Multi vegetation model evaluation of the Green Sahara climate regime

During the Quaternary, the Sahara desert was periodically colonized by vegetation, likely because of orbitally induced rainfall increases. However, the estimated hydrological change is not reproduced in climate model simulations, undermining confidence in projections of future rainfall. We evaluated the relationship between the qualitative information on past vegetation coverage and climate for the mid-Holocene using three different dynamic vegetation models. Compared with two available vegetation reconstructions, the models require 500–800 mm of rainfall over 20°–25°N, which is significantly larger than inferred from pollen but largely in agreement with more recent leaf wax biomarker reconstructions. The magnitude of the response also suggests that required rainfall regime of the early to middle Holocene is far from being correctly represented in general circulation models. However, intermodel differences related to moisture stress parameterizations, biases in simulated present-day vegetation, and uncertainties about paleosoil distributions introduce uncertainties, and these are also relevant to Earth system model simulations of African humid periods

A Systematic Framework to Rapidly Obtain Data on Patients with Cancer and COVID-19:CCC19 Governance, Protocol, and Quality Assurance

When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments

A Systematic Framework to Rapidly Obtain Data on Patients with Cancer and COVID-19: CCC19 Governance, Protocol, and Quality Assurance

When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments. When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments