These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

152 related articles for article (PubMed ID: 33784609)

  • 1. Machine learning for anomaly detection in cyanobacterial fluorescence signals.
    Almuhtaram H; Zamyadi A; Hofmann R
    Water Res; 2021 Jun; 197():117073. PubMed ID: 33784609
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements.
    Chaffin JD; Kane DD; Stanislawczyk K; Parker EM
    Environ Sci Pollut Res Int; 2018 Sep; 25(25):25175-25189. PubMed ID: 29943249
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sub-monthly time scale forecasting of harmful algal blooms intensity in Lake Erie using remote sensing and machine learning.
    Gupta A; Hantush MM; Govindaraju RS
    Sci Total Environ; 2023 Nov; 900():165781. PubMed ID: 37499836
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Classification machine learning to detect de facto reuse and cyanobacteria at a drinking water intake.
    Clements E; Thompson KA; Hannoun D; Dickenson ERV
    Sci Total Environ; 2024 Oct; 948():174690. PubMed ID: 38992351
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva.
    Derot J; Yajima H; Jacquet S
    Harmful Algae; 2020 Nov; 99():101906. PubMed ID: 33218452
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Fishing in greener waters: Understanding the impact of harmful algal blooms on Lake Erie anglers and the potential for adoption of a forecast model.
    Gill D; Rowe M; Joshi SJ
    J Environ Manage; 2018 Dec; 227():248-255. PubMed ID: 30199720
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Multiscale Mapping Assessment of Lake Champlain Cyanobacterial Harmful Algal Blooms.
    Torbick N; Corbiere M
    Int J Environ Res Public Health; 2015 Sep; 12(9):11560-78. PubMed ID: 26389930
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Ten-year survey of cyanobacterial blooms in Ohio's waterbodies using satellite remote sensing.
    Gorham T; Jia Y; Shum CK; Lee J
    Harmful Algae; 2017 Jun; 66():13-19. PubMed ID: 28602249
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Use of in vivo phycocyanin fluorescence to monitor potential microcystin-producing cyanobacterial biovolume in a drinking water source.
    McQuaid N; Zamyadi A; Prévost M; Bird DF; Dorner S
    J Environ Monit; 2011 Feb; 13(2):455-63. PubMed ID: 21157617
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Estimating microcystin levels at recreational sites in western Lake Erie and Ohio.
    Francy DS; Brady AM; Ecker CD; Graham JL; Stelzer EA; Struffolino P; Dwyer DF; Loftin KA
    Harmful Algae; 2016 Sep; 58():23-34. PubMed ID: 28073455
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.
    Kim JH; Shin JK; Lee H; Lee DH; Kang JH; Cho KH; Lee YG; Chon K; Baek SS; Park Y
    Water Res; 2021 Dec; 207():117821. PubMed ID: 34781184
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Recent advances in algal bloom detection and prediction technology using machine learning.
    Park J; Patel K; Lee WH
    Sci Total Environ; 2024 Aug; 938():173546. PubMed ID: 38810749
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Remote estimation of phycocyanin (PC) for inland waters coupled with YSI PC fluorescence probe.
    Song K; Li L; Tedesco L; Clercin N; Hall B; Li S; Shi K; Liu D; Sun Y
    Environ Sci Pollut Res Int; 2013 Aug; 20(8):5330-40. PubMed ID: 23397212
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting cyanobacterial biovolumes from phycocyanin fluorescence using a handheld fluorometer in the field.
    Thomson-Laing G; Puddick J; Wood SA
    Harmful Algae; 2020 Jul; 97():101869. PubMed ID: 32732055
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A novel single-parameter approach for forecasting algal blooms.
    Xiao X; He J; Huang H; Miller TR; Christakos G; Reichwaldt ES; Ghadouani A; Lin S; Xu X; Shi J
    Water Res; 2017 Jan; 108():222-231. PubMed ID: 27847147
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The magnitude and drivers of harmful algal blooms in China's lakes and reservoirs: A national-scale characterization.
    Huang J; Zhang Y; Arhonditsis GB; Gao J; Chen Q; Peng J
    Water Res; 2020 Aug; 181():115902. PubMed ID: 32505885
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges.
    Fournier C; Quesada A; Cirés S; Saberioon M
    Sci Total Environ; 2024 Jul; 932():172741. PubMed ID: 38679105
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Alternative alert system for cyanobacterial bloom, using phycocyanin as a level determinant.
    Ahn CY; Joung SH; Yoon SK; Oh HM
    J Microbiol; 2007 Apr; 45(2):98-104. PubMed ID: 17483793
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach.
    Shin J; Lee G; Kim T; Cho KH; Hong SM; Kwon DH; Pyo J; Cha Y
    Sci Total Environ; 2024 Feb; 912():169540. PubMed ID: 38145679
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning.
    Nelson NG; Muñoz-Carpena R; Phlips EJ; Kaplan D; Sucsy P; Hendrickson J
    Environ Sci Technol; 2018 Mar; 52(6):3527-3535. PubMed ID: 29478313
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.