Antibiotic resistance poses a great threat to global public health. Overuse of antibiotics is generally considered as the major factor contributing to it. However, little is known about whether ...non-antibiotic drugs could play potential roles in the emergence of antibiotic resistance.
We aimed to investigate whether antidepressant fluoxetine induces multiple antibiotic resistances and reveal underlying mechanisms.
Escherichia coli K12 was exposed to different concentrations of fluoxetine (0, 0.5, 5, 50 and 100 mg/L) and the resistant strains were isolated by plating on antibiotic containing plates. Resistant strains were randomly selected to determine the increase of minimum inhibition concentration (MIC) of multiple antibiotics. Genome-wide DNA sequencing was performed on cells cultured in lysogeny broth (LB) without any fluoxetine or antibiotics exposure. RNA sequencing and proteomic profiling of isolated mutants grown in LB with 100 mg/L fluoxetine were analyzed to reveal the underlying mechanisms.
Exposure of Escherichia coli to fluoxetine at 5–100 mg/L after repeated subculture in LB for 30 days promoted its mutation frequency resulting in increased resistance against the antibiotics chloramphenicol, amoxicillin and tetracycline. This increase was up to 5.0 × 107 fold in a dose-time pattern. Isolated mutants with resistance to one of these antibiotics also exhibited multiple resistances against fluoroquinolone, aminoglycoside, β-lactams, tetracycline and chloramphenicol. According to global transcriptional and proteomic analyses, the AcrAB-TolC pump together with the YadG/YadH transporter, a Tsx channel and the MdtEF-TolC pump have been triggered to export the antibiotics to the exterior of the cell. Whole-genome DNA analysis of the mutants further revealed that ROS-mediated mutagenesis (e.g., deletion, insertion, and substitution) of DNA-binding transcriptional regulators (e.g., marR, rob, sdiA, cytR and crp) to up-regulate the expression of efflux pumps, may further enhance the antibiotic efflux.
Our findings for the first time demonstrated that the exposure to antidepressant fluoxetine induces multiple antibiotic resistance in E. coli via the ROS-mediated mutagenesis.
•The exposure to antidepressant fluoxetine induces multiple antibiotic resistance in E. coli K12.•Multiple efflux pumps have been triggered by fluoxetine to export antibiotics.•ROS-mediated mutagenesis induced by fluoxetine enhances antibiotic efflux.•It is required to evaluate the potential impact of such chemicals on AMR.
We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers ...to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz .
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial ...pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.
Graphical Abstract
Graphical Abstract
Abstract Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast ...and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Neutrosophic theory studies objects whose values vary in the sets of elements and are not true or false, but in between, that can be called by neutral, indeterminate, unclear, vague, ambiguous, ...incomplete or contradictory quantities. In this paper, we firstly introduce preliminaries on granular calculus and analysis related to single-valued neutrosophic functions. Based on horizontal membership functions approach, we establish some basic arithmetic operations of single-valued neutrosophic numbers, that red allow us to directly introduce the terms of neutrosophic function in usual mathematical formulas. Additionally, we build metrics on the space of single-valued neutrosophic numbers induced from Hamming distance. Then, we define some backgrounds on the limit, derivative and integral of single-valued neutrosophic functions. Finally, in order to demonstrate the usable of our theoretical results, we present some applications to well-known problems arising in engineering such as logistic model, the inverted pendulum system, Mass - Spring - Damper model.
Shear strength of the soil is an important engineering parameter used in the design and audit of geo-technical structures. In this research, we aim to investigate and compare the performance of four ...machine learning methods, Particle Swarm Optimization - Adaptive Network based Fuzzy Inference System (PANFIS), Genetic Algorithm - Adaptive Network based Fuzzy Inference System (GANFIS), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), for predicting the strength of soft soils. For this purpose, case studies of 188 plastic clay soil samples collected from two major projects, Nhat Tan and Cua Dai bridges in Viet Nam have been used for generating training and testing datasets for constructing and validating the models. Validation and comparison of the models have been carried out using RMSE, and R. The results show that the PANFIS has the highest prediction capability (RMSE = 0.038 and R = 0.601), followed by the GANFIS (RMSE = 0.04 and R = 0.569), SVR (RMSE = 0.044 and R = 0.549), and ANN (RMSE = 0.059 and R = 0.49). It can be concluded that out of four models the PANFIS indicates as a promising technique for prediction of the strength of soft soils.
•We concentrated on the prediction of the shear strength of soft soils.•xANN were used.•Case studies of 188 plastic clay soil samples in Viet Nam have been used.•PANFIS indicates as a promising technique for prediction of strength of soft soils.
Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of ...forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. A geospatial database that contained records from 56 historical fires and nine explanatory variables was employed to train the standalone LWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factors.
•Developing four ensemble models for forest fire susceptibility mapping•Testing performance of CG, Bagging, Decorate, and Dagging ensemble learners•Reliable susceptibility mapping using the Dagging-LWL model (AUC = 0.983)•Providing insights for developing more advanced forest fire predictive models
Antibiotic resistance poses an increasing threat to public health. Horizontal gene transfer (HGT) promoted by antibiotics is recognized as a significant pathway to disseminate antibiotic resistance ...genes (ARGs). However, it is unclear whether non-antibiotic, anti-microbial (NAAM) chemicals can directly promote HGT of ARGs in the environment.
We aimed to investigate whether triclosan (TCS), a widely-used NAAM chemical in personal care products, is able to stimulate the conjugative transfer of antibiotic multi-resistance genes carried by plasmid within and across bacterial genera.
We established two model mating systems, to investigate intra-genera transfer and inter-genera transfer. Escherichia coli K-12 LE392 carrying IncP-α plasmid RP4 was used as the donor, and E. coli K-12 MG1655 or Pseudomonas putida KT2440 were the intra- and inter-genera recipients, respectively. The mechanisms of the HGT promoted by TCS were unveiled by detecting oxidative stress and cell membrane permeability, in combination with Nanopore sequencing, genome-wide RNA sequencing and proteomic analyses.
Exposure of the bacteria to environmentally relevant concentrations of TCS (from 0.02 μg/L to 20 μg/L) significantly stimulated the conjugative transfer of plasmid-encoded multi-resistance genes within and across genera. The TCS exposure promoted ROS generation and damaged bacterial membrane, and caused increased expression of the SOS response regulatory genes umuC, dinB and dinD in the donor. In addition, higher expression levels of ATP synthesis encoding genes in E. coli and P. putida were found with increased TCS dosage.
TCS could enhance the conjugative ARGs transfer between bacteria by triggering ROS overproduction at environmentally relevant concentrations. These findings improve our awareness of the hidden risks of NAAM chemicals on the spread of antibiotic resistance.
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•Triclosan promotes ARGs transfer at environmental concentrations.•Triclosan provokes bacteria oxidative stress and damage membrane.•Bacteria SOS response and ATP synthesis were elevated under triclosan exposure.•The applications of triclosan should be cautious and vigilant.
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and ...review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.
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