A switch to feeding on cycads generates parallel accelerated evolution of toxin tolerance in two clades of Eumaeus caterpillars (Lepidoptera: Lycaenidae)

We assembled a complete reference genome of Eumaeus atala, an aposematic cycad-eating hairstreak butterfly that suffered near extinction in the United States in the last century. Based on an analysis of genomic sequences of Eumaeus and 19 representative genera, the closest relatives of Eumaeus are Theorema and Mithras. We report natural history information for Eumaeus, Theorema, and Mithras. Using genomic sequences for each species of Eumaeus, Theorema, and Mithras (and three outgroups), we trace the evolution of cycad feeding, coloration, gregarious behavior, and other traits. The switch to feeding on cycads and to conspicuous coloration was accompanied by little genomic change. Soon after its origin, Eumaeus split into two fast evolving lineages, instead of forming a clump of close relatives in the phylogenetic tree. Significant overlap of the fast evolving proteins in both clades indicates parallel evolution. The functions of the fast evolving proteins suggest that the caterpillars developed tolerance to cycad toxins with a range of mechanisms including autophagy of damaged cells, removal of cell debris by macrophages, and more active cell proliferation.

The genus Eumaeus Hübner (Lycaenidae, Theclinae) arguably contains the most aposematically colored caterpillars and butterflies among the ∼4,000 Lycaenidae in the world (1–6). The brilliant red and gold gregarious caterpillars (Fig. 1) sequester cycasin from the leaves of their cycad food plants (Zamiaceae), which deters predators (3–9). Other secondary metabolites in cycads (e.g., 10‒11) may also deter predators. Eumaeus adults have a bright orange-red abdomen and an orange-red hindwing spot (except for one species) (Fig. 2). Blue and green iridescent markings are especially conspicuous on a black ground color. Eumaeus adults are among the largest lycaenids and have more rounded wings and a slower, more gliding flight than most Theclinae (1). Cycads are among the most primitive extant seed-plants (9), and the “plethora of aposematic attributes suggests a very ancient association between Eumaeus and the cycad host plants” (3).Open in a separate windowFig. 1.Caterpillars and pupae of Theorema eumenia (Top) and Eumaeus godartii (Bottom) in Costa Rica. Clockwise from Upper Left, second or third instar (length, ∼13 mm), fourth (final) instar (∼20 mm), pupa (∼18 mm), pupa (∼24 mm), fourth (final) instar (∼27 mm), second or third instar (∼20 mm). (Images from authors W.H. and D.H.J.).Open in a separate windowFig. 2.Adult wing uppersides and undersides. Eumaeus childrenae (two Upper Left images), E. atala (two Upper Right images), Theorema eumenia (two Lower Left images), and Mithras nautes (two Lower Right images). Scale bar, 1 cm.Eumaeus has been classified as a separate family (12–14), a genus in the Riodinidae (15‒16), or a monotypic subfamily or tribe of the Lycaenidae (17–20). Alternatively, others called it a typical member of the Neotropical Lycaenidae (21‒22). The evolutionary question behind this discordant taxonomic history is whether Eumaeus is a phylogenetically isolated lineage long associated with cycads (3) or an embedded clade in which a recent food plant shift to cycads resulted in the rapid evolution of aposematism. Recent molecular evidence for a limited number of taxa suggested the latter (23). To answer this question definitively, we analyzed genomic sequences of Eumaeus and its relatives.To trace the evolution of cycad feeding, we report the caterpillar food plants of the genera most closely related to Eumaeus and illustrate their immature stages (Fig. 1 and SI Appendix). This natural history information combined with analyses of genome sequences is the foundation for investigating the subsequent evolutionary impact on the Eumaeus genome of the switch to eating cycads.     

From the Cover: Summer warmth of the past six interglacials on Greenland

The relative warmth of mid-to-late Pleistocene interglacials on Greenland has remained unknown, leading to debates about the regional climate forcing that caused past retreat of the Greenland Ice Sheet (GrIS). We analyze the hydrogen isotopic composition of terrestrial biomarkers in Labrador Sea sediments through interglacials of the past 600,000 y to infer millennial-scale summer warmth on southern Greenland. Here, we reconstruct exceptionally warm summers in Marine Isotope Stage (MIS) 5e, concurrent with strong Northern Hemisphere summer insolation. In contrast, “superinterglacial” MIS11 demonstrated only moderate warmth, sustained throughout a prolonged interval of elevated atmospheric carbon dioxide. Strong inferred GrIS retreat during MIS11 relative to MIS5e suggests an indirect relationship between maximum summer temperature and cumulative interglacial mass loss, indicating strong GrIS sensitivity to duration of regional warmth and elevated atmospheric carbon dioxide.

The Greenland Ice Sheet (GrIS) is projected to contribute between +5 and +33 cm to global sea level by 2100 CE under continued strong anthropogenic forcing (1). Significant uncertainty in projections results, in part, from a lack of constraints on the regional terrestrial climate changes causing past large-scale ice sheet mass loss (2, 3). Extensive retreat of the GrIS likely occurred most recently during Marine Isotope Stage (MIS) 11 (ca. 425 to 375 thousand years before present [ka]), indicated by evidence of coniferous forest cover in southern Greenland coincident with a cessation in the delivery of glacially eroded silts to the Labrador Sea (4, 5) (Figs. 1 and ​and2).2). Curiously, Northern Hemisphere summer insolation and atmospheric carbon dioxide (CO₂) forcing were lower during MIS11 than other Pleistocene interglacials through which continental-scale ice persisted on Greenland. For example, the Last Interglacial (MIS5e) (ca. 130 to 115 ka) was associated with stronger Northern Hemisphere summer insolation and briefly higher atmospheric CO₂ concentrations (6, 7). Yet basal sections of seven ice cores contain ice deposited during MIS5e (8), suggesting ice was present on much of the island within this stage.Open in a separate windowFig. 1.Map of study region. Location of Eirik Drift core sites (black point), including Ocean Drilling Program Site 646 used in this study. Dotted lines separate silt provenances as in Fig. 2H (5, 12). White is the modern glacier extent. Solid lines are the modern schematic surface ocean currents: the West Greenland Current (WGC), Baffin Island Current (BIC), and Irminger Current (IC). The dashed line is the Deep Western Boundary Undercurrent (WBUC). Points are the Greenland ice cores (27), with ice dated to peak (dark red) or late (light red) MIS5e (28–31) and Holocene δ²H_C28 records (yellow) (17, 24). The inset map is of Lake El’Gygytgyn (Lake E) (9), Arctic Ocean core HLY-06 (10), and the Faroe Islands (FI) (25).Open in a separate windowFig. 2.Interglacial records from MIS13 to MIS1. Datasets are plotted on their own age scales and not synchronized, except those from the same sites. Formally defined MIS and peak substage (22) (as in Fig. 4) are shaded. (A) June 21st insolation at 65°N (6). (B) Atmospheric CO₂ concentration (7). (C) Site 646 δ²H_C28 (this study). Analytical error is smaller than point size (SI Appendix). (D) Site 646 δ²H_C28 as an anomaly relative to the last millennium. (E) Global benthic (blue) and Site 646 planktic foraminifera δ¹⁸O (black) (4, 22). (F) Stable carbon isotopes (δ¹³C, ‰ VPDB [Vienna Pee Dee Belemnite]) of Cibicidoides wuellerstorfi from U1305 (34). (G) SSTs from U1305 (summer: black and red error envelope; winter: black and blue error envelope) (11) and Site 646 (summer: red; winter: blue) (4). (H) Provenance of MD99-2227 silts as in Fig. 1 (5, 12). (I) Mean temperature of the warmest month (MTWM) from Lake El’Gygytgyn (9). (J) Site 646 pollen concentrations (4).Sparse paleoclimate evidence suggests that Arctic climate responded nonlinearly to global-scale forcings during past interglacials. For example, MIS11 was one of a few Pleistocene “superinterglacials” identified in the eastern Arctic, with inferred summer air temperatures 4 to 5 °C higher than the current interglacial (MIS1, the Holocene, 11.7 to 0 ka) (9) (Fig. 2I). Outstanding Arctic warmth during MIS11 is supported by ostracod assemblages in the Arctic Ocean, indicating summer sea surface temperatures (SSTs) 8 to 10 °C higher than modern (10). Yet regional Arctic temperatures likely differed; summer Labrador SSTs were cooler during MIS11 than MIS1 or MIS5e (4, 11) (Fig. 2G). Terrestrial climate on Greenland, where summer air temperature directly influences ice sheet mass balance, remains unconstrained by geologic evidence throughout most Pleistocene interglacials older than MIS5e, including MIS11.     

The role of lateral erosion in the evolution of nondendritic drainage networks to dendricity and the persistence of dynamic networks

Dendritic, i.e., tree-like, river networks are ubiquitous features on Earth’s landscapes; however, how and why river networks organize themselves into this form are incompletely understood. A branching pattern has been argued to be an optimal state. Therefore, we should expect models of river evolution to drastically reorganize (suboptimal) purely nondendritic networks into (more optimal) dendritic networks. To date, current physically based models of river basin evolution are incapable of achieving this result without substantial allogenic forcing. Here, we present a model that does indeed accomplish massive drainage reorganization. The key feature in our model is basin-wide lateral incision of bedrock channels. The addition of this submodel allows for channels to laterally migrate, which generates river capture events and drainage migration. An important factor in the model that dictates the rate and frequency of drainage network reorganization is the ratio of two parameters, the lateral and vertical rock erodibility constants. In addition, our model is unique from others because its simulations approach a dynamic steady state. At a dynamic steady state, drainage networks persistently reorganize instead of approaching a stable configuration. Our model results suggest that lateral bedrock incision processes can drive major drainage reorganization and explain apparent long-lived transience in landscapes on Earth.

