Dynamic characteristics of total and microcystin-producing Microcystis in a large deep reservoir

Li, Jingjing; Xian, Xuanxuan; Xiao, Xinyan; Li, Shuai; Yu, Xin

doi:10.1016/j.envpol.2023.122256

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…However, we cannot establish the causal links between pH and MICX based on this. The underlying process can be that phytoplankton photosynthesis affects both pH and MICX-production, meaning the relationship between pH and MICX was observed as a result of confounding; or it could also be that pH directly affects the growth of MICX-producing phytoplankton species and their metabolism process associated with MICX. , This is true for other observations of this study as well; we should take caution before making any causal claim and declare the underlying process.…”

Section: Discussionmentioning

confidence: 99%

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Wu,

Liang,

Qiu

2023

ACS EST Water

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Trace pollutants are widely observed in aquatic ecosystems and can significantly impact human health and the environment. Accurate prediction of trace pollutants and understanding their response to environmental drivers are key to environmental management, yet these tasks remain challenging. An important reason for this challenge is that monitoring data for trace pollutants are often left-censored, leading to biased estimation and inaccurate response-driver relationships. Here we propose a novel two-stage interpretable machine learning framework applicable to left-censored trace pollutant data. The two stages include (1) a classifier to predict the presence of the pollutant and (2) a regressor to predict the pollutant concentration if present. The two stages were followed by a model interpretation to understand the contribution of drivers to the presence and concentration of the pollutant accordingly. We take the prediction of microcystin (MICX) in lakes across the United States as a case study. Applying this framework to MICX consistently improved prediction accuracy, including prediction of its occurrence and concentration regardless of the algorithms and performance metrics used. The best-performing algorithm using the two-stage framework, compared with the baseline model, improves classification performance by 48% to 290% and the regression performance by 11% to 33%, depending on the metric used to evaluate the performance. The interpretable machine learning model also successfully revealed the impacts of the most important drivers on the presence of MICX and its concentration. Our results showed the advantages of this framework, including its interpretability to understand the driver-response relationship, ability to handle nonlinearity, better prediction performance, differentiation between the underlying processes, and potential to be generalized to other pollutants. As such, we anticipate that the framework we propose will be a starting point for using state-of-the-art interpretable machine learning models for predicting trace pollutants.

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Section: Discussionmentioning

confidence: 99%

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Wu,

Liang,

Qiu

2023

ACS EST Water

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“…It is well-known that nutrients (mainly nitrogen and phosphorus) trigger algal biomass. It appears that the concentration of nitrogen and phosphorus instead of the nitrogen/phosphorus ratio regulated Microcystis dominance (Li et al, 2023). Recently, hydrogen peroxide (7 mg/L) was successfully applied to control cyanobacteria in ponds apparently without influence on beneficial algae and aquatic animals (Ng et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a point-of-use device used for autoantibody analysis and its potential for following microcystin leucine-arginine exposure

Ma,

Loscher,

Parle-McDermott

et al. 2024

Front. Sens.

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Introduction: Globally, the need for measuring exposure to algal toxins has become urgent due to ever-increasing reports of contamination in sea and freshwater, in shellfish and fish stocks and in aerosols.Methods: To address this issue, we evaluated the potential of determining autoantibodies to a panel of biomarkers known to be elevated following exposure to the hepatotoxin microcystin leucine-arginine. The presence of autoantibodies, specific to four selected stress-response, metabolomic and chaperone biomarkers, namely, Heat shock protein 1, Triosephosphate isomerase, Peroxiredoxin 1 and Peroxiredoxin 2 was employed in screening 371 serum samples from microcystin-exposed individuals in Tanzania. In addition, the capacity of the LightDeck fluorescence-based detector, a point-of-use device, to monitor these autoantibody responses in comparison to enzyme-linked immunosorbent assay was evaluated.Results: By using the determination of autoantibodies to this novel panel of biomarkers an altered response was observed following microcystin exposure, with levels generally upregulated. The presence of elevated levels of microcystin leucine-arginine in water, as well as in food sources in Tanzania, may potentially have significant health effects on the population.Discussion: This novel biomarker panel may have potential for the detection of microcystin leucine-arginine exposure as well as various microcystin exposure-associated cancers (e.g., hepatocellular cancer and colorectal cancer). In addition, the utilisation of the LightDeck point-of-use device proved successful for the rapid analysis of this biomarker panel.

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Plant allelochemicals inhibit the growth and colony formation of Microcystis

Ma,

Wang,

Zhou

et al. 2024

J. Ocean. Limnol.

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Dynamic characteristics of total and microcystin-producing Microcystis in a large deep reservoir

Cited by 6 publications

References 34 publications

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Evaluation of a point-of-use device used for autoantibody analysis and its potential for following microcystin leucine-arginine exposure

Plant allelochemicals inhibit the growth and colony formation of Microcystis

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