scholarly journals Suitcase Lab: new, portable, and deployable equipment for rapid detection of specific harmful algae in Chilean coastal waters

2020 ◽  
Author(s):  
So Fujiyoshi ◽  
Kyoko Yarimizu ◽  
Yohei Miyashita ◽  
Joaquín Rilling ◽  
Jacquelinne J. Acuña ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Rapid Detection ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Lamp Assay ◽  
Field Studies ◽  
Harmful Algae ◽  
Molecular Diagnostic ◽  
Alexandrium Catenella ◽  
Molecular Diagnostic Test

AbstractPhytoplankton blooms, including harmful algal blooms (HABs), have serious impacts on ecosystems, public health, and productivity activities. Rapid detection and monitoring of marine microalgae are important in predicting and managing HABs. We developed a toolkit, the Suitcase Lab, to detect harmful algae species in the field. We demonstrated the Suitcase Lab’s capabilities for sampling, filtration, DNA extraction, and loop-mediated isothermal amplification (LAMP) detection in cultured Alexandrium catenella cells as well as Chilean coastal waters from four sites: Repollal, Isla García, Puerto Montt, and Metri. A LAMP assay using the Suitcase Lab in the field confirmed microscopic observations of A. catenella in samples from Repollal and Isla García. The Suitcase Lab allowed the rapid detection of A. catenella, within 2 h from the time of sampling, even at a single cell per milliliter concentrations, demonstrating its usefulness for quick and qualitative on-site diagnosis of target toxic algae species. This method is applicable not only to detecting harmful algae but also to other field studies that seek a rapid molecular diagnostic test.

scholarly journals Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

2021 ◽  
Vol 8 ◽  
Author(s):  
Sang-Soo Baek ◽  
JongCheol Pyo ◽  
Yong Sung Kwon ◽  
Seong-Jun Chun ◽  
Seung Ho Baek ◽  
...  
Keyword(s):  
Deep Learning ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Effective Means ◽  
Ocean Model ◽  
Alexandrium Catenella ◽  
The Public ◽  
Global Issue ◽  
Simulated Distribution ◽  
Classification And Regression

In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L–1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.

Growth performance of Alexandrium catenella from the Chilean fjords under different environmental drivers: plasticity as a response to a highly variable environment

2020 ◽  
Vol 42 (2) ◽  
pp. 119-134 ◽  
Author(s):  
Javier Paredes-Mella ◽  
Daniel Varela ◽  
Pamela Fernández ◽  
Oscar Espinoza-González
Keyword(s):  
Light Intensity ◽  
Growth Performance ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Optimal Growth ◽  
N:P Ratio ◽  
Environmental Drivers ◽  
Growth Responses ◽  
Variable Environment ◽  
Alexandrium Catenella

Abstract Alexandrium catenella, the main species associated with harmful algal blooms, has progressively increased its distribution through one of the most extensive and highly variable fjord systems in the world. In order to understand this successful expansion, we evaluated the effects of different salinities, light intensity, temperatures, nitrogen (N) forms and nitrogen/phosphate (N:P) ratio levels on the growth performance, using clones isolated from different locations across its wide geographic distribution. Results showed that the growth responses were plastic and, in some cases, different reaction norms among clones were observed. Despite plasticity, the optimal growth of A. catenella (i.e. highest growth rate and highest maximal cells density) was reached within a narrow thermal range (12–15°C), while salinity (20–30 PSU) and light intensity (20–120 μmol m−2 s−1) ranges were wider. These results are partially consistent with the highest cell densities recorded in the field. Furthermore, optimal growth was reached using reduced forms of nitrogen (i.e. urea and NH4+) and in unbalanced N:P ratios (18:1 and 30:1). These characteristics likely allow A. catenella to grow in highly variable environmental conditions and might partly explain the recent expansion of this species.

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