What should a drainage network look like? Fig. 1A shows a single channel, winding its way through the catchment so as to have access to water and sediment from unchannelized zones in the same manner as the dendritic (tree-like) network of Fig. 1B. It appears straightforward that the dendritic pattern is a model for nature, and the single channel is not. Dendritic drainage networks are called such because of their similarity to branching trees, and their patterns are “characterized by irregular branching in all directions” (1) with “tributaries joining at acute angles” (2). Drainage networks can also take on other forms such as parallel, pinnate, rectangular, and trellis in nature (2). However, drainage networks in their most basic form without topographic, lithologic, and tectonic constraints should tend toward a dendritic form (2). In addition, drainage networks that take a branching, tree-like form have been argued to be “optimal channel networks” that minimize total energy dissipation (3, 4). Therefore, we would expect that models simulating river network formation, named landscape evolution models (LEMs), that use the nondendritic pattern of Fig. 1A as an initial condition to massively reorganize and approach the dendritic steady state of Fig. 1B. To date, no numerical LEM has shown the ability to do this. Here, we present a LEM that can indeed accomplish such a reorganization. A corollary of this ability is the result that landscapes approach a dynamic, rather than static steady state.Open in a separate windowFig. 1.Schematic diagram of a nondendritic and a dendritic drainage network. This figure shows the Wolman Run Basin in Baltimore County, MD (A) drained by a single channel winding across the topography and (B) drained by a dendritic network of channels. Both networks have similar drainage densities (53, 54), but there is a stark difference between their stream ordering (53–56). This figure invites discussion as to how a drainage system might evolve from the configuration of A to that of B.There is indeed debate as to whether landscapes tend toward an equilibrium that is frozen or highly dynamic (5). Hack (6) hypothesized that erosional landscapes attain a steady state where “all elements of the topography are downwasting at the same rate.” This hypothesis has been tested in numerical models and small-scale experiments. Researchers found that numerical LEMs create static topographies (7, 8). In this state, erosion and uplift are in balance in all locations in the landscape, resulting in landscapes that are dissected by stable drainage networks in geometric equilibrium (9). The landscape has achieved geometric equilibrium in planform when a proxy for steady-state river elevation, named χ (10), has equal values across all drainage divides. In contrast, experimental landscapes (7, 11) develop drainage networks that persistently reorganize. Recent research on field landscapes suggests that drainage divides migrate until reaching geometric equilibrium (9), but other field-based research suggests that landscapes may never attain geometric equilibrium (12).The dynamism of the equilibrium state determines the persistence of initial conditions in experimental and model landscapes. It is important to understand initial condition effects (13) to better constrain uncertainty in LEM predictions. Kwang and Parker (7) demonstrate that numerical LEMs exhibit “extreme memory,” where small topographic perturbations in initial conditions are amplified and preserved during a landscape’s evolution (Fig. 2A). Extreme memory in the numerical models is closely related to the feasible optimality phenomenon found within the research on optimal channel networks (4). These researchers suggest that nature’s search for the most “stable” river network configuration is “myopic” and unable to find configurations that completely ignore their initial condition. In contrast to numerical models, experimental landscapes (7, 11) reach a highly dynamic state where all traces of initial surface conditions are erased by drainage network reorganization. It has been hypothesized that lateral erosion processes are responsible for drainage network reorganization in landscapes (7, 14); these processes are not included in most LEMs.Open in a separate windowFig. 2.A comparison of LEM-woLE (A) and LEM-wLE (B). Both models utilize the same initial condition, i.e., an initially flat topography with an embedded sinusoidal channel (1.27 m deep) without added topographic perturbations. Without perturbations, the landscape produces angular tributaries that are attached to the main sinusoidal channel (compare with SI Appendix, Fig. S7). Here, LEM-wLE quickly shreds the signal of the initial condition over time, removing the angular tributaries. By 10 RUs eroded the sinusoidal signal is mostly erased. After 100 RUs, the drainage network continues to reorganize itself (i.e., dynamic steady state). The landscape continues to reorganize as shown in Movies S1.Most widely used LEMs simulate incision into bedrock solely in the vertical direction. However, there is growing recognition that bedrock channels also shape the landscape by incising laterally (15, 16). Lateral migration into bedrock is important for the creation of strath terraces (17, 18) and the morphology of wide bedrock valleys (19–21). Recently, Langston and Tucker (22) developed a formulation for lateral bedrock erosion in LEMs. Here, we implement their submodel to explore the long-term behavior of LEMs that incorporate lateral erosion.The LEM submodel of Langston and Tucker (22) allows for channels to migrate laterally. By including this autogenic mechanism, we hypothesize that lateral bedrock erosion creates instabilities that 1) shred (23) the memory of initial conditions such as the unrealistic configurations of Fig. 1A and 2) produce landscapes that achieve a statistical steady state instead of a static one. By incorporating the lateral incision component (22) into a LEM, we aim to answer the following: 1) What controls the rate of decay of signals from initial conditions? 2) What are the frequency and magnitude of drainage reorganization in an equilibrium landscape? 3) What roles do model boundary conditions play in landscape reorganization?     

Drivers of fatal bird collisions in an urban center

Millions of nocturnally migrating birds die each year from collisions with built structures, especially brightly illuminated buildings and communication towers. Reducing this source of mortality requires knowledge of important behavioral, meteorological, and anthropogenic factors, yet we lack an understanding of the interacting roles of migration, artificial lighting, and weather conditions in causing fatal bird collisions. Using two decades of collision surveys and concurrent weather and migration measures, we model numbers of collisions occurring at a large urban building in Chicago. We find that the magnitude of nocturnal bird migration, building light output, and wind conditions are the most important predictors of fatal collisions. The greatest mortality occurred when the building was brightly lit during large nocturnal migration events and when winds concentrated birds along the Chicago lakeshore. We estimate that halving lighted window area decreases collision counts by 11× in spring and 6× in fall. Bird mortality could be reduced by ∼60% at this site by decreasing lighted window area to minimum levels historically recorded. Our study provides strong support for a relationship between nocturnal migration magnitude and urban bird mortality, mediated by light pollution and local atmospheric conditions. Although our research focuses on a single site, our findings have global implications for reducing or eliminating a critically important cause of bird mortality.

North America has lost nearly one-third of its birdlife in the last half-century, with migratory species experiencing particularly acute declines (1). Fatal collisions with built structures represent a major source of direct, human-caused bird mortality across North America, second only to predation by domestic cats (2). Estimates indicate that between 365 million and 988 million birds die annually in collisions with buildings in the United States, with another 16 million to 42 million annual deaths in Canada (2, 3). Birds may collide with glass windows because they reflect the surrounding environment or allow birds to perceive a seemingly open pathway to the interior of the building (4). For the billions of birds that migrate at night, outdoor lighting (e.g., streetlights and floodlights) and interior lighting from buildings may be disorienting and draw birds into built-up areas, at high risk to collide with infrastructure (5–8). Light pollution not only alters nocturnal migratory behavior on a large scale (5, 7), but is also an acute conservation concern. Nocturnal collisions with well-lit communication towers alone are estimated to kill appreciable percentages of the populations of sensitive species (9).Avian collisions with lighted structures have been documented in the scientific literature as early as the 19th century (10–12). In recent decades, this link between collisions and light pollution has been the subject of detailed investigation (8, 13–16). Observers of bird–building collisions and tower kills have long remarked on the apparent influence of meteorological factors such as cloud ceiling, fog, frontal passage, and abrupt changes in conditions, all of which have been associated with large mortality events (10, 13, 17–24). Steady-burning lights may be particularly hazardous (25). Due to high building density and intensity of artificial lighting, cities are of particular concern. Reports of mass collisions at lighted buildings in urban areas are frequent in both the popular and scientific press (13, 19–21, 26).Understanding, predicting, and preventing collision mortality are areas of active scientific inquiry and priorities for policymakers (1, 13). Collisions occur more frequently during migration seasons and impact numerous species of migratory birds (29), and recent work suggests that nocturnal migratory movements can be useful for predicting bird–window collisions (30). Lights-out programs, which encourage the public to extinguish outdoor lighting to protect migratory birds, are receiving increasing attention (13). The act of extinguishing lighting allows birds to immediately return to normal, safe behavior (7) and reduces mortality at lighted buildings (13). Presently, advisories are generally issued for a given time period (e.g., peak migration periods) or on specific nights when weather conditions are favorable for large migratory movements [e.g., using migration forecasting, (31, 32)].Here, we integrate meteorological, migration-intensity, and window-radiance data to understand how these factors interact to cause bird collisions. We use a 21-y dataset of fatal collisions recorded at a single large building (McCormick Place Lakeside Center) in Chicago, IL (Fig. 1), to understand the behavioral, environmental, and anthropogenic drivers of these mortality events. Chicago poses the greatest potential risk from light pollution to migrating birds of all cities in the United States (33), and over 40,000 dead birds have been recovered from McCormick Place alone since 1978 (Figs. 2 and ​and3).3). Since 2000, we have recorded the number of birds and the lighting status of each window bay during dawn collision monitoring. Nocturnal lighting at McCormick Place correlates positively with bird collisions in many songbird species (34), but this association has not been quantified in the context of other important factors, including migration intensity and weather conditions. We estimate the effect of window lighting on collision counts and assess how the intensity of nocturnal bird migration mediates this relationship. We also test whether wind and weather conditions may magnify these associations. Finally, we investigate the spatiotemporal scales at which weather and migration data best explain collision mortality, identifying the times of night and areas of airspace associated with these events.Open in a separate windowFig. 1.Location of McCormick Place along the Chicago lakefront. The Lakeside Center building monitored in this study is highlighted in red in a three-dimensional rendering.Open in a separate windowFig. 2.Summary of collisions recorded at McCormick Place and regional bird migration between 2000 and 2020. (Upper) Individual years are drawn in different colors. Dates are given for mortality events totaling more than 50 birds. Pie charts show the family (fam.) composition of collected birds, with families representing less than 5% of total collisions merged into a single “other” category. (Lower) Summed annual migration passage at the KLOT radar in estimated number of individual birds (years colored). (Lower, Inset) Summed seasonal passage totals in estimated number of birds crossing a 75-km transect, with each point representing a year. Estimates are based on methods from ref. 35.Open in a separate windowFig. 3.Recorded collisions by year and window lighting. (A) Collisions recorded at McCormick Place between 1982 and 2020 for spring (light gray) and fall (dark gray) seasons. Horizontal lines with numeric labels show average seasonal collision totals before and after the window-lighting regime changed from fully lighted to partially lighted in 1999. The year 1997 is not shown because construction limited access to the site during that year. (B) Mean recorded daily collisions by window-lighting status from 2000 to 2020.     

