Increment entropy as a measure of complexity for time series

Entropy has been a common index to quantify the complexity of time series in a variety of fields. Here, we introduce increment entropy to measure the complexity of time series in which each increment is mapped into a word of two letters, one letter corresponding to direction and the other corresponding to magnitude. The Shannon entropy of the words is termed as increment entropy (IncrEn). Simulations on synthetic data and tests on epileptic EEG signals have demonstrated its ability of detecting the abrupt change, regardless of energetic (e.g. spikes or bursts) or structural changes. The computation of IncrEn does not make any assumption on time series and it can be applicable to arbitrary real-world data.Comment: 12pages,7figure,2 table

Prediction of DNase I Hypersensitive Sites by Using Pseudo Nucleotide Compositions

DNase I hypersensitive sites (DHS) associated with a wide variety of regulatory DNA elements. Knowledge about the locations of DHS is helpful for deciphering the function of noncoding genomic regions. With the acceleration of genome sequences in the postgenomic age, it is highly desired to develop cost-effective computational methods to identify DHS. In the present work, a support vector machine based model was proposed to identify DHS by using the pseudo dinucleotide composition. In the jackknife test, the proposed model obtained an accuracy of 83%, which is competitive with that of the existing method. This result suggests that the proposed model may become a useful tool for DHS identifications

Potential pollen evidence for the 1933 M 7.5 Diexi earthquake and implications for post-seismic landscape recovery

The relationships between strong earthquakes, landslides, and vegetation destruction and the process of post-seismic recovery in tectonically active alpine valley areas have not been adequately documented. Here we show detailed pollen study results from a swamp located near the epicenter of the 1933 M 7.5 Diexi earthquake in eastern Qinghai-Tibetan Plateau (QTP) to reveal the impact of earthquake on vegetation, and the post-seismic recovery process. Based on(210)Pb-Cs-137 age model, the seismic event layer is well constrained. The earthquake event corresponds stratigraphically to a zone with the lowest pollen concentrations, the lowest pollen diversity, and a high frequency of non-arboreal pollen. Elaeagnaceae scrubs rapidly developed in post-seismic landscape recovery processes, which is important for reducing soil erosion and landslide activities. Natural ecological recovery is slow due to increasing human activities and historical climatic fluctuations