A set–reset latch is a basic building block of computers and can be used to store state information. Here, by testing the influence of the two logical input signals on the reliable set–reset latch logic operation in the bistable system, we found that there are two types of input signals, namely, suprathreshold and subthreshold signals. For the suprathreshold signals, reliable set–reset logic operation can be achieved without any driving forces and exhibits certain anti-interference ability; for the subthreshold signals, a single harmonic could induce correct set–reset latch logic operation but with a narrow optimal parameter region. The introduction of biharmonic-induced set–reset latch logic operation (logical vibrational resonance) could greatly expand the parameter region. Explanations for the above results were provided by taking the logical inputs as the dynamic bias to analyze the dynamic changes in the system. Finally, the results were further verified by circuit simulation and actual hardware circuit.
Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.