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

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications.

Routine Management of Microalgae Using Autofluorescence from Chlorophyll

From a high-potential biomass perspective, microalgae have recently attracted considerable attention due to their extensive application in many areas. Although studies searching for algal species with extensive application potential are ongoing, technical development for their assessment and maintenance of quality in culture are also critical and inescapable challenges. Considering the sensitivity of microalgae to environmental changes, management of algal quality is one of the top priorities for industrial applications. Helping substitute for conventional methods such as manual hemocytometry, turbidity, and spectrophotometry, this review presents an image-based, automated cell counter with a fluorescence filter to measure chlorophyll autofluorescence emitted by algae. Capturing chlorophyll-bearing cells selectively, the device accomplished precise qualification of algal numbers. The results for cell density using the device with fluorescence detection were almost identical to those obtained using hemocytometry. The automated functions of the device allow operators to reduce working hours, for not only cell density analysis but simultaneous multiparametric analysis such as cell size and algal status based on chlorophyll integrity. The automated device boldly supports further development of algal application and might contribute to opening up more avenues in the microalgal industry.

Metagenomics Sheds Light on the Ecology of Marine Microbes and Their Viruses

Advances brought about by omics-based approaches have revolutionized our understanding of the diversity and ecological processes involving marine archaea, bacteria, and their viruses. This broad review discusses recent examples of how genomics, metagenomics, and ecogenomics have been applied to reveal the ecology of these biological entities. Three major topics are covered in this revision: (i) the novel roles of microorganisms in ecosystem processes; (ii) virus-host associations; and (iii) ecological associations of microeukaryotes and other microbes. We also briefly comment on the discovery of novel taxa from marine ecosystems; development of a robust taxonomic framework for prokaryotes; breakthroughs on the diversity and ecology of cyanobacteria; and advances on ecological modelling. We conclude by discussing limitations of the field and suggesting directions for future research.

Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA

The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi) that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA (“ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning” (Gandola et al., 2016) [1]). This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements.