Plastic pollution in the ocean is a rapidly emerging global environmental concern, with high concentrations (up to 580,000 pieces per km2) and a global distribution, driven by exponentially increasing production. Seabirds are particularly vulnerable to this type of pollution and are widely observed to ingest floating plastic. We used a mixture of literature surveys, oceanographic modeling, and ecological models to predict the risk of plastic ingestion to 186 seabird species globally.

We here demonstrate that we can resolve the seasonality of the hydrologic cycle in the Amazon using an approach, opposite to general circulation models, in which we resolve convection and parameterize large-scale circulation as a function of the resolved convection. The results emphasize the key role of cloud albedo feedback and, in particular, of the morning fog layer in determining the diurnal course of surface heat fluxes and seasonality of the surface and atmospheric heat and water cycles.

Methane from enteric fermentation in the ruminant digestive system is a major contributor to anthropogenic greenhouse gas emissions in the United States and worldwide. Methane is also a net loss of feed energy to the animal. This study was undertaken to investigate the effect of a methane inhibitor on enteric methane emissions from lactating dairy cows. The experiment demonstrated that, under industry-relevant conditions, the inhibitor persistently decreased by 30% enteric methane emissions, without negatively affecting animal productivity.

Malnutrition is a challenge to the health and productivity of populations and is viewed as one of the five largest adverse health impacts of climate change. Nonetheless, systematic evidence quantifying these impacts is currently limited. Our aim was to assess the scientific evidence base for the impact of climate change on childhood undernutrition (particularly stunting) in subsistence farmers in low- and middle-income countries.

Original Source

A wide array of scientific disciplines and industries use radiocarbon analyses; for example, it is used in dating of archaeological specimens and in forensic identification of human and wildlife tissues, including traded ivory. Over the next century, fossil fuel emissions will produce a large amount of CO2 with no 14C because fossil fuels have lost all 14C over millions of years of radioactive decay. Atmospheric CO2, and therefore newly produced organic material, will appear as though it has “aged,” or lost 14C by decay.

Indoor residual spraying (IRS) is used to control visceral leishmaniasis (VL) in India, but it is poorly quality assured. Quality assurance was performed in eight VL endemic districts in Bihar State, India, in 2014. Residual dichlorodiphenyltrichloroethane (DDT) was sampled from walls using Bostik tape discs, and DDT concentrations [grams of active ingredient per square meter (g ai/m2)] were determined using HPLC. Pre-IRS surveys were performed in three districts, and post-IRS surveys were performed in eight districts.

Traditional approaches for development of antibodies are poorly suited to combating the emergence of novel pathogens, as they require multiple steps of laborious optimization and process adaptation for clinical development.

Traditional methods for estimating malaria transmission based on mosquito sampling are not standardized and are unavailable in many countries in sub-Saharan Africa. Such studies are especially difficult to implement when transmission is low, and low transmission is the goal of malaria elimination. Malaria-control efforts in Senegal have resulted in changes in population genomics evidenced by increased allele sharing among parasite genomes, often including genomic identity between independently sampled parasites.

The evolution of drug resistance is a major health threat. In chronic infections with rapidly mutating pathogens—including HIV, tuberculosis, and hepatitis B and C viruses—multidrug resistance can cause even aggressive combination drug treatment to fail. Oftentimes, individual drugs within a combination do not penetrate equally to all infected regions of the body.

The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs.