Dynamics of dissolved organic carbon in eutrophic Lake Taihu and its tributaries and their implications for bacterial abundance during autumn and winter

Ye, Linlin; Wu, Xiaodong; Liu, Bo; Yan, Dongdong; Kong, Fanxiang

doi:10.1080/02705060.2014.939108

Cited by 11 publications

(6 citation statements)

References 76 publications

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“…Electrical conductivity (EC) was measured in the Kokosing samples, which has been shown to be an indirect indicator for dissolved organic carbon (DOC) ( 42 – 44 ). Previous studies have found connections between DOC and ARG abundance ( 45 , 46 ).…”

Section: Resultsmentioning

confidence: 99%

An Ohio State Scenic River Shows Elevated Antibiotic Resistance Genes, Including Acinetobacter Tetracycline and Macrolide Resistance, Downstream of Wastewater Treatment Plant Effluent

et al. 2021

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Section: Resultsmentioning

confidence: 99%

An Ohio State Scenic River Shows Elevated Antibiotic Resistance Genes, Including Acinetobacter Tetracycline and Macrolide Resistance, Downstream of Wastewater Treatment Plant Effluent

et al. 2021

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“…2005; Ye et al . 2015). Because methane produced in the sediments accumulate in high amounts in the lower parts of the water column (Houser et al .…”

Section: Introductionmentioning

confidence: 99%

Vertical stratification of bacteria and archaea in sediments of a small boreal humic lake

Rissanen

Peura

Mpamah

et al. 2019

FEMS Microbiology Letters

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Although sediments of small boreal humic lakes are important carbon stores and greenhouse gas sources, the composition and structuring mechanisms of their microbial communities have remained understudied. We analyzed the vertical profiles of microbial biomass indicators (PLFAs, DNA and RNA) and the bacterial and archaeal community composition (sequencing of 16S rRNA gene amplicons and qPCR of mcrA ) in sediment cores collected from a typical small boreal lake. While microbial biomass decreased with sediment depth, viable microbes (RNA and PLFA) were present all through the profiles. The vertical stratification patterns of the bacterial and archaeal communities resembled those in marine sediments with well-characterized groups (e.g. Methanomicrobia , Proteobacteria , Cyanobacteria , Bacteroidetes ) dominating in the surface sediment and being replaced by poorly-known groups (e.g. Bathyarchaeota , Aminicenantes and Caldiserica ) in the deeper layers. The results also suggested that, similar to marine systems, the deep bacterial and archaeal communities were predominantly assembled by selective survival of taxa able to persist in the low energy conditions. Methanotrophs were rare, further corroborating the role of these methanogen-rich sediments as important methane emitters. Based on their taxonomy, the deep-dwelling groups were putatively organo-heterotrophic, organo-autotrophic and/or acetogenic and thus may contribute to changes in the lake sediment carbon storage.

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“…This shows high level of organic carbon (DOC) in the rivers. EC is an indirect indicator of DOC 25 , 50 , 51 and found to have associations with Acinetobacter -specific ARG and other ARG abundance 25 , 52 , 53 . Generally, A. baumannii in the environment can survive irrespective of the level of DO 54 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents

Ekundayo

Adewoyin

Ijabadeniyi

et al. 2023

Sci Rep

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A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models’ performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040–4.5828)] and Cubist [3.1736 (1.1012–4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00–83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes.

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Dynamics of dissolved organic carbon in eutrophic Lake Taihu and its tributaries and their implications for bacterial abundance during autumn and winter

Cited by 11 publications

References 76 publications

An Ohio State Scenic River Shows Elevated Antibiotic Resistance Genes, Including Acinetobacter Tetracycline and Macrolide Resistance, Downstream of Wastewater Treatment Plant Effluent

An Ohio State Scenic River Shows Elevated Antibiotic Resistance Genes, Including Acinetobacter Tetracycline and Macrolide Resistance, Downstream of Wastewater Treatment Plant Effluent

Vertical stratification of bacteria and archaea in sediments of a small boreal humic lake

Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents

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