dynactin (P150) retained on organelle membranes after treatment with mDIC-WT or mDIC-Mut. Membranes were prepared from RAW mouse macrophage cells. Rab7 is a marker for late endosomes. Actin is a loading control. Lower panel shows quantification of dynein band intensity across three experiments. The mDIC-Mut band intensity was always taken equal to 1 and mDIC-WT intensity calculated relative to mDIC-Mut. No significant difference is seen between mDIC-WT and mDIC-Mut treatments. Error bars, SEM. P value was calculated using Student’s t test. (C) Goat brain cytosol was treated with mDIC-WT or mDIC-Mut, followed by addition of exogenously polymerized MTs. Western blot shows levels of dynein (DIC) and dynactin (P150) that copelleted with MTs. Tubulin from the resuspended MT pellet is used for normalization. Lower panel shows quantification across three experiments. The mDIC-Mut band intensity was always taken equal to 1 and mDIC-WT intensity calculated relative to mDIC-Mut. Dynein and dynactin binding to MTs is reduced upon addition of mDIC-WT. Error bars, SEM. P values were calculated using Student’s t test.Two kinds of studies, classified here as in vitro and in vivo, have investigated dynein–dynactin function. In vitro, dynactin was suggested to work as a brake for dynein (8); however, others found dynactin to enhance processive motion of dynein-driven beads (2, 9, 10). Purified dynein and dynactin, when complexed with bicaudal-D (Bic-D) activated dynein for motion (3, 11–14). An elegant demonstration of such activation came from McKenney et al. (12, 15) and Schlager et al. (14). MT binding by dynactin was suggested to initiate processive transport along tyrosinated MTs in vitro by reducing detachment of the dynein–dynactin complex from MTs (12, 15). In vivo studies have relied on overexpression or genetic perturbation of dynactin subunits inside cells. Studies targeting the dynactin–MT binding in neurons suggested that dynactin is not needed once retrograde transport from neurite tips has been initiated (16, 17). Perturbing dynactin–MT binding had no effect on processivity and step size of dynein, but affected MT organization in cells (18). In vitro, however, a CAPGly antibody reduced run length of dynein–dynactin-driven motion (9, 19) and loss of the CAPGly domain increased dynein detachment from MTs (8). The in vitro assays described above are bereft of many (known and unknown) protein regulators of dynein. Further, native-like function requires motors to be assembled on a lipid membrane on the cargo (20), a component missing in most in vitro assays. On the other hand, in vivo studies often cannot extract mechanistic details of function at the single-cargo level and could also suffer from off-target effects arising from genetic perturbations that are introduced in the cells.To address these controversies, we fed micrometer-sized latex beads into Dictyostelium cells and allowed ingested beads to mature into phagosomes, wherein dynein and kinesin assembled in situ on the phagosome membrane. Phagosomes were then extracted from cells and motion and force generation of single phagosomes against an optical trap were interrogated ex vivo on polarity-labeled MTs (Fig. 1A). Most parameters of phagosome motion inside cells (7) were replicated in this ex vivo assay (21). Recombinant proteins and antibodies were then used to induce targeted perturbation of specific interactions within the dynein–dynactin machinery on phagosomes ex vivo (Fig. 1A). The consequence of these targeted perturbations was assayed by optical trapping and biochemical studies. Lastly, we reconstituted endogenous dynein–dynactin complexes on a lipid membrane that was assembled artificially on a bead (called supported lipid bilayer [SLB]). SLBs exhibit vigorous dynein–dynactin-driven motion, and targeted perturbation of dynein–dynactin on the SLBs replicates the effects seen on phagosomes. These SLB assays establish that the perturbations and their effects are indeed specific to the dynein–dynactin complex.Perturbing the interaction between dynein intermediate chain (DIC) and the CC1 domain of dynactin reduces the binding rate (K_ON) of dynein–dynactin to MTs, but has little effect on the detachment rate (K_OFF) of dyneins from the MT. In contrast, perturbing the dynactin–MT interaction increases K_OFF most appreciably when dynein attempts to move against opposing force (i.e., load) exerted by an optical trap. Therefore, the DIC–CC1 interaction maintains dynein in an initial (unloaded) conformation that binds rapidly to MTs (high K_ON) to initiate motion. Once motion is initiated, the dynactin–MT interaction ensures tenacious binding (low K_OFF) of dynein–dynactin to MTs against opposing force, allowing dynein–dynactin to compete against other motors and/or obstacles. We suggest a mechanism wherein a load-dependent communication operates across the dynein–dynactin scaffold and is necessary for the nanomachine to generate persistent force against opposition.     

Geometrical manipulation of complex supramolecular tessellations by hierarchical assembly of amphiphilic platinum(II) complexes

Here we report complex supramolecular tessellations achieved by the directed self-assembly of amphiphilic platinum(II) complexes. Despite the twofold symmetry, these geometrically simple molecules exhibit complicated structural hierarchy in a columnar manner. A possible key to such an order increase is the topological transition into circular trimers, which are noncovalently interlocked by metal···metal and π–π interactions, thereby allowing for cofacial stacking in a prismatic assembly. Another key to success is to use the immiscibility of the tailored hydrophobic and hydrophilic sidechains. Their phase separation leads to the formation of columnar crystalline nanostructures homogeneously oriented on the substrate, featuring an unusual geometry analogous to a rhombitrihexagonal Archimedean tiling. Furthermore, symmetry lowering of regular motifs by design results in an orthorhombic lattice obtained by the coassembly of two different platinum(II) amphiphiles. These findings illustrate the potentials of supramolecular engineering in creating complex self-assembled architectures of soft materials.

