Genome-wide sRNA and mRNA transcriptomic profiling insights into carbapenem-resistant Acinetobacter baumannii.

Introduction: Acinetobacter baumannii (AB) is rising as a human pathogen of critical priority worldwide as it is the leading cause of opportunistic infections in healthcare settings and carbapenem-resistant AB is listed as a "super bacterium" or "priority pathogen for drug resistance" by the World Health Organization. Methods: Clinical isolates of A. baumannii were collected and tested for antimicrobial susceptibility. Among them, carbapenem-resistant and carbapenem-sensitive A. baumannii were subjected to prokaryotic transcriptome sequencing. The change of sRNA and mRNA expression was analyzed by bioinformatics and validated by quantitative reverse transcription-PCR. Results: A total of 687 clinical isolates were collected, of which 336 strains of A. baumannii were resistant to carbapenem. Five hundred and six differentially expressed genes and nineteen differentially expressed sRNA candidates were discovered through transcriptomic profile analysis between carbapenem-resistant isolates and carbapenem-sensitive isolates. Possible binding sites were predicted through software for sRNA21 and adeK, sRNA27 and pgaC, sRNA29 and adeB, sRNA36 and katG, indicating a possible targeting relationship. A negative correlation was shown between sRNA21 and adeK (r = -0.581, P = 0.007), sRNA27 and pgaC (r = -0.612, P = 0.004), sRNA29 and adeB (r = -0.516, P = 0.020). Discussion: This study preliminarily screened differentially expressed mRNA and sRNA in carbapenem-resistant A. baumannii, and explored possible targeting relationships, which will help further reveal the resistance mechanism and provide a theoretical basis for the development of drugs targeting sRNA for the prevention and treatment of carbapenem-resistant A. baumannii infection.

Rank-Adaptive Tensor Completion Based on Tucker Decomposition.

Tensor completion is a fundamental tool to estimate unknown information from observed data, which is widely used in many areas, including image and video recovery, traffic data completion and the multi-input multi-output problems in information theory. Based on Tucker decomposition, this paper proposes a new algorithm to complete tensors with missing data. In decomposition-based tensor completion methods, underestimation or overestimation of tensor ranks can lead to inaccurate results. To tackle this problem, we design an alternative iterating method that breaks the original problem into several matrix completion subproblems and adaptively adjusts the multilinear rank of the model during optimization procedures. Through numerical experiments on synthetic data and authentic images, we show that the proposed method can effectively estimate the tensor ranks and predict the missing entries.

Frequency noise reduction of delay-coupled quantum cascade lasers.

This work theoretically investigates the frequency noise and spectral linewidth characteristics of mutually delay-coupled quantum cascade lasers, which are operated in the stable locking regime. We demonstrate that the mutual injection significantly reduces the frequency noise at proper coupling phases. However, the relative intensity noise is insensitive to the mutual injection. Influences of the pump current, the linewidth broadening factor, the coupling phase, and the delay time on the frequency noise are discussed as well. In addition, it is found that the appearance of multiple compound laser modes can deteriorate the frequency noise performance of the lasers.

Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design.

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.