Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.

Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by fundus images and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have utilized both genetic and image data for predicting AMD progression. Here we jointly used genotypes and fundus images to predict an eye as having progressed to late AMD with a modified deep convolutional neural network (CNN). In total, we used 31,262 fundus images and 52 AMD-associated genetic variants from 1,351 subjects from the Age-Related Eye Disease Study (AREDS) with disease severity phenotypes and fundus images available at baseline and follow-up visits over a period of 12 years. Our results showed that fundus images coupled with genotypes could predict late AMD progression with an averaged area under the curve (AUC) value of 0.85 (95%CI: 0.83-0.86). The results using fundus images alone showed an averaged AUC of 0.81 (95%CI: 0.80-0.83). We implemented our model in a cloud-based application for individual risk assessment.

CAM plant expansion favored indirectly by asymmetric climate warming and increased rainfall variability.

Recent observational evidence suggests that nighttime temperatures are increasing faster than daytime temperatures, while in some regions precipitation events are becoming less frequent and more intense. The combined ecological impacts of these climatic changes on crassulacean acid metabolism (CAM) plants and their interactions with other functional groups (i.e., grass communities) remain poorly understood. Here we developed a growth chamber experiment to investigate how two CAM-grass communities in desert ecosystems of the southwestern United States and northern Mexico respond to asymmetric warming and increasing rainfall variability. Grasses generally showed competitive advantages over CAM plants with increasing rainfall variability under ambient temperature conditions. In contrast, asymmetric warming caused mortality of both grass species (Bouteloua eriopoda and Bouteloua curtipendula) in both rainfall treatments due to enhanced drought stress. Grass mortality indirectly favored CAM plants even though the biomass of both CAM species Cylindropuntia imbricata and Opuntia phaeacantha significantly decreased. The stem's volume-to-surface ratio of C. imbricata was significantly higher in mixture than in monoculture under ambient temperature (both P < 0.0014); however, the difference became insignificant under asymmetric warming (both P > 0.1625), suggesting that warming weakens the negative effects of interspecific competition on CAM plant growth. Our findings suggest that while the increase in intra-annual rainfall variability enhances grass productivity, asymmetric warming may lead to grass mortality, thereby indirectly favoring the expansion of co-existing CAM plants. This study provides novel experimental evidence showing how the ongoing changes in global warming and rainfall variability affect CAM-grass growth and interactions in dryland ecosystems.