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Assessing microbial growth in drinking water using nucleic acid content and flow cytometry fingerprinting
Get PDFThis study utilizes flow cytometry (FCM) to evaluate the high nucleic acid (HNA) and low nucleic acid (LNA) content of intact cells for monitoring bacterial dynamics in drinking water treatment and supply systems. Our findings indicate that chlorine and nutrients differently impact components of bacterial populations. HNA bacteria, characterized by high metabolic rates, quickly react to nutrient alterations, making them suitable indicators of growth under varying water treatment and supply conditions. Conversely, LNA bacteria adapt to environments with stable, slowly degradable organics, reflecting distinct physiological characteristics. Changes in water treatment and supply conditions, such as chlorine dosing and nutrient inputs, significantly impact the ratio between HNA and LNA. FCM fingerprinting combined with cluster analysis provides a more sensitive evaluation of water quality by capturing a broader range of microbial characteristics compared to using only HNA/LNA ratios. This work advocates for multi-parameter data analysis to advance monitoring techniques for water treatment and supply processes.Engineering and Physical Sciences Research CouncilThe UK Engineering and Physical Sciences Research Council (EPSRC) and South East Water funded the work through an Engineering Doctoral Training Award (grant number: EP/L015412/1) to L.C.iScienc
A tiered assessment of human health risks associated with exposure to persistent, mobile and toxic chemicals via drinking water
Get PDFThere is increasing interest in chemicals which are persistent, mobile and toxic (PMT), primarily to protect drinking water. We present a tiered assessment of drinking water exposure and associated human health risks for 22 PMT substances. Worst-case exposure via drinking water is assumed to occur when wastewater is discharged to rivers which are then abstracted for water supply. Screening-level exposures assume daily per capita emissions based on REACH tonnage estimates, with removal in wastewater treatment calculated using SimpleTreat and no riverine dilution. Removal in water treatment was estimated for each substance assuming either conventional or advanced treatment processes. Higher tier spatially-resolved exposures used a gridded routing model which transfers chemical through the landscape based on flow directions derived from a 1 km digital elevation model. Emission was assumed to be proportional to population and no in-stream degradation was assumed. Exposures were calculated for 296 locations containing drinking water treatment works (WTWs) under mean discharge and Q95 (discharge exceeded 95% of the time). At low tiers, risk characterisation ratios (RCRs) were 1 for three substances under conventional treatment but were 1 for tetrachloroethylene (highest RCR) at up to 18 % of WTW locations under Q95 conditions assuming conventional treatment. However, RCRT was <1 for all locations assuming advanced treatment. Actual exposures will depend on catchment characteristics, but the model usefully allows prioritising higher risk chemicals and WTWs. Overall, the substances evaluated are unlikely to currently pose health risks, provided an appropriate level of water treatment is employed.National Institute for Health and Care Research, Department of Health and Social Care, Health and Safety Executive, Department for Environment, Food and Rural AffairsThis research was funded by the UK Drinking Water Inspectorate.Science of The Total Environmen
An organic-inorganic polyacrylamide-based surface imprinted quantum dots for the impedimetric and voltammetric detection of diazepam in saliva with smartphone readout
Get PDFDiazepam (DZP) is a muscle-relaxing, anxiety-relieving sedative drug; nonetheless, it is also an addictive drug that may be abused. This work reports on the development of a novel electrochemical nanosensor for diazepam using SiO2-encapsulated-3-mercaptopropionic acid-capped AuZnCeSeS quantum dots (QDs) overcoated with a molecularly imprinted polymer (MIP) on screen-printed carbon electrodes (SPCEs). Electrochemical, spectroscopic and electron microscopic characterization of the nanomaterial and modified electrode surface was carried out and is reported herein. Specifically, electrochemical characterization of the QDs/SPCE using cyclic voltammetry (CV) revealed that the QDs exhibit a higher electrode surface area whilst electrochemical impedance spectroscopy (EIS) characterization demonstrated a lower charge transfer resistance (Rct). To fabricate the electrochemical nanosensor, firstly, alloyed AuZnCeSeS QDs were synthesized in the organic phase and thereafter capped with 3-mercaptopropionic acid (MPA) via a ligand exchange reaction. The MPA-AuZnCeSeS QDs were encapsulated in a SiO2 layer to form a SiO2-MPA AuZnCeSeS QDs system. The QDs were drop-casted onto SPCEs to form a SiO2-MPA AuZnCeSeS QDs/SPCE transducer interface. Organic based acrylamide, used as a functional monomer, was electropolymerized via CV on the QDs/SPCE in the presence of the diazepam template with ethylene glycol dimethacrylate as a crosslinker and 2,2′-azobis(2-methylpropionitrile) as an initiator. Under optimum experimental conditions, DZP was detected using EIS and square wave voltammetry (SWV). Using a portable potentiostat and a hand-held smartphone-based potentiostat, DZP was quantitatively detected in saliva using the MIP@QDs/SPCE with a limit of detection (LOD) of 2.3 μM and 2.7 μM, respectively. The LOD for DZP from SWV analysis was 1.0 μM.ZY thanks UKRI NERC Fellowship grant (NE/R013349/2) and Leverhulme Trust Research Leadership Awards (RL-2022-041).Talant
