Arsenic speciation in freshwater fish using high performance liquid chromatography and inductively coupled plasma mass spectrometry.

Arsenic speciation in freshwater fish is crucial for providing meaningful consumption guidelines that allow the public to make informed decisions regarding its consumption. While marine fish have attracted much research interest due to their higher arsenic content, research on freshwater fish is limited due to the challenges in quantifying and identifying arsenic species present at trace levels. We describe here a sensitive method and its application to the quantification of arsenic species in freshwater fish.

Arsenic speciation in more than 1600 freshwater fish samples from fifty-three waterbodies in Alberta, Canada.

We report here arsenic speciation in 1643 freshwater fish samples, representing 14 common fish species from 53 waterbodies in Alberta, Canada. Arsenic species were extracted from fish muscle tissue. Arsenic species in the extracts were separated using anion-exchange high-performance liquid chromatography (HPLC) and quantified using inductively coupled plasma mass spectrometry (ICPMS).

Arsenic speciation in freshwater fish: challenges and research needs.

Food and water are the main sources of human exposure to arsenic. It is important to determine arsenic species in food because the toxicities of arsenic vary greatly with its chemical speciation. Extensive research has focused on high concentrations of arsenic species in marine organisms.

Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50.