Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds

1. Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.
2. We apply a framework for using K‐means clustering to classify bird behavior using points from short time interval GPS tracks. K‐means clustering is a well‐known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K‐means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free‐flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life‐stage‐ and age‐related variation in behavior.
3. After filtering for data quality, the K‐means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non‐moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.
4. The K‐means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short‐interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high‐dimensional movement data, it provides insight into small‐scale variation in behavior that would not be possible with many other analytical approaches.

     

Role of miR‐218‐GREM1 axis in epithelial‐mesenchymal transition of oral squamous cell carcinoma: An in vivo and vitro study based on microarray data

Oral squamous cell carcinoma (OSCC) is a prevalent cancer that develops in the head and neck area and has high annual mortality despite optimal treatment. microRNA‐218 (miR‐218) is a tumour inhibiting non‐coding RNA that has been reported to suppress the cell proliferation and invasion in various cancers. Thus, our study aims to determine the mechanism underlying the inhibitory role of miR‐218 in OSCC. We conducted a bioinformatics analysis to screen differentially expressed genes in OSCC and their potential upstream miRNAs. After collection of surgical OSCC tissues, we detected GREM1 expression by immunohistochemistry, RT‐qPCR and Western blot analysis, and miR‐218 expression by RT‐qPCR. The target relationship between miR‐218 and GREM1 was assessed by dual‐luciferase reporter gene assay. After loss‐ and gain‐of‐function experiments, OSCC cell proliferation, migration and invasion were determined by MTT assay, scratch test and Transwell assay, respectively. Expression of TGF‐β1, Smad4, p21, E‐cadherin, Vimentin and Snail was measured by RT‐qPCR and Western blot analysis. Finally, effects of miR‐218 and GREM1 on tumour formation and liver metastasis were evaluated in xenograft tumour‐bearing nude mice. GREM1 was up‐regulated, and miR‐218 was down‐regulated in OSCC tissues, and GREM1 was confirmed to be the target gene of miR‐218. Furthermore, after up‐regulating miR‐218 or silencing GREM1, OSCC cell proliferation, migration and invasion were reduced. In addition, expression of TGF‐β signalling pathway‐related genes was diminished by overexpressing miR‐218 or down‐regulating GREM1. Finally, up‐regulated miR‐218 or down‐regulated GREM1 reduced tumour growth and liver metastasis in vivo. Taken together, our findings suggest that the overexpression of miR‐218 may inhibit OSCC progression by inactivating the GREM1‐dependent TGF‐β signalling pathway.