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The balance between accuracy and computational complexity is currently a focal point of research in dynamical system modeling. From the perspective of model reduction, this paper addresses the mode selection strategy in Dynamic Mode Decomposition (DMD) by integrating an embedded fractal theory based on fractal dimension (FD). The existing model selection methods lack interpretability and exhibit arbitrariness in choosing mode dimension truncation levels. To address these issues, this paper analyzes the geometric features of modes for the dimensional characteristics of dynamical systems. By calculating the box counting dimension (BCD) of modes and the correlation dimension (CD) and embedding dimension (ED) of the original dynamical system, it achieves guidance on the importance ranking of modes and the truncation order of modes in DMD. To validate the practicality of this method, it is applied to the reduction applications on the reconstruction of the velocity field of cylinder wake flow and the force field of compressor blades. Theoretical results demonstrate that the proposed selection technique can effectively characterize the primary dynamic features of the original dynamical systems. By employing a loss function to measure the accuracy of the reconstruction models, the computed results show that the overall errors of the reconstruction models are below 5%. These results indicate that this method, based on fractal theory, ensures the model's accuracy and significantly reduces the complexity of subsequent computations, exhibiting strong interpretability and practicality. [ABSTRACT FROM AUTHOR]

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control. [ABSTRACT FROM AUTHOR]

Bacteria can be genetically engineered to act as therapeutic delivery vehicles in the treatment of tumors, killing cancer cells or activating the immune system. This is known as bacteria-mediated cancer therapy (BMCT). Tumor invasion, colonization and tumor regression are major biological events, which are directly associated with antitumor effects and are uncontrollable due to the influence of tumor microenvironments during the BMCT process. Here, we developed a genetic circuit for dynamically programming bacterial lifestyles (planktonic, biofilm or lysis), to precisely manipulate the process of bacterial adhesion, colonization and drug release in the BMCT process, via hierarchical modulation of the lighting power density of near-infrared (NIR) light. The deep tissue penetration of NIR offers us a modality for spatio-temporal and non-invasive control of bacterial genetic circuits in vivo. By combining computational modeling with a high-throughput characterization device, we optimized the genetic circuits in engineered bacteria to program the process of bacterial lifestyle transitions by altering the illumination scheme of NIR. Our results showed that programming intratumoral bacterial lifestyle transitions allows precise control of multiple key steps throughout the BMCT process and therapeutic efficacy can be greatly improved by controlling the localization and dosage of therapeutic agents via optimizing the illumination scheme. Programming intratumoral bacterial life style transitions allow precise control of multiple key steps throughout the bacteria-mediated cancer therapy process and therapeutic efficacy can be greatly improved by controlling the localization and dosage of therapeutic agents via optimizing the illumination scheme. [ABSTRACT FROM AUTHOR]

Based on the COMSOL software, body forces substituted into the Reynolds-averaged Navier–Stokes (RANS) equations as the source terms instead of the actual blade rows were improved to better predict the compressor performance. Improvements in parallel body force modeling were implemented, central to which were the local flow quantities. This ensured accurate and reliable off-design performance prediction. The parallel force magnitude mainly depended on the meridional entropy gradient extracted from three-dimensional (3D) steady single-passage RANS solutions. The COMSOL software could easily and accurately translate the pitchwise-averaged entropy into the grid points of the body force domain. A NASA Rotor 37 was used to quantify the improved body force model to represent the compressor. Compared with the previous model, the improved body force model was more efficient for the numerical calculations, and it agreed well with the experimental data and computational fluid dynamics (CFD) results. The results indicate that the improved body force model could quickly and efficiently capture the flow field through a turbomachinery blade row. [ABSTRACT FROM AUTHOR]

