Jihua Wang

TL;DR: The accuracy of the iterative use of predicted secondary structure and backbone torsion angles and dihedrals based on Cα atoms is higher than those of model structures from current state-of-the-art techniques, suggesting the potentially beneficial use of these predicted properties for model assessment and ranking.

TL;DR: It is demonstrated that a graphene single-crystal domain patterned into multiple channels can measure time- and concentration-dependent DNA hybridization kinetics and affinity reliably and sensitively, with a detection limit of 10 pM for DNA.

TL;DR: The time has come to finish off the final stretch of the long march towards protein secondary structure prediction as more powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques forsecondary structure prediction.

TL;DR: A newly developed method SPIDER2 is described that utilizes three iterations of deep learning neural networks to improve the prediction accuracy of several structural properties simultaneously and provides state-of-the-art, all-in-one accurate prediction of local structure and solvent accessible surface area.

TL;DR: Wang et al. as mentioned in this paper designed a pyroelectric nanogenerator by absorbing optical energy as surface enhanced Raman scattering (SERS) substrate for in-situ monitoring the complete oxidation reaction from 4-aminothiophenol (4-ATP) to 4-nitrothiophenolate (NTP) and the oxygen reduction reaction (ORR) intermediates.