We investigated the focal mechanism distributions using the dataset obtained by the F-net over a longer time duration after the mainshock than Asano et al. (2011), and performed the stress tensor inversion by the multiple inverse method (Otsubo et al., 2008).
We have followed the long term, steady state, 11 year modulation of the galactic cosmic ray (GCR) intensity for several solar cycles, using the dataset obtained with a variety of detectors on Earth (neutron monitors and ion chambers, at different global sites) as well as at the balloon altitudes.
Finally, an experiment of abbreviation disambiguation on the sense inventory yielded 0.984 accuracy and 0.986 F1 score using the dataset obtained from MEDLINE abstracts.
Moreover, complex models can be used to fit the datasets obtained as it is possible with this analysis technique to prove statistically that they are correct.
This analysis was further used to compare the datasets obtained for the different lectins, yielding information about the proximity of different sugars to one another.
For the specific purpose of our study, we used only the dataset obtained from monocytes.
FDR cut-off value 0.007 (i.e., a conservative cut-off value for correcting over 28 samples separately) was used to filter the dataset obtained before proceeding with further analysis.
In contrast, hierarchical clustering that uses all genes in the dataset obtained from an entire tumor specimen usually groups samples together based predominantly on the gene expression pattern in carcinoma cells as these cells often represent the majority of the cells within a tumor and often show the most variation in expression patterns.
Using such a multi-technique approach, the dataset obtained in this study not only characterizes the PDCs and related hazards at Merapi, but will allow comparisons with similar events at other volcanoes around the globe.
By extracting the homologous PPIs by using the "UniRef50" dataset obtained from the UniProt Reference Clusters (UniRef, http://www.uniprot.org/uniref/), we found that, among the 123751 edges derived from BioGrid database, only 431 edges (approximately 0.348%) are homologous PPIs and 86 edges are cancer PPIs; it means that most of the PPIs are not homologous PPIs.
