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.

New developments in fish amino acid nutrition: towards functional and environmentally oriented aquafeeds

Abstract: Recent evidence shows that some amino acids and their metabolites are important regulators of key metabolic pathways that are necessary for maintenance, growth, feed intake, nutrient utilization, immunity, behavior, larval metamorphosis, reproduction, as well as resistance to environmental stressors and pathogenic organisms in various fishes. Therefore, conventional definitions on essential and nonessential amino acids for fish are challenged by numerous discoveries that taurine, glutamine, glycine, proline and hydroxyproline promote growth, development, and health of aquatic animals. On the basis of their crucial roles in cell metabolism and physiology, we anticipate that dietary supplementation with specific amino acids may be beneficial for: (1) increasing the chemo-attractive property and nutritional value of aquafeeds with low fishmeal inclusion; (2) optimizing efficiency of metabolic transformation in juvenile and sub-adult fishes; (3) surpressing aggressive behaviors and cannibalism; (4) increasing larval performance and survival; (5) mediating timing and efficiency of spawning; (6) improving fillet taste and texture; and (7) enhancing immunity and tolerance to environmental stresses. Functional amino acids hold great promise for development of balanced aquafeeds to enhance the efficiency and profitability of global aquaculture production.

Cyber–Physical Systems: A Perspective at the Centennial

Abstract: Cyber-physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. Research on CPSs is fundamentally important for engineered systems in many important application domains such as transportation, energy, and medical systems. We overview CPS research from both a historical point of view in terms of technologies developed for early generations of control systems, as well as recent results on CPSs in many relevant research domains such as networked control, hybrid systems, real-time computing, real-time networking, wireless sensor networks, security, and model-driven development. We outline the potential for CPSs in many societally important application domains.

Graph Contrastive Learning with Augmentations

Abstract: Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different settings: semi-supervised, unsupervised, and transfer learning as well as adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods. We also investigate the impact of parameterized graph augmentation extents and patterns, and observe further performance gains in preliminary experiments. Our codes are available at this https URL.