Tessellation in two dimensions (2D) is a very old topic in geometry on how one or more shapes can be periodically arranged to fill a Euclidean plane without any gaps. Tessellation principles have been extensively applied in decorative art since the early times. In natural sciences, there has been a growing attention on creating ordered structures with increasingly complex architectures inspired by semi-regular Archimedean tilings (ATs) and quasicrystalline textures on account of their intriguing physical properties (1–5) and biological functions (6). Recent advances in this regard have been achieved in various fields of supramolecular science, including the programmable self-assembly of DNA molecules (7), coordination-driven assembly (8–10), supramolecular interfacial engineering (11–13), crystallization of organic polygons (14, 15), colloidal particle superlattices (16), and other soft-matter systems (17–20). Moreover, tessellation in 2D can overcome the topological frustration to generate complex semi- or non-regular patterns by using geometrically simple motifs. As exemplified by the self-templating assembly of spherical soft microparticles (21), a vast array of 2D micropatterns encoding non-regular tilings, such as rectangular, rhomboidal, hexagonal, and herringbone superlattices were obtained by layer-by-layer strategy at a liquid–liquid interface. Tessellation principles have also been extended to the self-assembly of giant molecules in three dimensions (3D). Superlattices with high space-group symmetry (Im3¯m, Pm3¯n, and P4₂/mnm) were reported in dendrimers and dendritic polymers by Percec and coworkers (22–24). Recently, Cheng and coworkers identified the highly ordered Frank–Kasper phases obtained from giant amphiphiles containing molecular nanoparticles (25–28). Despite such advancements made in the field of soft matter, an understanding of how structural ordering in supramolecular materials is influenced by the geometric factors of its constituent molecules has so far remained elusive.In light of these developments and the desire to explore the supramolecular systems, square-planar platinum(II) (Pt^II) polypyridine complexes may serve as an ideal candidate for model studies not only because of their intriguing spectroscopic and luminescence properties (29, 30), but also because of their propensity to form supramolecular polymers or oligomers via noncovalent Pt···Pt and π–π interactions (31–39). Although rod-shaped and lamellar structures are the most commonly observed in the self-assembly of planar Pt^II complexes (34–39), 2D-ordered nanostructures, such as the hexagonally packed columns (31, 40) and honeycomb-like networks (41–43), were recently first demonstrated by us.Herein, we report a serendipitous discovery of a C_2h-symmetric Pt^II amphiphile (Fig. 1A) that can hierarchically self-assemble into a 3D-ordered nanostructure with hexagonal geometry. Interestingly, this structurally anisotropic molecule possibly undergoes topological transition and interlocks to form its circular trimer by noncovalent Pt···Pt and π–π interactions (Fig. 1B). The resultant triangular motif is architecturally stabilized and preorganized for one-dimensional (1D) prismatic assembly (Fig. 1C). Together with the phase separation of the tailored hydrophobic and hydrophilic sidechains, an unusual and unique 3D hexagonal lattice is formed (Fig. 1D), in which the Pt centers adopt a rare rhombitrihexagonal AT-like order. Finally, the nanoarchitecture develops in a hierarchical manner on the substrate due to the homogeneous nucleation (Fig. 1E).Open in a separate windowFig. 1.Hierarchical self-assembly of Pt^II amphiphile into hexagonal ordering. (A) Space-filling (CPK) model of a C_2h-symmetric Pt^II amphiphile (1). All of the hydrogen atoms and counterions are omitted for clarity. (B) CPK representations of possible models of regular triangular, tetragonal, pentagonal, and hexagonal motifs formed with Pt···Pt and π–π stacking. These motifs possess a hydrophilic core (red) with various diameters wrapped by a hydrophobic shell comprising long alkyl chains (gray). (C) CPK representation of a 1D prismatic structure consisting of circular trimers with long-range Pt···Pt and π–π stacking. (D) CPK representation of a 3D columnar lattice constructed by the prismatic assemblies adopting a rare rhombitrihexagonal AT-like order. With the assistance of the phase separation, the hydrophobic domain serves as a discrete column associated with six prismatic neighbors. (E) Schematic representation of the nanoarchitecture with homogeneous orientation.     

Observation of non-Hermitian topology and its bulk–edge correspondence in an active mechanical metamaterial

Topological edge modes are excitations that are localized at the materials’ edges and yet are characterized by a topological invariant defined in the bulk. Such bulk–edge correspondence has enabled the creation of robust electronic, electromagnetic, and mechanical transport properties across a wide range of systems, from cold atoms to metamaterials, active matter, and geophysical flows. Recently, the advent of non-Hermitian topological systems—wherein energy is not conserved—has sparked considerable theoretical advances. In particular, novel topological phases that can only exist in non-Hermitian systems have been introduced. However, whether such phases can be experimentally observed, and what their properties are, have remained open questions. Here, we identify and observe a form of bulk–edge correspondence for a particular non-Hermitian topological phase. We find that a change in the bulk non-Hermitian topological invariant leads to a change of topological edge-mode localization together with peculiar purely non-Hermitian properties. Using a quantum-to-classical analogy, we create a mechanical metamaterial with nonreciprocal interactions, in which we observe experimentally the predicted bulk–edge correspondence, demonstrating its robustness. Our results open avenues for the field of non-Hermitian topology and for manipulating waves in unprecedented fashions.

