Glucagon-like peptide-1 (GLP-1) is an incretin hormone secreted from the gastrointestinal tract. It is best known for its glucose-dependent insulinotropic effects. GLP-1 is secreted in its intact (active) form (7-36NH) but is rapidly degraded by the dipeptidyl peptidase 4 (DPP-4) enzyme, converting >90% to the primary metabolite (9-36NH) before reaching the targets via the circulation. Although originally thought to be inactive or antagonistic, GLP-1 9-36NH may have independent actions, and it is therefore relevant to be able to measure it. Because reliable assays were not available, we developed a sandwich ELISA recognizing both GLP-1 9-36NH and nonamidated GLP-1 9-37. The ELISA was validated using analytical assay validation guidelines and by comparing it to a subtraction-based method, hitherto employed for estimation of GLP-1 9-36NH Its accuracy was evaluated from measurements of plasma obtained during intravenous infusions (1.5 pmol × kg × min) of GLP-1 7-36NH in healthy subjects and patients with type 2 diabetes. Plasma levels of the endogenous GLP-1 metabolite increased during a meal challenge in patients with type 2 diabetes, and treatment with a DPP-4 inhibitor fully blocked its formation. Accurate measurements of the GLP-1 metabolite may contribute to understanding its physiology and role of GLP-1 in diabetes.
Background
Due to their Arctic habitat and elusive nature, little is known about the narwhal (Monodon monoceros) and its foraging behaviour. Understanding its ability to catch prey is essential for understanding its ecological role, but also to assess its ability to withstand climate changes and anthropogenic activities. Narwhals produce echolocation clicks and buzzing sounds as part of their foraging behaviour and these can be used as indicators of prey capture attempts. However, acoustic data are expensive to store on the tagging devices and require complicated post-processing. The main goal of this paper is to predict prey capture attempts directly from acceleration and depth data. The aim is to apply broadly used statistical models with interpretable parameters. The ultimate goal is to be able to estimate prey consumption without the more demanding acoustic data.
Results
We predict narwhal buzzing activity using mixed-effects logistic regression models with 83 features extracted from acceleration and depth data as explanatory variables. The features encompass both instantaneous values as well as delayed values to capture behavioural patterns lasting several seconds. The data correlations were not strong enough to predict the exact timing of the buzzes, but were reliably able to detect buzzes within a few seconds. Most of the of the buzz predictions were within 2 s of an observed buzz (68%), increasing to 94% within 30 s. Conversely, 46% of the observed buzzes were within 2 s of a predicted buzz, increasing to 82% within 30 s. Additionally, the model performed well, although with a tendency towards underestimation of the number of buzzes per dive. In total, we predicted 17, 557 buzzes versus 25, 543 observed across data from 10 narwhals. Classifying foraging and non-foraging dives yielded a precision of 86% and a recall of 91%.
Conclusion
We conclude that narwhal foraging estimation through acceleration and depth data is a valid alternative or supplement to buzz recordings, even when using somewhat simple statistical methods, such as logistic regression. The methods in this paper can be extended to foraging detection in similar marine species and can aid instrument development.
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