The PPCD1 Mouse: Characterization of a Mouse Model for Posterior Polymorphous Corneal Dystrophy and Identification of a Candidate Gene

The PPCD1 mouse, a spontaneous mutant that arose in our mouse colony, is characterized by an enlarged anterior chamber resulting from metaplasia of the corneal endothelium and blockage of the iridocorneal angle by epithelialized corneal endothelial cells. The presence of stratified multilayered corneal endothelial cells with abnormal patterns of cytokeratin expression are remarkably similar to those observed in human posterior polymorphous corneal dystrophy (PPCD) and the sporadic condition, iridocorneal endothelial syndrome. Affected eyes exhibit epithelialized corneal endothelial cells, with inappropriate cytokeratin expression and proliferation over the iridocorneal angle and posterior cornea. We have termed this the “mouse PPCD1” phenotype and mapped the mouse locus for this phenotype, designated “Ppcd1”, to a 6.1 Mbp interval on Chromosome 2, which is syntenic to the human Chromosome 20 PPCD1 interval. Inheritance of the mouse PPCD1 phenotype is autosomal dominant, with complete penetrance on the sensitive DBA/2J background and decreased penetrance on the C57BL/6J background. Comparative genome hybridization has identified a hemizygous 78 Kbp duplication in the mapped interval. The endpoints of the duplication are located in positions that disrupt the genes Csrp2bp and 6330439K17Rik and lead to duplication of the pseudogene LOC100043552. Quantitative reverse transcriptase-PCR indicates that expression levels of Csrp2bp and 6330439K17Rik are decreased in eyes of PPCD1 mice. Based on the observations of decreased gene expression levels, association with ZEB1-related pathways, and the report of corneal opacities in Csrp2bptm1a(KOMP)Wtsi heterozygotes and embryonic lethality in nulls, we postulate that duplication of the 78 Kbp segment leading to haploinsufficiency of Csrp2bp is responsible for the mouse PPCD1 phenotype. Similarly, CSRP2BP haploinsufficiency may lead to human PPCD

Islands in the stream: revisiting methodological nationalism under conditions of globalization

Reference to the sovereign nation-state has traditionally been held to lie at the heart of the conceptualisation of inter-state relations in social scientific discourse. Recent debates have sought to problematise the centrality of this concept, particularly under the banner of globalisation. Specifically, it has been argued that contemporary practices have weakened national boundaries thereby undermining the effectiveness of state actors and the relevance and explanatory value of the national. In this paper we examine the maritime industry as a ‘critical case’, and demonstrate how in an extensively globalised context the national understood as legitimate authority, grounded in territory, is still necessary to understand certain key processes. We present a series of cases in which the normal, smooth operation of shipping is disturbed, in order to reveal operative practice. Our claim is that methodological nationalism continues to be a legitimate perspective even in an extensively globalised context

Accurate delineation of individual tree crowns in tropical forests from aerial <scp>RGB</scp> imagery using Mask <scp>R‐CNN</scp>

Tropical forests are a major component of the global carbon cycle and home to two‐thirds of terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2, which builds on the Mask R‐CNN computer vision framework to recognize the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper‐canopy trees. Detectree2 delineated 65 000 upper‐canopy trees across 14 km2 of aerial images. The skill of the automatic method in delineating unseen test trees was good (F1 score = 0.64) and for the tallest category of trees was excellent (F1 score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate‐size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open‐source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration. Python package available to install at https://github.com/PatBall1/Detectree2