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Peatland microbial community composition varies with respect to a range of biological and physicochemical variables. While the extent of peat degradation (humification) has been linked to microbial community composition along vertical stratification gradients within peatland sites, across-site variations have been relatively unexplored. In this study, we compared microbial communities across ten pristine Sphagnum-containing peatlands in the Adirondack Mountains, NY, which represented three different peat types—humic fen peat, humic bog peat, and fibric bog peat. Using 16S amplicon sequencing and network correlation analysis, we demonstrate that microbial community composition is primarily linked to peat type, and that distinct taxa networks distinguish microbial communities in each type. Shotgun metagenomic sequencing of the active water table region (mesotelm) from two Sphagnum-dominated bogs—one with fibric peat and one with humic peat—revealed differences in primary carbon degradation pathways, with the fibric peat being dominated by carbohydrate metabolism and hydrogenotrophic methanogenesis, and the humic peat being dominated by aliphatic carbon metabolism and aceticlastic methanogenesis. Our results suggest that peat humification is a major factor driving microbial community dynamics across peatland ecosystems.

The link between depression and anxiety status and cancer outcomes has been well-documented but remains unclear. We comprehensively quantified the association between depression and anxiety defined by symptom scales or clinical diagnosis and the risk of cancer incidence, cancer-specific mortality, and all-cause mortality in cancer patients. Pooled estimates of the relative risks (RRs) for cancer incidence and mortality were performed in a meta-analysis by random effects or fixed effects models as appropriate. Associations were tested in subgroups stratified by different study and participant characteristics. Fifty-one eligible cohort studies involving 2,611,907 participants with a mean follow-up period of 10.3 years were identified. Overall, depression and anxiety were associated with a significantly increased risk of cancer incidence (adjusted RR: 1.13, 95% CI: 1.06–1.19), cancer-specific mortality (1.21, 1.16–1.26), and all-cause mortality in cancer patients (1.24, 1.13–1.35). The estimated absolute risk increases (ARIs) associated with depression and anxiety were 34.3 events/100,000 person years (15.8–50.2) for cancer incidence and 28.2 events/100,000 person years (21.5–34.9) for cancer-specific mortality. Subgroup analyses demonstrated that clinically diagnosed depression and anxiety were related to higher cancer incidence, poorer cancer survival, and higher cancer-specific mortality. Psychological distress (symptoms of depression and anxiety) was related to higher cancer-specific mortality and poorer cancer survival but not to increased cancer incidence. Site-specific analyses indicated that overall, depression and anxiety were associated with an increased incidence risks for cancers of the lung, oral cavity, prostate and skin, a higher cancer-specific mortality risk for cancers of the lung, bladder, breast, colorectum, hematopoietic system, kidney and prostate, and an increased all-cause mortality risk in lung cancer patients. These analyses suggest that depression and anxiety may have an etiologic role and prognostic impact on cancer, although there is potential reverse causality; Furthermore, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. Early detection and effective intervention of depression and anxiety in cancer patients and the general population have public health and clinical importance.

Background Understanding the metabolic responses of wheat to drought stress is essential for developing strategies to enhance its resilience under water-deficit conditions. In this study, we investigated the metabolic and phenotypic responses of twelve Nordic spring wheat genotypes subjected to drought stress over 28 days in a high-throughput phenotyping facility. By integrating metabolic profiling with phenotypic assessments, we aimed to identify key metabolites and traits associated with drought tolerance. Results We identified nearly 200 metabolites that were differentially accumulated across four time points, including early drought and recovery phases. Of these, 25% were organic acids, 16.2% sugars and derivatives, 16.2% amino acids and derivatives, and 10.4% alkaloids, while the rest were mainly lipids, nucleotides and derivatives, and phenolic acids. Furthermore, 32 metabolites showed significant correlations with 17 phenotypic traits, highlighting potential biomarkers for drought tolerance. These metabolic markers could be utilized in screening programs to accelerate the breeding of drought-resilient spring wheat. Our findings suggest that metabolomic changes during drought stress and recovery involve critical pathways linked to osmoprotection, antioxidant activity, and energy metabolism, which differentiate tolerant from non-tolerant genotypes. Conclusion This study demonstrates the effectiveness of combining metabolomics with high-throughput phenotyping to dissect plant stress responses. By identifying key metabolic pathways and potential biomarkers for drought tolerance, our findings provide a valuable foundation for breeding climate-resilient wheat varieties. Moreover, this integrative approach enhances our understanding of plant adaptation to abiotic stress, contributing to future efforts in sustainable agriculture and food security.

Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. To get the best of the physical and statistical worlds, we propose a new framework - termed \"climate-invariant\" ML - incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid ML-physics simulations require domain-specific data and workflows that have been inaccessible to many ML experts. As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various ML baselines, alongside a hybrid baseline simulator, to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid ML-physics and high-fidelity climate simulations.