(Received 5 September 2023; accepted 20 March 2024; published 11 April 2024) Recent studies indicate that thermally produced dark matter will form highly concentrated, low -mass cusps in the early universe that often survive until the present. While these cusps contain a small fraction of the dark matter, their high density significantly increases the expected gamma-ray flux from dark matter annihilation, particularly in searches of large angular regions. We utilize 14 years of Fermi -LAT data to set strong constraints on dark matter annihilation through a detailed study of the isotropic gamma-ray background, excluding with 95% confidence dark matter annihilation to bb final states for dark matter masses below 120 GeV.

Crystal precipitation from aqueous solution occurs through multiple pathways. Besides the classical ion -byion addition, non-classical crystallization mechanisms, such as multi-ion polymer and nano-particle attachment, could be significant. These non-classical crystallization processes have been observed with advanced microscopy, yet detailed quantification of their contributions remains challenging. Building from paired Ca and Sr isotope observations, we develop a theoretical framework to quantify the contributions of classical and non-classical crystallization pathways on the precipitation of the calcium carbonate mineral calcite, a common precipitate in nature. We demonstrate that the classical crystallization pathway alone is insufficient to account for the observed isotope behaviors and, thus, the entire calcite precipitation process. We further present a surface kinetic model that incorporates non-classical crystallization pathways. This model enables the characterization of the roles of classical and non-classical crystallization mechanisms in calcite precipitation. The results suggest that the relative contribution of non-classical crystallization pathways increases with saturation state and can, under high supersaturation levels, be comparable to or greater than the classical pathway. The presented theoretical framework readily explains observed trace element partitioning and isotope fractionation behaviors during calcite precipitation and can be further expanded onto other mineral systems to gain insights into crystal growth mechanisms.

The eukaryotic algal CO2-concentrating mechanism (CCM) is based on a Rubisco-rich organelle called the pyrenoid, which is typically traversed by a network of thylakoid membranes. BST4 is a bestrophin-like transmembrane protein that has previously been identified in the model alga Chlamydomonas reinhardtii as a putative tether that could link the traversing thylakoid membrane network to the Rubisco matrix. In the present study, we show that BST4 forms a higher order complex assembly that localizes to the thylakoid network within the pyrenoid. However, investigation of a bst4 knock-out mutant in Chlamydomonas showed that the absence of BST4 did not result in a CCM-deficient phenotype and that BST4 is not necessary for the formation of the trans-pyrenoid thylakoids. Furthermore, heterologous expression of BST4 was not sufficient to facilitate the incorporation of thylakoids into a reconstituted Rubisco condensate in the land plant Arabidopsis. Subsequent analyses revealed that bst4 was under oxidative stress and showed enhanced non-photochemical quenching associated with CO2 limitation and over acidification of the thylakoid lumen. We conclude that the primary role of BST4 is not as a tethering protein, but rather as an ion channel involved in pH regulation in pyrenoid-based CCMs.

Many geoscience departments are taking steps to recruit and retain faculty from underrepresented groups. Here we interview 19 geoscientists who identify as an underrepresented race or gender who recently declined a tenure-track faculty job offer. A range of key factors influenced their decisions to accept or decline a position including commitment to diversity, equity, and inclusion (DEI) including personal identities, DEI initiatives, and mentorship; (in)civility during job interviews; values revealed in negotiation; and compatibility with personal life including family and geography. Many of the participants experienced hiring processes inconsistent with existing recommendations to increase faculty diversity. Therefore, we leverage our results to provide actionable recommendations for improving the equity and effectiveness of faculty recruitment efforts. We find that departments may doubly benefit from improving their culture: in addition to benefiting current members of the department, it may also help with recruitment.

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural ordinary differential equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data (Chen et al. 2018 Adv. Neural Inf. Process. Syst.). Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here, we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than autoregressive integrated moving average (ARIMA) models. We also find that untuned NODEs have a similar forecasting accuracy to untuned long-short term memory neural networks and both are outperformed in accuracy and precision by empirical dynamical modelling . However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy. We also discuss ways to improve the forecasting performance of NODEs. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.

We present an analysis of ground-based and JWST observations of SN 2022pul, a peculiar "03fg-like" (or "super-Chandrasekhar") Type Ia supernova (SN Ia), in the nebular phase at 338 days postexplosion. Our combined spectrum continuously covers 0.4-14 mu m and includes the first mid-infrared spectrum of a 03fg-like SN Ia. Compared to normal SN Ia 2021aefx, SN 2022pul exhibits a lower mean ionization state, asymmetric emission-line profiles, stronger emission from the intermediate-mass elements (IMEs) argon and calcium, weaker emission from iron-group elements (IGEs), and the first unambiguous detection of neon in a SN Ia. A strong, broad, centrally peaked [Ne ii] line at 12.81 mu m was previously predicted as a hallmark of "violent merger" SN Ia models, where dynamical interaction between two sub-M-Ch white dwarfs (WDs) causes disruption of the lower-mass WD and detonation of the other. The violent merger scenario was already a leading hypothesis for 03fg-like SNe Ia; in SN 2022pul it can explain the large-scale ejecta asymmetries seen between the IMEs and IGEs and the central location of narrow oxygen and broad neon. We modify extant models to add clumping of the ejecta to reproduce the optical iron emission better, and add mass in the innermost region (<2000 km s(-1)) to account for the observed narrow [O i] lambda lambda 6300, 6364 emission. A violent WD-WD merger explains many of the observations of SN 2022pul, and our results favor this model interpretation for the subclass of 03fg-like SNe Ia.

Compression of small molecules can induce solid-state reactions with products that are difficult or impossible to obtain through solution-phase synthesis. Of particular interest is the topochemical-like reaction of arenes to produce polymeric nanomaterials rich in sp3 carbon. However, high reaction onset pressures and poor control over high-pressure reaction selectivity remain significant challenges to be addressed. Herein, the incorporation of electron withdrawing/donating groups into π-stacked arenes is proposed as a strategy to reduce reaction barriers and onset pressures. Charge transfer cocrystals represent systems with optimal π-stacking and reduced energy barriers for intermolecular cycloaddition reactions, however, competing side-chain reactions between functional groups must also be considered. For the case of a diaminobenzene:tetracyanobenzene cocrystal, amidine formation between side groups is the first reaction to occur with an onset pressure near 9 GPa, as characterized using vibrational spectroscopy, X-ray diffraction, and computational studies. High-pressure reactivity is system-dependent and while functionalized arenes are predicted to exhibit reduced-barrier energy cycloaddition pathways, directed reactions between side groups can be used as a novel strategy for the formation unique polymeric materials.

A key challenge in materials discovery is to find high-temperature superconductors. Hydrogen and hydride materials have long been considered promising materials displaying conventional phononmediated superconductivity. However, the high pressures required to stabilize these materials have restricted their application. Here, we present results from high-throughput computation, considering a wide range of high-symmetry ternary hydrides from across the periodic table at ambient pressure. This large composition space is then reduced by considering thermodynamic, dynamic, and magnetic stability before direct estimations of the superconducting critical temperature. This approach has revealed a metastable ambient-pressure hydride superconductor, Mg2IrH6, with a predicted critical temperature of 160 K, comparable to the highest temperature superconducting cuprates. We propose a synthesis route via a structurally related insulator, Mg2IrH7, which is thermodynamically stable above 15 GPa, and discuss the potential challenges in doing so.