Volitional learning promotes theta phase coding in the human hippocampus

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4–12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3–8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.

Volitionally controlled—or “active”—learning has become a crucial topic in education, psychology, and neuroscience (1, 2). Behavioral studies show that memory benefits from voluntary action (3–5), putatively through a distinct modulation of attention, motivation, and cognitive control (2, 6). While these functions depend on widespread frontoparietal networks (7), a critical role of the hippocampus in coordinating volitional learning has been demonstrated in both humans (8) and rodents (9) (for a review see ref. 10). However, the mechanisms by which volition improves learning and memory are not well understood. Rodent recordings suggest that hippocampal theta oscillations (usually occurring between 4 and 12 Hz) might play a critical role, because they increase during voluntary movement (11) and active sensing (12). Consistently, human studies have shown volition-related theta power increases, although in a lower frequency range (typically between 3 and 8 Hz), during navigation in virtual (13, 14) and physical (15, 16) environments. It is believed that theta oscillations facilitate mnemonic processing by providing a temporal framework for the organization of stimulus-related neural codes (17). This is observed in the phenomenon of phase precession, where spatial locations represented by place cells in the rodent hippocampus are sequentially reactivated at distinct phases of theta oscillations (18). A similar phase coding mechanism underlies the representation of possible future scenarios in rats performing a spatial decision-making task, with early and late hippocampal theta phases representing current and prospective scenarios, respectively (19). It has been proposed (17) that these forms of neural phase coding support a range of cognitive processes, including multi-item working memory (20), episodic memory (21, 22), and mental time travel (23). In humans, this proposal has received empirical support from phase-amplitude coupling studies looking at the relationship between the amplitude of high-frequency activity and the phase of activity at a lower frequency, in particular theta (24–26). However, these analyses are agnostic to the specific content that is coupled to the theta phase and thus do not reflect “phase coding” in the narrower sense. Recent studies used multivariate analysis techniques to identify stimulus-specific representational signals at the high temporal resolution provided by human intracranial electroencephalography data (iEEG, see refs. 27, 28 for review). These analyses demonstrated the relevance of theta oscillations for hippocampal reinstatement of item-context associations (29), for the orchestration of content-specific representations of goal locations (30), and for word-object associations (31). However, it is unclear whether this mechanism is recruited when learning is volitionally controlled.Building on these empirical findings and methodological advances, we aimed to elucidate whether the improved memory performance typically observed in human active learning paradigms can be traced back to a hippocampal theta phase code. In particular, we hypothesized that during active learning, this theta phase code organizes and structures stimulus-specific memory representations. We analyzed electrophysiological activity from the hippocampus and widespread neocortical regions in epilepsy patients (n = 13, age = 33.5 ± 9.32) implanted with iEEG electrodes (total number of electrodes = 392; Fig. 1F) who performed a virtual reality (VR)-based navigation and memory task. Subjects navigated in a square virtual arena (Fig. 1A) and were asked to remember images of specific objects presented at distinct spatial locations indicated by red “boxes” located on the ground (Fig. 1B). Images were only visible when participants visited the red boxes and were hidden otherwise. Navigation occurred under two conditions: active (A) and passive (P) (Fig. 1B). In the active condition, participants could freely control their movements in visiting the stimulus sites while in the passive condition, they were exposed to the navigation path and order of image presentation generated by another participant (yoked design; Fig. 1 C and D). At the end of the experiment, the recognition memory for both the actively and passively learned items was tested (Fig. 1E). We predicted that active learning would enhance memory by promoting hippocampal theta phase coding of stimulus-specific memory representations.Open in a separate windowFig. 1.Experimental procedure, electrode implantation, and behavioral results. (A) Participants studied images presented at specific locations, indicated by red boxes located on the ground, in a square virtual environment (here shown from a bird’s eye perspective). (B) Stimulus presentation during the encoding phase of the experiment as seen by a participant. (C) Schematic timeline showing the main blocks of the experiment (A = active, P = passive, counterbalanced). (D) Detailed timeline of an example-encoding block. Participants freely determined the timings and materials of study in the active condition and were exposed to the trajectory of a different subject in the passive condition. (E) Timeline of the experiment at retrieval. (F) All electrodes included in the analyses (n = 392, MNI space), color coded by participant identity. (G) Receiver operating characteristic (ROC) curves for each subject (gray) and grand average (red). (H) Proportion of correct items for all stimuli as a function of confidence. (I) Proportion of remembered items (Left) and of high-confidence remembered items (Right) for active and passive conditions. *P < 0.05; ***P < 0.001.     

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.     

Hematite reconstruction of Late Triassic hydroclimate over the Colorado Plateau

Hematite is the most abundant surficial iron oxide on Earth resulting from near-surface processes that make it important for addressing numerous geologic problems. While red beds have proved to be excellent paleomagnetic recorders, the early diagenetic origin of hematite in these units is often questioned. Here, we validate pigmentary hematite (“pigmentite”) as a proxy indicator for the Late Triassic environment and its penecontemporaneous origin by analyzing spectrophotometric measurements of a 14.5-My–long red bed sequence in scientific drill core CPCP-PFNP13-1A of the Chinle Formation, Arizona. Pigmentite concentrations in the red beds track the evolving pattern of the Late Triassic monsoon and indicate a long-term rise in aridity beginning at ∼215 Ma followed by increased oscillatory climate change at ∼213 Ma. These monsoonal changes are attributed to the northward drift of the Colorado Plateau as part of Laurentia into the arid subtropics during a time of fluctuating CO₂. Our results refine the record of the Late Triassic monsoon and indicate significant changes in rainfall proximal to the Adamanian–Revueltian biotic transition that thus may have contributed to apparent faunal and floral events at 216 to 213 Ma.

Hematite is usually represented by two phases in rocks, sediments, and soils. One is specularite, with opaque, relatively large, and commonly euhedral crystals. The other is a fine-grained, often poorly crystalline phase that provides most of hematite’s characteristic color. Both are capable of carrying a magnetic remanence, yet each has a separate characteristic magnetic coercivity and thermal unblocking spectra (1, 2). Specularite crystals typically yield a red streak on porcelain plates that cause the mechanical breakdown of the sample into small particles: grains with diameters less than 100 nm are generally orange, those of about 500 nm are red, and sizes around 1,500 nm appear dark purple (3). Visible light reflectance increases and wavelengths get shorter as hematite grain size becomes smaller, with changes from yellow-red to blue-red accompanying increasing diameter (4). The magnetic behavior of iron oxides is viscous/unstable with decreasing grain size, and thus, ultrafine (<100 nm) hematite is perhaps unreliable for paleomagnetic studies (5, 6).Understanding the origins of these minerals and their timing of formation within geologic units has been an ongoing pursuit, and the study of North American red beds has been instrumental to such inquiries (7–10). Some research has long suggested that red bed color has had a protracted geologic history and hematite was formed considerably after the deposition of the host rock (11). The usefulness of hematite in red beds as a paleoclimate indicator at the time of deposition, or as a carrier of a penecontemporaneous record of the Earth’s magnetic field, is thus impaired if the hematite formed much later in time through diagenetic alteration (12). Similarly, red bed paleomagnetic remanence has been argued to have been acquired through a chemical remanent magnetization that postdates deposition by millions or tens of millions of years (13, 14). However, Triassic–Jurassic hematite-bearing red shales, mudstones, and siltstones (as well as magnetite-bearing gray-to-black shales) of the Newark Basin record magnetozones that represent the polarity of the geomagnetic field close to the time of deposition, as shown by internal comparisons of magnetizations and 20-ky–level cyclostratigraphy (15). This study along with others suggests that thermal unblocking patterns of coercivity spectra demonstrate the contribution of detrital specularite to the characteristic remanence, whereas the red pigment phase has been shown to carry a paleomagnetic overprint (1, 2, 7, 9, 16). This has supported the idea that the colorization of the red beds derives from a secondary diagenetic product, formed sometime after the specularite acquired characteristic remanence. Newark Basin–wide lake cycles and polarity magnetozones occur in consistent mutual stratigraphic relationships at different sites separated by 4 to 42 km and crosscut red-gray color boundaries (17) reflecting specular hematite versus magnetite remanence (15). Recent tests of these interpretations using hematite-bearing red beds of the Chinle Formation further support a very early acquisition of the characteristic magnetizations by the specularite (18, 19). However, color changes of the Newark Basin red beds have been correlated with sedimentary and moisture cycles that were caused by orbital climate forcing of the Late Triassic monsoon (8). This insinuates that at least some of the pigmentary hematite is indeed ancient and may provide a record of how hydrated a past soil environment was, relating to rainfall.Red beds of the Chinle Formation of the Colorado Plateau in Arizona (Fig. 1) are one of the most geographically exposed Late Triassic terrestrial sedimentary archives of western Pangaea. These deposits preserve the Adamanian–Revueltian biotic turnover, involving the largest magnitude faunal and floral change on land during all but the final part of the Late Triassic (20, 21). Although magneto-chronostratigraphic evidence constrains the Adamanian–Revueltian turnover to 216 through 213 Ma (19), its cause and rapidity is still open to debate (22–24).Open in a separate windowFig. 1.(A) Pangaean geography according to a 220-Ma mean composite paleopole (56) with the Chinle Formation (CPCP, Colorado Plateau Coring Project) and the Newark Basin (NBCP, Newark Basin Coring Project) indicated by filled circles connected by arrows to their relative positions at 200 Ma by open circles (25). (B) Lithostratigraphic unit names, predominant colors, and thickness of the Chinle Formation in PFNP-core 1A (18, 19, 25). (C) DRS results showing changes of the position of the characteristic absorption band for hematite. Note the very good agreement between the red (purple) colors of the core and wavelengths <550 nm (>550 nm).We studied mudstones of the Chinle Formation in scientific drill CPCP-PFNP13-1A (hereafter PFNP-core 1A) (Fig. 1B) drilled at Chinde Point of the Petrified Forest National Park of Arizona in the United States (25). Chinle mudstones are overprinted with paleosols and thought to have been developed within a fluvial floodplain depositional environment (22, 25, 26). We acquired diffuse reflectance spectroscopy (DRS) measurements from these paleosol-bearing mudstones to evaluate the controls on hematite production within the ancient soils. Hematite variations registered to the core chronostratigraphy provide details on the timing of monsoonal rainfall changes that can be compared with other records to reconstruct the environmental history of Late Triassic age strata of the Colorado Plateau (Fig. 2).Open in a separate windowFig. 2.(A) Magneto-chronostratigraphy of the Chinle Formation in PFNP‐core 1A (18, 19, 25). BFB is the stratigraphic position of the Black Forest Bed dated within the core (18). (B) Second derivative values at ∼535 to 570 nm of the intensity of hematite reflectance measured using DRS (this study). The thick red line is a six-point moving average of the (red circles) measured values. Hematite concentrations increase (more arid) toward the right. The green dashed line is the interval correlative to the Adamanian–Revueltian (A–R) biotic change (19, 24). The orange star is the date of the Manicouagan impact crater (MIC) (59, 60). (C) Outcrop data of variations in mean annual rainfall (millimeters) from the CIA–K transfer function, with radioisotopic zircon dates for the BFB and volcaniclastic horizons in the upper Sonsela Member (22, 26). (D) Hematite data stretched to time using the two accumulation rate intervals of ref. (19), with 1 being ∼30 m/My and 2 being ∼10 m/My. (E) Mean annual rainfall stretched to time as in D. (F) Chile magnetostratigraphy stretched to time as in D. (G) Late Triassic and Early Jurassic atmospheric pCO₂ (57). The ± range bracketing the pCO2 estimates is approximated using Monte Carlo simulation with the red trend line placed by the eye (57). (H) Newark–Hartford astrochronostratigraphic polarity time scale (APTS) (17, 56). The gray shading indicates the magnetostratigraphic interval (chrons E14 and E15) that constrains the drawdown of pCO₂ at ∼215 to 212 Ma. The gray lines indicate the interval of the Chinle magnetostratigraphy correlative to the Newark–Hartford APTS.     

