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Abstract Holsnøy, Norway, offers a world-class natural laboratory for studying the impact of fluid on subducting lower crust. Holsnøy is composed of dry, metastable lower crustal granulite that was infiltrated by fluids along shear zones and seismic fractures during subduction. The infiltration facilitated the localized growth of eclogite facies mineral assemblages along the fluid flow pathways. The duration of the eclogite facies metamorphism, however, remains uncertain. Previous garnet diffusion chronometry studies have estimated timescales ranging from hundreds of years to millions of years based on diffusional relaxation between metastable granulite facies garnet cores and eclogite facies garnet rims and fractures. The shorter timescales are inferred from extremely sharp Ca gradients across chemical contacts present in some garnets whereas the longer timescales are from wider Mg and Fe profiles present in all garnets. The different timescale estimates have led to divergent models for the region’s tectonometamorphic evolution. Here we show that the sharp Ca contacts can be explained by diffusion-induced compositional stress. As Ca is significantly larger than Mg and Fe, its movement strains the crystal lattice and generates stress that limits the relaxation of sharp chemical contacts. When compositional stress is accounted for, the sharp contacts yield timescales that are consistent with the wider Mg and Fe diffusion profiles. We determine that eclogite facies conditions (670–700 °C, 1.5–2.2 GPa) lasted a maximum of c. 300 kyr. The relatively short duration of eclogite facies conditions requires that multiple transient heating events were superimposed on a longer (>106 yr) overall timescale of metamorphism. Granulite facies garnet cores are surrounded by multiple generations of eclogite facies rims formed by interface-coupled dissolution–reprecipitation (ICDR) reactions. The garnet rims indicate two rapid, regional-scale fluid pulses and additional smaller, more localized pulses. The fluid pulses may be linked to episodes of seismic moment release as well as transient heating via exothermic hydration reactions and/or shear deformation. Our model results predict up to 400 MPa of differential stress at the garnet core–rim contacts, consistent with observed eclogite facies microfractures that extend into relic granulite facies garnet cores. The microfractures indicate that ICDR was aided by compositional stress: diffusion ahead of the reaction front generated stress and fracturing that created porosity for further ICDR. Thus, compositional stress can markedly impact both diffusion systematics and intracrystalline deformation. Together, these results show that despite their brevity, transient thermal, fluid flux, and/or baric episodes may exert the primary controls on the mineralogical and rheological development of subducted lithologies, in contrast to the long, slow burial and exhumation typically envisioned for regional metamorphism. 

ABSTRACT Neuromesodermal progenitors (NMPs) are a vertebrate cell type that contribute descendants to both the spinal cord and the mesoderm. The undifferentiated bipotential NMP state is maintained when both Wnt signaling is active and Sox2 is present. We used transgenic zebrafish reporter lines to live-image both Wnt activity and Sox2 levels in NMPs and observed a unique cellular ratio in NMPs compared to NMP-derived mesoderm or neural tissue. We used this unique signature to identify the previously unknown anatomical position of a progenitor population that gives rise to midline tissues of the floor plate of the spinal cord and the mesodermal notochord. Thus, quantification of the active Wnt signaling to Sox2 ratio can be used to predict and identify cells with neuromesodermal potential. We also developed the auxin-inducible 2 degron system for use in zebrafish to test the temporal role that Sox2 plays during midline formation. We found that ectopic Sox2 in the presence of Wnt activity holds cells in the undifferentiated floor plate/notochord progenitor state, and that degradation of the ectopic Sox2 is required for cells to adopt a notochord fate. 

ABSTRACT Using the youngest detrital-zircon date(s) of a sedimentary deposit to constrain its maximum depositional age (MDA) is a widespread and growing application of geochronology. Most MDA studies analyze zircon grains at random, but this strategy can be costly and inefficient in cases where the youngest age group is only a small fraction of the population. We propose that handpicking sharply faceted zircon grains will increase the likelihood of encountering first-cycle zircon that have not undergone significant sedimentary transport, thus producing MDA estimates that are closer to the depositional age. We evaluate this procedure by conducting intra-sample comparisons of randomly selected versus handpicked zircon separates from 30 samples analyzed via laser-ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS). Our results show that handpicking zircon produces an overall shift towards younger ages in comparison to their randomly analyzed counterparts by an average of ∼ 406 Myr. In randomly analyzed separates, only 1.6% of grains were within 5 Myr of an independent estimate of the MDA, while handpicked separates contained 14.2%, an approximately nine-fold increase. However, handpicking can also lead to selection of older grains if they have been minimally transported, as with one handpicked Mesozoic sample that yielded 81% of ∼ 1.1 Ga zircon interpreted to be derived from a local granitic source. Handpicking is most effective in samples where young, sharply faceted grains are diluted by older, rounded grains, as with one sample that exhibited an ∼ 18-fold increase in the proportion of near-depositional-age zircons relative to its counterpart where grain selection was random. Because handpicking zircon imparts a severe bias on the resulting U–Pb age distribution, we recommend that two separate aliquots be used for quantitative provenance characterization through random analysis and MDA analysis through handpicking. 