The inclusion of non-Hermitian features in topological insulators has recently seen an explosion of activity. Exciting developments include tunable wave guides that are robust to disorder (1–3), structure-free systems (4, 5), and topological lasers and pumping (6–10). In these systems, active components are introduced to typically 1) break time-reversal symmetry to create topological insulators with unidirectional edge modes (1–5) and 2) pump topologically protected edge modes, thus harnessing Hermitian topology in non-Hermitian settings (6–9, 11). In parallel, extensive theoretical efforts have generalized the concept of a topological insulator to truly non-Hermitian phases that cannot be realized in Hermitian materials (12–14). However, such non-Hermitian topology and its bulk–edge correspondence remain a matter of intense debate. On the one hand, it has been argued that the usual bulk–edge correspondence breaks down in non-Hermitian settings, while on the other hand, new topological invariants specific to non-Hermitian systems have been proposed to capture particular properties of their edge modes (15–20).Here, focusing on a non-Hermitian version of the Su–Schrieffer–Heeger (SSH) model (15–17, 21) with an odd number of sites (Fig. 1A), we find that a change in the bulk non-Hermitian topological invariant is accompanied by a localization change in the zero-energy edge modes. This finding suggests the existence of a bulk–edge correspondence for this type of truly non-Hermitian topology. We further construct a mechanical analogue of the non-Hermitian quantum model (Fig. 1B) and create a mechanical metamaterial (Fig. 1C) in which we observe the predicted correspondence between the non-Hermitian topological invariant and the topological edge mode. In particular, we report that the edge mode in the non-Hermitian topological phase has a peculiar nature, as it is localized on the rigid rather than the floppy side of the mechanical metamaterial.Open in a separate windowFig. 1.Quantum-to-classical mapping of a chain with non-Hermitian topology. (A) An SSH chain with two sublattices, A (in red) and B (in blue), augmented with nonreciprocal variations in the hopping amplitudes (indicated by ±ε). (B) The nonreciprocal classical analog of the augmented SSH chain, in which the classical masses (in red) correspond to the A sites in the quantum model, while the nonreciprocal springs (in blue) are analogous to the B sites. (C) Picture of the mechanical metamaterial realizing the nonreciprocal classical analogue of the augmented SSH model.     

Trapping a cross-linked lysine–tryptophan radical in the catalytic cycle of the radical SAM enzyme SuiB

The radical S-adenosylmethionine (rSAM) enzyme SuiB catalyzes the formation of an unusual carbon–carbon bond between the sidechains of lysine (Lys) and tryptophan (Trp) in the biosynthesis of a ribosomal peptide natural product. Prior work on SuiB has suggested that the Lys–Trp cross-link is formed via radical electrophilic aromatic substitution (rEAS), in which an auxiliary [4Fe-4S] cluster (AuxI), bound in the SPASM domain of SuiB, carries out an essential oxidation reaction during turnover. Despite the prevalence of auxiliary clusters in over 165,000 rSAM enzymes, direct evidence for their catalytic role has not been reported. Here, we have used electron paramagnetic resonance (EPR) spectroscopy to dissect the SuiB mechanism. Our studies reveal substrate-dependent redox potential tuning of the AuxI cluster, constraining it to the oxidized [4Fe-4S]²⁺ state, which is active in catalysis. We further report the trapping and characterization of an unprecedented cross-linked Lys–Trp radical (Lys–Trp•) in addition to the organometallic Ω intermediate, providing compelling support for the proposed rEAS mechanism. Finally, we observe oxidation of the Lys–Trp• intermediate by the redox-tuned [4Fe-4S]²⁺ AuxI cluster by EPR spectroscopy. Our findings provide direct evidence for a role of a SPASM domain auxiliary cluster and consolidate rEAS as a mechanistic paradigm for rSAM enzyme-catalyzed carbon–carbon bond-forming reactions.

The radical S-adenosylmethionine (rSAM) enzyme superfamily is the largest known in nature, with over 570,000 annotated and predominantly uncharacterized members spanning all domains of life (1–4). The uniting feature of rSAM enzymes is a [4Fe-4S] cluster, usually bound by a CX₃CX₂C motif that catalyzes reductive cleavage of SAM to form L-Met and a strongly oxidizing 5′-deoxyadenosyl radical (5′-dA•) (5–7). Recent studies on a suite of rSAM enzymes have revealed the presence of a previously unknown organometallic intermediate in this process, termed Ω, in which the 5′-C of 5′-dA• is bound to the unique iron of the [4Fe-4S] cluster (Fig. 1A) (8, 9). Homolysis of the Fe–C bond ultimately liberates 5′-dA•, which abstracts a hydrogen atom from substrate to initiate a profoundly diverse set of chemical reactions in both primary and secondary metabolism, including DNA, cofactor, vitamin, and antibiotic biosynthesis (5, 10–13).Open in a separate windowFig. 1.(A) Accepted scheme for radical initiation in rSAM enzymes. (B) X-ray crystal structure of SuiB (PDB ID: 5V1T). The RS domain, SPASM domain, and RiPP recognition element are rendered blue, green, and pink, respectively. [4Fe-4S] clusters are shown as spheres with the distances separating them indicated. (C) Lys–Trp cross-link formation (20) catalyzed by SuiB. The carbon–carbon bond installed by SuiB is shown in red. (D and E) Previously proposed EAS (D) and rEAS (E) mechanisms for SuiB-catalyzed Lys–Trp cross-link formation.Of the 570,000 rSAM enzyme superfamily members, over a quarter (∼165,000 genes from the Enzyme Function Initiative-Enzyme Similarity Tool) possess C-terminal extensions, called SPASM and twitch domains, which bind auxiliary Fe-S clusters (4, 14–19). The SPASM domain typically binds two auxiliary Fe-S clusters and is named after the rSAM enzymes involved in the synthesis of subtilosin, pyrroloquinoline quinone, anaerobic sulfatase, and mycofactocin. The twitch domain is a truncated SPASM domain and only binds one auxiliary cluster (15). Despite the wide prevalence of these domains and the characterization of several different SPASM/twitch rSAM enzymes by spectroscopic and structural studies, direct evidence for their catalytic function(s) has remained elusive.We previously performed functional and structural characterization on the SPASM rSAM enzyme SuiB (Fig. 1B), which is involved in the biosynthesis of a ribosomal peptide natural product in human and mammalian microbiome streptococci (14, 20–22). SuiB introduces an unusual carbon–carbon bond onto its substrate peptide, SuiA, between the sidechains of Lys2 and Trp6 (Fig. 1C). The mechanism for this transformation is of broader relevance, as a number of enzymes, such as RrrB, PqqE, and MqnC (2, 23, 24), are known to join unactivated aliphatic and aromatic carbons to generate sp³-sp² cross-links. A general mechanistic paradigm for this class of transformations is not yet available. For SuiB, two pathways have been proposed (20), one through a typical electrophilic aromatic substitution (EAS) mechanism, which is involved in other enzyme-catalyzed indole modifications, such as indole prenylation or flavin adenine dinucleotide (FAD)-enzyme-dependent indole chlorination (25–27). In this pathway, the 5′-dA• generates an alkyl radical, which upon a second one-electron oxidation, creates an α,β-unsaturated amide electrophile with which the indole sidechain reacts via Michael addition (Fig. 1D). Lanthionine cross-links observed in diverse lanthipeptides are built by this general scheme, though via heterolytic chemistry, with Cys acting as the nucleophile (28, 29). Alternatively, a radical electrophilic aromatic substitution (rEAS) reaction has been proposed, wherein the alkyl radical, formed by 5′-dA•, would react with the indole sidechain to generate a radical σ complex, a cross-linked Lys–Trp radical (Lys–Trp•), which upon oxidation and rearomatization would yield product (Fig. 1E). In both mechanisms, AuxI is proposed as an oxidant. Although this role for an rSAM auxiliary cluster has been previously suggested (30, 31), it has yet to be directly demonstrated experimentally. Mechanistic studies have favored the rEAS pathway (20); however, intermediates in the reaction of SuiB and enzymes that catalyze similar reactions have not yet been detected (15).In the current work, we sought to differentiate between the proposed mechanisms by trapping intermediates in the catalytic cycle of SuiB and characterizing them using electron paramagnetic resonance (EPR) spectroscopy. We report observation of three transient reaction intermediates, most importantly the sought-after Lys–Trp•, which is fundamentally different from previously characterized Trp radicals, as it is cross-linked and carries an indole tetrahedral center. We also provide evidence for AuxI as the oxidant of the Lys–Trp• intermediate as well as insights into redox potential changes of Fe-S clusters in SuiB that accompany SuiA binding. Together, our findings support the rEAS pathway for formation of the sp³-sp² cross-link and carry important implications for other enzymes that catalyze related transformations.     