From the Cover: Neurophysiological coordination of duet singing

Coordination of behavior for cooperative performances often relies on linkages mediated by sensory cues exchanged between participants. How neurophysiological responses to sensory information affect motor programs to coordinate behavior between individuals is not known. We investigated how plain-tailed wrens (Pheugopedius euophrys) use acoustic feedback to coordinate extraordinary duet performances in which females and males rapidly take turns singing. We made simultaneous neurophysiological recordings in a song control area “HVC” in pairs of singing wrens at a field site in Ecuador. HVC is a premotor area that integrates auditory feedback and is necessary for song production. We found that spiking activity of HVC neurons in each sex increased for production of its own syllables. In contrast, hearing sensory feedback produced by the bird’s partner decreased HVC activity during duet singing, potentially coordinating HVC premotor activity in each bird through inhibition. When birds sang alone, HVC neurons in females but not males were inhibited by hearing the partner bird. When birds were anesthetized with urethane, which antagonizes GABAergic (γ-aminobutyric acid) transmission, HVC neurons were excited rather than inhibited, suggesting a role for GABA in the coordination of duet singing. These data suggest that HVC integrates information across partners during duets and that rapid turn taking may be mediated, in part, by inhibition.

Animals routinely rely on sensory feedback for the control of their own behavior. In cooperative performances, such sensory feedback can include cues produced by other participants (1–8). For example, in interactive vocal communication, including human speech, individuals take turns vocalizing. This “turn taking” is a consequence of each participant responding to auditory cues from a partner (4–6, 9, 10). The role of such “heterogenous” (other-generated) feedback in the control of vocal turn taking and other cooperative performances is largely unknown.Plain-tailed wrens (Pheugopedius euophrys) are neotropical songbirds that cooperate to produce extraordinary duet performances but also sing by themselves (Fig. 1A) (4, 10, 11). Singing in plain-tailed wrens is performed by both females and males and used for territorial defense and other functions, including mate guarding and attraction (1, 11–16). During duets, female and male plain-tailed wrens take turns, alternating syllables at a rate of between 2 and 5 Hz (Fig. 1A) (4, 11).Open in a separate windowFig. 1.Neural control of solo and duet singing in plain-tailed wrens. (A) Spectrogram of a singing bout that included male solo syllables (blue line, top) followed by a duet. Solo syllables for both sexes (only male solo syllables are shown here) are sung at lower amplitudes than syllables produced in duets. Note that the smeared appearance of wren syllables in spectrograms reflects the acoustic structure of plain-tailed wren singing. (B and C) Each bird has a motor system that is used to produce song and sensory systems that mediate feedback. (B) During solo singing, the bird hears its own song, which is known as autogenous feedback (orange). (C) During duet singing, each bird hears both its own singing and the singing of its partner, known as heterogenous feedback (green). The key difference between solo and duet singing is heterogenous feedback that couples the neural systems of the two birds. This coupling results in changes in syllable amplitude and timing in both birds.There is a categorical difference between solo and duet singing. In solo singing, the singing bird receives only autogenous (hearing its own vocalization) feedback (Fig. 1B). The partner may hear the solo song if it is nearby, a heterogenous (other-generated) cue. In duet singing, birds receive both heterogenous and autogenous feedback as they alternate syllable production (Fig. 1C). Participants use heterogenous feedback during duet singing for precise timing of syllable production (4, 11). For example, when a male temporarily stops participating in a duet, the duration of intersyllable intervals between female syllables increases (4), showing an effect of heterogenous feedback on the timing of syllable production.How does the brain of each wren integrate heterogenous acoustic cues to coordinate the precise timing of syllable production between individuals during duet performances? To address this question, we examined neurophysiological activity in HVC, a nucleus in the nidopallium [an analogue of mammalian cortex (17, 18)]. HVC is necessary for song learning, production, and timing in species of songbirds that do not perform duets (19–24). Neurons in HVC are active during singing and respond to playback of the bird’s own learned song (25–27). In addition, recent work has shown that HVC is also involved in vocal turn taking (19).To examine the role of heterogenous feedback in the control of duet performances, we compared neurophysiological activity in HVC when female or male wrens sang solo syllables with syllables sung during duets. Neurophysiological recordings were made in awake and anesthetized pairs of wrens at the Yanayacu Biological Station and Center for Creative Studies on the slopes of the Antisana volcano in Ecuador. We found that heterogenous cues inhibited HVC activity during duet performances in both females and males, but inhibition was only observed in females during solo singing.     

Interplays of electron and nuclear motions along CO dissociation trajectory in myoglobin revealed by ultrafast X-rays and quantum dynamics calculations

Ultrafast structural dynamics with different spatial and temporal scales were investigated during photodissociation of carbon monoxide (CO) from iron(II)–heme in bovine myoglobin during the first 3 ps following laser excitation. We used simultaneous X-ray transient absorption (XTA) spectroscopy and X-ray transient solution scattering (XSS) at an X-ray free electron laser source with a time resolution of 80 fs. Kinetic traces at different characteristic X-ray energies were collected to give a global picture of the multistep pathway in the photodissociation of CO from heme. In order to extract the reaction coordinates along different directions of the CO departure, XTA data were collected with parallel and perpendicular relative polarizations of the laser pump and X-ray probe pulse to isolate the contributions of electronic spin state transition, bond breaking, and heme macrocycle nuclear relaxation. The time evolution of the iron K-edge X-ray absorption near edge structure (XANES) features along the two major photochemical reaction coordinates, i.e., the iron(II)–CO bond elongation and the heme macrocycle doming relaxation were modeled by time-dependent density functional theory calculations. Combined results from the experiments and computations reveal insight into interplays between the nuclear and electronic structural dynamics along the CO photodissociation trajectory. Time-resolved small-angle X-ray scattering data during the same process are also simultaneously collected, which show that the local CO dissociation causes a protein quake propagating on different spatial and temporal scales. These studies are important for understanding gas transport and protein deligation processes and shed light on the interplay of active site conformational changes and large-scale protein reorganization.

