A New Genus of Miniaturized and Pug-Nosed Gecko from South America (Sphaerodactylidae: Gekkota)

Sphaerodactyl geckos comprise five genera distributed across Central and South America and the Caribbean. We estimated phylogenetic relationships among sphaerodactyl genera using both separate and combined analyses of seven nuclear genes. Relationships among genera were incongruent at different loci and phylogenies were characterized by short, in some cases zero-length, internal branches and poor phylogenetic support at most nodes. We recovered a polyphyletic Coleodactylus, with Coleodactylus amazonicus being deeply divergent from the remaining Coleodactylus species sampled. The C. amazonicus lineage possessed unique codon deletions in the genes PTPN12 and RBMX while the remaining Coleodactylus species had unique codon deletions in RAG1. Topology tests could not reject a monophyletic Coleodactylus, but we show that short internal branch lengths decreased the accuracy of topology tests because there were not enough data along these short branches to support one phylogenetic hypothesis over another. Morphological data corroborated results of the molecular phylogeny, with Coleodactylus exhibiting substantial morphological heterogeneity. We identified a suite of unique craniofacial features that differentiate C. amazonicus not only from other Coleodactylus species, but also from all other geckos. We describe this novel sphaerodactyl lineage as a new genus, Chatogekko gen. nov. We present a detailed osteology of Chatogekko, characterizing osteological correlates of miniaturization that provide a framework for future studies in sphaerodactyl systematics and biology

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Structural evolution drives diversification of the large LRR-RLK gene family

Cells are continuously exposed to chemical signals that they must discriminate between and respond to appropriately. In embryophytes, the leucine‐rich repeat receptor‐like kinases (LRR‐RLKs) are signal receptors critical in development and defense. LRR‐RLKs have diversified to hundreds of genes in many plant genomes. Although intensively studied, a well‐resolved LRR‐RLK gene tree has remained elusive. To resolve the LRR‐RLK gene tree, we developed an improved gene discovery method based on iterative hidden Markov model searching and phylogenetic inference. We used this method to infer complete gene trees for each of the LRR‐RLK subclades and reconstructed the deepest nodes of the full gene family. We discovered that the LRR‐RLK gene family is even larger than previously thought, and that protein domain gains and losses are prevalent. These structural modifications, some of which likely predate embryophyte diversification, led to misclassification of some LRR‐RLK variants as members of other gene families. Our work corrects this misclassification. Our results reveal ongoing structural evolution generating novel LRR‐RLK genes. These new genes are raw material for the diversification of signaling in development and defense. Our methods also enable phylogenetic reconstruction in any large gene family