Synergistic air pollution exposure elevates depression risk: a cohort study
Get PDFDepression is a leading mental health disorder worldwide, contributing substantially to the global disease burden. While emerging evidence suggests links between specific air pollutants and depression, the potential interactions among multiple pollutants remain underexplored. Here we show the influence of six common air pollutants on depressive symptoms among middle-aged and older Chinese adults. In single-pollutant models, a 10 μg m−3 increase in SO2, CO, PM10, and PM2.5 is associated with increased risks of depressive symptoms, with odds ratios (95% confidence intervals) of 1.276 (1.238–1.315), 1.007 (1.006–1.008), 1.066 (1.055–1.078), and 1.130 (1.108–1.153), respectively. In two-pollutant models, SO2 remains significantly associated with depressive symptoms after adjusting for other pollutants. Multi-pollutant models uncover synergistic effects, with SO2, CO, NO2, PM10, and PM2.5 exhibiting significant interactions, identifying SO2 as the primary driver of these associations. Mediation analyses further indicate that cognitive and physical impairments partially mediate the relationship between air pollution and depressive symptoms. These findings underscore the critical mental health impacts of air pollution and highlight the need for integrated air quality management strategies. Targeted mitigation of specific pollutants, particularly SO2, is expected to significantly enhance public mental health outcomes.The authors gratefully acknowledge funding from Project LH2021E097, supported by the Natural Science Foundation of Heilongjiang Province, Project QMPT-2007, supported by Harbin Medical University, with the support of the China Scholarship Council.Environmental Science and Ecotechnolog
Boundary objects at play in the world's greenest football club
Get PDFIn the face of increasing environmental and societal pressures, organizations are gradually moving away from merely reducing their detrimental effects toward making positive impacts. One sizeable sector of economic activity, that is frequently overlooked, is that of sport, of which football is the largest in terms of economic value, fan base and global cultural influence. It is only relatively recently that outliers in the football industry have transitioned from being purely profit‐motivated to being both socially and environmentally aware. This transition is challenging since it is being undertaken within the often aggressively masculine environment, as well as the deep‐seated socio‐historical origins and contexts of the individual clubs and the sport as a whole. One such outlying football club is Forest Green Rovers which appears to have navigated this journey successfully. However, research has yet to understand “how” this has been achieved. This study addresses this gap through a 4‐year examination of the social and environmental initiatives of Forest Green Rovers. This lower‐league “club on the hill” is globally recognized for its novel approaches and solutions. Through examining the various Boundary Objects that aid in uniting disparate social groups in order to effect considerable changes to the “match day experience” and to stakeholders' consumption behaviors, it explains how their pragmatic, syntactic, and semantic functions combine to create an accepted suite of socially and environmentally beneficial initiatives.Strategic Chang
Deep autoencoders for unsupervised anomaly detection in wildfire prediction
Get PDFWildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a unique approach, differentiating from classical supervised learning, and addressed the gap in unsupervised wildfire prediction using autoencoders and clustering techniques for anomaly detection. Historical weather and normalized difference vegetation index data sets of Australia for 2005–2021 were utilized. Two main unsupervised approaches were analyzed. The first used a deep autoencoder to obtain latent features, which were then fed into clustering models, isolation forest, local outlier factor and one‐class support vector machines for anomaly detection. The second approach used a deep autoencoder to reconstruct the input data and use reconstruction errors to identify anomalies. Long Short‐Term Memory autoencoders and fully connected (FC) autoencoders were employed in this part, both in an unsupervised way learning only from nominal data. The FC autoencoder outperformed its counterparts, achieving an accuracy of 0.71, an F1‐score of 0.74, and an MCC of 0.42. These findings highlight the practicality of this method, as it effectively predicts wildfires in the absence of ground truth, utilizing an unsupervised learning technique.Earth and Space Scienc
Decision support system for sustainable hydrogen production: case study of Saudi Arabia