A new sea-level record for the Neogene/Quaternary boundary reveals transition to a more stable East Antarctic Ice Sheet

Sea-level rise resulting from the instability of polar continental ice sheets represents a major socioeconomic hazard arising from anthropogenic warming, but the response of the largest component of Earth’s cryosphere, the East Antarctic Ice Sheet (EAIS), to global warming is poorly understood. Here we present a detailed record of North Atlantic deep-ocean temperature, global sea-level, and ice-volume change for ∼2.75 to 2.4 Ma ago, when atmospheric partial pressure of carbon dioxide (pCO₂) ranged from present-day (>400 parts per million volume, ppmv) to preindustrial (<280 ppmv) values. Our data reveal clear glacial–interglacial cycles in global ice volume and sea level largely driven by the growth and decay of ice sheets in the Northern Hemisphere. Yet, sea-level values during Marine Isotope Stage (MIS) 101 (∼2.55 Ma) also signal substantial melting of the EAIS, and peak sea levels during MIS G7 (∼2.75 Ma) and, perhaps, MIS G1 (∼2.63 Ma) are also suggestive of EAIS instability. During the succeeding glacial–interglacial cycles (MIS 100 to 95), sea levels were distinctly lower than before, strongly suggesting a link between greater stability of the EAIS and increased land-ice volumes in the Northern Hemisphere. We propose that lower sea levels driven by ice-sheet growth in the Northern Hemisphere decreased EAIS susceptibility to ocean melting. Our findings have implications for future EAIS vulnerability to a rapidly warming world.

