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Abstract BackgroundClimate change is expected to alter fire return intervals in cold and wet forests in the northwestern United States. This coupled with an expected rise in prescribed fires to restore healthy forests, disproportionately increases risk to saplings of tree species adapted to colder and wetter environments that have low fire resistance. To assess this potential impact, we evaluated the impacts of increasing fire intensity onPicea engelmanniiandThuja plicatasapling physiology, morphology, and mortality. This was achieved using established pyro-ecophysiology experiments where saplings were subjected to controlled surface fires across a range of fire intensities and post-fire growth, physiology and mortality were assessed up to 7 months post-fire. ResultsIn this study we demonstrate that the probability of mortality in the saplings of these two conifer species displays a sigmoidal increase with increasing fire intensity. At fire radiative energy dosage levels < 0.6 MJ m⁻², the observed mortality in both species was lower than predicted by existing crown scorch-based models due to their limited sensitivity at small diameters. Prior to sapling death, chlorophyll fluorescence transiently recovers before a rapid decline, though the timing varies by species and fire intensity dosage. A new general sapling mortality model derived from 7 conifer species is presented. ConclusionsOur results provide predictive tools that managers could use to make informed decisions on the potential impacts of fires on conifer saplings growing in cold and wet environments. Results from both species suggest that chlorophyll fluorescence temporal trends could serve as a potential early warning indicator of fire-induced tree mortality, however, future work should explore whether similar responses are observable using remote sensing data from solar-induced chlorophyll fluorescence and assess potential mechanisms underlying this signal. The general sapling mortality model presented in this paper appears to provide an improved method of predicting conifer sapling mortality over existing approaches, however, research is needed to develop coefficients to adjust the model with tree age and environmental factors. Further studies could also explore whether phenotypic plasticity is driving observed tree responses to fire from plants grown from similar environments. 

Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation masks. To address this, we propose a generative deep learning framework that produces high-quality image-mask pairs as auxiliary training data. Unlike traditional generative models that separate data generation from model training, ours uses multi-level optimization for end-to-end data generation. This allows segmentation performance to guide the generation process, producing data tailored to improve segmentation outcomes. Our method demonstrates strong generalization across 11 medical image segmentation tasks and 19 datasets, covering various diseases, organs, and modalities. It improves performance by 10–20% (absolute) in both same- and out-of-domain settings and requires 8–20 times less training data than existing approaches. This greatly enhances the feasibility and cost-effectiveness of deep learning in data-limited medical imaging scenarios. 

Spectral indices are widely used to assess vegetation fire severity following wildland fires. Although essential, ground-based assessments of how such indices change due to varying fire intensities remain limited, especially with deciduous tree species that exhibit resprouting. In this paper, we evaluate the efficacy of detecting post-fire physiological change and top kill in quaking aspen (Populus tremuloides) saplings using differenced spectral indices. Saplings (n = 64) were burned under controlled conditions over a range of discrete fire intensity levels from 0 to 4.0 MJ m−2, and reflectance was collected pre-fire and at six post-fire intervals up to 16 weeks. Ten spectral indices (CCI, CSI, MIRBI, NDVIL8, NBR, NBRL8, PRI, SAVI, SW-NIRratio, and SW-SWratio) were calculated, differenced from pre-fire, and related to the change in net photosynthesis and top kill. Fire intensity most strongly influenced the observed spectral changes at weeks 1–2 post-fire, especially for ΔCSI, ΔCCI, and ΔPRI. Pre- to post-fire change in net photosynthesis was strongly related (Tjur’s R2 > 0.5) with ΔCCI, ΔCSI, ΔNBRL8, and the ΔSW–NIR ratio at one week post-fire. Of the spectral indices assessed, ΔCCI and ΔPRI were most effective at predicting top kill. This study illustrates the potential of spectral indices for monitoring vegetation fire severity in deciduous tree species. 

BackgroundAlthough fires can cause tree mortality or reduce post-fire growth in trees of all ages, and models exist that predict fire-induced mortality in mature trees, the development of predictive models of how fires impact younger trees has received less attention. AimsTo assess whether inclusion of fire behaviour metrics alongside pre- and post-fire sapling morphological traits improve the prediction of fire-induced tree mortality as compared to existing models. MethodsIn this study, we subjected Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) and Pinus monticola var. minima Lemmon saplings to increasing levels of fire intensity and evaluated models to predict immediate and delayed post-fire mortality. Key resultsFor Pinus monticola, the optimal model relied on the post-fire crown volume scorched, while for Pseudotsuga menziesii the optimal model used flame height and fire radiative energy. We show that while Pinus monticola saplings exhibit immediate fire-induced mortality, Pseudotsuga menziesii saplings are prone to delayed fire-induced mortality. ImplicationsEven in younger trees, crown volume scorched and related metrics remain consistent predictors of fire-induced tree mortality. Future studies should track mortality over extended periods to ensure that developed models better represent delayed fire-induced tree mortality. 