In order to effectively identify the signs of instability in the aerodynamic system of an axial compressor, a wavelet singular spectral entropy algorithm incorporated within the wavelet transform, singular value decomposition and information entropy is proposed to describe the distribution complexity of the spatial modalities in the flow field. This kind of identification design can accurately distinguish the boundary between the stable and unstable states of the internal flow field from the view of a dynamic system. On the basis of the information entropy algorithm, the wavelet singular spectral entropy algorithm is designed to integrate with the advantages of wavelet transform analysis on the time-frequency localization and singular value decomposition for signal processing and data mining together. So that the quantitative analysis of the definition of rebuilding a system image can be achieved by the solution of wavelet singular spectral entropy. This method can automatically extract the transient information of the space mode in the time-frequency domain. It effectively avoids the shortcoming that the feature extraction on spatial information cannot be accomplished from multiple angles with the single information entropy algorithm. In the data processing of instability signals under different speeds, the wavelet singular spectral entropy algorithm shows a greater advantage in the early warning for compressor stall. The result shows that the value of the wavelet singular spectral shows an obvious mutation when the aerodynamic system approaches the instability boundary. According to the threshold set, the identification hybrid algorithm can detect the stall precursor about 23~96 r in advance. Compared to the single information entropy algorithm, the hybrid wavelet singular spectral entropy algorithm is able to shift to an earlier precursor identification by about 11~82 r. This established hybrid identification algorithm accounts for the nonlinearity of the aerodynamic system, providing a new perspective for the nonlinear system instability identification. [ABSTRACT FROM AUTHOR]

Effective and accurate diagnosis of engine health is key to ensuring the safe operation of engines. Inlet distortion is due to the flow or the pressure variations. In the paper, an acoustic emission (AE) online monitoring technique, which has a faster response time compared with the ordinary vibration monitoring technique, is used to study the inlet distortion of an engine. The results show that with the deterioration of the inlet distortion, the characteristic parameters of AE signals clearly evolve in three stages. Stage I: when the inlet distortion J ≤ 30%, the characteristic parameters of the AE signal increase as J increases and the amplitude saturates at J = 23%, faster than the other three parameters (the strength, the root mean square (RMS), and the average signal level (ASL)). Stage II: when the inlet distortion 30% < J ≤ 43.64%, all the parameters saturate with only slight fluctuations as J increases and the engine works in an unstable statue. Stage III: when the inlet distortion J > 43.64%, the engine is prone to surge. Furthermore, an intelligent recognition method of the engine inlet distortion based on a unit parameter entropy and the back propagation (BP) neural network is constructed. The recognition accuracy is as high as 97.5%, and this method provides a new approach for engine health management. [ABSTRACT FROM AUTHOR]

Focusing on the identification of dynamic system stability, a hybrid neural network model is proposed in this research for the rotating stall phenomenon in an axial compressor. Based on the data fusion of the amplitude of the spatial mode, the nonlinear property is well characterized in the feature extraction of the rotating stall. This method of data processing can effectively avoid the inaccurate recognition of single or multiple measuring sensors only depending on pressure. With the analysis on the spatial mode, a chaotic characteristic was shown in the development of the amplitude with the first-order spatial mode. With the prerequisite of revealing the essence of this dynamic system, a hybrid radial basis function (RBF) neural network was adopted to represent the properties of the system by artificial intelligence learning. Combining the advantages of the methods of K-means and Gradient Descent (GD), the Chaos–K-means–GD–RBF fusion model was established based on the phase space reconstruction of the chaotic sequence. Compared with the two methods mentioned above, the calculation accuracy was significantly improved in the hybrid neural network model. By taking the strategy of global sample entropy and difference quotient criterion identification, a warning of inception can be suggested in advance of 12.3 revolutions (296 ms) with a multi-step prediction before the stall arrival. [ABSTRACT FROM AUTHOR]

