Agricultural and urban practices are correlated to changes in the resistome of riverine systems.

The objective of this study was to comprehensively characterise the resistome, the collective set of antimicrobial resistance genes in a given environment, of two rivers, from their source to discharge into the sea, as these flow through areas of different land use. Our findings reveal significant differences in the riverine resistome composition in areas of different land uses, with increased abundance and diversity of AMR in downstream agricultural and urban locations, with the resistome in urban areas more similar to the resistome in wastewater. The changes in resistome were accompanied by changes in microbial communities, with a reduction in microbial diversity in downstream agricultural and urban affected areas, driven mostly by increased relative abundance in the phyla, Bacteroidetes and Proteobacteria. These results provide insight into how pollution associated with agricultural and urban activities affects microbial communities and influences AMR in aquatic water bodies. These results add valuable insights to form effective strategies for mitigating and preserving aquatic ecosystems. Overall, our study highlights the critical role of the environment in the development and dissemination of AMR and underscores the importance of adopting a One Health approach to address this global public health threat.

IoT Data Quality Assessment Framework Using Adaptive Weighted Estimation Fusion.

Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications' varying data quality requirements pose a challenge, as each application requires a unique data quality process. This creates scalability issues as the number of applications increases, and it also has financial implications, as it would require a separate data pipeline for each application. To address this challenge, this paper proposes a novel approach integrating fusion methods into end-to-end data quality assessment to cater to different applications within a single data pipeline. By using real-time and historical analytics, the study investigates the effects of each fusion method on the resulting data quality score and how this can be used to support different applications. The study results, based on two real-world datasets, indicate that Kalman fusion had a higher overall mean quality score than Adaptive weighted fusion and Naïve fusion. However, Kalman fusion also had a higher computational burden on the system. The proposed solution offers a flexible and efficient approach to addressing IoT applications' diverse data quality needs within a single data pipeline.

Bacteriophages from faecal contamination are an important reservoir for AMR in aquatic environments.

Bacteriophages have been shown to play an important role in harbouring and propagating antibiotic resistance genes (ARGs). Faecal matter contains high levels of phages, suggesting that faecal contamination of water bodies may lead to increased antimicrobial resistance (AMR) levels due to increased phage loading in aquatic environments. In this study, we assessed whether faecal pollution of three rivers (Rivers Liffey, Tolka, and Dodder) was responsible for increased levels of ARGs in phage particles using established phage-faecal markers, focusing on four ARGs (blaTEM, tet(O), qnrS, and sul1). We observed all four ARGs in phage fractions in all three rivers, with ARGs more frequently observed in agricultural and urban sampling sites compared to their source. These findings highlight the role of faecal pollution in environmental AMR and the impact of agricultural and urban activities on water quality. Furthermore, our results suggest the importance of including phages as indicators when assessing environmental AMR, as they serve as significant reservoirs of resistance genes in aquatic environments. This study provides important insights into the role of faecal pollution and phages in the prevalence of AMR in the environment and the need for their inclusion in future studies to provide a comprehensive understanding of environmental AMR.

Land use as a critical determinant of faecal and antimicrobial resistance gene pollution in riverine systems.

The WHO recognises antimicrobial resistance (AMR) as a global health threat. The environment can act as a reservoir, facilitating the exchange and the physical movement of resistance. Aquatic environments are at particular risk of pollution, with large rivers subject to pollution from nearby human, industrial or agricultural activities. The land uses associated with these activities can influence the type of pollution. One type of pollution and a likely contributor to AMR pollution that lowers water quality is faecal pollution. Both pose an acute health risk and could have implications for resistance circulating in communities. The effects of land use are typically studied using physiochemical parameters or in isolation of one another. However, this study aimed to investigate the impact of different land uses on riverine systems. We explored whether differences in sources of faecal contamination are reflected in AMR gene concentrations across agricultural and urban areas. Water quality from three rivers impacted by different land uses was assessed over one year by quantifying faecal indicator bacteria (FIB), microbial source tracking markers (MST) and AMR genes. In addition, a multiparametric analysis of AMR gene pollution was carried out to understand whether agricultural and urban areas are similarly impacted. Faecal indicators varied greatly, with the highest levels of FIB and the human MST marker observed in urban regions. In addition, these faecal markers correlated with AMR genes. Similarly, significant correlations between the ruminant MST marker and AMR gene levels in agriculture areas were observed. Overall, applying multiparametric analyses to include AMR gene levels, separation and clustering of sites were seen based on land use characterisation. This study suggests that differences in prescription of antimicrobials used in animal and human healthcare may influence environmental resistomes across agricultural and urban areas. In addition, public health risks due to exposure to faecal contamination and AMR genes are highlighted.