The instability of polar continental ice sheets in a warmer future is an issue of major societal concern (1–5). Based on linear extrapolation of recent sea-level rise (2), mean global sea level could increase by 65 ± 12 cm by 2100 relative to the 2005 baseline, consistent with Intergovernmental Panel on Climate Change projections (1) of a ∼30- to 100-cm increase by 2100. Further, satellite observations (4) document substantial mass loss of both the Greenland Ice Sheet (GIS) and the West Antarctic Ice Sheet (WAIS) over the past decade—the two ice sheets that are most susceptible to global warming because of rapidly rising Arctic air temperatures (1) (GIS) and vulnerability to ocean-atmospheric warming (5, 6) (WAIS). The mass balance of the much larger EAIS and its contribution to ongoing sea-level change, however, remain poorly constrained (1).The role of atmospheric partial pressure of carbon dioxide (pCO₂) as a driver of long-term changes in ice volume and sea level over the Cenozoic Era (past ∼66 My) is widely documented (7–9) and there is compelling evidence (6, 10–12) of East Antarctic Ice Sheet (EAIS) retreat during warm intervals of the Pliocene epoch between ∼5.3 and 3.3 Ma when pCO₂ levels (13, 14) last reached values close to the present day (∼400 parts per million volume [ppmv]; Fig. 1 A and B and see SI Appendix, section S1). However, there is disagreement over EAIS behavior under pCO₂ levels (13) similar to those of preindustrial Quaternary times (<280 ppmv). A compilation of marine geochemical paleo-sea-level and pCO₂ records suggests that the EAIS was stable under these conditions (7). In contrast, while the amplitudes of change are controversial (15) (SI Appendix, section S2), sea-level reconstructions from paleoshorelines (16) and benthic geochemical data (9, 17, 18) (Fig. 2) imply EAIS melting during the Quaternary “super-interglacials” of Marine Isotope Stage (MIS) 11 (∼400 ka) and 31 (∼1.07 Ma) under relatively low pCO₂ conditions. Supporting evidence for EAIS retreat during the most recent “super-interglacial” MIS 11 comes from isotope measurements in mineral deposits recording past changes in subglacial East Antarctic waters (19), as well as records of ice-rafted debris (IRD) and detrital sediment neodymium isotopes from offshore the Wilkes Subglacial Basin (20). The latter records (20) also indicate EAIS retreat during the last interglacial MIS 5e (∼120 ka). Melting of the EAIS as inferred in the late Quaternary was likely driven by ocean–atmosphere warming around Antarctica and grounding-line retreat in response to ice–ocean interactions (19, 20).Open in a separate windowFig. 1.Neogene to Quaternary climate and sea-level evolution. (A) LR04 stack (21) for the past 5 My; arrow indicates the iNHG (∼3.6 to 2.4 Ma) and its culmination (thick-arrowed interval) (22); green line indicates the benthic δ¹⁸O level associated with MIS 101. (B) Atmospheric pCO₂ estimates of refs. 13 (blue) and 23 (purple) for the past 5 My; the late Quaternary glacial–interglacial pCO₂ range (1) is indicated as preindustrial pCO₂ band. Yellow shading in A and B highlights the study interval (∼2.75 to 2.4 Ma). (C and D) Site U1313 benthic δ¹⁸O and Mg/Ca raw data, respectively. (E) Site U1313 deep-sea temperature. (F) Site U1313 δ¹⁸O_sw-based sea level relative to present (black line); blue shading: 95% probability interval from Monte Carlo simulations (2σ); red line: threshold (11.6 msle) above which a smaller-than-present EAIS is signaled (24–26); m = marine part of EAIS, t = terrestrial part of EAIS. Glacials are highlighted in gray.Open in a separate windowFig. 2.Implication of different sea-level-δ¹⁸O_sw conversions for estimates of interglacial ice-volume loss. y axis shows lower-than-modern δ¹⁸O_sw values (∆δ¹⁸O_sw) and the x axis (log-scale) the corresponding sea-level increase for commonly used conversion factors (27–29) (0.011 [black], 0.010 [purple], and 0.008 ‰⋅m⁻¹ [red]) and those for Antarctica only (11) (0.014 ‰⋅m⁻¹) ignoring (yellow) and incorporating (brown) the impact of its marine-based ice sheets. Stars mark ∆δ¹⁸O_sw for interglacials of this study and corresponding sea-level equivalents in dependence of the conversion applied. Orange, blue, and purple diamonds show the same for MIS 31, 11 (18), and 5e (17), respectively. Vertical lines indicate the sea-level increase resulting from complete melting of the GIS (+7.3 m), WAIS (+4.3 m), and EAIS (+53.3 m) (24–26).To further investigate past EAIS response to climate forcing we studied the Neogene/Quaternary transition when mean pCO₂ (13, 23) fell from levels similar to the anthropogenically perturbed values of today into the Quaternary range, leading to progressive high-latitude cooling and the intensification of Northern Hemisphere Glaciation (21, 30–33) (iNHG; Fig. 1 A and B). Our approach is based on a simple approximation that, once estimated past global sea level exceeds 11.6 m sea-level equivalent (msle) above modern, which corresponds to the complete melting of the present-day GIS [7.3 msle (24, 25)] and the marine- and land-based WAIS [3.4 and 0.9 msle (25, 26), respectively], EAIS instability (i.e., a retreat from its present-day size) can be inferred (see SI Appendix, section S4.1 for details). We quantified sea-level and ice-volume changes for the interval ∼2.75 to 2.4 Ma (MIS G7 to 95) by measuring the oxygen-isotope composition (δ¹⁸O) and Mg/Ca ratio in well-preserved benthic foraminiferal calcite (Oridorsalis umbonatus) from Integrated Ocean Drilling Program (IODP) Site U1313 [41°0′N, 32°57′W; 3,426-m water depth (34)] in the North Atlantic Ocean (Fig. 1 C and D). Using this approach we reconstructed changes in seawater δ¹⁸O (δ¹⁸O_sw), a proxy for global sea level and continental ice volume (35). This was done by 1) calculating bottom-water temperatures (BWT) derived from Mg/Ca (36) (Fig. 1E), 2) combining Mg/Ca-derived BWTs with δ¹⁸O to determine δ¹⁸O_sw (37) (Fig. 1F), and 3) converting δ¹⁸O_sw to sea level using a relationship between changes in sea level and δ¹⁸O_sw of 0.011 ‰⋅m⁻¹ (27) (Materials and Methods and Fig. 1F). Ninety-five percent probability intervals calculated through Monte Carlo simulations for individual sea-level data points yield an average uncertainty for our sea-level estimates of ± 28 m (∼2σ [SD]) (Materials and Methods and Fig. 1F), roughly equivalent to the decay/growth of ice four times greater than the GIS. Our approach was validated by reconstructing δ¹⁸O_sw for the recent (∼0 to 7 ka) at IODP Site U1313 and for late Holocene core-top (multicorer) samples from a neighboring site (MSM58) which are indistinguishable from the observed modern-day values (see Materials and Methods and SI Appendix, section S4.2.8 for details).     

Heading perception depends on time-varying evolution of optic flow

There is considerable support for the hypothesis that perception of heading in the presence of rotation is mediated by instantaneous optic flow. This hypothesis, however, has never been tested. We introduce a method, termed “nonvarying phase motion,” for generating a stimulus that conveys a single instantaneous optic flow field, even though the stimulus is presented for an extended period of time. In this experiment, observers viewed stimulus videos and performed a forced-choice heading discrimination task. For nonvarying phase motion, observers made large errors in heading judgments. This suggests that instantaneous optic flow is insufficient for heading perception in the presence of rotation. These errors were mostly eliminated when the velocity of phase motion was varied over time to convey the evolving sequence of optic flow fields corresponding to a particular heading. This demonstrates that heading perception in the presence of rotation relies on the time-varying evolution of optic flow. We hypothesize that the visual system accurately computes heading, despite rotation, based on optic acceleration, the temporal derivative of optic flow.

