Biodiversity and its responses to environmental changes is a central issue in ecology, and for society. Almost all microbial biodiversity researches focus on species richness and abundance but ignore the interactions among different microbial species/populations. However, determining the interactions and their relationships to environmental changes in microbial communities is a grand challenge, primarily due to the lack of information on the network structure among different microbial species/populations. Here, a novel random matrix theory (RMT)-based conceptual framework for identifying functional ecological gene networks (fEGNs) is developed with the high throughput functional gene array hybridization data from the grassland microbial communities in a long-term FACE (Free Air CO2 Enrichment) experiment. Both fEGNs under elevated CO2 (eCO2) and ambient CO2 (aCO2) possessed general characteristics of many complex systems such as scale-free, small-world, modular and hierarchical. However, the topological structure of the fEGNs is distinctly different between eCO2 and aCO2, suggesting that eCO2 dramatically altered the interactions among different microbial functional groups/populations. In addition, the changes in network structure were significantly correlated with soil carbon and nitrogen dynamics, and plant productivity, indicating the potential importance of network interactions in ecosystem functioning. Elucidating network interactions in microbial communities and their responses to environmental changes are fundamentally important for research in microbial ecology, systems microbiology, and global change.

Rising medical costs is a real and imminent problem; as a major health care services provider, Medicare covers nearly 16% of the U.S. population. This paper aims to investigate factors that a ect the cost and quality of acute care hospitals reim- bursed by Medicare. Multiple datasets from the Center for Medicare and Medicaid Services and IRS were merged to evaluate the e ect of hospital ownership and local socioeconomic levels on hospital performance, based on three main quality metrics: average cost per bene ciary, 30-day readmission rate, and hospital acquired conditions (HAC) score. Both variables were found to have statistically signi cant e ects. In addition, the results showed that CMS policy may have unfairly penalized hospitals serving more vulnerable populations, by using a metric that did not consider hospital location in lower income neighborhoods.