N6-methyladenosine (m6A) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of m6A sites identified from different species using high-throughput experiments provides a rich resource to construct in-silico approaches for identifying m6A sites. The existing m6A predictors are developed using conventional machine-learning (ML) algorithms and most are species-centric. In this paper, we develop a novel cross-species deep-learning classifier based on bidirectional Gated Recurrent Unit (BGRU) for the prediction of m6A sites. In comparison with conventional ML approaches, BGRU achieves outstanding performance for the Mammalia dataset that contains over fifty thousand m6A sites but inferior for the Saccharomyces cerevisiae dataset that covers around a thousand positives. The accuracy of BGRU is sensitive to the data size and the sensitivity is compensated by the integration of a random forest classifier with a novel encoding of enhanced nucleic acid content. The integrated approach dubbed as BGRU-based Ensemble RNA Methylation site Predictor (BERMP) has competitive performance in both cross-validation test and independent test. BERMP also outperforms existing m6A predictors for different species. Therefore, BERMP is a novel multi-species tool for identifying m6A sites with high confidence. This classifier is freely available at http://www.bioinfogo.org/bermp.

In the field of bionic soft robots and microrobots, artificial muscle materials have exhibited unique potential for cutting-edge applications. However, current mainstream thermal-responsive artificial muscles based on semicrystalline polymers (SCPs), despite their excellent physical properties, suffer from the limitation of environmental stimuli in practice, while their photodriven counterparts adopting liquid crystal elastomers (LCEs) lack ductility. Herein, a novel multifunctional programmable artificial muscle with a unique patch-sewing structure formed by π−π stacking between azobenzene groups was designed, which combined the advantages of SCPs and LCEs. The nanocomposite demonstrated a unique combination between artificial muscle performance (46.5 times the energy density and 26.6 times the power density of human skeletal muscles) and programmability (274.84% strain and 100% shape-memory recovery rate within 1 s). Meanwhile, coupling the photoisomerization of azobenzene and the photothermal conversion of gold nanorods, the cycle of deformation triggered by ultraviolet light and restoring by infrared light could be accomplished rapidly within 30 s. A COMSOL Multiphysics model was established and the corresponding finite element analysis verified the photoactuation and captured the general principle of light initiation in elastomers. These demonstrate that the multifunctional programmable elastomer is promising for artificial muscle applications, especially for photoinduced actuation.

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This paper presented an automatic morphological method to extract a vascular tree using an angiogram. Under the assumption that vessels are connected in a local linear pattern in a noisy environment, the algorithm decomposes the vessel extraction problem into several consecutive morphological operators, aiming to characterize and distinguish different patterns on the angiogram: background, approximate vessel region and the boundary. It started with a contrast enhancement and background suppression process implemented by subtracting the background from the original angiogram. The background was estimated using multiscale morphology opening operators by varying the size of structuring element on each pixel. Subsequently, the algorithm simplified the enhanced angiogram with a combined fuzzy morphological opening operation, with linear rotating structuring element, in order to fit the vessel pattern. This filtering process was then followed by simply setting a threshold to produce approximate vessel region. Finally, the vessel boundaries were detected using watershed techniques with the obtained approximate vessel centerline, thinned result of the obtained vessel region, as prior marker for vessel structure. Experimental results using clinical digitized vascular angiogram and some comparative performance of the proposed algorithm were reported.

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