James Gibson first remarked that the instantaneous motion of points on the retina (Fig. 1A) can be formally described as a two-dimensional (2D) field of velocity vectors called the “optic flow field” (or “optic flow”) (1). Such optic flow, caused by an observer’s movement relative to the environment, conveys information about self-motion and the structure of the visual scene (1–15). When an observer translates in a given direction along a straight path, the optic flow field radiates from a point in the image with zero velocity, or singularity, called the focus of expansion (Fig. 1B). It is well known that under such conditions, one can accurately estimate one’s “heading” (i.e., instantaneous direction of translation in retinocentric coordinates) by simply locating the focus of expansion (SI Appendix). However, if there is angular rotation in addition to translation (by moving along a curved path or by a head or eye movement), the singularity in the optic flow field will be displaced such that it no longer corresponds to the true heading (Fig. 1 C and D). In this case, if one estimates heading by locating the singularity, the estimate will be biased away from the true heading. This is known as the rotation problem (14).Open in a separate windowFig. 1.Projective geometry, the rotation problem, time-varying optic flow, and the optic acceleration hypothesis. (A) Viewer-centered coordinate frame and perspective projection. Because of motion between the viewpoint and the scene, a 3D surface point traverses a path in 3D space. Under perspective projection, the 3D path of this point projects onto a 2D path in the image plane (retina), the temporal derivative of which is called image velocity. The 2D velocities associated with all visible points define a dense 2D vector field called the optic flow field. (B–D) Illustration of the rotation problem. (B) Optic flow for pure translation (1.5-m/s translation speed, 0° heading, i.e., heading in the direction of gaze). Optic flow singularity (red circle) corresponds to heading (purple circle). (C) Pure rotation, for illustrative purposes only and not corresponding to any experimental condition (2°/s rightward rotation). (D) Translation + rotation (1.5 m/s translation speed, 0° heading, 2°/s rightward rotation). Optic flow singularity (red circle) is displaced away from heading (purple circle). (E) Three frames from a video depicting movement along a circular path with the line-of-sight initially perpendicular to a single fronto-parallel plane composed of black dots. (F) Time-varying evolution of optic flow. The first optic flow field reflects image motion between the first and second frames of the video. The second optic flow field reflects image motion between the second and third frames of the video. For this special case (circular path), the optic flow field evolves (and the optic flow singularity drifts) only due to the changing depth of the environment relative to the viewpoint. (G) Illustration of the optic acceleration hypothesis. Optic acceleration is the derivative of optic flow over time (here, approximated as the difference between the second and first optic flow fields). The singularity of the optic acceleration field corresponds to the heading direction. Acceleration vectors autoscaled for visibility.Computer vision researchers and vision scientists have developed a variety of algorithms that accurately and precisely extract observer translation and rotation from optic flow, thereby solving the rotation problem. Nearly all of these rely on instantaneous optic flow (i.e., a single optic flow field) (4, 9, 16–25) with few exceptions (26–29). However, it is unknown whether these algorithms are commensurate with the neural computations underlying heading perception.The consensus of opinion in the experimental literature is that human observers can estimate heading (30, 31) from instantaneous optic flow, in the absence of additional information (5, 10, 15, 32–34). Even so, there are reports of systematic biases in heading perception (11); the visual consequences of rotation (eye, head, and body) can bias heading judgments (10, 15, 35–37), with the amount of bias typically proportional to the magnitude of rotation. Other visual factors, such as stereo cues (38, 39), depth structure (8, 10, 40–43), and field of view (FOV) (33, 42–44) can modulate the strength of these biases. Errors in heading judgments have been reported to be greater when eye (35–37, 45, 46) or head movements (37) are simulated versus when they are real, which has been taken to mean that observers require extraretinal information, although there is also evidence to the contrary (10, 15, 33, 40, 41, 44, 47–50). Regardless, to date no one has tested whether heading perception (even with these biases) is based on instantaneous optic flow or on the information available in how the optic flow field evolves over time. Some have suggested that heading estimates rely on information accumulated over time (32, 44, 51), but no one has investigated the role of time-varying optic flow without confounding it with stimulus duration (i.e., the duration of evidence accumulation).In this study, we employed an application of an image processing technique that ensured that only a single optic flow field was available to observers, even though the stimulus was presented for an extended period of time. We called this condition “nonvarying phase motion” or “nonvarying”: The phases of two component gratings comprising each stationary stimulus patch shifted over time at a constant rate, causing a percept of motion in the absence of veridical movement (52). Phase motion also eliminated other cues that may otherwise have been used for heading judgments, including image point trajectories (15, 32) and their spatial compositions (i.e., looming) (53, 54). For nonvarying phase motion, observers exhibited large biases in heading judgments in the presence of rotation. A second condition, “time-varying phase motion,” or “time-varying,” included acceleration by varying the velocity of phase motion over time to match the evolution of a sequence of optic flow fields. Doing so allowed observers to compensate for the confounding effect of rotation on optic flow, making heading perception nearly veridical. This demonstrates that heading perception in the presence of rotation relies on the time-varying evolution of optic flow.     

Developmental influence on evolutionary rates and the origin of placental mammal tooth complexity

Development has often been viewed as a constraining force on morphological adaptation, but its precise influence, especially on evolutionary rates, is poorly understood. Placental mammals provide a classic example of adaptive radiation, but the debate around rate and drivers of early placental evolution remains contentious. A hallmark of early dental evolution in many placental lineages was a transition from a triangular upper molar to a more complex upper molar with a rectangular cusp pattern better specialized for crushing. To examine how development influenced this transition, we simulated dental evolution on “landscapes” built from different parameters of a computational model of tooth morphogenesis. Among the parameters examined, we find that increases in the number of enamel knots, the developmental precursors of the tooth cusps, were primarily influenced by increased self-regulation of the molecular activator (activation), whereas the pattern of knots resulted from changes in both activation and biases in tooth bud growth. In simulations, increased activation facilitated accelerated evolutionary increases in knot number, creating a lateral knot arrangement that evolved at least ten times on placental upper molars. Relatively small increases in activation, superimposed on an ancestral tritubercular molar growth pattern, could recreate key changes leading to a rectangular upper molar cusp pattern. Tinkering with tooth bud geometry varied the way cusps initiated along the posterolingual molar margin, suggesting that small spatial variations in ancestral molar growth may have influenced how placental lineages acquired a hypocone cusp. We suggest that development could have enabled relatively fast higher-level divergence of the placental molar dentition.

