Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

A multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor for global prediction of ammonia-nitrogen concentration in wastewater treatment processes

Ammonia-nitrogen concentration is a key water quality indicator, which reflects changes in pollutant components during wastewater treatment processes. The timely and accurate detection results contribute to optimizing control and operational management of wastewater treatment plants (WWTPs), but current detection methods only focus on the effluent location. This paper proposes a multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor to achieve the global prediction of ammonia-nitrogen concentration. Firstly, the wastewater treatment process is divided into several independent subsystems depending on the reaction mechanism, and the variable selection is performed using mutual information. Subsequently, the bidirectional long short-term memory network (Bi-LSTM) is employed to construct a model for predicting ammonia-nitrogen concentration within each subsystem, and the outputs between neighboring subsystems are incorporated as a set of new variables added into the training dataset to strengthen their connection. Finally, to address performance degradation caused by environmental factors, a probability density function (PDF)-based dynamic moving window method is proposed to enhance the robustness. The effectiveness and superiority of the proposed soft sensor are validated in the Benchmark Simulation Model no. 1 (BSM1). The experimental results demonstrate that the proposed soft sensor can accurately predict the global ammonia-nitrogen concentration in the face of different weather conditions including sunny, rainy, and stormy days. This study contributes to the stable operation of WWTPs with higher treatment efficiency and lower economic costs.

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and -1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

Long short-term memory models of water quality in inland water environments

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.

Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge

Background

The annual death toll of over 1.2 million worldwide is attributed to infections caused by resistant bacteria, driven by the significant impact of antibiotic misuse and overuse in spreading these bacteria and their associated antibiotic resistance genes (ARGs). While limited data suggest the presence of ARGs in beach environments, efficient prediction tools are needed for monitoring and detecting ARGs to ensure public health safety. This study aims to develop interpretable machine learning methods for predicting ARGs in beach waters, addressing the challenge of black-box models and enhancing our understanding of their internal mechanisms.

Methods

In this study, we systematically collected beach water samples and subsequently isolated bacteria from these samples using various differential and selective media supplemented with different antibiotics. Resistance profiles of bacteria were determined by using Kirby-Bauer disk diffusion method. Further, ARGs were enumerated by using the quantitative polymerase chain reaction (qPCR) to detect and quantify ARGs. The obtained qPCR data and hydro-meteorological were used to create an ML model with high prediction performance and we further used two explainable artificial intelligence (xAI) model-agnostic interpretation methods to describe the internal behavior of ML model.

Results

Using qPCR, we detected blaCTX-M, blaNDM, blaCMY, blaOXA, blatetX, blasul1, and blaaac(6'-Ib-cr) in the beach waters. Further, we developed ML prediction models for blaaac(6'-Ib-cr), blasul1, and blatetX using the hydro-metrological and qPCR-derived data and the models demonstrated strong performance, with R2 values of 0.957, 0.997, and 0.976, respectively.

Conclusions

Our findings show that environmental factors, such as water temperature, precipitation, and tide, are among the important predictors of the abundance of resistance genes at beaches.

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model-agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6'-lb-cr), sul1, and tetX with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling.

Deep Learning and Antibiotic Resistance

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

Distribution and model prediction of antibiotic resistance genes in Weishan Lake based on the indication of Chironomidae larvae

The widespread contamination of antibiotic resistance genes (ARGs) in freshwater environment are becoming a serious challenge to human health and ecological safety. Rapid and efficient monitoring of ARGs pollution is of great significance to ARGs control. Water, bottom mud, and fish have all been used to indicate ARG contamination in aquatic environments. However, it is unclear whether macrobenthic invertebrates in the food chain of aquatic environments can be indicators of ARG contamination. In this study, we demonstrated that ARGs including tetA gene, sul2 gene, and km gene were distributed in Chironomidae larvae in Weishan Lake. The ARG distribution was related to animal species, body parts, sampling sites, time, urban environment, animal farming, south-to-north water diversion, food chain, antibiotics, and water storage. Mathematical model predictions of ARG contamination in Weishan Lake were constructed based on the structural equation model (SEM) and the distribution of ARG sul2 in Chironomidae larvae. Influencing factors such as water storage, metal elements, antibiotic, and temperature were found to be closely related to the prediction of ARG contamination. This study provided a new indicator for ARG contamination in freshwater environments and a method to predict ARGs contamination.