Enzymatic functions frequently involve local motions at the active site as well as large-amplitude motions of the protein, and the two are often strongly correlated. Many chemical processes at the active sites take place as a result of the interplay between atomic movement and electronic structural changes in response to external stimuli such as light, ligand binding, heat or electric field. While reaction kinetics can be predicted from thermodynamic properties, the intrinsic time scales for fundamental chemical events, such as bond breakage and formation, are often unresolved due to challenges in examining rapid electronic and atomic movements in real time. Advanced X-ray sources, especially those with intense photon bursts within the time scale of fundamental chemical events (i.e., femtoseconds), enable structural characterization in terms of the electronic and atomic motions. Combining such ultrashort X-ray pulses with laser excitation, we are able to detect the interplay of ultrafast electron and nuclear motions in the photodissociation of an axial CO ligand from the iron center in the heme site of myoglobin (Mb) (Fig. 1). The same process has been extensively studied due to numerous functions of heme or other iron porphyrins in hemoproteins, including electron transfer, catalytic oxidation or reduction of metabolites, neutralization of damaging reactive species, and famously the binding of diatomics such as dioxygen, carbon monoxide, and nitric oxide for transportation and sensing (1–6). Because the dissociation of diatomic ligands, such as CO and NO, can be synchronized through optical excitation of the porphyrin, diatomic ligand binding in hemoproteins is amenable to scrutiny by dynamic structural and electronic spectroscopies (1, 7–14). Several X-ray diffraction, solution scattering, and X-ray spectroscopy (including X-ray absorption and emission) studies have been carried out using intense X-ray pulses from synchrotron and X-ray free electron laser sources (11, 14–19). In this report, we focus on the correlations between the electronic structural change of the iron center and these nuclear motions. To investigate these correlations, we used X-ray transient absorption spectroscopy/scattering and theoretical calculations to detect and project detailed trajectories for the CO departure from Fe(II) in the heme site of bovine Mb.Open in a separate windowFig. 1.(A) Mb structure with heme in pink surrounded by helices of the protein. (B) Mb active site structural changes following CO photolysis. Upon photoexcitation, ground state MbCO (green) loses its bond to CO and adopts a square pyramidal structure with His93 (pink), resulting in the doming of the porphyrin where the Fe (red) comes out of the plane of the macrocycle. Structures are from photolysed MbCO trapped at low temperature (12) and ground state (9) MbCO, where their crystal structures are aligned by their respective porphyrin carbons.In carbonmonoxymyoglobin (MbCO), the low-spin (LS) Fe(II) center has a pseudo-octahedral coordination geometry, ligated with four nitrogens (N_p) from the heme, the nitrogen of an axial histidine (N_His, His93), and CO, a strong field ligand. Previous studies have pointed out that upon excitation of the heme Soret or Q band, photolysis occurs within ∼50 fs, although there is an ongoing debate about the mechanism of CO photodissociation and the subsequent relaxation of the heme, as well as the possible role of intermediate spin states, similar to those observed in photoexcited iron Tris(bipyridine) (20) and ferrous cytochrome c (14, 15, 21). With the loss of CO, the LS state of Fe(II) transforms to a high-spin (HS) state and adopts square-pyramidal pentacoordination with the axial histidine His93 moving ∼0.3 Å out of the porphyrin plane, perturbing the position of the alpha helix in which it sits (Fig. 1B) (4, 8, 22, 23). Protein control of this movement is critical, both because it is the first step of the mechanism of cooperativity in hemoglobin O₂ binding and because it may lead to a conformational rearrangement of the heme pocket that allows CO to escape and avoid geminate recombination (24).This CO photodissociation from MbCO, as well as the photolysis of other diatomics such as NO, and the subsequent recombination dynamics have been assessed using X-ray transient absorption (XTA) spectroscopy (Fig. 2) at synchrotron sources with ∼100-ps time resolution (19, 25), using Fe K-edge X-ray absorption near edge structure (XANES) spectral features shown in Fig. 2. The main differences between the spectral features of MbCO and Mb are an edge shift to a lower energy and a preedge conversion from two sharp peaks to one broader and weaker peak. These changes are consistent with loss of CO and a conversion of Fe(II) from LS to HS in Mb, as supported by optical and vibrational spectroscopic studies (8, 26).Open in a separate windowFig. 2.Fe K-edge XANES and difference spectra measured after CO photodissociation with 100 ps time resolution (19). Energies selected for measurement of polarization-dependent dynamics are marked in dashed vertical cyan lines: line 1, 7.112 keV, the depletion of the ground state transition of 1s → 3d_z2, 3d_x2-y2 character; line 2, 7.115 keV, the disappearance of the preedge peak associated with the CO back bonding antibonding orbital; line 3, 7.118 keV, the rising edge shoulder that appears in MbFe(II); line 4, 7.123 keV, the edge shift; and line 5, 7.172 keV, an EXAFS energy where changes are purely based on changes in the local geometry.While heme vibrational cooling was observed by time-resolved Raman techniques on the time scales of a few to tens of ps (26, 27), and optical transient absorption spectroscopy shows the development of broad excited state absorption features with lifetimes of ∼300 fs and 3 ps, there is an active discussion in the literature as to whether these features can be assigned to an excited-state evolution through a series of electronic intermediate states/species (28–31) or to an exclusively vibrational relaxation pathway (31–33). Because Fe K-edge XTA is sensitive to both the heme iron electronic configuration and the local structural geometry during heme relaxation, measurements of the XANES should distinguish between these mechanisms but only if very fast time scales are resolvable. In this regard, XTA at Linac Coherent Light Source (LCLS) provides a rare opportunity to investigate these relaxation processes with a technique with both high temporal and structural resolution.Although the photodissociation of CO from heme and the concomitant LS to HS transition and heme doming motion are well-known phenomena, many fundamental transformations in terms of electronic and nuclear motions that result in the CO departure are not well understood. The recent works on cytochtome c and NO-bound myoglobin have made progress in the timing of the spin state transitions on the femtosecond time scale and the identification of the intermediate spin state (14, 15). However, it is far from clear how the spin state change was induced electronically, how the iron spin state is related to the Fe–CO distance, and when the heme doming takes place as the Fe–CO distance elongates in dissociation processes. Understanding these correlations has important implications for other chemical and enzymatic processes involving ligand dissociation. It is therefore of great interest to link dynamic structural and electronic changes at the heme during ligand dissociation to more large-scale conformational changes, especially on the time scale of ligand departure and heme doming, which is expected to take place on tens of femtoseconds to a few picoseconds time scales.In order to address the dynamic interplay between electronic and nuclear motions that are beyond the Born–Oppenheimer approximation, we carried out combined XTA and X-ray transient scattering measurements during CO photodissociation with sub–100-fs time resolution at the X-ray Pump-Probe (XPP) facility of the LCLS. The kinetics of the spin state transition and the nuclear motion associated with doming, as well as the global motions of the protein matrix, have revealed a series of correlated events as CO departs from the heme. Although the exact trajectory of CO departure in terms of Fe–CO distance and other structural parameters is still difficult to resolve, such processes can be simulated via quantum mechanical calculations to model this process for the Fe(II) center and the ligands directly bound to the metal. The results provide the energetics of different excited states as well as their trajectories as functions of local structural changes, such as Fe–CO distance and heme relaxation, to distinguish the effect of different structural factors on the overall structural dynamics. These calculations also allow us to predict XTA features without and with the structural relaxation of the heme, providing insight into the interplays between the electronic spin state/configuration and corresponding nuclear motions as a function of the Fe–CO distance.     

INAUGURAL ARTICLE by a Recently Elected Academy Member:The 2018 reawakening and eruption dynamics of Steamboat Geyser,the world’s tallest active geyser

Steamboat Geyser in Yellowstone National Park’s Norris Geyser Basin began a prolific sequence of eruptions in March 2018 after 34 y of sporadic activity. We analyze a wide range of datasets to explore triggering mechanisms for Steamboat’s reactivation and controls on eruption intervals and height. Prior to Steamboat’s renewed activity, Norris Geyser Basin experienced uplift, a slight increase in radiant temperature, and increased regional seismicity, which may indicate that magmatic processes promoted reactivation. However, because the geothermal reservoir temperature did not change, no other dormant geysers became active, and previous periods with greater seismic moment release did not reawaken Steamboat, the reason for reactivation remains ambiguous. Eruption intervals since 2018 (3.16 to 35.45 d) modulate seasonally, with shorter intervals in the summer. Abnormally long intervals coincide with weakening of a shallow seismic source in the geyser basin’s hydrothermal system. We find no relation between interval and erupted volume, implying unsteady heat and mass discharge. Finally, using data from geysers worldwide, we find a correlation between eruption height and inferred depth to the shallow reservoir supplying water to eruptions. Steamboat is taller because water is stored deeper there than at other geysers, and, hence, more energy is available to power the eruptions.

On 15 March 2018, Steamboat Geyser in Yellowstone National Park’s Norris Geyser Basin (Fig. 1; refs. 1, 2) had its first major eruption following a 3.5-y dormancy. Since then, Steamboat has erupted 109 times as of 31 July 2020—already a greater number than any previous active phase on record (Fig. 2). Its eruption plumes can reach heights that exceed 115 m (2, 3), which is currently taller than any other geyser worldwide. Steamboat’s new active phase, thus, drew significant public attention and widespread press coverage. The renewed eruptions highlighted some fundamental open questions about intermittent natural processes that result from localized input of energy and mass and, more specifically, on geyser dynamics (4):

• Why did Steamboat become active again?
• What processes or thermodynamic conditions control the interval between its eruptions?
• Why are Steamboat’s eruptions tall compared to other geysers’?

