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Polyaniline (PANI) onto indium-doped tin-oxide (ITO)-coated glass samples were prepared by electroopolymerization in 0.5 M H2SO4 solution. Structure and morphology characterization of the PANI films demonstrated that the films were grown onto ITO substrates in the form of polycrystalline microbelts separated by micropores. By analysing the UV-Vis absorption spectra of the PANI films, the energy bandgap was found to be approximately 2.75 eV. The PANI/ITO films exhibited a good reversible electrochromic display (ECD) performance when cycled in 0.1 M LiClO4 + pro-pylene carbonate. The response time of the ECD coloration was found to be as small as 15 s and the coloration efficiency was found to be 8.85 cm2 C-1. After 100 cycles of the ECD performance, the cyclic voltammetry curve of the working electrode maintained unchanged. This demonstrates that the electropolymerized PANI films can be served as a good candidate for ECD applications, taking advantage of their excellent properties in terms of chemical stability.
MicroRNAs (miRNAs) are short
(~22nt) non-coding RNAs that play an indispensable role in gene regulation of
many biological processes. Most of current computational, comparative, and
non-comparative methods commonly classify
human precursor micro- RNA (pre-miRNA) hairpins from both genome pseudo
hairpins and other non-coding RNAs (ncRNAs). Although there were a few
approaches achieving promising results in applying class imbalance learning
methods, this issue has still not solved completely and successfully yet by the
existing methods because of imbalanced class distribution in the datasets. For
example, SMOTE is a famous and general over-sampling method addressing this
problem, however in some cases it cannot improve or sometimes reduces classification performance. Therefore,
we developed a novel over-sampling method named incre-mental- SMOTE to
distinguish human pre-miRNA hairpins from both genome pseudo hairpins and other
ncRNAs. Experimental results on pre-miRNA datasets from Batuwita et al. showed that our method achieved
better Sensitivity and G-mean than the control (no over- sampling), SMOTE,
and several successsors of modified SMOTE
including safe-level-SMOTE and border-line-SMOTE. In addition, we also
applied the novel method to five imbalanced benchmark datasets from UCI Machine
Learning Repository and achieved improvements in Sensitivity and G-mean.
These results suggest that our method outperforms SMOTE and several successors
of it in various biomedical classification problems including miRNA classification.