Microplastics shape microbial interactions and affect the dissemination of antibiotic resistance genes in different full-scale wastewater treatment plants.

Wastewater treatment plants (WWTPs) pose a potential threat to the environment because of the accumulation of antibiotic resistance genes (ARGs) and microplastics (MPs). However, the interactions between ARGs and MPs, which have both indirect and direct effects on ARG dissemination in WWTPs, remain unclear. In this study, spatiotemporal variations in different types of MPs, ten ARGs (sul1, sul2, tetA, tetO, tetM, tetX, tetW, qnrS, ermB, and ermC), class 1 integron integrase (intI1) and transposon Tn916/1545 in three typical WWTPs were characterized. Sul1, tetO, and sul2 were the predominant ARGs in the targeted WWTPs, whereas the intI1 and transposon Tn916/1545 were positively correlated with most of the targeted ARGs. Saccharimonadales (4.15 %), Trichococcus (2.60 %), Nitrospira (1.96 %), Candidatus amarolinea (1.79 %), and SC-I-84 (belonging to phylum Proteobacteria) (1.78 %) were the dominant genera. Network and redundancy analyses showed that Trichococcus, Faecalibacterium, Arcobacter, and Prevotella copri were potential hosts of ARGs, whereas Candidatus campbellbacteria and Candidatus kaiserbacteria were negatively correlated with ARGs. The potential hosts of ARGs had a strong positive correlation with polyethylene terephthalate, silicone resin, and fluor rubber and a negative correlation with polyurethane. Candidatus campbellbacteria and Candidatus kaiserbacteria were positively correlated with polyurethane, whereas potential hosts of ARGs were positively correlated with polypropylene and fluor rubber. Structural equation modeling highlighted that intI1, transposon Tn916/1545 and microbial communities, particularly microbial diversity, dominated the dissemination of ARGs, whereas MPs had a significant positive correlation with microbial abundance. Our study deepens the understanding of the relationships between ARGs and MPs in WWTPs, which will be helpful in designing strategies for inhibiting ARG hosts in WWTPs.

Metagenomic insight into the prevalence and driving forces of antibiotic resistance genes in the whole process of three full-scale wastewater treatment plants.

The spread of antibiotic resistance genes (ARGs) is an emerging global health concern, and wastewater treatment plants (WWTPs), as an essential carrier for the occurrence and transmission of ARGs, deserves more attention. Based on the Illumina NovaSeq high-throughput sequencing platform, this study conducted a metagenomic analysis of 18 samples from three full-scale WWTPs to explore the fate of ARGs in the whole process (influent, biochemical treatment, advanced treatment, and effluent) of wastewater treatment. Total 70 ARG subtypes were detected, among which multidrug, aminoglycoside, tetracycline, and macrolide ARGs were most abundant. The different treatment processes used for three WWTPs were capable of reducing ARG diversity, but did not significantly reduce ARG abundance. Compared to that by denitrification filters, the membrane bioreactor (MBR) process was advantageous in controlling the prevalence of multidrug ARGs in WWTPs. Linear discriminant analysis Effect Size (LEfSe) suggested g_Nitrospira, g_Curvibacter, and g_Mycobacterium as the key bacteria responsible for differential ARG prevalence among different WWTPs. Meanwhile, adeF, sul1, and mtrA were the persistent antibiotic resistance genes (PARGs) and played dominant roles in the prevalence of ARGs. Proteobacteria and Actinobacteria were the host bacteria of majority ARGs in WWTPs, while Pseudomonas and Nitrospira were the most crucial host bacteria influencing the dissemination of critical ARGs (e.g., adeF). In addition, microbial richness was determined to be the decisive factor affecting the diversity and abundance of ARGs in wastewater treatment processes. Overall, regulating the abundance of microorganisms and key host bacteria by selecting processes with microbial interception, such as MBR process, may be beneficial to control the prevalence of ARGs in WWTPs.

Insight into the spatiotemporal distribution of antibiotic resistance genes in estuarine sediments during long-term ecological restoration.