Open in a separate windowFig. 1.Geologic map of Norris Geyser Basin and its surrounding area (after refs. 1, 2). Locations of key geysers, thermal springs, streams, seismic station YNM, and boreholes Y12, C2, and Y9 are shown on the map. Inset map shows the location of Norris Geyser Basin relative to the Yellowstone Caldera, boundaries of Yellowstone National Park, GPS station NRWY, relevant USGS streamgages, and the region of seismicity analyzed in this work.Open in a separate windowFig. 2.Steamboat’s major eruptions in the GeyserTimes database as of 31 July 2020, excluding one eruption in 1911. We note that this database undercounts eruptions in the 1960s and differs from the yearly counts published in White et al. (2); see Eruption Datasets for details. (A) Cumulative eruptions since 1961, prior to which there was a 50-y dormancy. Most eruptions occur during active phases. (B) Comparative progression of the three active phases. We define the eruptions on 2 September 1961, 13 January 1982, and 15 March 2018 as the beginning of the 1960s, 1980s, and late-2010s active phases, respectively.Major eruptions of Steamboat may be a manifestation of deeper magmatic processes. Wicks et al. (5) proposed that renewed eruptions at Steamboat resulted from uplift episodes of Norris Geyser Basin from 2013 to 2014 and again since 2016. The inflation was attributed to magma intrusion in the late 1990s and ascent of magmatic volatiles to shallow depths (5). If this interpretation of geodetic data is correct, the questions listed are not just related to geysers, but are also connected to larger-scale magmatic processes and volcanic hazards, possibly including hydrothermal explosions, such as those that have occurred in Norris Geyser Basin (2, 6).Here, we first provide an overview of Steamboat Geyser’s geologic setting, physical characteristics, and eruptive behavior, and then address the three outlined questions with a combination of observations and models. We use seismic, ground-deformation, hydrologic, geochemical, and satellite-based thermal infrared data to look for changes correlated with Steamboat’s reactivation. To determine what influences its eruption intervals, we search for empirical relations among intervals and ejected water volumes, meteorological data, ground deformation, and seismicity. Last, we use chemical geothermometry (7) and the thermodynamic properties of eruptions to identify controls on eruption heights.     

Large ecosystem-scale effects of restoration fail to mitigate impacts of land-use legacies in longleaf pine savannas

Ecological restoration is a global priority, with potential to reverse biodiversity declines and promote ecosystem functioning. Yet, successful restoration is challenged by lingering legacies of past land-use activities, which are pervasive on lands available for restoration. Although legacies can persist for centuries following cessation of human land uses such as agriculture, we currently lack understanding of how land-use legacies affect entire ecosystems, how they influence restoration outcomes, or whether restoration can mitigate legacy effects. Using a large-scale experiment, we evaluated how restoration by tree thinning and land-use legacies from prior cultivation and subsequent conversion to pine plantations affect fire-suppressed longleaf pine savannas. We evaluated 45 ecological properties across four categories: 1) abiotic attributes, 2) organism abundances, 3) species diversity, and 4) species interactions. The effects of restoration and land-use legacies were pervasive, shaping all categories of properties, with restoration effects roughly twice the magnitude of legacy effects. Restoration effects were of comparable magnitude in savannas with and without a history of intensive human land use; however, restoration did not mitigate numerous legacy effects present prior to restoration. As a result, savannas with a history of intensive human land use supported altered properties, especially related to soils, even after restoration. The signature of past human land-use activities can be remarkably persistent in the face of intensive restoration, influencing the outcome of restoration across diverse ecological properties. Understanding and mitigating land-use legacies will maximize the potential to restore degraded ecosystems.

In the face of historic extinction rates and declines to ecosystem functioning (1–3), ecological restoration has emerged as a global priority (4, 5). In turn, large commitments to restoration have been pledged, such as the Bonn Challenge, a global effort to reforest 350 million ha by 2030 (5–7), and 2021 to 2030 has been termed by the United Nations General Assembly “The Decade on Ecosystem Restoration” (8). Yet, restoration is a developing field, with extensive practical and conceptual challenges that must be overcome for these ambitious goals to be met (9, 10).One such major challenge to restoration is presented by land-use legacies (11), where the altered characteristics of ecosystems persist after cessation of human land uses, such as agriculture and forestry (12–16). Land-use legacies may affect restoration success through soils that have been modified by agriculture (e.g., refs. 12 and 17), or due to slow natural reestablishment of plant species following human land-use abandonment (18, 19). Land-use legacies are potentially far-reaching, with an estimated 10 to 44 million square kilometers of the terrestrial biosphere currently recovering from human land uses abandoned since 1700 (20), an area ∼4.5 times larger than the United States.The study of land-use legacies is challenging for several reasons (21), and novel research is needed to meet these challenges. First, the initial selection of land for human use is not random, leading to the potential for land-use legacies to be confounded with the environmental conditions that were amenable for human land-use conversion. For example, forests on level, productive soils are more likely to be converted to farm fields than areas with poor soils or sloped topography (22). Once farming is abandoned, these lands may have different environmental conditions from lands that were never farmed, either due to the farming itself or because of initial site-selection bias. Thus, studies on the effects of land-use legacies must carefully control for land-use decision making. Second, land-use legacies can persist for variable amounts of time, leading to uncertainty about when recovery will occur without active restoration interventions. In some cases, land-use legacies last for centuries or even millennia (e.g., refs. 12, 17, 23, 24), whereas in other cases ecological properties may recover over the course of decades (e.g., refs. 25 and 26). This variation may be caused by climate, ecosystem type, soil type, intensity of the original land use, or the identity of the property under study (15). A third challenge is that land-use legacies, as well as restoration, influence entire ecosystems, yet studies of these effects typically focus on one or a few properties of the ecological system—often plants or soils (14, 27, 28). Yet, it is likely that various ecological properties—including abiotic attributes, biodiversity, interactions among species, and others—will recover at different rates following disturbance and during restoration (9, 29). Thus, to comprehensively interpret land-use legacies and guide restoration practices, studies are needed that evaluate recovery across diverse ecological properties.Despite the likely need for active restoration to overcome some land-use legacies, it remains unclear to what extent current restoration practices counteract land-use legacies. In some cases, restoration activities can, at least in part, ameliorate land-use legacies (e.g., refs. 25 and 30). Yet, in other cases legacies persist even in the face of intensive restoration activities and, in fact, land-use legacies can influence restoration outcomes by altering the biotic or abiotic template onto which restoration operates (e.g., ref. 31). The reasons for this variation remain unclear but—as with land-use legacies themselves—may relate to differences among studies in the ecological property under investigation. Experiments are needed that apply restoration manipulations to areas with different land-use histories, followed by assessments spanning diverse ecological properties, to understand how these historical and contemporary human influences interact.We overcame these challenges through a synthesis of land-use legacy effects within a large-scale restoration experiment. Our experiment spans 27 replicate blocks and controls for potential biases resulting from past land-use decisions by pairing adjacent 1 ha plots with or without a history of crop cultivation (Fig. 1; ref. 32). Plots with a cultivation history were farmed for corn and cotton before agriculture was abandoned in 1951; the fields were subsequently reforested with native pine trees (33). Plots without a cultivation history support naturally regenerated native pines with a prominent native hardwood tree component due to history of fire suppression (32). We randomly applied restoration treatments to half of the plots in our study, thereby avoiding site selection issues, which can bias restoration studies (34).Open in a separate windowFig. 1.Map of the large-scale experiment at the Savannah River Site in South Carolina, where the effects of land-use legacies and restoration were measured. Plots were 1 ha (100 × 100 m), grouped into 27 blocks, and were located within longleaf pine patches either with or without a history of agriculture and subsequent pine plantation land use. Half of the plots received an experimental restoration tree-thinning treatment and half were unrestored controls.Our study took place within the longleaf pine ecosystem, which is a component of the North American Coastal Plain biodiversity hotspot (35). Longleaf pine ecosystems are in need of restoration because habitat loss and degradation resulting from widespread fire suppression, agricultural legacies, and conversion to plantation forests have left an estimated 3% of historical range intact [i.e., maintained by fire and undisturbed by recent histories of intensive human land use such as agriculture or plantation conversion (36)]. Most former longleaf pine savannas are presently high-density pine plantations or fire-suppressed, hardwood-encroached woodlands and restoration efforts often seek to reestablish historical savanna conditions over the course of years to decades through tree clearing and reinstatement of a frequent surface-fire regime (36). Prior to our study, all plots in our experiment were fire suppressed. The major goals of restoration in this and other fire-suppressed savanna ecosystems (37) are to reinstate open-canopy conditions, which promotes a high density and diversity of native species and various species interactions, such as pollination (36). Our restoration treatment involved clearing trees within half of the plots to produce savanna conditions and leaving trees at high density in the remaining plots; we also conducted prescribed fire management across all plots.We considered responses of 45 ecological properties, spanning four broad categories: abiotic conditions, the abundance of organisms, the diversity of species, and interactions among species. We asked three questions:

• 1)What are the legacies of intensive human land use? To address this question, we compared plots with and without a history of agriculture and subsequent pine planting, which had not undergone restoration (Fig. 2). We hypothesized that agricultural legacies would affect soil attributes, including through compaction, reduced organic matter, elevated phosphorus, and altered pH (15), and that the history of pine planting would influence aspects of ecosystem structure including through canopy closure and leaf-litter accumulation (32). Together, the combination of these land-use legacy effects would suppress the abundance and diversity of plants and other taxa and thereby alter various species interactions (14, 27).Open in a separate windowFig. 2.Agricultural/plantation land-use legacies and restoration influenced 45 ecological properties in longleaf pine savannas. (A) The four experimental treatments include plots without (−) and with (+) restoration and plots without (−) and with (+) agricultural/plantation history. The colored bars show four comparisons made among treatments, which correspond to matching colored points in the graph on the right. (B) The effects of restoration (Restoration − Ag. history, Restoration + Ag. history; blue and green points) were roughly twice the magnitude of land-use legacy effects (Ag. history − Restoration; orange point). There was no evidence that agricultural/plantation history alters the outcome of restoration (no difference between Restoration − Ag. history and Restoration + Ag. history; blue versus green points) nor that restoration ameliorated legacy effects present in unrestored plots (no difference between Ag. history − Restoration and Ag. history + Restoration; red versus orange points). We consider Hedges’ g values of 0.2 small-, 0.5 medium-, and above 0.8 large-magnitude effects.
• 2a)What is the response to restoration?
• 2b)Do land-use legacies alter the outcome of restoration? To address these questions, we compared plots that had received the restoration tree-thinning treatment to plots that had not received restoration (Fig. 2). We then considered whether the outcome of 6 y of restoration differed for plots with and without a history of agriculture/plantation forestry. We hypothesized that restoration would influence numerous abiotic and biotic attributes, by reducing canopy closure and leaf-litter accumulation; these changes would increase the abundance and diversity of plants and other taxa by reinstating the open savanna conditions to which longleaf pine species are adapted (38, 39). We further hypothesized that restoration would alter species interactions by increasing the abundance and diversity of plants, arthropods, and small mammals. We expected that many of these effects of restoration would be of similar magnitude for plots with and without agricultural histories because this treatment reinstated open savanna conditions regardless of land-use history (Fig. 2).
• 3)Does restoration ameliorate land-use legacies? To address this question, we evaluated whether agricultural legacies present prior to restoration (i.e., those identified in question 1) were no longer evident after restoration (Fig. 2). We hypothesized that, by reinstating open savanna conditions, restoration would ameliorate land-use legacy effects related to above-ground ecosystem structure (e.g., canopy closure and leaf-litter accumulation) and the abundance and diversity of mobile taxa (e.g., bees and small mammals) but not others that change slowly, such as soil variables and the diversity and abundance of less-mobile taxa such as plants and soil microbes (40, 41). Thus, we expected for restoration to more clearly ameliorate those legacy effects tied to plantation forestry, than those associated with agricultural history.

