Serratia marcescens is a significant opportunistic pathogen that can infect hospitalized or immunocompromised patients and result in serious illnesses. This prominent human-pathogenic bacteria contains over a third of all proteins whose functions are unknown. We extended this network by leveraging data from the interactome of protein–protein interactions, which we defined as the collection of all interactions between evolutionarily conserved proteins in other bacteria. We found that these interactions dramatically improved our ability to predict the function of a protein, allowing us to propose functional hypotheses for S. marcescens proteins with unknown functions. The interaction network depicted here comprises 36,507 edges and 1814 nodes and features a scale-free topology. The potential networks are found to have a substantial biological bias rather than the consequence of erroneous interactions, according to an in silico sanity check. The interactome-based analysis reported 272 essential proteins from S. marcescens and further found that 42 proteins are non-host homologous. This study also found 21 sub-network motifs with themes related to the biosynthesis of secondary metabolites, biosynthesis of amino acids, environmental variations in microbial metabolism, a two-component system, ABC transporters, ribosomes, carbon and purine metabolism, flagellar assembly, oxidative phosphorylation, and homologous recombination, Biosynthesis of folic acid, lipopolysaccharides, aminoacyl-tRNA, RNA degradation, and DNA replication. Bottleneck proteins and proteins that straddle several sub-networks and signaling pathways are described in addition to the functional annotation of each protein in the network. The list of possible interactions between the S. marcescens strains offers a structure for formulating original theories and arranging experiments for wet-lab studies. The predicted core protein–protein interaction networks can be a valuable and adaptable tool for S. marcescens researchers.
Plant-derived compounds have attracted considerable attention in the field of antimicrobial therapy. This interest is primarily due to their natural origin and historical evidence of their use in traditional medicine systems. These derivatives are a rich reservoir of chemical diversity that has a promising potential for the development and production of new antimicrobial agents with the least amount of side effects and risks of drug resistance. However, the delivery of plant-derived antimicrobial agents, especially through the topical route, poses significant challenges. As the largest organ of the body, the skin acts as a first barrier against the entrance of microbial pathogens. A primary limitation to transdermal delivery of plant-derived antimicrobial agents is their complex molecular structures, which often prevent effective absorption through the skin. Therefore, developing and promoting an effective local drug delivery system to increase the potential of antimicrobial therapy is very important and effective in public health. This review discusses delivery strategies for plant-derived antimicrobial agents aimed at the bioavailability and stability of these compounds as well as their mode of action, ensuring targeted delivery to the site of infection with long-lasting effects and minimizing side effects. Besides, various topical drug delivery platforms are analyzed, including nanoparticles, liposomes, and innovative application methods such as microneedles.
This paper presents a sparse identification of nonlinear dynamic systems (SINDy) for a diesel engine air path system and nonlinear model predictive control (NMPC) with the SINDy model to attain good control performance. The air path system control is well known as a challenging problem, and many studies have been presented such as traditional model-based control design and machine learning. However, these conventional approaches still have some difficulties including the control performance and design costs. In this paper, we obtain the model of the air path system in a data-driven manner using the SINDy algorithm and construct the offset-free NMPC with the SINDy model. SINDy is a suitable modeling method for controlling a complicated air path system, owing to its characteristics of high computational efficiency, high learning efficiency, high modeling accuracy, and applicability to complex systems. Additionally, NMPC provides high control performance under constraints. The proposed offset-free NMPC with the SINDy model is verified through the simulations. The results show that the coefficient of determination of the SINDy model provided over 90
Aluminum (Al) alloys used for automobile engine components are subjected to fatigue loading at high temperatures over 1/2 of their melting temperatures Tm. The fatigue damage must be evaluated by a method that considers the effect of both the plastic and creep deformations because creep deformation occurs in the fatigue process at 1/2Tm. In this paper, the plastic-creep separation method is first applied to a casting Al alloy subjected to low cycle fatigue (LCF) loading, and a fatigue damage law is derived considering the effect of the plastic and creep damages on the fatigue life. Since Al alloy engine components are used at cyclically changing temperatures from room temperature to over 1/2Tm, the fatigue damage law is adapted to the fatigue life due to cyclic thermal loading employing temperature-dependence parameters. Finally, the fatigue damage law is applied to the fatigue life evaluation for the thermo-mechanical fatigue (TMF) test that reproduces a real used condition of engines.
This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.
