A low-cost automated digital microscopy platform for automatic identification of diatoms

This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ[EN] Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatomsSIThis research was funded by the Spanish Government under the AQUALITAS-RETOS project with Ref. CTM2014-51907-C2-2-R-MINEC

Fully automatic detection and classification of phytoplankton specimens in digital microscopy images

©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article Rivas-Villar, D., Rouco, J., Carballeira, R., Penedo, M. G., & Novo, J. (2021). “Fully automatic detection and classification of phytoplankton specimens in digital microscopy images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 200(105923), 105923. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2020.105923.[Abstract]: Background and objective: The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore, highly desirable to reduce the workload of the specialists and facilitate the process. Methods: This manuscript proposes a novel fully automatic methodology to perform phytoplankton analyses in digital microscopy images of water samples taken with a regular light microscope. In particular, we propose a method capable of analysing multi-specimen images acquired using a simplified systematic protocol. In contrast with prior approaches, this enables its use without the necessity of an expert taxonomist operating the microscope. The system is able to detect and segment the different existing phytoplankton specimens, with high variability in terms of visual appearances, and to merge them into colonies and sparse specimens when necessary. Moreover, the system is capable of differentiating them from other similar objects like zooplankton, detritus or mineral particles, among others, and then classify the specimens into defined target species of interest using a machine learning-based approach. Results: The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the ”Other” group, a set of relevant toxic and interesting species widely spread over the samples. Conclusions: The proposed methodology provided accurate results in all the designed steps given the complexity of the problem, particularly in terms of specimen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.This work is supported by the European Regional Development Fund (ERDF) of the European Union and Xunta de Galicia through Centro de Investigación del Sistema Universitario de Galicia, ref. ED431G 2019/01.Xunta de Galicia; ED431G 2019/0

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms

Distribution and Function of marine Bacteroidetes

Members of the phylum Bacteroidetes play a pivotal role in degrading organic matter and appear everywhere in marine and freshwater systems, from coastal to open ocean, from polar to equatorial, from surface waters down to the deep sea as well as in association with aggregates and with phytoplankton blooms. The studies described in this thesis elaborate on the distribution and function of marine Bacteroidetes. Specifically their association with spring phytoplankton blooms, substrate association by direct surface attachment and their genetic capability of degrading high molecular weight organic matter and in particular polysaccharides were examined. The Bacteroidetes distribution and community structure were analyzed at a temporal scale, by investigating the responses of distinct bacteroidetal clades during and after spring phytoplankton blooms of four consecutive years at the coastal station Helgoland Roads. It could be shown by automated microscopic cell counting that shortly after the chlorophyll a maximum concentration Bacteroidetes increased to more than 50% of the total bacterioplankton community during spring seasons. The Bacteroidetes community comprised only a few dominant genera, which accounted together for more than half of the Bacteroidetes. Each year a distinct succession pattern of the clades Ulvibacter, Formosa A, and Polaribacter was observed with relative abundances of single clades with up to 20%. Furthermore, members of the Bacteroidetes inhabited not only the free-living fraction, but they were also found attached to diatoms. Although a quantification of attached Bacteroidetes was difficult, qualitative observations were made. For example members of this phylum attach frequently to the diatom Chaetoceros spp., which is commonly blooming in spring at Helgoland Roads. The clades Polaribacter and Formosa A were identified as dominating among those Chaetoceros-associated Bacteroidetes. In contrast, Ulvibacter was not found attached to Chaetoceros, but to Asterionella spp., another diatom genus occurring in spring blooms. Since members of Bacteroidetes are the first in responding to algal blooms and attached even to distinct diatom species, we investigated their genetic potential to degrade algal derived organic matter. In particular we searched for the presence of polysaccharide utilization loci (PULs) in fosmids retrieved from two contrasting provinces of the North Atlantic Ocean. In total 14 PULs were identified, six on fosmids from the northern station and eight on fosmids from the southern station. Among those PULs one seems to be involved in xylan degradation and four were identified as potential laminarin degradation PULs. Interestingly, GHs were identified which had been assumed to be unique among terrestrial Flavobacteria, suggesting a higher capability of open ocean Bacteroidetes clades for organic matter degradation than previously anticipated

Automatic system for the detection and recognition of phytoplankton in digital microscope imaging