Whether developmental processes bias or constrain morphological adaptation is a long-standing question in evolutionary biology (1–4). Many of the distinctive features of a species derive from pattern formation processes that establish the position and number of anatomical structures (5). If developmental processes like pattern formation are biased toward generating only particular kinds of variation, adaptive radiations may often be directed along developmental–genetic “lines of least resistance” (2, 4, 6, 7). Generally, the evolutionary consequences of this developmental bias have been considered largely in terms of how it might influence the pattern of character evolution (e.g., refs. 1, 2, 8–10). But development could also influence evolutionary rates by controlling how much variation is accessible to natural selection in a given generation (11).For mammals, the dentition is often the only morphological system linking living and extinct species (12). Correspondingly, tooth morphology plays a crucial role in elucidating evolutionary relationships, time calibrating phylogenetic trees, and reconstructing adaptive responses to past environmental change (e.g., refs. 13–15). One of the most pervasive features of dental evolution among mammals is an increase in the complexity of the tooth occlusal surface, primarily through the addition of new tooth cusps (16, 17). These increases in tooth complexity are functionally and ecologically significant because they enable more efficient mechanical breakdown of lower-quality foods like plant leaves (18).Placental mammals are the most diverse extant mammalian group, comprising more than 6,000 living species spread across 19 extant orders, and this taxonomic diversity is reflected in their range of tooth shapes and dietary ecologies (12). Many extant placental orders, especially those with omnivorous or herbivorous ecologies (e.g., artiodactyls, proboscideans, rodents, and primates), convergently evolved a rectangular upper molar cusp pattern from a placental ancestor with a more triangular cusp pattern (19–21). This resulted from separate additions in each lineage of a novel posterolingual cusp, the "hypocone'''' [sensu (19)], to the tritubercular upper molar (Fig. 1), either through modification of a posterolingual cingulum (“true” hypocone) or another posterolingual structure, like a metaconule (pseudohypocone) (19). The fossil record suggests that many of the basic steps in the origin of this rectangular cusp pattern occurred during an enigmatic early diversification window associated with the divergence and early radiation of several placental orders (20, 21; Fig. 1). However, there remains debate about the rate and pattern of early placental divergence (22–24). On the one hand, most molecular phylogenies suggest that higher-level placental divergence occurred largely during the Late Cretaceous (25, 26), whereas other molecular phylogenies and paleontological analyses suggest more rapid divergence near the Cretaceous–Paleogene (K–Pg) boundary (21, 24, 27–29). Most studies agree that ecological opportunity created in the aftermath of the K–Pg extinction probably played an important role in ecomorphological diversification within the placental orders (30, 31). But exactly how early placentals acquired the innovations needed to capitalize on ecological opportunity remains unclear. Dental innovations, especially those which facilitated increases in tooth complexity, may have been important because they would have promoted expansion into plant-based dietary ecologies left largely vacant after the K–Pg extinction event (32).Open in a separate windowFig. 1.Placental mammal lineages separately evolved complex upper molar teeth with a rectangular cusp pattern composed of two lateral pairs of cusps from a common ancestor with a simpler, triangular cusp pattern. Many early relatives of the extant placental orders, such as Eritherium, possessed a hypocone cusp and a more rectangular primary cusp pattern. Examples of complex upper molars are the following: Proboscidea, the gomphothere Anancus; Rodentia, the wood mouse Apodemus; and Artiodactyla, the suid Nyanzachoerus.Mammalian tooth cusps form primarily during the “cap” and “bell” stage of dental development, when signaling centers called enamel knots establish the future sites of cusp formation within the inner dental epithelium (33, 34). The enamel knots secrete molecules that promote proliferation and changes in cell–cell adhesion, which facilitates invagination of the dental epithelium into an underlying layer of mesenchymal cells (34, 35). Although a range of genes are involved in tooth cusp patterning (36–38), the basic dynamics can be effectively modeled using reaction–diffusion models with just three diffusible morphogens: an activator, an inhibitor, and a growth factor (39–41). Candidate activator genes in mammalian tooth development include Bmp4, Activin A, Fgf20, and Wnt genes, whereas potential inhibitors include Shh and Sostdc, and Fgf4 and Bmp2 have been hypothesized to act as growth factors (38, 40–43). In computer models of tooth development, activator molecules up-regulated in the underlying mesenchyme stimulate differentiation of overlying epithelium into nondividing enamel knot cells. These in turn secrete molecules that inhibit further differentiation of epithelium into knot cells, while also promoting cell proliferation that creates the topographic relief of the cusp (40). Although many molecular, cellular, and physical processes have the potential to influence cusp formation, and thereby tooth complexity (35, 37), parameters that control the strength and conductance of the activator and inhibitor signals, the core components of the reaction–diffusion cusp patterning mechanism (39, 40) are likely to be especially important.Here, we integrate a previous computer model of tooth morphogenesis called ToothMaker (41), with simulations of trait evolution and data from the fossil record (Fig. 2), to examine the developmental origins of tooth complexity in placental mammals. Specifically, we ask the following: 1) What developmental processes can influence how many cusps form? 2) How might these developmental processes influence the evolution of tooth cusp number, especially rates? And 3) what developmental changes may have been important in the origins of the fourth upper molar cusp, the hypocone, in placental mammal evolution?Open in a separate windowFig. 2.Workflow for simulations of tooth complexity evolution. (A) Tooth shape is varied for five signaling and growth parameters in ToothMaker. (B) From an ancestral state, each parameter is varied in 2.5% increments up to a maximum of ± 50% of the ancestral state. (C) Tooth complexity and enamel knot (EK) pattern were quantified for each parameter combination. Tooth complexity was measured using cusp number/EK number and OPC. ToothMaker and placental upper second molars were classified into categories based on EK/cusp pattern. (D) The parameter space was populated with pattern and tooth complexity datums to build a developmental landscape. (E) Tooth complexity evolution was simulated on each developmental landscape. (F) Resulting diversity and pattern of tooth complexity was compared with placental mammal molar diversity.     

