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  1. Abstract

    With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

     
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    Free, publicly-accessible full text available December 1, 2025
  2. Climate change has led to an alarming increase in the frequency and severity of wildfires worldwide. While it is known that amphibians have physiological characteristics that make them highly susceptible to fire, the specific impacts of wildfires on their symbiotic skin bacterial communities (i.e., bacteriomes) and infection by the deadly chytrid fungus, Batrachochytrium dendrobatidis, remain poorly understood. Here, we address this research gap by evaluating the effects of fire on the amphibian skin bacteriome and the subsequent risk of chytridiomycosis. We sampled the skin bacteriome of the Neotropical species Scinax squalirostris and Boana leptolineata in fire and control plots before and after experimental burnings. Fire was linked with a marked increase in bacteriome beta dispersion, a proxy for skin microbial dysbiosis, alongside a trend of increased pathogen loads. By shedding light on the effects of fire on amphibian skin bacteriomes, this study contributes to our broader understanding of the impacts of wildfires on vulnerable vertebrate species. 
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    Free, publicly-accessible full text available December 1, 2025
  3. Abstract Questions

    Anthropogenic climate change is causing increases in the severity of wildland fire in many parts of the world. At the same time, post‐fire succession is occurring under new, warmer temperatures that are projected to continue increasing. Despite this, the combined effects of uncharacteristically high burn severity and increased ambient temperature on post‐fire community composition remain poorly understood. We ask how post‐fire forest understorey community composition and species richness are influenced by (1) burn severity, (2) experimental warming, and (3) years since fire.

    Location

    Museum Fire Scar,Pinus ponderosaforest, Arizona, United States.

    Methods

    We established 120 1‐m2quadrats in unburned, low‐ and high‐severity locations nine months after a mixed‐severity fire. Half of the plots were subject to experimental warming via open‐top warming chambers that elevated daytime temperatures by 1.079°C, simulating near‐term anthropogenic warming. Plant composition data were collected annually for three years. Relationships between community composition, burn severity, and experimental warming were analyzed using repeated‐measures PERMANOVA and linear mixed‐effects models.

    Results

    Composition differed significantly according to burn severity, time since fire, and their interaction, while experimental warming did not significantly influence composition. Species richness significantly increased in burned areas compared to unburned control within two years of fire.

    Conclusions

    Our results suggest that near‐term temperature increases, driven by anthropogenic climate change, will have little effect on community composition relative to fire severity. High‐severity fire drove large, rapid changes in plant composition compared to unburned controls, favoring exotic annuals in a historically perennial‐dominated plant community.

     
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  4. Abstract

    Randomized controlled trials (RCT’s) allow researchers to estimate causal effects in an experimental sample with minimal identifying assumptions. However, to generalize or transport a causal effect from an RCT to a target population, researchers must adjust for a set of treatment effect moderators. In practice, it is impossible to know whether the set of moderators has been properly accounted for. I propose a two parameter sensitivity analysis for generalizing or transporting experimental results using weighted estimators. The contributions in the article are threefold. First, I show that the sensitivity parameters are scale-invariant and standardized, and introduce an estimation approach for researchers to account for both bias in their estimates from omitting a moderator, as well as potential changes to their inference. Second, I propose several tools researchers can use to perform sensitivity analysis: (1) numerical measures to summarize the uncertainty in an estimated effect to omitted moderators; (2) graphical summary tools to visualize the sensitivity in estimated effects; and (3) a formal benchmarking approach for researchers to estimate potential sensitivity parameter values using existing data. Finally, I demonstrate that the proposed framework can be easily extended to the class of doubly robust, augmented weighted estimators.

     
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  5. N-heterocyclic carbenes (NHCs) have grown in popularity in recent years due to their superior surface stability on metal nanoparticles and surfaces. This stability is often characterized experimentally by studying the σ-donation and π-backbonding as measured through NHC-selenium adduct NMR and the Huynh Electronic Parameter (HEP), respectively. However, recent work with NHCs on metal clusters suggests that the ligands can adopt a variety of orientations on the surface. Thus, the surface may have a pronounced impact on the σ-donation and π-backbonding observed for these NHCs. In this work, we aim to determine how well these experimental characterizations compare to trends observed via bond decomposition analysis. 
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  6. Abstract

    Droughts are predicted to become more frequent and intense in many tropical regions, which may cause shifts in plant community composition. Especially in diverse tropical communities, understanding how traits mediate demographic responses to drought can help provide insight into the effects of climate change on these ecosystems. To understand tropical tree responses to reduced soil moisture, we grew seedlings of eight species across an experimental soil moisture gradient at the Luquillo Experimental Forest, Puerto Rico. We quantified survival and growth over an 8‐month period and characterized demographic responses in terms of tolerance to low soil moisture—defined as survival and growth rates under low soil moisture conditions—and sensitivity to variation in soil moisture—defined as more pronounced changes in demographic rates across the observed range of soil moisture. We then compared demographic responses with interspecific variation in a suite of 11 (root, stem, and leaf) functional traits, measured on individuals that survived the experiment. Lower soil moisture was associated with reduced survival and growth but traits mediated species‐specific responses. Species with relatively conservative traits (e.g., high leaf mass per area), had higher survival at low soil moisture whereas species with more extensive root systems were more sensitive to soil moisture, in that they exhibited more pronounced changes in growth across the experimental soil moisture gradient. Our results suggest that increasing drought will favor species with more conservative traits that confer greater survival in low soil moisture conditions.

