ABSTRACT
The prediction of extreme events, from avalanches and droughts to tsunamis and epidemics, depends on the formulation and analysis of relevant, complex dynamical systems. Such dynamical systems are characterized by high intrinsic dimensionality with extreme events having the form of rare transitions that are several standard deviations away from the mean. Such systems are not amenable to classical order-reduction methods through projection of the governing equations due to the large intrinsic dimensionality of the underlying attractor as well as the complexity of the transient events. Alternatively, data-driven techniques aim to quantify the dynamics of specific, critical modes by utilizing data-streams and by expanding the dimensionality of the reduced-order model using delayed coordinates. In turn, these methods have major limitations in regions of the phase space with sparse data, which is the case for extreme events. In this work, we develop a novel hybrid framework that complements an imperfect reduced order model, with data-streams that are integrated though a recurrent neural network (RNN) architecture. The reduced order model has the form of projected equations into a low-dimensional subspace that still contains important dynamical information about the system and it is expanded by a long short-term memory (LSTM) regularization. The LSTM-RNN is trained by analyzing the mismatch between the imperfect model and the data-streams, projected to the reduced-order space. The data-driven model assists the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system state. We assess the developed framework on two challenging prototype systems exhibiting extreme events. We show that the blended approach has improved performance compared with methods that use either data streams or the imperfect model alone. Notably the improvement is more significant in regions associated with extreme events, where data is sparse.
Subject(s)
Models, Theoretical , Nonlinear Dynamics , Computer Simulation , Memory, Long-Term , Memory, Short-Term , Neural Networks, ComputerABSTRACT
Bacillus amyloliquefaciens HB-26, a Gram-positive bacterium was isolated from soil in China. SDS-PAGE analysis showed this strain secreted six major protein bands of 65, 60, 55, 34, 25 and 20 kDa. A bioassay of this strain reveals that it shows specific activity against P. brassicae and nematode. Here we describe the features of this organism, together with the draft genome sequence and annotation. The 3,989,358 bp long genome (39 contigs) contains 4,001 protein-coding genes and 80 RNA genes.
ABSTRACT
Four new alkylated anthraquinone analogues (1-4) were isolated from a soil actimomycete Streptomyces sp. WS-13394. The structures of compounds 1-4 were elucidated to be 1,4,6-trihydroxy-8-alkylanthraquinones by means of spectroscopic methods, including UV, one dimensional (1D), 2D-NMR and MS spectrometry. All compounds showed activities against BGC-823 and MCF-7 with IC50 from 0.99 to 3.54 µg/mL, while 2 exhibited cytotoxicity against HepG2, A875, BGC-823 and MCF-7 with IC50 2.29, 4.90, 0.99, and 1.66 µg/mL, respectively.