Classification Of Diatoms Research Articles

SC-DiatomNet: An Efficient and Accurate Algorithm for Diatom Classification

Detecting the quantity and diversity of diatoms is of great significance in areas such as climate change, water quality assessment, and oil exploration. Here, an efficient and accurate object detection model, named SC-DiatomNet, is proposed for diatom detection in complex environments. This model is based on the YOLOv3 architecture and uses the K-means++ algorithm for anchor box clustering on the diatom dataset. A convolutional block attention module is incorporated in the feature extraction network to enhance the model’s ability to recognize important regions. A spatial pyramid pooling module and adaptive anchor boxes are added to the encoder to improve detection accuracy for diatoms of different sizes. Experimental results show that SC-DiatomNet can successfully detect and classify diatoms accurately without reducing detection speed. The recall, precision, and F1 score were 94.96%, 94.21%, and 0.94, respectively. It further improved the mean average precision (mAP) of YOLOv3 by 9.52% on the diatom dataset. Meanwhile, the detection accuracy was improved compared with those of other advanced deep learning algorithms. SC-DiatomNet has potential applications in water quality analysis and monitoring of harmful algal blooms.

A high-efficiency local and global detector for diatom-based drowning diagnosis

Detection and classification of diatoms has been considered as the most effective method for the drowning diagnosis in forensic practice. However, challenges (such as limited samples, complex background interference, and detecting multiple objects) remain during detection and classification. To address these challenges, we propose a MDMS (multi-scale dynamic multi-head self-attention)-based deep learning network for the diatom-based drowning diagnosis. This framework uses TFFT (two-stage full fine-tuning training strategy) to solve the challenges of limited diatom samples and detecting multiple objects, and builds a multi-scale dynamic multi-head self-attention module to extract the local and global features of diatom images. In the meantime, we introduce an online hard example mining strategy to attenuate the complex background interference. The experimental results show that the proposed framework can effectively reduce the missing and false detection rates of diatom objects, with the mAP (mean Average Precision) reaching 92.434%, which is better than the result using the mainstream methods.

A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet.

Diatoms are a type of algae with many species. Accurate and quick classification of diatom species is important in many fields, such as water quality analysis and weather change forecasting. Traditional methods for diatom classification, specifically morphological taxonomy and molecular detection, are time-consuming and may not provide satisfactory performance. However, in recent years, deep learning has demonstrated impressive performance in this task, just like other image classification problems. On the other hand, networks with more layers do not guarantee increased accuracy. While increasing depth can be useful in capturing complex features and patterns, it also introduces challenges such as vanishing gradients, overfitting, and optimization challenges. Therefore, in our work, we propose DiatomNet, a lightweight convolutional neural network (CNN) model that can classify diatom species accurately while requiring low computing resources. A recently introduced dataset consisting of 3,027 diatom images and 68 diatom species is used to train and evaluate the model. The model is compared with well-known and successful CNN models (i.e., AlexNet, GoogleNet, Inceptionv3, ResNet18, VGG16, and Xception) and their customized versions obtained with transfer learning. The comparison is based on several success metrics: accuracy, precision, recall, F-measure, number of learnable parameters, training, and prediction time. Eventually, the experimental results reveal that DiatomNet outperforms the other models regarding all metrics with just a few exceptions. Therefore, it is a lightweight but strong candidate for diatom classification tasks.

A comparative assessment of time-consuming and laborious diatom analysis：Brief experimentation with suggestion of automatic identification

Diatom testing is considered a useful method for providing supportive evidence for the diagnosis of drowning in forensic pathology. However, various factors remain controversial for recognizing diatoms, such as being time-consuming and laborious and influencing the consistency of the results. Given the absence of precise and well-defined studies on this subject, this study aimed to determine the relationship between the ability to identify diatoms and researchers with different technical backgrounds. A total of 55 samples from 18 cases, including water, lungs, liver, and kidneys, were treated using the microwave digestion-vacuum filtration-automated scanning electron microscopy (MD-VF-Auto SEM), which was used to compare diatom analyses among three groups of well-trained forensic pathologists (FPs), trained junior employees (JEs), and new trainees (TEs). In addition to achieving similar accuracy of positive findings from drowning cases, counting efficiency was evaluated based on taxonomy records and counting time after viewing more than 5500 diatom images. In contrast to the higher counting efficiency of the JE group than that of the TE group, we observed a statistically significant difference (p < 0.05) in the diatom classification between these two groups. Based on our experiments, an efficient analysis for automatically identifying and classifying diatoms is urgently required.

Can Prediction and Understanding of Water Quality Variation Be Improved by Combining Phosphorus Source and Waterbody Condition Parameters?

