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Key aspects of international policy, such as those pertaining to migration and trade, manifest in the physical world at international political borders; for this reason, borders are of interest to political science studying the impacts and implications of these policies. While some prior efforts have worked to characterize features of borders us- ing trained human coders and crowdsourcing, these are limited in scale by the need for manual annotations. In this paper, we present a new task, dataset, and baseline approaches for estimating the legibility of international political borders automatically and on a global scale. Our contributions are to (1) define the border legibility estimation task; (2) collect a dataset of overhead (aerial) imagery for the entire world’s international borders, (3) propose several classical and deep-learning-based approaches to establish a baseline for the task, and (4) evaluate our algorithms against a validation dataset of crowdsourced legibility com- parisons. Our results on this challenging task confirm that while low-level features can often explain border legibility, mid- and high-level features are also important. Finally, we show preliminary results of a global analysis of legibility, confirming some of the political and geographic influences of legibility. 

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational software suite, Domain Analysis and Motif Matcher (DAMM), that analyzes peptide-binding cleft sequence identity as compared with human PDZ domains and that can be used in combination with literature searches of known human PDZ-interacting sequences to predict target specificity in choanoflagellate PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with <100 μM affinity, a value commonly considered the threshold for cellular PDZ–peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contribute to investigations into choanoflagellate signaling and how it informs metazoan evolution.