Pseudomonas aeruginosa (P. aeruginosa) is a Gram-negative opportunistic pathogen that infects patients with cystic fibrosis, burn wounds, immunodeficiency, chronic obstructive pulmonary disorder (COPD), cancer, and severe infection requiring ventilation, such as COVID-19. P. aeruginosa is also a widely-used model bacterium for all biological areas. In addition to continued, intense efforts in understanding bacterial pathogenesis of P. aeruginosa including virulence factors (LPS, quorum sensing, two-component systems, 6 type secretion systems, outer membrane vesicles (OMVs), CRISPR-Cas and their regulation), rapid progress has been made in further studying host-pathogen interaction, particularly host immune networks involving autophagy, inflammasome, non-coding RNAs, cGAS, etc. Furthermore, numerous technologic advances, such as bioinformatics, metabolomics, scRNA-seq, nanoparticles, drug screening, and phage therapy, have been used to improve our understanding of P. aeruginosa pathogenesis and host defense. Nevertheless, much remains to be uncovered about interactions between P. aeruginosa and host immune responses, including mechanisms of drug resistance by known or unannotated bacterial virulence factors as well as mammalian cell signaling pathways. The widespread use of antibiotics and the slow development of effective antimicrobials present daunting challenges and necessitate new theoretical and practical platforms to screen and develop mechanism-tested novel drugs to treat intractable infections, especially those caused by multi-drug resistance strains. Benefited from has advancing in research tools and technology, dissecting this pathogen’s feature has entered into molecular and mechanistic details as well as dynamic and holistic views. Herein, we comprehensively review the progress and discuss the current status of P. aeruginosa biophysical traits, behaviors, virulence factors, invasive regulators, and host defense patterns against its infection, which point out new directions for future investigation and add to the design of novel and/or alternative therapeutics to combat this clinically significant pathogen.

We study the occurrence and dynamics of rogue waves in three-dimensional deep water using phase-resolved numerical simulations based on a high-order spectral (HOS) method. We obtain a large ensemble of nonlinear wave-field simulations (M = 3 in HOS method), initialized by spectral parameters over a broad range, from which nonlinear wave statistics and rogue wave occurrence are investigated. The HOS results are compared to those from the broad-band modified nonlinear Schrödinger (BMNLS) equations. Our results show that for (initially) narrow-band and narrow directional spreading wave fields, modulational instability develops, resulting in non-Gaussian statistics and a probability of rogue wave occurrence that is an order of magnitude higher than linear theory prediction. For longer times, the evolution becomes quasi-stationary with non-Gaussian statistics, a result not predicted by the BMNLS equations (without consideration of dissipation). When waves spread broadly in frequency and direction, the modulational instability effect is reduced, and the statistics and rogue wave probability are qualitatively similar to those from linear theory. To account for the effects of directional spreading on modulational instability, we propose a new modified Benjamin-Feir index for effectively predicting rogue wave occurrence in directional seas. For short-crested seas, the probability of rogue waves based on number frequency is imprecise and problematic. We introduce an area-based probability, which is well defined and convergent for all directional spreading. Based on a large catalogue of simulated rogue wave events, we analyse their geometry using proper orthogonal decomposition (POD). We find that rogue wave profiles containing a single wave can generally be described by a small number of POD modes.

Virtual screening (VS) can be accomplished in either ligand- or structure-based methods. In recent times, an increasing number of 2D fingerprint and 3D shape similarity methods have been used in ligand-based VS. To evaluate the performance of these ligand-based methods, retrospective VS was performed on a tailored directory of useful decoys (DUD). The VS performances of 14 2D fingerprints and four 3D shape similarity methods were compared. The results revealed that 2D fingerprints ECFP_2 and FCFP_4 yielded better performance than the 3D Phase Shape methods. These ligand-based methods were also compared with structure-based methods, such as Glide docking and Prime molecular mechanics generalized Born surface area rescoring, which demonstrated that both 2D fingerprint and 3D shape similarity methods could yield higher enrichment during early retrieval of active compounds. The results demonstrated the superiority of ligand-based methods over the docking-based screening in terms of both speed and hit enrichment. Therefore, considering ligand-based methods first in any VS workflow would be a wise option.

The generalized inverse L † of the Laplacian matrix of a connected graph is examined and some of its properties are established. In some physical and chemical considerations the quantity r ij = (L †) ii + (L †) jj − (L †) ij − (L †) ji is encountered; it is called resistance distance. Based on the results obtained for L † we prove some previously known and deduce some new properties of the resistance distance.