Abstract Extreme ultraviolet (EUV; 100–912 Å) photons influence the formation and evolution of planets by ionizing hydrogen and other species, but the EUV radiation of most exoplanet host stars is poorly constrained. This work applies the differential emission measure (DEM) technique to infer the EUV spectra of eight stars previously observed with high signal-to-noise ratios by the Extreme Ultraviolet Explorer (EUVE; 1992–2002) spanning spectral types from M to F: AD Leo (MV), $\mathcal{E}$Eri (KV),κ¹Ceti (GV), Procyon (FIV–V),αCen A + B (GV + KV), andξBoo A + B (GV + KV). The model spectra are accurate to within a factor of 3 for the majority of individual EUVE flux density data points and accurate to within 30% of the integrated 100–300 Å fluxes. We provide the atomic data used for each star’s calculations, X-ray/EUV/far-ultraviolet (noncontemporaneous) observational inputs, the DEM models, and the model-predicted EUV spectra (extending beyond the 90–510 Å range of the EUVE spectra) and compare the results to archival EUVE data. We also find that the relative contributions of the transition region and corona to a star’s EUV emission vary significantly among the few stars analyzed here. The flare-driven variability of the corona is greater than the transition region, so the EUV spectrum of each star will have stochastic flare excess contributions at different wavelengths depending on the star’s temperature structure, flare behavior, and activity cycle. We conclude with a discussion of the implications of this variability for analyses that use EUV estimates to study planetary atmospheric evolution. 

BackgroundRecent studies looking to advance knowledge of fire-effects on trees have used both heated water baths and experimental laboratory fires to apply heat to plant tissues. AimsWe assessed whether heated water baths and experimental laboratory fires caused xylem cell wall deformation and increased vulnerability to embolism. MethodsUsing Pinus ponderosa and Pinus monticola saplings, we measured impacts using both heated water bath treatments and experimental laboratory fires, with parameters elucidated by prior studies that observed effects associated with lethal outcomes. Key resultsWe show that increased vulnerability to embolism only occurred in one of the species tested when using the heated water baths and did not occur in either species when using the laboratory fire treatments. Neither treatment caused xylem cell deformations. ConclusionsHeated water baths may generate misleading results in some species and therefore should be used with caution when researching effects due to wildland fires. ImplicationsFuture studies should assess the potential of other common fire dynamics proxy methods. 

Abstract Teeth scans are essential for many applications in orthodontics, where the teeth structures are virtualized to facilitate the design and fabrication of the prosthetic piece. Nevertheless, due to the limitations caused by factors such as viewing angles, occlusions, and sensor resolution, the 3D scanned point clouds (PCs) could be noisy or incomplete. Hence, there is a critical need to enhance the quality of the teeth PCs to ensure a suitable dental treatment. Toward this end, we propose a systematic framework including a two-step data augmentation (DA) technique to augment the limited teeth PCs and a hybrid deep learning (DL) method to complete the incomplete PCs. For the two-step DA, we first mirror and combine the PCs based on the bilateral symmetry of the human teeth and then augment the PCs based on an iterative generative adversarial network (GAN). Two filters are designed to avoid the outlier and duplicated PCs during the DA. For the hybrid DL, we first use a deep autoencoder (AE) to represent the PCs. Then, we propose a hybrid approach that selects the best completion to the teeth PCs from AE and a reinforcement learning (RL) agent-controlled GAN. Ablation study is performed to analyze each component’s contribution. We compared our method with other benchmark methods including point cloud network (PCN), cascaded refinement network (CRN), and variational relational point completion network (VRC-Net), and demonstrated that the proposed framework is suitable for completing teeth PCs with good accuracy over different scenarios. 

Abstract DNA methylation at cytosine bases (5-methylcytosine, 5mC) is a heritable epigenetic mark regulating gene expression. While enzymes that metabolize 5mC are well-characterized, endogenous signaling molecules that regulate DNA methylation machinery have not been described. We report that physiological nitric oxide (NO) concentrations reversibly inhibit the DNA demethylases TET and ALKBH2 by binding to the mononuclear non-heme iron atom forming a dinitrosyliron complex (DNIC) and preventing cosubstrates from binding. In cancer cells treated with exogenous NO, or endogenously synthesizing NO, 5mC and 5-hydroxymethylcytosine (5hmC) increase, with no changes in DNA methyltransferase activity. 5mC is also significantly increased in NO-producing patient-derived xenograft tumors from mice. Genome-wide methylome analysis of cells chronically treated with NO (10 days) shows enrichment of 5mC and 5hmC at gene-regulatory loci, correlating with altered expression of NO-regulated tumor-associated genes. Regulation of DNA methylation is distinctly different from canonical NO signaling and represents a unique epigenetic role for NO.