Musculoskeletal modeling offers an invaluable insight into the spine biomechanics.
Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application.
The approach, which is based on field observations and robust statistical modeling, offers an alternative for stream restoration practitioners to more traditional streambank erosion prediction methods that underperform in the region, and may have applicability elsewhere.
Our results demonstrate that topic modeling offers an opportunity for use in the identification of hidden variables (topics) embedded in gene expression profiles.
Therefore, microenvironment modeling offers an effective mean of estimating population exposures to these pollutants without the considerable logistical difficulties of personal sampling.
Deciphering this complex network remains challenging, however our comparative network motif modeling offers an extensible and robust experimental framework to understand the dynamics of large-scale network in terms of elementary network patterns.
