Radioisotopic chronology of Ocean Anoxic Event 1a: Framework for analysis of driving mechanisms.

The timing, tempo, and causative mechanisms of Ocean Anoxic Event 1a (OAE1a), one of several such abrupt perturbations of the Mesozoic global carbon cycle, remain uncertain. Mudstones interbedded with tuffs in Hokkaido, Japan preserve carbon and osmium isotope shifts recording OAE1a. U-Pb zircon ages of tuffs constrain the OAE1a onset to 119.

Trends in Pediatric Hospital Admissions Caused or Contributed by SARS-CoV-2 Infection in England.

Objective: To investigate the changing characteristics of SARS-CoV-2-related pediatric hospital admissions over time.

Study Design: This was a national, observational cohort study from July 1, 2020, to August 31, 2023, using English population-linked electronic health records. We identified 45 203 children younger than 18 years old in whom SARS-CoV-2 either caused or contributed to hospitalization, excluding those admitted with "incidental" infection.

Start of the COVID-19 Pandemic and Palliative Care Unit Utilization: A Retrospective Cohort Study.

Context: People with noncancer diagnoses have poorer access to palliative care units (PCUs) or hospices compared to those with cancer diagnoses. The COVID-19 pandemic disrupted how specialist palliative care services were delivered and utilized.

Objective: To determine the association between the start of the COVID-19 pandemic and PCU/hospice utilization in hospitalized individuals with cancer and noncancer diagnoses with specialist palliative care needs.

Protracted metallogenic and magmatic evolution of the Kirazlı epithermal Au-Ag and porphyry Cu deposits, Biga Peninsula, NW Turkey: evidence from zircon U-Pb, muscovite Ar/Ar, and molybdenite Re-Os geochronology.

Unlabelled: The Kirazlı deposit is located at the center of the Biga Peninsula metallogenic province, in a geological setting characterized by an extensional tectonic environment. A NNW-SSE trending high-sulfidation (HS) orebody with a total reserve of 33.86 Mt @ 0.

Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches.

Background: The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening.

Objective: This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app.

Methods: Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used.