Cold seep formation from salt diapir-controlled deep biosphere oases.

Deep sea cold seeps are sites where hydrogen sulfide, methane, and other hydrocarbon-rich fluids vent from the ocean floor. They are an important component of Earth's carbon cycle in which subsurface hydrocarbons form the energy source for highly diverse benthic micro- and macro-fauna in what is otherwise vast and spartan sea scape. Passive continental margin cold seeps are typically attributed to the migration of hydrocarbons generated from deeply buried source rocks. Many of these seeps occur over salt tectonic provinces, where the movement of salt generates complex fault systems that can enable fluid migration or create seals and traps associated with reservoir formation. The elevated advective heat transport of the salt also produces a chimney effect directly over these structures. Here, we provide geophysical and geochemical evidence that the salt chimney effect in conjunction with diapiric faulting drives a subsurface groundwater circulation system that brings dissolved inorganic carbon, nutrient-rich deep basinal fluids, and potentially overlying seawater onto the crests of deeply buried salt diapirs. The mobilized fluids fuel methanogenic archaea locally enhancing the deep biosphere. The resulting elevated biogenic methane production, alongside the upward heat-driven fluid transport, represents a previously unrecognized mechanism of cold seep formation and regulation.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.