Links across ecological scales: Plant biomass responses to elevated CO2.

The degree to which elevated CO2 concentrations (e[CO2 ]) increase the amount of carbon (C) assimilated by vegetation plays a key role in climate change. However, due to the short-term nature of CO2 enrichment experiments and the lack of reconciliation between different ecological scales, the effect of e[CO2 ] on plant biomass stocks remains a major uncertainty in future climate projections. Here, we review the effect of e[CO2 ] on plant biomass across multiple levels of ecological organization, scaling from physiological responses to changes in population-, community-, ecosystem-, and global-scale dynamics. We find that evidence for a sustained biomass response to e[CO2 ] varies across ecological scales, leading to diverging conclusions about the responses of individuals, populations, communities, and ecosystems. While the distinct focus of every scale reveals new mechanisms driving biomass accumulation under e[CO2 ], none of them provides a full picture of all relevant processes. For example, while physiological evidence suggests a possible long-term basis for increased biomass accumulation under e[CO2 ] through sustained photosynthetic stimulation, population-scale evidence indicates that a possible e[CO2 ]-induced increase in mortality rates might potentially outweigh the effect of increases in plant growth rates on biomass levels. Evidence at the global scale may indicate that e[CO2 ] has contributed to increased biomass cover over recent decades, but due to the difficulty to disentangle the effect of e[CO2 ] from a variety of climatic and land-use-related drivers of plant biomass stocks, it remains unclear whether nutrient limitations or other ecological mechanisms operating at finer scales will dampen the e[CO2 ] effect over time. By exploring these discrepancies, we identify key research gaps in our understanding of the effect of e[CO2 ] on plant biomass and highlight the need to integrate knowledge across scales of ecological organization so that large-scale modeling can represent the finer-scale mechanisms needed to constrain our understanding of future terrestrial C storage.

Effect of hybridization composition and glycerin content on novel corn starch/nata de coco plastic film: Thermal, mechanical, and degradation study.

The objective of this work was to develop a plastic film from food sources with excellent thermal, mechanical, and degradability performance. Corn starch (CS)/nata de coco (NDC) were hybridized with addition of glycerin as plasticizer at different weight ratio and weight percent, respectively. Sample analysis found that the hybridization of CS with NDC improved the film forming properties, mechanical and thermal, degradation properties, as well as hydrophobicity and solubility of the film up to 0.5:0.5 wt hybrid ratio. The properties of the films were highly affected by the homogeneity of the sample during hybridization, with high NDC amount (0.3:0.7 wt CS:NDC) showing poor hydrophobicity, and mechanical and thermal properties. The glycerin content, however, did not significantly affect the hydrophobicity, water solubility, and degradability properties of CS/NDC film. Hybridization of 0.5:0.5 wt CS/NDC with 2 phr glycerin provided the optimum Young's modulus (15.67 MPa) and tensile strength (1.67 MPa) properties.

A meta-analysis of global fungal distribution reveals climate-driven patterns.

The evolutionary and environmental factors that shape fungal biogeography are incompletely understood. Here, we assemble a large dataset consisting of previously generated mycobiome data linked to specific geographical locations across the world. We use this dataset to describe the distribution of fungal taxa and to look for correlations with different environmental factors such as climate, soil and vegetation variables. Our meta-study identifies climate as an important driver of different aspects of fungal biogeography, including the global distribution of common fungi as well as the composition and diversity of fungal communities. In our analysis, fungal diversity is concentrated at high latitudes, in contrast with the opposite pattern previously shown for plants and other organisms. Mycorrhizal fungi appear to have narrower climatic tolerances than pathogenic fungi. We speculate that climate change could affect ecosystem functioning because of the narrow climatic tolerances of key fungal taxa.

Resource-ratio theory predicts mycorrhizal control of litter decomposition.

Ecosystems with ectomycorrhizal plants have high soil carbon : nitrogen ratios, but it is not clear why. The Gadgil effect, where competition between ectomycorrhizal and saprotrophic fungi for nitrogen slows litter decomposition, may increase soil carbon. However, experimental evidence for the Gadgil effect is equivocal. Here, we apply resource-ratio theory to assess whether interguild fungal competition for different forms of organic nitrogen can affect litter decomposition. We focus on variation in resource input ratios and fungal resource use traits, and evaluate our model's predictions by synthesizing prior experimental literature examining ectomycorrhizal effects on litter decomposition. In our model, resource input ratios determined whether ectomycorrhizal fungi suppressed saprotrophic fungi. Recalcitrant litter inputs favored the former over the latter, allowing the Gadgil effect only when such inputs predominated. Although ectomycorrhizal fungi did not always hamper litter decomposition, ectomycorrhizal nitrogen uptake always increased carbon : nitrogen ratios in litter. Our meta-analysis of empirical studies supports our theoretical results: ectomycorrhizal fungi appear to slow decomposition of leaf litter only in forests where litter inputs are highly recalcitrant. We thus find that the specific contribution of the Gadgil effect to high soil carbon : nitrogen ratios in ectomycorrhizal ecosystems may be smaller than predicted previously.

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis.

Motivation: Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools. Results: The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions. Availability and implementation: https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele. Contact: darrell.hurt@nih.gov.

DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS.

The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.