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R^2, and root mean standard error values of 0.42 and 0.83 [log(cells mL^-1)], respectively. All simulated plankton results closely matched the measured concentrations. Particularly, the simulated cyanobacteria showed good agreement with the measured cell concentration, with an R² value of 0.72. In addition, our simulated result showed good agreement in peak concentration compared to observations. However, a limitation remained in following the temporal variation of Tintinnopsis sp. and Bosmia sp. Using an importance map from the TF model, water temperature, total phosphorus, and total nitrogen were identified as significant variables influencing phytoplankton and zooplankton blooms. Overall, our study demonstrated that DL can be used for modeling HABs at the phylum/class and genus levels.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

Profiling of the spatiotemporal distribution, risks, and prioritization of antibiotics in the waters of Laizhou Bay, northern China

We investigated the spatiotemporal distributions, risks, and prioritization of 15 widely used antibiotics in Laizhou Bay (LZB). Water samples (145) were collected from LZB and its estuaries and analyzed. Twelve antibiotics, with total concentrations of 241-1450 and 69-289 ng L^-1 in estuarine water and seawater, respectively, were detected, with the contributions of norfloxacin, ciprofloxacin, and amoxicillin exceeding 70%. Amoxicillin was firstly determined, which contributed to 20% and 46% of the total antibiotics during summer and spring, respectively. Higher antibiotic concentrations were observed in the sea located adjacent to aquaculture bases and the Yellow River Estuary, which are significantly influenced by mariculture and riverine inputs, respectively. Veterinary antibiotics showed higher total concentrations in summer compared to spring, indicating a higher degree of their usage in mariculture in summer. The antibiotic mixtures posed high risk to algae and low to medium risks to crustaceans and fish. Amoxicillin and norfloxacin were identified as high-risk pollutants. Additionally, amoxicillin and ciprofloxacin showed medium to high resistance development risks. Previous studies on antibiotics in the LZB did not determined amoxicillin and thus underestimated antibiotic contamination, ecological risk, and resistance development risk. Amoxicillin, norfloxacin, and ciprofloxacin should be prioritized in risk management.

Impact of sewer overflow on public health: A comprehensive scientometric analysis and systematic review

Sewer overflow (SO), which has attracted global attention, poses serious threat to public health and ecosystem. SO impacts public health via consumption of contaminated drinking water, aerosolization of pathogens, food-chain transmission, and direct contact with fecally-polluted rivers and beach sediments during recreation. However, no study has attempted to map the linkage between SO and public health including Covid-19 using scientometric analysis and systematic review of literature. Results showed that only few countries were actively involved in SO research in relation to public health. Furthermore, there are renewed calls to scale up environmental surveillance to safeguard public health. To safeguard public health, it is important for public health authorities to optimize water and wastewater treatment plants and improve building ventilation and plumbing systems to minimize pathogen transmission within buildings and transportation systems. In addition, health authorities should formulate appropriate policies that can enhance environmental surveillance and facilitate real-time monitoring of sewer overflow. Increased public awareness on strict personal hygiene and point-of-use-water-treatment such as boiling drinking water will go a long way to safeguard public health. Ecotoxicological studies and health risk assessment of exposure to pathogens via different transmission routes is also required to appropriately inform the use of lockdowns, minimize their socio-economic impact and guide evidence-based welfare/social policy interventions. Soft infrastructures, optimized sewer maintenance and prescreening of sewer overflow are recommended to reduce stormwater burden on wastewater treatment plant, curtail pathogen transmission and marine plastic pollution. Comprehensive, integrated surveillance and global collaborative efforts are important to curtail on-going Covid-19 pandemic and improve resilience against future pandemics.