For instance, the structure of the benchmarks readily allowed for the generation of survey questions.
On the other hand, the volume ratio of this structure to the benchmarking two-scale sphere is around 14.76%, indicating that the increase of Jsc is attributed to the shape rather than the volume because larger nanoparticles generally induce stronger surface plasmon and therefore a larger Jsc.
Quite remarkably, it grants a better precision in terms of its ability to identify the underlying structure in each of the benchmark datasets.
We tested our model on the RA benchmark (Sivasubramanian et al., 2009) (we used 53 of the 54 structures in the benchmark excluding the PDB: 2AI0 case because in RA benchmark the H3 loop was uncorrectly reported as a six-residue loop) and our results are more accurate than those provided by RA, considered the best method available at present, on the same benchmark.
Agent-based models follow a 'generative paradigm' [15] because they serve to model agents' individual behavior in a bottom-up fashion and then evaluate the resulting macro structure against the benchmark of empirical observations or theoretical expectations of this macro structure.
Here, we found that we were able to recover the 3D structures of our benchmark RNAs even though some MOHCA-seq constraints were not satisfied by our final models.
We randomly select 100 pairs of subtomograms for each of the benchmark structures.
Furthermore, a modified version of the same genetic algorithm is used to identify a state-space representation of the benchmark structure.
The results show that, even for the real data, the proposed method can reliably detect, locate and assess damage of the benchmark structure by inferring substructure stiffness losses using the identified modal parameters from the calibration and monitoring stages.
For each sequence of the benchmark, we derive reference structures from Rfam, by constrained folding of the associated Rfam consensus structure of the sequence.
