Protease-producing bacteria are known to play an important role in degrading sedimentary particular organic nitrogen, and yet, their diversity and extracellular proteases remain largely unknown. In this paper, the diversity of the cultivable protease-producing bacteria and their extracellular proteases in the sediments of the South China Sea was investigated. The richness of the cultivable protease-producing bacteria reached 10(6) cells/g in all sediment samples. Analysis of the 16S rRNA gene sequences revealed that the predominant cultivated protease-producing bacteria are Gammaproteobacteria affiliated with the genera Pseudoalteromonas, Alteromonas, Marinobacter, Idiomarina, Halomonas, Vibrio, Shewanella, Pseudomonas, and Rheinheimera, with Alteromonas (34.6%) and Pseudoalteromonas (28.2%) as the predominant groups. Inhibitor analysis showed that nearly all the extracellular proteases from the bacteria are serine proteases or metalloproteases. Moreover, these proteases have different hydrolytic ability to different proteins, reflecting they may belong to different kinds of serine proteases or metalloproteases. To our knowledge, this study represents the first report of the diversity of bacterial proteases in deep-sea sediments.

Background:The mechanism of marine elastin degradation is unclear. Results: A novel M23 metalloprotease pseudoalterin from a marine bacterium degraded elastin by cleaving both the glycyl bonds and the peptide bonds involved in cross-linking. Conclusion: Pseudoalterin adopts a novel elastolytic mechanism different from other M23 metalloproteases. Significance: The results shed light on the mechanism of marine elastin degradation.

Background:The mechanism of marine collagen degradation is largely unknown. Results: Myroicolsin, a subtilisin-like protease from a marine bacterium, was characterized, and its collagenolytic mechanism was studied. Conclusion: Myroicolsin has a novel domain structure and a unique collagen degradation mechanism compared with other subtilisin-like proteases. Significance: This study provides new insights into the mechanism of subtilisin-like proteases' collagenolysis and marine nitrogen cycling.

No abstract

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence.The wave function and the temporal evolution contain all information of quantum physics. However, they might be possibly the hardest to grasp in the classical world. Seventy years ago, Feynman proposed a path integral approach which has been viewed as the "sum over paths or histories" version of quantum mechanics, i.e. the wave function can be represented as a coherent superposition of contributions of all possible spatio-temporal paths [1,2]. Even though the Feynman's path integral (FPI) has been considered as the most fundamental way to interpret the quantum mechanics and answer what is the nature of measurements, the complete characterization of quantum wavepacket with all possible paths is formidable due to track ergodicity. Typically, only a very limited amount of paths could be accessed, and therefore only a reduced amount of information of quantum wavepacket could be obtained in different approximation methods so far.The development history of semiclassical methods based on FPI in strong-field physics, from the strong-field approximation (SFA) to the Coulomb corrected strongfield approximation (CCSFA) and quantum trajectory Monte Carlo methods, also proves that the more trajectories have been adopted, the more information could be extracted . As a result, despite of the notable success of these methods, there still exist a large number of unexplored regimes, including the open question about whether one could truly achieve the quantumclassical correspondence. Actually, with increasingly so-phisticated experiments, the limitation of existing semiclassical methods based on FPI for reproducing and explaining some quantum phenomena has been becoming increasingly evident due to the limited amount of paths, especially for the new attosecond measurements where a series of high-resolution photoelectron spectra with different pump-probe delays are needed to obtain attosecond time-resolved movies of electrons [25][26][27][...