Bacterial and Bacteriophage Antibiotic Resistance in Marine Bathing Waters in Relation to Rivers and Urban Streams.

Fecal pollution of surface water may introduce bacteria and bacteriophages harboring antibiotic resistance genes (ARGs) into the aquatic environment. Watercourses discharging into the marine environment, especially close to designated bathing waters, may expose recreational users to fecal pollution and therefore may increase the likelihood that they will be exposed to ARGs. This study compares the bacterial and bacteriophage ARG profiles of two rivers (River Tolka and Liffey) and two small urban streams (Elm Park and Trimleston Streams) that discharge close to two marine bathing waters in Dublin Bay. Despite the potential differences in pollution pressures experienced by these waterways, microbial source tracking analysis showed that the main source of pollution in both rivers and streams in the urban environment is human contamination. All ARGs included in this study, bla TEM , bla SHV , qnrS, and sul1, were present in all four waterways in both the bacterial and bacteriophage fractions, displaying a similar ARG profile. We show that nearshore marine bathing waters are strongly influenced by urban rivers and streams discharging into these, since they shared a similar ARG profile. In comparison to rivers and streams, the levels of bacterial ARGs were significantly reduced in the marine environment. In contrast, the bacteriophage ARG levels in freshwater and the marine were not significantly different. Nearshore marine bathing waters could therefore be a potential reservoir of bacteriophages carrying ARGs. In addition to being considered potential additional fecal indicators organism, bacteriophages may also be viewed as indicators of the spread of antimicrobial resistance.

Identifying Sources of Faecal Contamination in a Small Urban Stream Catchment: A Multiparametric Approach.

Small urban streams discharging in the proximity of bathing waters may significantly contribute to the deterioration of water quality, yet their impact may be overlooked. This study focuses on the Elm Park stream in the city of Dublin that is subject to faecal contamination by unidentified sources. The aim of the study was to identify a minimum number of "sentinel" sampling stations in an urban catchment that would provide the maximum amount of information regarding faecal pollution in the catchment. Thus, high-resolution sampling within the catchment was carried out over the course of 1 year at 11 stations. Faecal indicator bacteria were enumerated and microbial source tracking (MST) was employed to evaluate human pollution. In addition, ammonium, total oxidised nitrogen, and phosphorus levels were monitored to determine if these correlated with faecal indicator and the HF183 MST marker. In addition, the effect of severe weather events on water quality was assessed using automated sampling at one of the identified "sentinel" stations during baseflow and high flow conditions over a 24-h period. Our results show that this urban stream is at times highly contaminated by point source faecal pollution and that human faecal pollution is pervasive in the catchment. Correlations between ammonium concentrations and faecal indicator bacteria (FIB) as well as the human MST marker were observed during the study. Cluster analysis identified four "sentinel" stations that provide sufficient information on faecal pollution in the stream, thus reducing the geographical complexity of the catchment. Furthermore, ammonium levels strongly correlated with FIB and the human HF183 MST marker under high flow conditions at key "sentinel" stations. This work demonstrates the effectiveness of pairing MST, faecal indicators, and ammonium monitoring to identify "sentinel" stations that could be more rapidly assessed using real-time ammonium readouts to assess remediation efforts.

crAssphage as a human molecular marker to evaluate temporal and spatial variability in faecal contamination of urban marine bathing waters.