The part of expression noise that is brought about by transcriptional regulation (represented here as NTR) is an important criterion for estimating the regulatory mode of a gene. However, characterization of NTR is an under-explored area, and there is little knowledge regarding the genome-wide NTR in the model pathogen Pseudomonas aeruginosa. Here, with a library of dual-color transcriptional reporters, we estimated the NTR for over 90% of the promoters in P. aeruginosa. Most promoters exhibit low NTR, while 42 and 115 promoters with high NTR were screened out in the exponential and the stationary growth phases, respectively. Specifically, a rearrangement of NTR was found in promoters involved in amino acid metabolism when bacteria enter the exponential phase. In addition, during the stationary phase, high NTR was found in a wide range of iron-related promoters involving siderophore synthesis and heme uptake, ExsA-regulated promoters involving bacterial virulence, and FleQ-regulated promoters involving biofilm development. We also found a large-scale negative dependence of transcriptional regulation between high-NTR promoters belonging to different functional categories. Our findings offer a global view of transcriptional heterogeneity in P. aeruginosa. IMPORTANCE The phenotypic diversity of Pseudomonas aeruginosa is frequently observed in research, suggesting that bacteria adopt strategies such as bet-hedging to survive ever-changing environments. Gene expression noise (GEN) is the major source of phenotypic diversity. Large GEN from transcriptional regulation (represented as NTR) represent an evolutionary necessity to maintain the copy number diversity of certain proteins in the population. Here, we provide a system-wide view of NTR in P. aeruginosa under nutrient-rich and stressed conditions. High NTR was found in genes involved in flagella biosynthesis and amino acid metabolism under both conditions. Specially, iron acquisition genes exhibited high NTR in the stressed condition, suggesting a great diversity of iron physiology in P. aeruginosa. We further revealed a global negative dependence of transcriptional regulation between those high-NTR genes under the stressed condition, suggesting a mutually exclusive relationship between different bacterial survival strategies.

Bacterial pathogens operate by tightly controlling the pathogenicity to facilitate invasion and survival in host. While small molecule inducers can be designed to modulate pathogenicity to perform studies of pathogen-host interaction, these approaches, due to the diffusion property of chemicals, may have unintended, or pleiotropic effects that can impose limitations on their use. By contrast, light provides superior spatial and temporal resolution. Here, using optogenetics we reengineered GacS of the opportunistic pathogen Pseudomonas aeruginosa , signal transduction protein of the global regulatory Gac/Rsm cascade which is of central importance for the regulation of infection factors. The resultant protein (termed YGS24) displayed significant light-dependent activity of GacS kinases in Pseudomonas aeruginosa . When introduced in the Caenorhabditis elegans host systems, YGS24 stimulated the pathogenicity of the Pseudomonas aeruginosa strain PAO1 in a brain-heart infusion and of another strain, PA14, in slow killing media progressively upon blue-light exposure. This optogenetic system provides an accessible way to spatiotemporally control bacterial pathogenicity in defined hosts, even specific tissues, to develop new pathogenesis systems, which may in turn expedite development of innovative therapeutics.

Quantification of the rate of direct repeat deletion (DRD) is an important aspect in the research of DNA rearrangement. The widely used tetracycline selection method usually introduces antibiotic pressure to the tested organism, which may interfere with the DRD process. Also the length of repeat arm (LRA) is limited by the length of the TetR coding sequence. On the basis of the fluorescent microscopy and high-throughput imaging processing, here we developed a two-module genetic circuit, termed TFDEC (which stands for three-color fluorescence-based deletion event counter), to quantify the DRD rate under neutral conditions. DRD events were determined from the state of a three-state fluorescent logic gate constructed through coupling of an OR gate and an AND gate. TFDEC was applied in Pseudomonas aeruginosa , and we found that the DRD rate was RecA-dependent for long repeat arms (>500 bp) and RecA-independent for short repeat arms (<500 bp), which was consistent with the case in Escherichia coli . In addition, the increase of DRD rate followed an S-shaped curve with the increase of LRA, while treating cells with ciprofloxacin did not change the LRA-dependence of DRD. We also detected a significant increased DRD rate for long repeat arms in the uvrD (8-fold) and radA (4-fold) mutants. Our results show that the TFDEC method could be used as a complement tool for quantification of the DRD rate in the future.

Pseudomonas aeruginosa can cause severe infections in humans. This bacterium often adopts a biofilm lifestyle that is hard to treat. In several previous studies, the PprA-PprB two-component system (TCS), which controls the expression of type IVb pili, BapA adhesin, and CupE fimbriae, was shown to be involved in biofilm formation (M. Romero, H. Silistre, L. Lovelock, V. J. Wright, K.-G. Chan, et al., Nucleic Acids Res 46:6823-6840, 2018, https://doi.org/10.1093/nar/gky324; S. de Bentzmann, C. Giraud, C. S. Bernard, V. Calderon, F. Ewald F, et al., PLoS Pathog 8:e1003052, 2012, https://doi.org/10.1371/journal.ppat.1003052). However, signals or environmental conditions that can trigger the PprA-PprB TCS are still unknown, and the molecular mechanisms of PprB-mediated biofilm formation are poorly characterized. Here, we report that carbon starvation stress (CSS) can induce the expression of pprB and genes in the PprB regulon. CSS-induced pprB transcription is mediated by the stress response sigma factor RpoS rather than the two-component sensor PprA. We also observed a strong negative regulation of PprB on the transcription of itself. Further experiments showed that PprB overexpression greatly enhanced cell-cell adhesion (CCA) and cell-surface adhesion (CSA) in P. aeruginosa Specifically, under the background of PprB overexpression, both the BapA adhesin and CupE fimbriae displayed positive effects on CCA and CSA, while the type IVb pili showed an unexpected negative effect on CCA and no effect on CSA. In addition, expression of the PprB regulon genes were significantly increased in 3-day colony biofilms, indicating a possible carbon limitation state. The CSS-RpoS-PprB-Bap/Flp/CupE pathway identified in this study provides a new perspective on the process of biofilm formation in carbon-limited environments. IMPORTANCE Typically, the determination of the external signals that can trigger a regulatory system is crucial to understand the regulatory logic and inward function of that system. The PprA-PprB two-component system was reported to be involved in biofilm formation in Pseudomonas aeruginosa , but the signals triggering this system are unknown. In this study, we found that carbon starvation stress (CSS) induces transcription of pprB and genes in the PprB regulon through an RpoS-dependent pathway. Increased PprB expression leads to enhanced cell-cell adhesion (CCA) and cell-surface adhesion (CSA) in P. aeruginosa Both CCA and CSA are largely dependent on the Bap secretion system and are moderately dependent on the CupE fimbriae. Our findings suggest that PprB reinforces the structure of biofilms under carbon-limited conditions, and the Bap secretion system and CupE fimbriae are two potential targets for biofilm treatment., (Copyright © 2019 Wang et al.)

The attachment of bacterial cells to a surface is implicated in the formation of biofilms. Although the surface-related behaviors in this process, such as single cell motility and surface sensing, have been investigated intensively, the precise information of separation distance between the attached cells and the surface has remained unclear. Here, we set a prism-based total internal reflection dark-field microscope (p-TIRDFM) combined with the microfluidic method to image the separation distance of single attached cells. We directly observed that bacterial cells attached to the surface with one nearest touchpoint, and it gradually changed to two touchpoints, respectively, for the two offspring with the cell division. We first monitored the fluctuation of the relative distance on nanometer scale when cells twitch on a surface and further established the relationship between the twitching velocity and the separation distance. The results indicated that the moving cells are a considerable distance apart from the surface and the separation distance fluctuated more widely than immobile cells.

The second messenger signaling molecule cyclic diguanylate monophosphate (c-di-GMP) drives the transition between planktonic and biofilm growth in many bacterial species. Pseudomonas aeruginosa has two surface sensing systems that produce c-di-GMP in response to surface adherence. Current thinking in the field is that once cells attach to a surface, they uniformly respond by producing c-di-GMP. Here, we describe how the Wsp system generates heterogeneity in surface sensing, resulting in two physiologically distinct subpopulations of cells. One subpopulation has elevated c-di-GMP and produces biofilm matrix, serving as the founders of initial microcolonies. The other subpopulation has low c-di-GMP and engages in surface motility, allowing for exploration of the surface. We also show that this heterogeneity strongly correlates to surface behavior for descendent cells. Together, our results suggest that after surface attachment, P. aeruginosa engages in a division of labor that persists across generations, accelerating early biofilm formation and surface exploration., Competing Interests: CA, CL, JP, Jd, AX, KZ, BT, LH, FJ, CH, GW, MP No competing interests declared, (© 2019, Armbruster et al.)

We present a method capable of detecting single slow-growing and growth-arrested cells in a bacterial culture composed of physiologically and phenotypically different cells. Unlike the use of transcriptional reporters to gauge the metabolic activities in cells, here, we fuse two different fluorescent proteins with distinctive maturation rates to construct a timer to directly determine the growth rate of single Pseudomonas aeruginosa cells. We demonstrate that the dual-color fluorescent timer can indicate the slow-growing and growth-arrested cells from bacterial cultures in the presence of various environmental stresses, including nutrient starvation or antibiotic treatments, which greatly expand the methods for detecting and isolating persister cells.

