ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments.

[1]  N. Wannicke,et al.  Climate change and regulation of hepatotoxin production in Cyanobacteria. , 2014, FEMS microbiology ecology.

[2]  M. Zeder,et al.  Automated Quantification and Sizing of Unbranched Filamentous Cyanobacteria by Model-Based Object-Oriented Image Analysis , 2010, Applied and Environmental Microbiology.

[3]  S. Atsumi,et al.  Cyanobacterial biofuel production. , 2012, Journal of biotechnology.

[4]  N. Salmaso,et al.  Global expansion of toxic and non-toxic cyanobacteria: effect on ecosystem functioning , 2015, Biodiversity and Conservation.

[5]  P. J. García Nieto,et al.  Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain). , 2011 .

[6]  S. Gkelis,et al.  Anthropogenic and climate-induced change favors toxic cyanobacteria blooms: Evidence from monitoring a highly eutrophic, urban Mediterranean lake , 2014 .

[7]  I. Falconer.  Toxic cyanobacterial bloom problems in Australian waters: risks and impacts on human health , 2001 .

[8]  S. Tabassum,et al.  Exploring Marine Cyanobacteria for Lead Compounds of Pharmaceutical Importance , 2012, TheScientificWorldJournal.

[9]  A spline fitting algorithm for identifying cell filaments in bright field micrographs , 2012 .

[10]  Blahoslav Marsálek,et al.  Detection and estimation of potentially toxic cyanobacteria in raw water at the drinking water treatment plant by in vivo fluorescence method. , 2007, Water research.

[11]  P. Albertano,et al.  Toxic blooms of Planktothrix rubescens (Cyanobacteria/Phormidiaceae) in three waterbodies in Italy , 2003 .

[12]  Janet F. Neff,et al.  Human Illnesses and Animal Deaths Associated with Freshwater Harmful Algal Blooms—Kansas , 2015, Toxins.

[13]  A. D. Poularikas,et al.  Automated sizing, counting and identification of zooplankton by pattern recognition , 1984 .

[14]  C. Mougin,et al.  Cyanobacterial toxins: modes of actions, fate in aquatic and soil ecosystems, phytotoxicity and bioaccumulation in agricultural crops. , 2014, Chemosphere.

[15]  Mary C. Watzin,et al.  Evaluation of sampling and screening techniques for tiered monitoring of toxic cyanobacteria in lakes , 2008 .

[16]  Lucas J. Stal,et al.  BASIC: Baltic Sea cyanobacteria. An investigation of the structure and dynamics of water blooms of cyanobacteria in the Baltic Sea responses to a changing environment. , 2003 .

[17]  P. Albertano,et al.  Characterization of biofilm‐forming cyanobacteria for biomass and lipid production , 2012, Journal of applied microbiology.

[18]  K. V. Embleton,et al.  Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method , 2003 .

[19]  P. Albertano,et al.  Evaluating biomass of Baltic filamentous cyanobacteria by image analysis , 2000 .

[20]  R. M. Willis,et al.  Biodiesel production by simultaneous extraction and conversion of total lipids from microalgae, cyanobacteria, and wild mixed-cultures. , 2011, Bioresource technology.

[21]  P. Albertano,et al.  Cyanobacterial picoplankton from the Central Baltic Sea: cell size classification by image-analyzed fluorescence microscopy , 1997 .

[22]  L. Nedbalová,et al.  Quantification of pelagic filamentous microorganisms in aquatic environments using the line-intercept method , 2001 .

[23]  Jamie Bartram,et al.  Toxic Cyanobacteria in Water: a Guide to Their Public Health Consequences, Monitoring and Management Chapter 2. Cyanobacteria in the Environment 2.1 Nature and Diversity 2.1.1 Systematics , 2022 .

[24]  W. Uhlmann,et al.  Automated Phytoplankton Analysis by a Pattern Recognition Method , 1978 .

[25]  Bernhard Ernst,et al.  Determination of the filamentous cyanobacteria Planktothrix rubescens in environmental water samples using an image processing system , 2006 .

[26]  A. J. Kaufman,et al.  Early Earth: Cyanobacteria at work , 2014 .

[27]  H. Paerl,et al.  Climate change: a catalyst for global expansion of harmful cyanobacterial blooms. , 2009, Environmental microbiology reports.

[28]  Peter D. Hunter,et al.  Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes , 2010 .

[29]  C. Brönmark,et al.  Food-chain length alters community responses to global change in aquatic systems , 2013 .

[30]  L. Backer,et al.  Cyanobacteria and Algae Blooms: Review of Health and Environmental Data from the Harmful Algal Bloom-Related Illness Surveillance System (HABISS) 2007–2011 , 2015, Toxins.

[31]  P. Albertano,et al.  Morphometric variability of the genus Nodularia (Cyanobacteria) in Baltic natural communities , 2003 .

[32]  L. T. Tan.  Pharmaceutical agents from filamentous marine cyanobacteria. , 2013, Drug discovery today.

[33]  D. N. Kim,et al.  Fast Fourier Transform - Algorithms and Applications , 2010 .

[34]  H. Paerl,et al.  Climate change: links to global expansion of harmful cyanobacteria. , 2012, Water research.

[35]  H. Paerl,et al.  Health Effects of Toxic Cyanobacteria in U.S. Drinking and Recreational Waters: Our Current Understanding and Proposed Direction , 2015, Current Environmental Health Reports.

[36]  C. Gobler,et al.  The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change , 2012 .

[37]  P. J. García Nieto,et al.  Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain) , 2013 .

[38]  Automated measurements of filamentous cyanobacteria by digital image analysis , 2007 .