A solution to a sex ratio puzzle in Melittobia wasps

The puzzling sex ratio behavior of Melittobia wasps has long posed one of the greatest questions in the field of sex allocation. Laboratory experiments have found that, in contrast to the predictions of theory and the behavior of numerous other organisms, Melittobia females do not produce fewer female-biased offspring sex ratios when more females lay eggs on a patch. We solve this puzzle by showing that, in nature, females of Melittobia australica have a sophisticated sex ratio behavior, in which their strategy also depends on whether they have dispersed from the patch where they emerged. When females have not dispersed, they lay eggs with close relatives, which keeps local mate competition high even with multiple females, and therefore, they are selected to produce consistently female-biased sex ratios. Laboratory experiments mimic these conditions. In contrast, when females disperse, they interact with nonrelatives, and thus adjust their sex ratio depending on the number of females laying eggs. Consequently, females appear to use dispersal status as an indirect cue of relatedness and whether they should adjust their sex ratio in response to the number of females laying eggs on the patch.

Sex allocation has produced many of the greatest success stories in the study of social behaviors (1–4). Time and time again, relatively simple theory has explained variation in how individuals allocate resources to male and female reproduction. Hamilton’s local mate competition (LMC) theory predicts that when n diploid females lay eggs on a patch and the offspring mate before the females disperse, the evolutionary stable proportion of male offspring (sex ratio) is (n − 1)/2n (Fig. 1) (5). A female-biased sex ratio is favored to reduce competition between sons (brothers) for mates and to provide more mates (daughters) for those sons (6–8). Consistent with this prediction, females of >40 species produce female-biased sex ratios and reduce this female bias when multiple females lay eggs on the same patch (higher n; Fig. 1) (9). The fit of data to theory is so good that the sex ratio under LMC has been exploited as a “model trait” to study the factors that can constrain “perfect adaptation” (4, 10–13).Open in a separate windowFig. 1.LMC. The sex ratio (proportion of sons) is plotted versus the number of females laying eggs on a patch. The bright green dashed line shows the LMC theory prediction for the haplodiploid species (5, 39). A more female-biased sex ratio is favored in haplodiploids because inbreeding increases the relative relatedness of mothers to their daughters (7, 32). Females of many species adjust their offspring sex ratio as predicted by theory, such as the parasitoid Nasonia vitripennis (green diamonds) (82). In contrast, the females of several Melittobia species, such as M. australica, continue to produce extremely female-biased sex ratios, irrespective of the number of females laying eggs on a patch (blue squares) (15).In stark contrast, the sex ratio behavior of Melittobia wasps has long been seen as one of the greatest problems for the field of sex allocation (3, 4, 14–21). The life cycle of Melittobia wasps matches the assumptions of Hamilton’s LMC theory (5, 15, 19, 21). Females lay eggs in the larvae or pupae of solitary wasps and bees, and then after emergence, female offspring mate with the short-winged males, who do not disperse. However, laboratory experiments on four Melittobia species have found that females lay extremely female-biased sex ratios (1 to 5% males) and that these extremely female-biased sex ratios change little with increasing number of females laying eggs on a patch (higher n; Fig. 1) (15, 17–20, 22). A number of hypotheses to explain this lack of sex ratio adjustment have been investigated and rejected, including sex ratio distorters, sex differential mortality, asymmetrical male competition, and reciprocal cooperation (15–18, 20, 22–26).We tested whether Melittobia’s unusual sex ratio behavior can be explained by females being related to the other females laying eggs on the same patch. After mating, some females disperse to find new patches, while some may stay at the natal patch to lay eggs on previously unexploited hosts (Fig. 2). If females do not disperse, they can be related to the other females laying eggs on the same host (27–31). If females laying eggs on a host are related, this increases the extent to which relatives are competing for mates and so can favor an even more female-biased sex ratio (28, 32–35). Although most parasitoid species appear unable to directly assess relatedness, dispersal behavior could provide an indirect cue of whether females are with close relatives (36–38). Consequently, we predict that when females do not disperse and so are more likely to be with closer relatives, they should maintain extremely female-biased sex ratios, even when multiple females lay eggs on a patch (28, 35).Open in a separate windowFig. 2.Host nest and dispersal manners of Melittobia. (A) Photograph of the prepupae of the leaf-cutter bee C. sculpturalis nested in a bamboo cane and (B) a diagram showing two ways that Melittobia females find new hosts. The mothers of C. sculpturalis build nursing nests with pine resin consisting of individual cells in which their offspring develop. If Melittobia wasps parasitize a host in a cell, female offspring that mate with males inside the cell find a different host on the same patch (bamboo cane) or disperse by flying to other patches.We tested whether the sex ratio of Melittobia australica can be explained by dispersal status in a natural population. We examined how the sex ratio produced by females varies with the number of females laying eggs on a patch and whether or not they have dispersed before laying eggs. To match our data to the predictions of theory, we developed a mathematical model tailored to the unique population structure of Melittobia, where dispersal can be a cue of relatedness. We then conducted a laboratory experiment to test whether Melittobia females are able to directly access the relatedness to other females and adjust their sex ratio behavior accordingly. Our results suggest that females are adjusting their sex ratio in response to both the number of females laying eggs on a patch and their relatedness to the other females. However, relatedness is assessed indirectly by whether or not they have dispersed. Consequently, the solution to the puzzling behavior reflects a more-refined sex ratio strategy.