Abstract Twice in the Cryogenian Period (720–635 Ma), during the Sturtian and Marinoan glaciations, ice sheets extended to equatorial latitudes for millions of years. These climate extremes have been interpreted to record the Snowball climate state, in which all of Earth’s oceans were covered with ice. During a Snowball Earth, the hydrological cycle would have been curtailed and silicate weathering greatly reduced. In this scenario, deep ocean chemistry should have evolved toward mantle values through hydrothermal exchange at mid-ocean ridges. Specifically, seawater strontium isotopes (87Sr/86Sr) are predicted to exhibit unradiogenic mantle-like values. However, cap carbonates that overlie the Cryogenian glacial deposits have yielded radiogenic 87Sr/86Sr values similar to those of seawater prior to glaciation, inconsistent with the central geochemical prediction of the Snowball Earth hypothesis. Here we report the discovery of 87Sr/86Sr values of 0.7034 in marine carbonate and authigenic barite that rest directly above Sturtian glacial deposits in Dhofar, Oman. These values record either a local unradiogenic source or Snowball Earth deep-water values that have not been previously identified. If it is a global signal, these new data and geochemical modeling support an extreme Snowball Earth scenario with near-complete ice cover and define one of the largest geochemical perturbations to seawater in Earth history. 

Groundwater, a crucial natural resource on a global scale, plays a significant role in Texas, impacting various essential ecosystem services either directly or indirectly. Despite efforts of state- and community-level regulations and conservation efforts, there is an ongoing trend of declining groundwater levels in the state of Texas. In this study, we utilized the systems thinking and system dynamics modeling approach to better understand this problem and investigate possible leverage points to achieve more sustainable groundwater resource levels. After conceptualizing a causal loop diagram (CLD) of the underlying feedback structure of the issue (informed by the existing literature), a small system dynamics (SD) model was developed to connect the feedback factors identified in the CLD to the stocks (groundwater level) and flows (recharge rate and groundwater pumping) that steer the behaviors of groundwater systems across time. After completing model assessment, experimental simulations were conducted to evaluate the current state relative to simulated treatments for improved irrigation efficiency, restricted pumping rates, cooperative conservation protocols among users, and combination strategy (of all treatments above) in the long-term. Results showed that groundwater stress (and the associated repercussions on related ecosystem service) could be alleviated with a combination strategy, albeit without complete groundwater level recovery. 

Large language models have the ability to generate text that mimics patterns in their inputs. We introduce a simple Markov Chain sequence modeling task in order to study how this in-context learning (ICL) capability emerges. In our setting, each example is sampled from a Markov chain drawn from a prior distribution over Markov chains. Transformers trained on this task form \emph{statistical induction heads} which compute accurate next-token probabilities given the bigram statistics of the context. During the course of training, models pass through multiple phases: after an initial stage in which predictions are uniform, they learn to sub-optimally predict using in-context single-token statistics (unigrams); then, there is a rapid phase transition to the correct in-context bigram solution. We conduct an empirical and theoretical investigation of this multi-phase process, showing how successful learning results from the interaction between the transformer's layers, and uncovering evidence that the presence of the simpler unigram solution may delay formation of the final bigram solution. We examine how learning is affected by varying the prior distribution over Markov chains, and consider the generalization of our in-context learning of Markov chains (ICL-MC) task to n-grams for n is greater than 2. 

Colloids can be used either as model systems for directed assembly or as the necessary building blocks for making functional materials. Previous work primarily focused on assembling colloids under a single external field, where controlling particle−particle interactions is limited. This work presents results under a combination of electric and magnetic fields. When these two fields are orthogonally applied, we can independently tune the magnitude and direction of the dipolar attraction and repulsion between the particles. As a result, we obtain well-aligned, highly dense, but individually separated linear chains at intermediate particle concentrations. Both the inter- and intrachain spacings can be tuned by adjusting the particle concentration and relative strengths of both fields. At high particle concentrations and by tuning the electric field frequency, the individual microspheres can assemble into colloidal oligomers such as trimers, tetramers, heptamers, and nonamers in response to the electric field due to the synergy between dipolar and electrohydrodynamic interactions. These oligomers, in turn, serve as building blocks for making hierarchical structures with finer architectures upon superimposing a one-dimensional (1D) magnetic field. In addition to experiments, Monte Carlo (MC) simulations have been performed on colloids confined near the electrode, interacting through a Stockmayer-like potential. They faithfully reproduce key observations in the experiments. Our work demonstrates the potential of using orthogonal electric and magnetic fields to assemble diversified types of highly aligned structures for applications in high-strength composites, optical materials, or structured battery electrodes. 

Global herbicide-resistant weed populations continue rising due to selection pressures exerted by herbicides. Despite this, herbicides continue to be farmers’ preferred weed-control method due to cost and efficiency relative to physical or biological methods. However, weeds developing resistance to herbicides not only challenges crop production but also threatens ecosystem services by disrupting biodiversity, reducing soil health, and impacting water quality. Our objective was to develop a simulation model that captures the feedback between weed population dynamics, agricultural management, profitability, and farmer decision-making processes that interact in unique ways to reinforce herbicide resistance in weeds. After calibration to observed data and evaluation by subject matter experts, we tested alternative agronomic, mechanical, or intensive management strategies to evaluate their impact on weed population dynamics. Results indicated that standalone practices enhanced farm profitability in the short term but lead to substantial adverse ecological outcomes in the long term, indicated by elevated herbicide resistance (e.g., harm to non-target species, disrupting natural ecosystem functions). The most management-intensive test yielded the greatest weed control and farm profit, albeit with elevated residual resistant seed bank levels. We discuss these findings in both developed and developing-nation contexts. Future work requires greater connectivity of farm management and genetic-resistance models that currently remain disconnected mechanistically.