Exponentially growing bacteria in a well-mixed planktonic culture are generally assumed to be physiologically and phenotypically uniform and distinct from their genetically identical counterparts living in biofilms. Using a combination of high spatiotemporal microscopy and a bacterial tracking algorithm, in this study, we showed that planktonic cells of Pseudomonas aeruginosa differently attached to surfaces even when they remained in the exponential phase. We consistently observed that fast- and slow-attaching phenotypes coexist in planktonic cells, regardless of their growth phase. Furthermore, we found that (i) the distinct attaching phenotypes of planktonic cells resulted from the differential production of Psl and (ii) the RsmYZ/RsmA signaling pathway mainly regulated the differential production of Psl. Our results indicate that the differential production of Psl in P. aeruginosa plays a significant role in biofilm development and formation. IMPORTANCE The attachment of planktonic cells to surfaces is the first and most crucial step in biofilm formation. In this paper, we show that planktonic cells of Pseudomonas aeruginosa differently attach to surfaces. Typically, in the later exponential phase, approximately 80% of the cells can quickly attach to surfaces within 15 min, whereas approximately 20% of the cells slowly attach to surfaces, which greatly affects the initial stage of biofilm formation in the presence of flows. This is because fast-attaching cells are more likely to attach on surfaces to form microcolonies, whereas slow-attaching cells are more likely to remain in the mobile phase. This scenario is different from the previous understanding of biofilm formation in the initial stage, in which planktonic cells were thought to uniformly attach to surfaces. Most notably, the results of this study show that the different attachment manner of planktonic cells to surfaces affects the subsequent stages of biofilm formation. This research highlights that the phenotypic variations in planktonic cells plays significant roles in various stages of biofilm formation., (Copyright © 2018 Yang et al.)

In this paper, we present a new strategy for microprinting dense bacterial communities with a prescribed organization on a substrate. Unlike conventional bioprinting techniques that require bioinks, through optogenetic manipulation, we directly manipulated the behaviors of Pseudomonas aeruginosa to allow these living bacteria to autonomically form patterned biofilms following prescribed illumination. The results showed that through optogenetic manipulation, patterned bacterial communities with high spatial resolution (approximately 10 μm) could be constructed in 6 h. Thus, optogenetic manipulation greatly increases the range of available bioprinting techniques.

Understanding the mechanisms that promote cooperative behaviors of bacteria in their hosts is of great significance to clinical therapies. Environmental stress is generally believed to increase competition and reduce cooperation in bacteria. Here, we show that bacterial cooperation can in fact be maintained because of environmental stress. We show that Pseudomonas aeruginosa regulates the secretion of iron-scavenging siderophores in the presence of different environmental stresses, reserving this public good for private use in protection against reactive oxygen species when under stress. We term this strategy "conditional privatization". Using a combination of experimental evolution and theoretical modeling, we demonstrate that in the presence of environmental stress the conditional privatization strategy is resistant to invasion by non-producing cheaters. These findings show how the regulation of public goods secretion under stress affects the evolutionary stability of cooperation in a pathogenic population, which may assist in the rational development of novel therapies.

Synthetic biologists have attempted to solve real-world problems, such as those of bacterial biofilms, that are involved in the pathogenesis of many clinical infections and difficult to eliminate. To address this, we employed a blue light responding system and integrated it into the chromosomes of Pseudomonas aeruginosa. With making rational adaptions and improvements of the light-activated system, we provided a robust and convenient means to spatiotemporally control gene expression and manipulate biological processes with minimal perturbation in P. aeruginosa. It increased the light-induced gene expression up to 20-fold. Moreover, we deliberately introduced a functional protein gene PA2133 containing an EAL domain to degrade c-di-GMP into the modified system, and showed that the optimally engineered optogenetic tool inhibited the formation of P. aeruginosa biofilms through the induction of blue light, resulting in much sparser and thinner biofilms. Our approach establishes a methodology for leveraging the tools of synthetic biology to guide biofilm formation and engineer biofilm patterns with unprecedented spatiotemporal resolution. Furthermore, the findings suggest that the synthetic optogenetic system may provide a promising strategy that could be applied to control and fight biofilms.