Evaluating sulfurization as a blue carbon sink in a southern California salt marsh - Lena R. Capece; Melea Bailey; Max Morrison; Alexandra A. Phillips; Lewis Sharpnack; Sam M. Webb; Dana C. Brenner; Maya Gomes; Morgan R. Raven (Limnology and Oceanography, 2025)
Blue carbon ecosystems such as seagrass meadows, mangrove forests, and salt marshes are important carbon sinks that can store carbon for millennia. Recently, organic matter sulfurization and pyritization have been proposed as mechanisms of net carbon storage in blue carbon ecosystems. At our study site, organic sulfur that is resistant to acid hydrolysis (protokerogen) is an order of magnitude less abundant than pyrite sulfur, suggesting a dominance of pyritization over sulfurization. The C/N ratios and carbon isotope compositions suggest that nearly half of total organic carbon and ≥ 80% of protokerogen is composed of marsh plant material. Sediment protokerogen appears to be sulfurized based on its low δ 34 S values (− 10‰), abundance of disulfides, and higher S/C ratio (~ 1.0%) relative to potential biogenic sulfur sources. However, the interpretation of protokerogen δ 34 S values is complicated by the wide range in sulfur isotope compositions of marsh plants. Evidence for sulfurization occurs within the shallowest sediments across different vegetation zones, yielding consistent products, while pyritization appears to be more sensitive to alterations in sediment redox conditions. Based on organic sulfur and pyrite content, sulfurization may be a more spatially consistent process than pyritization, with implications for carbon storage. The relative abundance of pyrite and protokerogen organic sulfur indicates that pyritization is favored at our study site, but this is likely to vary across the spectrum of blue carbon ecosystems.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Comparison of low-carbon pathways for California - Geoffrey M. Morrison; Sonia Yeh; Anthony R. Eggert; Christopher Yang; James H. Nelson; Jeffery B. Greenblatt; Raphael Isaac; Mark Z. Jacobson; Josiah Johnston; Daniel M. Kammen; Ana Mileva; Jack Moore; et al. (Climatic Change, 2015)