WRc (United Kingdom)

Marine aquaculture tailwater, rich in nitrogen and phosphorus, threatens aquatic ecosystems and human health. This study optimized constructed wetlands (CWs) by bioaugmenting with arbuscular mycorrhizal fungi (AMF) and intertidal wetland sediment (IWS) to enhance pollutant removal under variable salinity. Four bioaugmenting strategies─AMF inoculation (A), IWS inoculation (I), combined AMF-IWS (AI), and control (CK)─were tested at salinities of 5 and 10 ppt. Results showed that AI CWs achieved the highest removal rates of NH4+-N (up to 76.59 vs 49.91% in CK), total inorganic nitrogen (up to 87.59 vs 73.49% in CK), and inorganic phosphorus (up to 77.94 vs 26.80% in CK), maintaining superior and stable performance across both low and high salinity conditions. Microbial analyses indicated that IWS introduction enriched key genera (Denitromonas, Thauera, and Desulfuromonas), while AMF promoted plant–microbe interactions that contributed to pollutant reduction. Functional gene predictions revealed that the combined inoculation enhanced denitrification while suppressing nitrification and dissimilatory nitrate reduction pathways. These findings demonstrate the synergistic benefits of AMF and IWS in CWs and provide new insights into bioaugmented wetland design for the treatment of saline wastewater in marine aquaculture.

Biochemical assays are essential tools in biological research and drug discovery, but optimisation of these assays is often a challenging and lengthy process due to the wide range of input variables and the complex effects of these variables on one another. Traditional ‘one-factor-at-a-time’ optimisation is both time-consuming and fails to explore the full range of input combinations. In contrast, the modern ‘design of experiments’ (DoE) approach enables simultaneous investigation of multiple input variables and their interactions, leading to more information-rich and efficient experimentation. We therefore sought to apply DoE to the optimisation of a new fluorescence-based assay for the enzyme RecBCD, a helicase–nuclease–ATPase complex involved in bacterial stress responses. A novel ‘functional data analysis’ (FDA) approach was used to predict the shape of RecBCD reaction curves in response to different combinations of input variables, which successfully identified assay conditions suitable for drug screening. Collectively, this work delivers a new assay for the antibiotic target RecBCD and demonstrates the potential of DoE and FDA to accelerate biochemical assay development.

River pollution is a global challenge recognized as unacceptable by citizens. Despite increasing awareness and investment in river water quality monitoring worldwide, current monitoring strategies fail to well characterise river health. In particular, the spatial and temporal resolution at which river health is currently monitored is insufficient and falls short to identify e.g., pollution spikes and point pollution sources. At the same time, the rise in citizen engagement in river monitoring, driven by increased awareness and widespread availability of smart phones and other monitoring technologies, has generated opportunities to overcome current monitoring barriers.    In this paper, we share our experience collaborating with the community group Friends of Bradford’s Becks (FoBB, UK) to use citizen-collected photos for AI-based detection of health indicators, leading to enhanced river health management. More specifically, FoBB has collected around 100,000 photos of the streams that flow through and under Bradford. These images offer insights into the health of the becks, including specific pollution issues such as discharging overflows, sewage litter, discolouration, amongst other things, as well as how pollution has changed in time and space. The number of photos makes analysis challenging. In this project, we used AI models for automatic image labelling and prototyped several landing solutions for embedding the labelling model into a tool usable by citizens.   The project was initially set up in a Hackathon, funded by Natural England, and aimed to develop solutions using AI models. The landing solutions employed classification and object detection deep learning models to assist citizens by offering automatic detection of river health indicators. This not only reduces the cost of reporting but also improves the quality of reporting with comprehensive labels. Through community engagement, high spatio-temporal resolution data can be collected from citizens to fill the data gaps, including pollution levels, natural habitat conditions, and biodiversity. Additionally, while collecting the data, the deep learning models can be further fine-tuned to better assist citizens and managers in river health assessment and management. In summary, the project presents a holistic approach to river health management, combining the strengths of AI with the insights and engagement of local communities. The success of this approach in Bradford offers a template for similar initiatives globally, marking a step towards more informed and responsive river health management strategies.

The current study proposes a new strategy for using small hydroelectric turbines in downstream river branches with the least amount of construction and the lowest cost by comparing two different methods of installing the turbines, the first by installing the turbines at the river's bottom and the second by installing the turbines on floating boats. The methodology of this article is based on predicting the distribution of velocities through the watercourse using experimental data collected at various points in the river's depth, and then predicting the resulting electrical power for different sizes of turbines, as well as estimating the number of turbines for each row and the number of rows along the river. Therefore, Investigate the proposed systems. The proposed small hydropower system's economic viability and environmental impact are investigated in this article. According to the nature of the waterway, the best diameter of a turbine that can be used is 1.5 m based on water velocities and river depths. The proposed power plant generated 25.8 kW per single turbine row, with an estimated cost of produced power (0.035 USD/kWh) of approximately 20 turbines installed per row. Compared to other renewable energy sources, the proposed hydropower system is cost-effective and environmentally friendly, as generating electricity with the proposed small hydropower plant could reduce annual carbon dioxide emissions by 368 tones of CO2 per single turbine row.

To assess deviation from a standardised structure of surgical time-out procedures in a multidisciplinary referral hospital. An observational process audit was performed on a convenience sample of surgical cases. A fly-on-the-wall observer assessed surgical time-out procedures in real-time. Pre-induction and recovery checklists were not assessed. Observations were recorded on standardised reporting forms including a checklist and free text. Analysis was performed using a validated framework of four conceptual domains: the purpose, occasion, audience and content of the communication. Field notes were taken to allow retrospective verification of assessments. Observations were compared to a predefined standardised surgical time-out procedure structure. Twenty surgical time-out procedures were observed from a mixture of procedure types. Although all were performed at the specified time and place, only eight (40%) were considered to have fully achieved their purpose with potentially important sections of the communication being omitted in the others. Individuals were not ready for communication to begin in 14 (70%) and distractions occurred in 11 surgical time-out procedures (55%). In seven surgical time-out procedures (35%) superfluous information was communicated. In a busy operating theatre environment, surgical time-out procedures may not be performed as they are intended. Communication during surgical time-out procedures should be audited to highlight opportunities for improvement.

A model was developed to simulate the clogging of a field-scale stormwater harvesting system with high flow filters over 2.5-year.

A negatively charged nanofiltration membrane was prepared with a high selectivity for the rejection of both multivalent and monovalent ions during seawater pre-treatment.