BackgroundThe nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function.ResultsWe present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download.ConclusionsSimulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.
Rapid growth of modern technologies is bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud. Many data mining methods have been proposed for fraud detection. Nevertheless, there is always a contradiction that most methods are irrelevant to transaction sequence, yet sequence-related methods usually cannot learn information at single-transaction level well. In this paper, a new "withinbetweenwithin" sandwichstructured sequence learning architecture has been proposed by stacking an ensemble model, a deep sequential learning model and another top-layer ensemble classifier in proper order. Moreover, attention mechanism has also been introduced in to further improve performance. Models in this structure have been manifested to be very efficient in scenarios like fraud detection, where the information sequence is made up of vectors with complex interconnected features.
The visual quality of architectural heritage is crucial to the preservation of architectural features, enhancement of the environmental quality, and conservation of the sustainable development and adaptive use of architectural heritage. Few studies have explored the visual behavior characteristics of rural architectural heritage and which elements influence visual perception. Our study used eye-tracking technology to explore this issue. The results indicate that participants have different visual behavior characteristics for architectural heritage in different scenarios, with five eye movement metrics showing statistical differences. Featured elements attracted more visual attention. The visual behavior characteristics were related to the area, relative area, distance from center, and perimeter. Based on the results, decision-makers can target the sustainable and virtuous development of architectural heritage and enhance environmental quality.
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