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Mass cultivation of the dinoflagellate Alexandrium pacificum for gonyautoxin-1,4 production
Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma
Elevated basophils in peripheral blood predict lower extremity deep venous thrombosis in non-surgical patients with spontaneous intracerebral hemorrhage
Three-dimensional thermomechanical modelling beneath Guerrero, Mexico, and its relation to the occurrence of interplate seismic phenomena
Generalized q-rung picture linguistic Schweizer and Sklar aggregation operators and their application in decision making
Nonlinear association between serum 25-hydroxyvitamin D concentrations and lung function in the United States adult population with COPD
Research on status monitoring and positioning compensation system for digital twin of parallel robots
A bayesian approach for parameterizing and predicting plasmid conjugation dynamics
One-dimensional convolutional neural network-based analysis of dielectric spectral abundance in liquid-phase media
Dietary patterns and risk of all-cause mortality in individuals with dyslipidemia based on a prospective cohort in Guizhou China
Utilizing deep belief network optimized by balanced Manta ray foraging optimization algorithm for estimating the shear Wall’s shear strength
Predictive validity of the sequential organ failure assessment score for mortality in patients with acute respiratory distress syndrome in Vietnam
EEG connectivity and BDNF correlates of fast motor learning in laparoscopic surgery
Design and implementation of time-based fault tolerance technique for solar PV system reliability improvement in different applications
Abstract This paper investigates the application of time-based fault tolerance techniques in solar photovoltaic (PV), DC–DC converter, battery, and permanent magnet synchronous motor (PMSM) systems. The fault tolerance techniques are like open circuit switch-level, leg-level, module-level, and measurement-level. By leveraging time-based monitoring and analysis, these techniques enable early detection, isolation, and recovery from various faults, enhancing system reliability and availability. The study focuses on fault scenarios within the 0.15–0.3 time per/second frame, a critical window for rapid fault response. Specific techniques, including time-based fault detection, isolation, and recovery strategies are explored in detail. The OPAL-RT HIL testing platform is used to validate the simulation results and conduct tests to evaluate the efficiency of various methods. The results demonstrate the performance of the fault-tolerant systems and the implementation of effective time-based techniques in solar PV applications. Finally, this work can be useful for researchers who want to learn how solar PV systems, batteries, and PMSM systems behave in fault situations followed by the conclusion.
Image instance segmentation based on diffusion model improved by step noisy
Unveiling the anti-inflammatory mechanism of exogenous hydrogen sulfide in Kawasaki disease based on network pharmacology and experimental validation
Wastewater based genomic surveillance key to population level monitoring of AmpC/ESBL producing Escherichia coli
Abstract Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we leverage the power of whole genome sequencing (WGS) to screen AmpC- and extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli from 77 composite samples obtained from 10 wastewater treatment plants across Finland. We found a high abundance of multidrug-resistant AmpC/ESBL-producing E. coli and significant differences in the diversity of AMR genes between the sampled cities. The in silico analysis of 73 short-read genome sequences shows the clonally diverse isolates consisting of 30 sequence types (STs), including the globally distributed pandemic ST131 clone. The CTX-M ESBL genes were detected in 86.3% (63/73) of the isolates concurrently with the blaTEM-1 (31.5%, 23/73) and blaOXA-1 (9.6%, 7/73) genes. The most prevalent ESBL genes were blaCTX-M-15 (46.6%, 34/73), blaCTX-M-27 (16.4%, 12/73), blaCTX-M-14 (4.1%, 3/73), and blaCTX-M-55 (4.1%, 3/73). Two isolates harboured the carbapenemase resistance gene, blaKPC-2 and blaNDM-1, respectively. In addition, WGS predicted phenotypic resistance against aminoglycosides, beta-lactams, cephalosporins, quinolones, sulfonamides, carbapenems, and polymyxins. The diversity of antibiotic- and stress-resistance genes correlated with the clinical incidence reported in the Finnish AMR report. Core-genome MLST revealed two wastewater genomic clusters but no genomic clusters among human and wastewater ST131 isolates. Our findings suggest the circulation of distinct clonal lineages of AmpC/ESBL-producing E. coli across Finland, with variations in AMR gene diversity and abundance by wellbeing service county. Also, our findings underscore the fact that wastewater surveillance could be key to population-level monitoring of AmpC/ESBL-producing Escherichia coli and can serve as complementary data to guide public health decisions. We propose longitudinal WGS-based epidemiology as an economically feasible approach for global AMR surveillance, pathogen evolution, and prediction of AMR.