Get PDFThe global energy sector is undergoing a transition towards sustainable sources, with hydrogen emerging as a promising alternative due to its high energy content and clean-burning properties. The integration of hydrogen into the energy landscape represents a significant advancement towards a cleaner, greener future. This paper introduces an innovative decision support system (DSS) that combines multi-criteria decision-making (MCDM) and decision tree methodologies to optimize hydrogen production decisions in emerging economies, using Saudi Arabia as a case study. The proposed DSS, developed using MATLAB Web App Designer tools, evaluates various scenarios related to demand and supply, cost and profit margins, policy implications, and environmental impacts, with the goal of balancing economic viability and ecological responsibility. The study's findings highlight the potential of this DSS to guide policymakers and industry stakeholders in making informed, scalable, and flexible hydrogen production decisions that align with sustainable development goals. The novel DSS framework integrates two key influencing factors technical and logistical by considering components such as data management, modeling, analysis, and decision-making. The analysis component employs statistical and economic methods to model and assess the costs and benefits of eleven strategic scenarios, while the decision-making component uses these results to determine the most effective strategies for implementing hydrogen production to minimize risks and uncertainties.The authors acknowledge the funding from Ministry of Energy Saudi ArabiaEnergy for Sustainable Developmen
Vitacrystallography: structural biomarkers of breast cancer obtained by X-ray scattering
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Breast cancer ranks as the most prevalent cancer among women. Current screening includes regular mammography and subsequent biopsy if the mammography results are abnormal. These procedures are costly and uncomfortable. We propose an alternative non-invasive method based on X-ray scattering. Using a machine learning approach, we have examined almost 3000 measurements of cancerous and non-cancerous samples belonging to 110 patients and shown excellent results on cancer/non-cancer separation. This can lead to patient-friendly, fast, and economical solutions for breast cancer screening to complement mammography and reduce biopsy. It should be emphasized that this approach can be readily extended to other types of cancer and even other diseases.
Abstract
With breast cancer being one of the most widespread causes of death for women, there is an unmet need for its early detection. For this purpose, we propose a non-invasive approach based on X-ray scattering. We measured samples from 107 unique patients provided by the Breast Cancer Now Tissue Biobank, with the total dataset containing 2958 entries. Two different sample-to-detector distances, 2 and 16 cm, were used to access various structural biomarkers at distinct ranges of momentum transfer values. The biomarkers related to lipid metabolism are consistent with those of previous studies. Machine learning analysis based on the Random Forest Classifier demonstrates excellent performance metrics for cancer/non-cancer binary decisions. The best sensitivity and specificity values are 80% and 92%, respectively, for the sample-to-detector distance of 2 cm and 86% and 83% for the sample-to-detector distance of 16 cm.Cancer
Self-organising maps for comparing flying performance using different inceptors
Get PDFThis paper addresses a new data analysis method which is suitable to cluster flight data and complement current exceedance-based flight data monitoring programmes within an airline. The data used for this study consists of 296 simulated approaches from 4.5 NM to 1 NM to the runway threshold, flown by 74 participants (both pilots and non-pilots) with either a conventional sidestick or a gamepad in the future flight simulator at Cranfield University. It was clustered and analysed with the use of Kohonen’s Self-Organising Maps (SOM) algorithm. The results demonstrate that SOM can be a meaningful indicator for safety analysts to accurately cluster both optimal and less-optimal flying performance. This methodology can therefore complement current deviation-based flight data analyses by highlighting day-to-day as well as exceptionally good performance, bridging the cap of current analyses with safety-II principles.21st International Conference, EPCE 2024, Held as Part of the 26th HCI International Conference, HCII 202
Modulating the properties of brown alga alginate-based fibers using natural cross-linkers for sustainable textile and fashion applications
Get PDFSeaweed-derived alginate shows promise in the textile industry as a sustainable alternative to synthetic and natural materials. However, challenges arise due to its low mechanical strength. We addressed this limitation by sustainably extracting alginates from European brown algae and employing novel manufacturing methods. Using natural cross-linkers, such as chitosan, ferulic acid, and citric acid, we have successfully modulated the mechanical properties of alginate fibers. Mechanical properties of ferulic acid and citric acid-cross-linked alginate solutions were spinnable, producing fibers with a diameter of 73–75 μm. Ferulic acid cross-linked alginate fibers exhibited stiffness, with a tensile strength of 52.97 MPa and a strain percentage of 20.77, mechanical properties comparable to those of wool, polyester, and rayon. In contrast, citric acid-cross-linked fibers showed partial elasticity, with a tensile strength of 14.35 MPa and a strain percentage of 45.53, comparable to those of nylon. This ability to control the mechanical properties of seaweed-derived fibers represents a significant advancement for their application in sustainable textiles and the fashion industry.Biotechnology and Biological Sciences Research Council (BBSRC)Engineering and Physical Sciences Research Council (EPSRC)This project was supported by a grant from the Biomass Biorefinery Network (BBNet), a BBSRC/EPSRC funded Network in Industrial Biotechnology and Bioenergy (BBSRC NIBB) BB/S009779/1 and by the H2020 Project GENIALG (Grant Agreement No. 727892).ACS Omeg