Calli Essential Oils Synergize with Lawsone against Multidrug Resistant Pathogens.

The fast development of multi-drug resistant (MDR) organisms increasingly threatens global health and well-being. Plant natural products have been known for centuries as alternative medicines that can possess pharmacological characteristics, including antimicrobial activities. The antimicrobial activities of essential oil (Calli oil) extracted from the Calligonum comosum plant by hydro-steam distillation was tested either alone or when combined with lawsone, a henna plant naphthoquinone, against MDR microbes. Lawsone showed significant antimicrobial activities against MDR pathogens in the range of 200-300 µg/mL. Furthermore, Calli oil showed significant antimicrobial activities against MDR bacteria in the range of 180-200 µg/mL, Candida at 220-240 µg/mL and spore-forming Rhizopus fungus at 250 µg/mL. Calli oil's inhibition effect on Rhizopus, the major cause of the lethal infection mucormycosis, stands for 72 h, followed by an extended irreversible white sporulation effect. The combination of Calli oil with lawsone enhanced the antimicrobial activities of each individual alone by at least three-fold, while incorporation of both natural products in a liposome reduced their toxicity by four- to eight-fold, while maintaining the augmented efficacy of the combination treatment. We map the antimicrobial activity of Calli oil to its major component, a benzaldehyde derivative. The findings from this study demonstrate that formulations containing essential oils have the potential in the future to overcome antimicrobial resistance

ℓ<inf>2</inf>–ℓ<inf>∞</inf> proportional–integral observer design for systems with mixed time-delays under round–robin protocol

The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, FP-21-42; National Natural Science Foundation of China, 61873148; 61873169; 61933007; Alexander von Humboldt Foundation of Germany

A Resilient Approach to Distributed Filter Design for Time-Varying Systems under Stochastic Nonlinearities and Sensor Degradation

This paper is concerned with the distributed filtering problem for a class of discrete time-varying systems with stochastic nonlinearities and sensor degradation over a finite horizon. A two-step distributed filter algorithm is proposed where the sensor nodes collaboratively estimate the states of the plant by exploiting the information from both the local and neighboring nodes. The goal of this paper is to design the distributed filters over a wireless sensor network subject to given sporadic communication topology. Moreover, a resilient operation is guaranteed to suppress random perturbations on the actually implemented filter gains. An upper bound is first derived for the filtering error covariance by utilizing an inductive method and such an upper bound is subsequently minimized via iteratively solving a quadratic optimization problem. To account for the topological information of the sensor networks, a novel matrix simplification technique is utilized to preserve the sparsity of the gain matrices in accordance with the given topology and the analytical parameterization is obtained for the gain matrices of the desired sub-optimal filter. Furthermore, a sufficient condition is established to guarantee the mean-square boundedness of the estimation errors. Numerical simulation is carried out to verify the effectiveness of the proposed filtering algorithm

Antimicrobial peptides in human corneal tissue of patients with fungal keratitis

Background Fungal keratitis (FK) is the leading cause of unilateral blindness in the developing world. Antimicrobial peptides (AMPs) have been shown to play an important role on human ocular surface (OS) during bacterial, viral and protozoan infections. In this study, our aim was to profile a spectrum of AMPs in corneal tissue from patients with FK during the active pase of infection and after healing.Methods OS samples were collected from patients at presentation by impression cytology and scraping. Corneal button specimens were collected from patients undergoing therapeutic penetrating keratoplasty for management of severe FK or healed keratitis. Gene expression of human beta-defensin (HBD)-1, -2, -3 and -9, S100A7, and LL-37 was determined by quantitative real-time PCR.Results Messenger RNA expression (mRNA) for all AMPs was shown to be significantly upregulated in FK samples. The levels of HBD-1 and -2 mRNA were found to be elevated in 18/20 FK samples. Whereas mRNA for HBD-3 and S100A7 was upregulated in 11/20 and HBD9 was increased in 15/20 FK samples. LL-37 mRNA showed moderate upregulation in 7/20 FK samples compared with controls. In healed scar samples, mRNA of all AMPs was found to be low and matching the levels in controls.Conclusion AMP expression is a consistent feature of FK, but not all AMPs are equally expressed. HBD-1 and -2 are most consistently expressed and LL-37 the least, suggesting some specificity of AMP expression related to FK. These results will help to identify HBD sequence templates for designing FK-specific peptides to test for therapeutic potential

Fitness Tradeoffs of Antibiotic Resistance in Extraintestinal Pathogenic Escherichia coli

Evolutionary trade-offs occur when selection on one trait has detrimental effects on other traits. In pathogenic microbes, it has been hypothesized that antibiotic resistance trades off with fitness in the absence of antibiotic. Although studies of single resistance mutations support this hypothesis, it is unclear whether trade-offs are maintained over time, due to compensatory evolution and broader effects of genetic background. Here, we leverage natural variation in 39 extraintestinal clinical isolates of Escherichia coli to assess trade-offs between growth rates and resistance to fluoroquinolone and cephalosporin antibiotics. Whole-genome sequencing identifies a broad range of clinically relevant resistance determinants in these strains. We find evidence for a negative correlation between growth rate and antibiotic resistance, consistent with a persistent trade-off bet

A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay

In this paper, the parameter identification problem of the lateral flow immunoassay (LFIA) devices is investigated via a new switching delayed particle swarm optimization (SDPSO) algorithm. By evaluating an evolutionary factor in each generation, the velocity of the particle can adaptively adjust the model according to a Markov chain in the proposed SDPSO method. During the iteration process, the SDPSO can adaptively select the inertia weight, acceleration coefficients, locally best particle pbest and globally best particle gbest in the swarm. It is worth highlighting that the pbest and the gbest can be randomly selected from the corresponding values in the previous iteration. That is, the delayed information of the pbest and the gbest can be exploited to update the particle’s velocity in current iteration according to the evolutionary states. The strategy can not only improve the global search but also enhance the possibility of eventually reaching the gbest. The superiority of the proposed SDPSO is evaluated on a series of unimodal and multimodal benchmark functions. Results demonstrate that the novel SDPSO algorithm outperforms some well-known PSO algorithms in aspects of global search and efficiency of convergence. Finally, the novel SDPSO is successfully exploited to estimate the unknown time-delay parameters of a class of nonlinear state-space LFIA model.This work was supported in part by the Royal Society of the U.K., the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of China under Grant 61403319, the Fujian Natural Science Foundation under Grant 2015J05131, and the Fujian Provincial Key Laboratory of Eco-Industrial Green Technology

Mechanisms for reducing low back pain: a mediation analysis of a multifaceted intervention in workers in elderly care

Purpose A multifaceted workplace intervention consisting of participatory ergonomics, physical training, and cognitive–behavioural training (CBT) has shown effectiveness for reducing low back pain (LBP). However, the mechanisms of action underlying these intervention components are not well understood. Methods This was a mediation analysis of a cluster-randomised controlled trial of a multifaceted intervention in 420 workers in elderly care. Mediation analysis was carried out via structural equation modelling. Potential mediators investigated were: fear-avoidance beliefs, perceived muscle strength, use of assistive devices at work and perceived physical exertion at work. LBP outcomes assessed were: days with LBP, LBP intensity and days with bothersome LBP. Results There were no significant indirect effects of the intervention on LBP outcomes. There were significant effects of the intervention on both fear-avoidance measures [β = − 0.63, 95% CI (1.23, 0.03); β = − 1.03, 95% CI (− 1.70, − 0.34)] and the use of assistive devices [β = − 0.55, 95% CI (− 1.04, − 0.05)], but not on perceived muscle strength [β = − 0.18, 95% CI (− 0.50, 0.13)] or physical exertion [β = − 0.05, 95% CI (− 0.40, 0.31)]. The only potential mediator with a significant effect on LBP outcomes was physical exertion, which had a significant effect on LBP intensity [β = 0.14, 95% CI (0.04, 0.23)]. Conclusions A multifaceted intervention consisting of participatory ergonomics, physical training, and CBT was able to decrease fear-avoidance beliefs and increase use of assistive devices in the workplace. However, these changes did not explain the effect of any of the intervention components on days with LBP, LBP intensity and days with bothersome LBP

Robust filtering for a class of nonlinear stochastic systems with probability constraints

This paper is concerned with the probability-constrained filtering problem for a class of time-varying nonlinear stochastic systems with estimation error variance constraint. The stochastic nonlinearity considered is quite general that is capable of describing several well-studied stochastic nonlinear systems. The second-order statistics of the noise sequence are unknown but belong to certain known convex set. The purpose of this paper is to design a filter guaranteeing a minimized upper-bound on the estimation error variance. The existence condition for the desired filter is established, in terms of the feasibility of a set of difference Riccati-like equations, which can be solved forward in time. Then, under the probability constraints, a minimax estimation problem is proposed for determining the suboptimal filter structure that minimizes the worst-case performance on the estimation error variance with respect to the uncertain second-order statistics. Finally, a numerical example is presented to show the effectiveness and applicability of the proposed method