Texas A&M University

About: Texas A&M University is a education organization based out in College Station, Texas, United States. It is known for research contribution in the topics: Population & Gene. The organization has 72169 authors who have published 164372 publications receiving 5764236 citations.

Formation of the Isthmus of Panama

Abstract: The formation of the Isthmus of Panama stands as one of the greatest natural events of the Cenozoic, driving profound biotic transformations on land and in the oceans. Some recent studies suggest that the Isthmus formed many millions of years earlier than the widely recognized age of approximately 3 million years ago (Ma), a result that if true would revolutionize our understanding of environmental, ecological, and evolutionary change across the Americas. To bring clarity to the question of when the Isthmus of Panama formed, we provide an exhaustive review and reanalysis of geological, paleontological, and molecular records. These independent lines of evidence converge upon a cohesive narrative of gradually emerging land and constricting seaways, with formation of the Isthmus of Panama sensu stricto around 2.8 Ma. The evidence used to support an older isthmus is inconclusive, and we caution against the uncritical acceptance of an isthmus before the Pliocene.

A Legume Ethylene-Insensitive Mutant Hyperinfected by Its Rhizobial Symbiont

Abstract: Development of the Rhizobium-legume symbiosis is controlled by the host plant, although the underlying mechanisms have remained obscure. A mutant in the annual legume Medicago truncatula exhibits an increase of more than an order of magnitude in the number of persistent rhizobial infections. Physiological and genetic analyses indicate that this same mutation confers insensitivity to the plant hormone ethylene for multiple aspects of plant development, including nodulation. These data support the hypothesis that ethylene is a component of the signaling pathway controlling rhizobial infection of legumes.

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

Abstract: We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring performance while being much more flexible and efficient. DeblurGAN-V2 is based on a relativistic conditional GAN with a double-scale discriminator. For the first time, we introduce the Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-V2. It can flexibly work with a wide range of backbones, to navigate the balance between performance and efficiency. The plug-in of sophisticated backbones (e.g. Inception ResNet v2) can lead to solid state-of-the-art performance. Meanwhile, with light-weight backbones (e.g. MobileNet and its variants), DeblurGAN-V2 becomes 10-100 times faster than the nearest competitors, while maintaining close to state-of-the-art results, implying the option of real-time video deblurring. We demonstrate that DeblurGAN-V2 has very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. In addition, we show the architecture to be effective for general image restoration tasks too. Our models and codes will be made available upon acceptance.