[Abstract] The quality of water can be compromised by the proliferation of toxic species of phytoplankton. When these blooms occur in rivers and reservoirs used for the water supply, this event can have a negative impact on human health. Currently, to determine the existence of risk, experts rudimentarily monitor phytoplankton populations by sampling and analysing the water. This analysis consits on the identification of dangerous species and its biologic volume. All in all, this process is long and tedious when the amount of samples that need to be analysed in order to obtain a quality and representative measure is taken into account, which also needs to be carried out periodically for each water source. The taxonomic process requires broad experience and training for the personnel involved. The automation of these tasks is highly desirable as it would free the experts from part of the work at the same time as that eliminates subjective factors that may impact in the overall quality of the process. In this work the intention is to help experts starting from images obtained directly from a conventional microscope, differentiating it from other similar works in the state of the art that use specific hardware. Computer vision techniques will be used to detect candidate individuals and artificial intelligence methods to recognise relevant phytoplankton species, that is, the toxic ones, distinguishing them from the rest of objects in the images like, for example, inorganic materials. Finally, the phytoplankton organisms will be classified to obtain a metric that counts the dangerous ones and so be able to analyse the quality of the water.[Resumo] A calidade da auga pode verse ameazada pola proliferación de especies tóxicas de fitoplancto. Cando estas proliferacións ocorren en ríos e encoros utilizados na subministración de auga potable este feito pode ter impactos negativos na saúde humana. Actualmente, para determinar a existencia de risco, os expertos monitorizan, de forma rudimentaria, as poboacións de fitoplancto mediante a recolección de mostras e a súa correspondente análise. Esta análise consiste na identificación das especies perigosas e o rexistro do seu volume biolóxico. Con todo, este proceso resulta longo e tedioso se se ten en conta a cantidade de mostras a analizar para poder ofrecer unhas métricas fiables e representativas, as cales se deben realizar periodicamente para cada unha das fontes de auga destinadas ao consumo. Así mesmo, o proceso taxonómico require unha ampla experiencia e formación específica do persoal involucrado. A automatización destas tarefas é moi desexable xa que libera aos expertos de parte do traballo, á vez que evita factores subxectivos que poidan influír na calidade global do proceso. Neste traballo preténdese axudar aos expertos partindo de imaxes obtidas directamente do microscopioconvencional, diferenciándoo de traballos similares do estado do arte que requiren hardware específico. Empregaranse técnicas de procesado de imaxe e visión artificial para detectar individuos candidatos e técnicas de intelixencia artificial para recoñecer as especies de fitoplancto relevantes, é dicir, as tóxicas, distinguíndoas do resto de obxectos nas imaxes, como, por exemplo, materiais inertes ou inorgánicos. Por último, os microogranismos de fitoplancto son clasificados para obter unha métrica que contabilice os perigosos e poder, así, analizar a calidade da auga.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2018/201

In situ real-time Zooplankton Detection and Classification

Zooplankton plays a key-role on Earth’s ecosystem, emerging in the oceans and rivers in great quantities and diversity, making it an important and rather common topic on scientific studies. It serves as prey for many large living beings, such as fish and whales, and helps to keep the food chain stabilized by acting not only as prey to other animals but also as a consumer of phytoplankton, the main producers of oxygen on the planet. Zooplankton are also good indicators of environmental changes, such as global warming or rapid fluctuations in carbon dioxide in the atmosphere, since their abundance and existence is dependent on many environmental factors that indicate such changes. Not only is it important to study the numbers of zooplankton in the water masses, but also to know of what different species these numbers are composed of, as different species can provide information of different environmental attributes. In this thesis a possible solution for the zooplankton in situ detection and classification problem in real-time is proposed using a portable deep learning approach based on CNNs (Convolutional Neural Networks) deployed on INESC TEC’s MarinEye system. The proposed solution makes use of two different CNNs, one for the detection problem and another for the classification problem, running in MarinEye’s plankton imaging system, and portability is guaranteed by the use of the Movidius™ Neural Compute Stick as the deep learning motor in the hardware side. The software was implemented as a ROS node, which guarantees not only portability but facilitates communication between the imaging system and other MarinEye’s modules.O zooplâncton representa um papel fundamental no ecossistema do planeta, surgindo nos oceanos e rios em grandes quantidades numa elevada diversidade de espécies, sendo um objecto de estudo comum em publicações e artigos produzidos pela comunidade científica. A sua importância vem de entre outros factores do facto de ser a principal fonte de alimento de uma grande parte da vida marinha, desde pequenos peixes a baleias, e de ser um grande consumidor de fitoplâncton, a principal fonte de oxigénio do planeta. O zooplâncton é também um bom indicador de alterações ambientais, como o aquecimento global ou variações rápidas na quantidade de dióxido de carbono na atmosfera, uma vez que a sua abundância depende de diversos factores ambientais relacionados com tais mudanças, sendo não só importante perceber em que quantidades existe nas massas de água do planeta, mas também por que diferentes espécies está distribuído. Nesta tese é apresentada uma possível solução para a deteção e classificação de zooplâncton in situ e em tempo real, recorrendo a uma abordagem facilmente portável de Deep Learning, baseada em Redes Neuronais Convolucionais implementado no sistema MarinEye do INESC TEC. A solução proposta faz uso de duas arquitecturas de redes diferentes, uma dedicada à tarefa de deteção do zooplâncton, e outra dedicada `a sua classificação, implementadas no módulo de aquisição de imagens de plâncton do sistema MarinEye. A portabilidade e flexibilidade do sistema foi garantida através do uso da Movidius™ Neural Compute Stick como motor de deep learning, assim como da implementação do software como um nó de ROS, que garante não só a portabilidade do sistema, como também permite uma facilidade de comunicação entre os diferentes módulos do MarinEye