     

Identifying the origin of local flexibility in a carbohydrate polymer

Correlating the structures and properties of a polymer to its monomer sequence is key to understanding how its higher hierarchy structures are formed and how its macroscopic material properties emerge. Carbohydrate polymers, such as cellulose and chitin, are the most abundant materials found in nature whose structures and properties have been characterized only at the submicrometer level. Here, by imaging single-cellulose chains at the nanoscale, we determine the structure and local flexibility of cellulose as a function of its sequence (primary structure) and conformation (secondary structure). Changing the primary structure by chemical substitutions and geometrical variations in the secondary structure allow the chain flexibility to be engineered at the single-linkage level. Tuning local flexibility opens opportunities for the bottom-up design of carbohydrate materials.

Natural polymers adopt a multitude of three-dimensional structures that enable a wide range of functions (1). Polynucleotides store and transfer genetic information; polypeptides function as catalysts and structural materials; and polysaccharides play important roles in cellular structure (2–6), recognition (5), and energy storage (7). The properties of these polymers depend on their structures at various hierarchies: sequence (primary structure), local conformation (secondary structure), and global conformation (tertiary structure).Automated solid-phase techniques provide access to these polymers with full sequence control (8–12). The correlation between the sequence, the higher hierarchy structures, and the resulting properties is relatively well established for polynucleotides (13, 14) and polypeptides (15, 16), while comparatively little is known for polysaccharides (17). Unlike polypeptides and polynucleotides, polysaccharides are based on monosaccharide building blocks that can form multiple linkages with different configurations (e.g., α- or β-linkages) leading to extremely diverse linear or branched polymers. This complexity is exacerbated by the flexibility of polysaccharides that renders structural characterization by ensemble-averaged techniques challenging (17). Imaging single-polysaccharide molecules using atomic force microscopy has revealed the morphology and properties of polysaccharides at mesoscopic, submicrometer scale (18–22). However, imaging at such length scales precludes the observation of individual monosaccharide subunits required to correlate the polysaccharide sequence to its molecular structure and flexibility, the key determinants of its macroscopic functions and properties (23).Imaging polysaccharides at subnanometer resolution by combining scanning tunnelling microscopy (STM) and electrospray ion-beam deposition (ES-IBD) (24, 25) allows for the observation of their monosaccharide subunits to reveal their connectivity (26–28) and conformation space (29). Here, we use this technique to correlate the local flexibility of an oligosaccharide chain to its sequence and conformation, the lowest two structural hierarchies. By examining the local freedom of the chain as a function of its primary and secondary structures, we address how low-hierarchy structural motifs affect local oligosaccharide flexibility—an insight critical to the bottom-up design of carbohydrate materials (30).We elucidate the origin of local flexibility in cellulose, the most abundant polymer in nature, composed of glucose (Glc) units linked by β-1,4–linkages (31–33). Unveiling what affects the flexibility of cellulose chains is important because it gives rise to amorphous domains in cellulose materials (34–37) that change the mechanical performance and the enzyme digestibility of cellulose (38). Cellohexaose, a Glc hexasaccharide (Fig. 1A), was used as a model for a single-cellulose chain as it has been shown to resemble the cellulose polymer behavior (12). Modified analogs prepared by Automated Glycan Assembly (AGA) (11, 12) were designed to manipulate particular intramolecular interactions responsible for cellulose flexibility. Cellohexaose, ionized as a singly deprotonated ion in the gas phase ([M-H]⁻¹) was deposited on a Cu(100) surface held at 120 K by ES-IBD (24) (Materials and Methods). The ions were landed with 5-eV energy, well suited to access diverse conformation states of the molecule without inducing any chemical change in the molecule (29). The resulting cellohexaose observed in various conformation states allowed its mechanical flexibility (defined by the variance in the geometrical bending between two residues) to be quantified for every intermonomer linkage. The observed dependence of local flexibility on the oligosaccharide sequence and conformation thus exemplifies how primary and secondary structures tune the local mechanical flexibility of a carbohydrate polymer.Open in a separate windowFig. 1.STM images of cellohexaose (AAAAAA) and its analogs (AXAAXA). Structures and STM images of cellohexaose (A) and its substituted analogs (B–E). Cellohexaose contains six Glcs (labeled as A; colored black) linked via β-1,4–glycosidic bonds. The cellohexaose analogs contain two substituted Glcs, as the second and the fifth residues from the nonreducing end, that have a single methoxy (–OCH₃) at C(3) (labeled as B; colored red), two methoxy groups at C(3) and C(6) (labeled as C; colored green), a single carboxymethoxy (–OCH₂COOH) at C(3) (labeled as D; colored blue), and a single fluorine (–F) at C(3) (labeled as F; colored purple).The effect of the primary structure on the chain flexibility was explored using sequence-defined cellohexaose analogs (Fig. 1). Cellohexaose, AAAAAA (Fig. 1A), was compared with its substituted analogs, ABAABA, ACAACA, ADAADA, and AFAAFA (written from the nonreducing end) (Fig. 1 B–E), where A is Glc, B is Glc methylated at OH(3), C is Glc methylated at OH(3) and OH(6), D is Glc carboxymethylated at OH(3), and F is Glc deoxyfluorinated at C(3). These substitutions are designed to alter the intramolecular hydrogen bonding between the first and the second as well as between the fourth and fifth Glc units (Fig. 1). These functional groups also affect the local steric environment (i.e., the bulky carboxymethyl group) (Fig. 1D) and the local electronic properties (i.e., the electronegative fluorine group) (Fig. 1E). When compared with the unsubstituted parent cellohexaose, these modified cellohexaoses exhibit different aggregation behavior and are more water soluble (12).All cellohexaose derivatives adsorbed on the surface were imaged with STM at 11 K (Fig. 1). The oligosaccharides were deposited as singly deprotonated species and were computed to adsorb on the surface via a single covalent RO–Cu bond, except for ADAADA which was deposited as doubly deprotonated species and was computed to adsorb on the surface via two covalent RCOO–Cu bonds (R = sugar chain). All cellohexaoses appear as chains containing six protrusions corresponding to the six constituent Glcs. The unmodified cellohexaose chains (Fig. 1A) mainly adopt a straight geometry, while the substituted cellohexaoses (Fig. 1 B–E) adopt both straight- and bent-chain geometries. Chemical substitution thus increases the geometrical freedom of the cellulose chain, consistent with the reported macroscopic properties (12).Large-chain bending between neighboring Glc units is observed exclusively for the substituted cellohexaose (Fig. 1). The large, localized bending reveals the substitution site and allows for the nonreducing and the reducing ends of the chain to be identified. These chains are understood to bend along the surface plane via the glycosidic linkage without significant tilting of the pyranose ring that remains parallel to the surface (illustrated in SI Appendix, Fig. S1), as indicated by the ∼2.0-Å height of every Glc (29).The bending angle measured for AA and AX linkages (Fig. 2; Materials and Methods has analysis details) shows that, while both AA and AX prefer the straight, unbent geometry, AX displays a greater variation of bending angles than AA. AX angular distribution is consistently ∼10° wider than that for AA, indicating that AX has a greater conformational freedom than AA. This increased bending flexibility results from the absence of the intramolecular hydrogen bonding between OH(3) and O(5) of the neighboring residue. Methylation of OH(6), in addition to methylation of OH(3), results in similar flexibility (Fig. 2 B and C), suggesting the greater importance of OH(3) in determining the bending flexibility. Steric effects were found to be negligible since AD displayed similar flexibility to other less bulky AX linkages.Open in a separate windowFig. 2.Bending flexibility of AA linkage and substituted AX linkages. Chain bending (Fig. 1) is quantified as an angle formed between two neighboring Glcs (Materials and Methods). The results are given in A for AA, in B for AB, in C for AC, in D for AD, and in E for AF, showing that AX (where X = B, C, D, F) has a higher conformational freedom than AA. The angle distributions (bin size: 10°) are fitted with a Gaussian (solid line) shown with its half-width half-maximum. The computed potential energy curves are shown with the half-width at 0.4 eV and fitted with a parabola to estimate its stiffness (k; in millielectronvolts per degree²).Density functional theory (DFT) calculations support the observations, showing that substitution of OH(3) decreases the linkage stiffness by up to ∼40% (Fig. 2). Replacing OH(3) with other functional groups weakens the interglucose interactions by replacing the OH(3)··O(5) hydrogen bond with weak Van der Waals interactions. The similar flexibility between AB and AC linkages is attributed to the similar strength of the interglucose OH(2)··OH(6) hydrogen bond in AB (Fig. 2B) and the OH(2)··OMe(6) hydrogen bond in AC (Fig. 2C). The negligible steric effect in AD is attributed to the positional and rotational freedom of the bulky moiety that prevents any “steric clashes” and diminishes the contribution of steric repulsion in the potential energy curve. Comparing the potential landscape in the gas phase and on the surface shows that the stiffness of the adsorbed cellohexaoses is primarily dictated by their intramolecular interactions instead of molecule–surface interactions (SI Appendix, Fig. S2). Primary structure alteration by chemical substitution modifies the interglucose hydrogen bonds and enables chain flexibility to be locally engineered at the single-linkage level.We subsequently investigate how molecular conformation (secondary structure) affects the local bending flexibility. We define the local secondary structure as the geometry formed between two Glcs, here exemplified by the local twisting of the chain (Fig. 3). The global secondary structure is defined as the overall geometry formed by all Glcs in the chain, here exemplified by the linear and cyclic topologies of the chain (Fig. 4).Open in a separate windowFig. 3.Bending flexibility of untwisted and twisted AA linkages. (A) STM image of a cellohexaose containing two types of AA linkages: untwisted (HH and VV) and twisted (HV and VH; from the nonreducing end). The measured bending angles and the computed potential curve are given in B for HH, in C for HV, and in D for VV, showing that the twisted linkage (HV) is more flexible than the untwisted ones (HH and VV). In the molecular structures, interunit hydrogen bonds are given as dotted blue lines, and the pyranose rings are colored red for the horizontal ring (H) and green for vertical (V). The angle distributions (bin size: 10°) are fitted with a Gaussian distribution (solid line) labeled with its peak and half-width half-maximum. The computed potential curves are labeled with its half-width at 0.4 eV and fitted with a parabola to estimate its stiffness (k; in millielectronvolts per degree²).Open in a separate windowFig. 4.Bending flexibility of AA linkage in linear (LIN) and cyclic (CYC) chains. STM image, measured bending angle distribution, and computed potential of AA linkage are given in A for a linear cellohexaose conformer and in B for a cyclic cellohexaose conformer, showing that chain flexibility is reduced in conformations with cyclic topology. The same data are given in C for α-cyclodextrin that is locked in a conformation with cyclic topology. The measured angles (bin size: 10°) are each fitted with a Gaussian distribution (solid line) labeled with its peak and half-width half-maximum. The computed potentials are each labeled with its half-width at 0.4 eV and fitted with a parabola to estimate its stiffness (k; in millielectronvolts per degree²).The effect of local secondary structure on chain flexibility is exemplified by the bending flexibility of twisted and untwisted linkages in a cellohexaose chain (Fig. 3A). The untwisted and twisted linkages are present due to the Glc units observed in two geometries, H or V (Fig. 3), distinguished by their heights (h). H (h ∼ 2.0 Å) is a Glc with its pyranose ring parallel to the surface, while V (h ∼ 2.5 Å) has its ring perpendicular to the surface (29). These lead to HH and VV as untwisted linkages and HV and VH (written from nonreducing end) as twisted linkages.The twisted linkage is more flexible than the untwisted one, as shown by the unimodal bending angles for the untwisted linkage (HH and VV in Fig. 3 B and D, respectively) and the multimodal distribution for the twisted linkage (HV in Fig. 3C). DFT calculations attribute the increased bending flexibility to the reduction of accessible interunit hydrogen bonds from two to one. Linkage twisting increases the distance between the hydrogen-bonded pair, which weakens the interaction between Glc units and increases the flexibility at the twisting point. The increase in local chain flexibility conferred by chain twisting shows how local secondary structures affect chain flexibility.The effect of the global secondary structure on the local chain flexibility was examined by comparing the local bending flexibility of cellohexaose chains possessing different topologies. Cellohexaose can adopt either linear (Figs. 3A and ​and4A)4A) or cyclic topology (Fig. 4B), the latter characterized by the presence of a circular, head-to-tail hydrogen bond network (29). The cyclic conformation of cellohexaose is enabled by the head-to-tail chain folding from the 60° chain bending of the VV linkage. The VV segment in the cyclic chain is stiffer than in the linear chain since the bending angle distribution for the cyclic chain is 6° narrower than that for the linear chain. The observation is corroborated by DFT calculations that show that the VV linkage in the cyclic chain is about three times stiffer than that in the linear chain.To characterize the degree of chain stiffening due to the linear-to-cyclic chain folding, we compare the flexibility of the cyclic cellohexaose and α-cyclodextrin (an α-1,4–linked hexaglucose covalently locked in cyclic conformation). The α-cyclodextrin provides the referential local flexibility for a cyclic oligosaccharide chain. Strikingly, the local flexibility in α-cyclodextrin was found to be identical to that in the cyclic cellohexaose, as evidenced by the similar width of the bending angle distribution and the computed potentials (Fig. 4 B and C). The similar stiffness indicates that the folding-induced stiffening in cellohexaose is a general topological effect unaffected by the type of the interactions that give the cyclic conformation (noncovalent hydrogen bond in cellohexaose vs. covalent bond in α-cyclodextrin). The folding-induced stiffening is the result of the creation of a circular spring network that restricts the motion of Glc units and reduces their conformational freedom. The folding-induced stiffening reported here provides a mechanism by which carbohydrate structures can be made rigid. The dependence of the local chain flexibility on the chain topology shows how global secondary structures modify local flexibility.Using cellulose as an example, we have quantified the local flexibility of a carbohydrate polymer and identified structural factors that determine its flexibility. Modification of the carbohydrate primary structure by chemical substitution alters the mechanical flexibility at the single-linkage level. Changing secondary structure by chain twisting and folding provides additional means to modify the flexibility of each linkage. Control of these structural variables enables tuning of polysaccharide flexibility at every linkage as a basis for designing and engineering carbohydrate materials (30). Our general approach to identify structural factors affecting the flexibility of a specific molecular degrees of freedom in a supramolecular system should aid the design of materials and molecular machines (39) and the understanding of biomolecular dynamics.     