Bathing water quality may be negatively impacted by diffuse pollution arising from urban and agricultural activities and wildlife, it is therefore important to be able to differentiate between biological and geographical sources of faecal pollution. crAssphage was recently described as a novel human-associated microbial source tracking marker. This study aimed to evaluate the performance of the crAssphage marker in designated bathing waters. The sensitivity and specificity of the crAss_2 marker was evaluated using faecal samples from herring gulls, dogs, sewage and a stream impacted by human pollution (n = 80), which showed that all human impacted samples tested positive for the marker while none of the animal samples did. The crAss_2 marker was field tested in an urban marine bathing water close to the discharge point of human impacted streams. In addition, the bathing water is affected by dog and gull fouling. Analysis of water samples taken at the compliance point every 30 min during a tidal cycle following a rain event showed that the crAss_2 and HF183 markers performed equally well (Spearman correlation ρ = 0.84). The levels of these marker and faecal indicators (Escherichia coli, intestinal enterococci, somatic coliphages) varied by up to 2.5 log10 during the day. Analysis of a high-tide transect perpendicular to the shoreline revealed high levels of localised faecal contamination 1 km offshore, with a concomitant spike in the gull marker. In contrast, both the crAss_2 and HF183 markers remained at a constant level, showing that human faecal contamination is homogenously distributed, while gull pollution is localised. Performance of the crAss_2 and HF183 assay was further evaluated in bimonthly compliance point samples over an 18-month period. The co-occurrence between the crAss_2 and HF183 markers in compliance sampling was 76%. A combination of both markers should be applied in low pollution impacted environments to obtain a high confidence level.

Correlation between antimicrobial resistance and faecal contamination in small urban streams and bathing waters.

Antibiotic resistance represents the greatest challenge to healthcare systems around the world. As antibiotic resistance genes (ARGs) are shed in faeces, many studies have focused on how wastewater effluent contributes to ARG pollution in rivers. However, small urban streams and bathing waters not impacted by treated wastewater have received little attention though they may be important reservoirs of ARGs. The main objective of this study was to assess the extent to which ARG and faecal pollution impact small urban streams and bathing waters and to determine if there is a relationship between these contaminants. For one year, bi-monthly water samples were collected from two urban streams and Dublin city's three designated bathing waters. The Liffey Estuary, that receives treated wastewater, was also sampled. The sul1, tet(O), qnrS, blaTEM, blaSHV and blaCTX-M ARGs were quantified. E. coli and intestinal enterococci levels were determined and the source of faecal pollution (human, dog, gull) quantified by microbial source tracking. Our results show that the Liffey Estuary, the urban streams and the bathing waters are highly impacted by ARGs and human faeces. There were clear correlations between all of the studied faecal indicators and ARGs in the Liffey Estuary. In the urban streams relationships were observed for only some of the ARGs and faecal indicators, which is likely a result of non-continuous sewage leaks and overflows to the streams. Similarly, only some ARGs correlated with faecal indicators in the urban bathing waters. The source of ARGs in the bathing waters is likely to be multifaceted as we detected sporadic dog and gull faecal markers. This study demonstrates that small urban streams and bathing waters are reservoirs of ARGs and that they may pose a previously unrecognised public health risk as they have the potential to transmit enteric pathogens and antibiotic resistance determinants.

An Evaluation of Graphical Formats for the Summary of Activities of Daily Living (ADLs).

Activities of Daily Living systems (ADLs) and the User Interface (UI) design principles used to implement them empowers the elderly to continue living a normal daily routine. The daily monitoring of activities for most Assisted Living (AL) systems demands/necessitates accurate daily user interaction, and the design principles for these systems often focus on the UI usability for the elder, not the caregiver/family member. This paper reviews Ambient Assisted Living (AAL) and ADLs UI designs and evaluates the usability of ADLs visualisation tools for caregivers. Results indicate that the UI presenting information in a bar graph format was the preferred option for respondents, as 60% chose this summarisation method over the alternative line graph UI, which had 38% of respondents selecting this format for information representation. Therefore, when designing Ambient Assisted Living (AAL) UIs, it is recommended that short periods of time are best presented in a pie graph format in combination with a bar graph format for representing extended timeline information to caregivers about their loved ones.

A Smart and Balanced Energy-Efficient Multihop Clustering Algorithm (Smart-BEEM) for MIMO IoT Systems in Future Networks.

Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work-Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity.

A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.