Meiofauna Biodiversity and Ecology

Meiofauna are small organisms ranging 30–500 μm in body size, inhabiting marine sediments and other substrata all over the world, even the most extreme ones. We can find many different meiofaunal species in a very small handful of sediment, with the most varied and curious shapes, that share peculiar lifestyles, ecological relationships, and evolutionary traits. They contribute significantly to the processes and functioning of marine ecosystems, thanks to their high abundance and taxonomical diversity, fast turnover and metabolic rates. Some meiofaunal taxa have also revealed their considerable utility in the evaluation of the ecological quality of coastal marine sediments in accordance with European Directives. Therefore, understanding the distribution patterns of their biodiversity and identifying the factors that control it at a global level and in different types of habitats is of great importance. Due to their very small morphological characteristics utilized for the taxonomical identification of these taxa, the suite of necessary skills in taxonomy, and the general taxonomic crisis, many young scientists have been discouraged to tackle meiofauna systematics. The papers collected in this book, however, bring together important themes on the biology, taxonomy, systematics, and ecology of meiofauna, thanks to the contribution of researchers from around the world from the USA, Brazil, Costa Rica, Mexico, Cuba, Italy, Belgium, France, Denmark, Russia, Kuwait, Vietnam, and South Korea. This was certainly an additional opportunity to build a more solid network among experts in this field and contribute to increasing the visibility of these tiny organisms. A special thanks to Prof. Wonchoel Lee for the wonderful taxonomic drawings of the species described in this volume that contribute to make our cover unique

Meiofauna Biodiversity and Ecology

Sedimentary habitats cover the vast majority of the ocean floor and constitute the largest ecosystem on Earth. These systems supply fundamental services to human beings, such as food production and nutrient recycling. It is well known that meiofauna are an abundant and ubiquitous component of sediments, even though their biodiversity and importance in marine ecosystem functioning remain to be fully investigated. In this book, the meiofaunal biodiversity trends in marine habitats worldwide are documented, along with the collection of empirical evidence on their role in ecosystem services, such as the production, consumption, and decomposition of organic matter, and energy transfer to higher and lower trophic levels. Meiofaunal activities, like feeding and bioturbation, induce changes in several physico-chemical and biological properties of sediments, and might increase the resilience of the benthic ecosystem processes that are essential for the supply of ecosystem goods and services required by humans. As a key component of marine habitats, the taxonomical and functional aspects of the meiofaunal community are also used for the ecological assessment of the sediments’ quality status, providing important information on the anthropogenic impact of benthos

Exocrine glands of the caligid copepod Lepeophtheirus salmonis (Kroyer, 1837)

A variety of different functions have been attributed to the secretions of copepod exocrine glands. Such secretions have been suggested to possess, amongst other properties, hydrodynamic, predator deterrent, cuticle hardening and antifoulant activities. The nature of the secretion of a selected group of exocrine glands of the caligid copepod Lepeophtheirus salmonis was examined. Firstly, the number and pattern of distribution of the defined glands of L. salmon is were identified in all life-stages. In comparison, several species of parasitic, commensal and freeliving copepods and other crustaceans were examined to determine the extent of the distribution of such glands within the Crustacea. Histochemical techniques and ultrastructural analysis of glandular tissues were utilised to suggest the probable biochemical characteristics of the gland contents and revealed that mucus and protein were components of the gland secretions. TEM analysis revealed a considerable mucus layer covering the body cuticle of L. salmonis. This layer was presumed to be derived from the exocrine glands of this species. To characterise the secretion more precisely specific enzyme assays, selective stammg procedures and high performance thin layer chromatography (HPTLC) were employed. These techniques indicated that the peroxidatic enzyme catalase was present in the glands at significantly higher levels than in the general body tissues. This enzyme was shown to be contained within the secretory vesicles of the glands. Having positively identified an enzymatic component of the gland tissues, acrylamide gel electrophoresis was undertaken to specifically determine the molecular weight and quaternary structure of the enzyme. Further studies using this technique focused on identifying secreted enzymes of L. salmonis and correlated such proteins to those identified in samples of gland tissue. A four subunit, catalase of between 260 and 280kDa was demonstrated to be present in the secretions of L. salmonis. The fmal stage of the work tested the hypothesised functions of the secretions of the identified glands of L. salmon is. The gland secretions of L. salmonis were demonstrated not to possess an antibacterial activity against some common species of marine bacteria whilst the deliberate removal of the external mucus layer significantly affected the hydrodynamic characteristics of L. salmonis