     
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  7. Structures with specific graded geometries or properties can cause spatial separation and local field enhancement of wave energy. This phenomenon is called rainbow trapping, which manifests itself as stopping the propagation of waves at different locations according to their frequencies. In acoustics, most research on rainbow trapping has focused on wave propagation in one dimension. This research examined the elastic wave trapping performance of a two-dimensional (2D) axisymmetric grooved phononic crystal plate structure. The performance of the proposed structure is validated using numerical simulations based on finite element analysis and experimental measurements using a laser Doppler vibrometer. It is found that rainbow trapping within the frequency range of 165–205 kHz is achieved, where elastic waves are trapped at different radial distances in the plate. The results demonstrate that the proposed design is capable of effectively capturing elastic waves across a broad frequency range of interest. This concept could be useful in applications such as filtering and energy harvesting by concentrating wave energy at different locations in the structure.

     
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    Free, publicly-accessible full text available March 1, 2025
  8. Abstract

    Quartz crystals with zircon inclusions were synthesized using a piston-cylinder apparatus to experimentally evaluate the use of inclusions in “soft” host minerals for elastic thermobarometry. Synthesized zircon inclusion strains and, therefore, pressures (Pinc) were measured using Raman spectroscopy and then compared with the expected inclusion strains and pressures calculated from elastic models. Measured inclusion strains and inclusion pressures are systematically more tensile than the expected values and, thus, re-calculated entrapment pressures are overestimated. These discrepancies are not caused by analytical biases or assumptions in the elastic models and strain calculations. Analysis shows that inclusion strain discrepancies progressively decrease with decreasing experimental temperature in the α-quartz field. This behavior is consistent with inelastic deformation of the host–inclusion pairs induced by the development of large differential stresses during experimental cooling. Therefore, inclusion strains are more reliable for inclusions trapped at lower temperature conditions in the α-quartz field where there is less inelastic deformation of the host–inclusion systems. On the other hand, entrapment isomekes of zircon inclusions entrapped in the β-quartz stability field plot along the α–β quartz phase boundary, suggesting that the inclusion strains were mechanically reset at the phase boundary during experimental cooling and decompression. Therefore, inclusions contained in soft host minerals can be used for elastic thermobarometry and inclusions contained in β-quartz may provide constraints on thePTat which the host–inclusion system crossed the phase boundary during exhumation.

     
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  9. PNAS (Ed.)
    While low-temperature Nuclear Magnetic Resonance (NMR) holds great promise for the analysis of unstable samples and for sensitizing NMR detection, spectral broadening in frozen protein samples is a common experimental challenge. One hypothesis explaining the additional linewidth is that a variety of conformations are in rapid equilibrium at room temperature and become frozen, creating an inhomogeneous distribution at cryogenic temperatures. Here, we investigate conformational heterogeneity by measuring the backbone torsion angle (Ψ) in Escherichia coli Dihydrofolate Reductase (DHFR) at 105 K. Motivated by the particularly broad N chemical shift distribution in this and other examples, we modified an established NCCN Ψ experiment to correlate the chemical shift of Ni+1 to Ψi. With selective 15N and 13C enrichment of Ile, only the unique I60-I61 pair was expected to be detected in 13C’-15N correlation spectrum. For this unique amide, we detected three different conformation basins based on dispersed chemical shifts. Backbone torsion angles Ψ were determined for each basin: 114 ± 7° for the major peak and 150 ± 8° and 164 ± 16° for the minor peaks as contrasted with 118° for the X-ray crystal structure (and 118° to 130° for various previously reported structures). These studies support the hypothesis that inhomogeneous distributions of protein backbone torsion angles contribute to the lineshape broadening in low-temperature NMR spectra. 
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    Free, publicly-accessible full text available February 28, 2025
  10. Abstract

    Quantifying ecosystem resilience to disturbance is important for understanding the effects of disturbances on ecosystems, especially in an era of rapid global change. However, there are few studies that have used standardized experimental disturbances to compare resilience patterns across abiotic gradients in real‐world ecosystems. Theoretical studies have suggested that increased return times are associated with increasing variance during recovery from disturbance. However, this notion has rarely been explicitly tested in field, in part due to the challenges involved in obtaining long‐term experimental data. In this study, we examined resilience to disturbance of 12 coastal marsh sites (five low‐salinity and seven polyhaline [=salt] marshes) along a salinity gradient in Georgia, USA. We found that recovery times after experimental disturbance ranged from 7 to >127 months, and differed among response variables (vegetation height, cover and composition). Recovery rates decreased along the stress gradient of increasing salinity, presumably due to stress reducing plant vigor, but only when low‐salinity and polyhaline sites were analyzed separately, indicating a strong role for traits of dominant plant species. The coefficient of variation of vegetation cover and height in control plots did not vary with salinity. In disturbed plots, however, the coefficient of variation (CV) was consistently elevated during the recovery period and increased with salinity. Moreover, higher CV values during recovery were correlated with slower recovery rates. Our results deepen our understanding of resilience to disturbance in natural ecosystems, and point to novel ways that variance can be used either to infer recent disturbance, or, if measured in areas with a known disturbance history, to predict recovery patterns.

     
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