Pinpointing the neural signatures of single-exposure visual recognition memory

Memories of the images that we have seen are thought to be reflected in the reduction of neural responses in high-level visual areas such as inferotemporal (IT) cortex, a phenomenon known as repetition suppression (RS). We challenged this hypothesis with a task that required rhesus monkeys to report whether images were novel or repeated while ignoring variations in contrast, a stimulus attribute that is also known to modulate the overall IT response. The monkeys’ behavior was largely contrast invariant, contrary to the predictions of an RS-inspired decoder, which could not distinguish responses to images that are repeated from those that are of lower contrast. However, the monkeys’ behavioral patterns were well predicted by a linearly decodable variant in which the total spike count was corrected for contrast modulation. These results suggest that the IT neural activity pattern that best aligns with single-exposure visual recognition memory behavior is not RS but rather sensory referenced suppression: reductions in IT population response magnitude, corrected for sensory modulation.

Under the right conditions, we are very good at remembering the images that we have seen: we can remember thousands of images after viewing each only once and only for a few seconds (1, 2). How our brains support this remarkable ability, often called “visual recognition memory” (3), is not well understood. The most prominent proposal to date suggests that memories about whether images have been encountered before are signaled in high-level visual brain areas such as inferotemporal cortex (IT) and perirhinal cortex via adaptation-like reductions of the population response to repeated as compared to novel stimuli, a phenomenon referred to as repetition suppression (RS) (4–9). Repetition suppression exhibits the primary attributes needed to account for the vast capacity of single-exposure visual memory behavior: response decrements in subsequent exposures are selective for image identity (even after viewing an extensive sequence of other images), and last for several minutes to hours (5, 6, 10). RS has also been shown to account for behavior in an image recognition memory task: a linear decoder with positive weights can predict single-exposure visual recognition memory behavior from neural responses in IT cortex (10).Despite the fact that the RS hypothesis is consistent with available evidence, it seems likely to be too simplistic an explanation for visual recognition memory encoding. In particular, it is well known that sensory neurons such as those of IT cortex are modulated not only by image memory, but also by stimulus properties such as image contrast (11). It is thus unclear whether and how these stimulus-induced effects interfere with judgments of whether images are novel or have been encountered before, and if they do not, how image memory can be decoded from neural responses in a way that disambiguates it from changes in these stimulus properties. To investigate this, we measured behavioral and neural responses of monkeys trained to report whether images were novel or repeated while disregarding image contrast (Fig. 1A).Open in a separate windowFig. 1.Visual memory behavior. (A) The contrast-invariant, single-exposure visual memory task. The monkeys viewed a sequence of images and reported whether they were novel (never seen before) or repeated (seen exactly once) while ignoring randomized changes in contrast. Monkeys were trained to saccade to one of two response targets to indicate their choice (red arrows). Images were repeated with a randomly chosen delay between the first and repeated presentation (“n-back”). (B) Images were displayed at one of two contrast levels, yielding two conditions for novel images, high (H) and low (L), and four conditions for repeated images: HH (repeated H preceded by novel H), LL (repeated L preceded by novel L), HL (repeated L preceded by novel H), and LH (repeated H preceded by novel L). The four repeated conditions were organized into same-contrast and mixed-contrast groups depending on whether the initial and repeated presentations were at the same or different contrasts, respectively. (C) Behavioral performance for the data pooled across monkeys in the task, where small black dots indicate average performance for an individual session and large colored dots indicate the average performance across sessions. A measure of contrast invariance, I, was computed as the ratio of the variance across contrast conditions and the variance with respect to the maximally contrast-modulated pattern after taking overall performance into account, subtracted from 1 (SI Appendix, SI Methods). Insets illustrate the expected behavioral pattern with minimal (I = 0) and maximal (I = 1) contrast invariance.