In this study, we aimed to investigate the long-term spatiotemporal changes in hydrodynamics, antibiotics, nine typical subtypes of antibiotic resistance genes (ARGs), class 1 integron gene (intI1), and microbial communities in the sediments of a semi-enclosed estuary during ecological restoration with four treatment stages (influent (#1), effluent of the biological treatment area (#2), oxic area (#3), and plant treatment area (#4)). Ecological restoration of the estuary reduced common pollutants (nitrogen and phosphorus) in the water, whereas variations in ARGs showed noticeable seasonal and spatial features. The absolute abundance of ARGs at sampling site #2 considerably increased in autumn and winter, while it significantly increased at sampling site #3 in spring and summer. The strong intervention of biological treatment (from #1 to #2) and aerators (from #2 to #3) in the estuary substantially affected the distribution of ARGs and dominant antibiotic-resistant bacteria (ARB). The dominant ARB (Thiobacillus) in estuarine sediments may have low abundance but important dissemination roles. Meanwhile, redundancy and network analysis revealed that the microbial communities and intl1 were key factors related to ARG dissemination, which was affected by spatial and seasonal ecological restoration. A positive correlation between low flow velocity and certain ARGs (tetM, tetW, tetA, sul2, and ermC) was observed, implying that flow optimization should also be considered in future ecological restoration to remediate ARGs. Furthermore, the absolute abundance of ARGs can be utilized as an index to evaluate the removal capacity of ARGs by estuarine restoration.

Distribution and drivers of antibiotic resistance genes in brackish water aquaculture sediment.

Brackish water aquaculture has brought numerous economic benefits, whereas anthropogenic activities in aquaculture may cause the dissemination of antibiotic resistance genes (ARGs) in brackish water sediments. The intricate relationships between environmental factors and microbial communities as well as their role in ARGs dissemination in brackish water aquaculture remain unclear. This study applied PCR and 16S sequencing to identify the variations in ARGs, class 1 integron gene (intI1) and microbial communities in brackish water aquaculture sediment. The distribution of ARGs in brackish water aquaculture sediment was similar to that in freshwater aquaculture, and the sulfonamide resistance gene sul1 was the indicator of ARGs. Proteobacteria and Firmicutes were the dominant phyla, and Paenisporosarcina (p_ Firmicutes) was the dominant genus. The results of correlation, network and redundancy analysis indicated that the microbial community in the brackish water aquaculture sediment was function-driven. The neutral model and variation partitioning analysis were used to verify the ecological processes of the bacterial community. The normalized stochasticity ratio showed that pond bacteria community was dominated by determinacy, which was affected by aquaculture activities. The total nitrogen and organic matter influenced the abundance of ARGs, while Proteobacteria and Thiobacillus (p_Proteobacteria) were the key antibiotic-resistant hosts. Our study provides insight into the prevalence of ARGs in brackish water aquaculture sediments, and indicates that brackish water aquaculture is a reservoir of ARGs.

Victim Detection and Localization in Emergencies.

Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.

WiFi FTM and UWB Characterization for Localization in Construction Sites.

A high-precision location is becoming a necessity in the future Industry 4.0 applications that will come up in the near future. However, the construction sector remains particularly obsolete in the adoption of Industry 4.0 applications. In this work, we study the accuracy and penetration capacity of two technologies that are expected to deal with future high-precision location services, such as ultra-wide band (UWB) and WiFi fine time measurement (FTM). For this, a measurement campaign has been performed in a construction environment, where UWB and WiFi-FTM setups have been deployed. The performance of UWB and WiFi-FTM have been compared with a prior set of indoors measurements. UWB seems to provide better ranging estimation in LOS conditions but it seems cancelled by reinforcement concrete for propagation and WiFi is able to take advantage of holes in the structure to provide location services. Moreover, the impact of fusion of location technologies has been assessed to measure the potential improvements in the construction scenario.

Metagenomic insights into the spatiotemporal responses of antibiotic resistance genes and microbial communities in aquaculture sediments.

The dissemination of antibiotic resistance genes (ARGs) in aquaculture systems is a potential threat to environmental safety and human health. However, the spatiotemporal distribution pattern of ARGs and key factors associated with their dissemination in aquaculture sediments remain unclear. In this study, ARGs, mobile genetic elements, microbial community composition, heavy metal contents, and nutrient contents of samples collected from a whole culture cycle of fish in a representative aquaculture farm were characterized. The distribution patterns of nine subtypes of ARGs (tetW, tetM, tetA, ermC, ermB, sul1, sul2, floR, and qnrS) showed clear spatiotemporal differences. The absolute abundance of ARGs in aquaculture sediments was higher in winter and in rivers of the aquaculture farm. Proteobacteria was the dominant phylum in all sediment samples. The results of network and redundancy analyses confirmed that the Dechloromonas, Candidatus Accumulibacter, Smithella, Geobacter, and Anaeromyxobacter belonging to Proteobacteria were positively correlated with ARGs, suggesting that these microbial species are potential hosts of corresponding ARGs. Our study highlights that the microbial community is the determining factor for ARG dissemination. Strategies for inhibiting these potential hosts of ARGs should be developed based on controllable factors.

WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning.

High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.