Assessing mechanisms for microbial taxa and community dynamics using process models.

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer-resource model with a neutral model in stochastic differential equations.

Disentangling direct from indirect relationships in association networks.

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks.

Functionally Graded, Bone- and Tendon-Like Polyurethane for Rotator Cuff Repair.

Critical considerations in engineering biomaterials for rotator cuff repair include bone-tendon-like mechanical properties to support physiological loading and biophysicochemical attributes that stabilize the repair site over the long-term. In this study, UV-crosslinkable polyurethane based on quadrol (Q), hexamethylene diisocyante (H), and methacrylic anhydride (M; QHM polymers), which are free of solvent, catalyst, and photoinitiator, is developed. Mechanical characterization studies demonstrate that QHM polymers possesses phototunable bone- and tendon-like tensile and compressive properties (12-74 MPa tensile strength, 0.

Random sampling process leads to overestimation of β-diversity of microbial communities.

The site-to-site variability in species composition, known as β-diversity, is crucial to understanding spatiotemporal patterns of species diversity and the mechanisms controlling community composition and structure. However, quantifying β-diversity in microbial ecology using sequencing-based technologies is a great challenge because of a high number of sequencing errors, bias, and poor reproducibility and quantification. Herein, based on general sampling theory, a mathematical framework is first developed for simulating the effects of random sampling processes on quantifying β-diversity when the community size is known or unknown.