Phosphorus (P) pollution impairs river systems globally. There is long-standing interest in understanding catchment source P loads to inform mitigation to improve water quality. However, P sources to the hydrosphere differ individually in discharge behaviour, P intensity, bioavailability, and cumulative impacts. River condition also varies (e.g., riparian disturbance, climate change impacts) such that source and river resilience are likely synergistic but poorly studied controls on water quality variation. To challenge the use of overly-simplistic factors (e.g., basic soils and landcover) in empirical catchment pollution source-impact assessments, we pooled spatial data according to conceptual aspects of P source mechanisms and waterbody riparian condition. These were related empirically to P concentrations and loads, and trophic diatom indices, for 19 Scottish catchments (~10–250 km2) representing some mechanistic aspects of pollution loading and river impacts. Sources of P from septic tanks and farmyards influenced loads and ecological impacts. Some secondary calculations pooling spatial data such as septic tank source-delivery methods were novel, involving complex, but available, soil water flowpath data. In contrast, inclusion of channel condition and farmyard P loads used simple aerial imagery. Multiple Factor Analysis combined with Redundancy Analysis showed that source P loads expressed as bioavailable forms of P were better explanatory factors of diatom classification groups than stream soluble reactive P concentrations, although used together they improved explanation further. Riparian quality metrics were less powerful predictors than expected, likely with more scale-dependant effects on ecological functions than can be quantified by visual condition assessment on isolated short reaches. There was strong justification for examining separate P fractions (total, dissolved, particulate and bioavailable forms) by distinct catchment source types to understand better nutrient dynamics across land to waters, ecosystem degradation and waterbody impacts in the contemporary hydrosphere.

Diversity and ecology of benthic diatoms in a mire with narrow pH gradient

Mires are specific freshwater habitats with great importance for global biodiversity. The present study investigated diatoms in different habitats of a Đon Močvar mire and their relationship with environmental parameters, as well as the use of diatom indicator values and the German Red List for conservation purposes. The study was conducted monthly from January to November 2012. Diatoms were sampled simultaneously with water for physical and chemical properties in three different habitats. A total of 50 diatom taxa were identified. Based on their relative abundance and diversity, habitat-related environmental factors shaped the diatom community when seasonal dynamics were less important. Multivariate analysis showed that chemical oxygen demand (COD) and conductivity were the most significant environmental variables describing diatom communities. The indicator values characterized the diatoms as a typical mire community and proved to be a reliable starting point for future monitoring and management. Diatom classification by German Red List revealed a dominance of species with priority for conservation, suggesting that the Đon Močvar should have conservation status. This study brings a better understanding of the response of diatoms to living in specific habitats and the basis for future studies on ecological characteristics of new or rare taxa.

Use of seasonal epilithic diatom assemblages to evaluate ecological status in Irish lakes

Periphyton is increasingly used to assess the biological condition of lakes. The predominance of relatively shallow exposed waterbodies in Ireland implies that epilithic diatoms should have a prominent role as bioindicators for the WFD; but epilithic diatom ecology in these Irish systems is poorly described. We characterised spring and summer diatom populations in diverse periphyton communities in 91 Irish lake waterbodies. Six assemblage clusters of diatom species were identified, and variation in their assemblages was predominately associated with environmental gradients of acidity-alkalinity, total phosphorus, and true colour. Diatom composition differed amongst high status lakes in three a priori defined lake alkalinity groups commonly used as a diatom typology for lake classification. However, when waterbodies were evaluated with a lake trophic diatom index (LTDI) their distribution of ‘expected-values’ was equivalent. The LTDI was highly correlated with total phosphorus but also had similar responses in the three lake alkalinity types. These findings indicated that lake typology had a limited role for diatom classification with the LTDI in Ireland. Matched spring and summer sampling across wide environmental gradients allowed the role of seasonality to be investigated. Relatively few benthic diatoms had a significant seasonal indicator value suggesting that unbiased monitoring of the epilithon is possible during any month of the growing season in comparable temperate waterbodies. However, temporal variability was lower at low values of the LTDI and increased unpredictably on a lake by lake basis thereafter. Given that lake diatom classification has been validated we suggest that the approach could be developed and used for more targeted assessment objectives.

Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy.

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

Protoraphis Simonsen, a newly recorded marine epizoic diatom genus for China

Epizoic diatoms on marine copepods are common in nature and may have a special ecological relationship with their hosts. However, this special ecological group is not well known, and it has only rarely been studied in the China seas. To address this knowledge gap, the species diversity and classification of epizoic diatoms on planktonic copepods were studied with samples collected from the East China Sea. In the present study, a marine araphid diatom genus Protoraphis and its type species, Pr. hustedtiana, were observed and identified by light and electron microscopy, thus representing the first record of this genus and its type species in China. This genus is characterized by a median sternum strongly bent to opposite sides and terminate in two transapical grooves at the valve ends. Protoraphis hustedtiana was found to be epizoic on the posterior body appendages and segments of the marine calanoid copepod Candacia bradyi. An internal view shows a complex, ear-shaped process that is close to the apical slit field. The ecological habitats and geographical distributions of Protoraphis were also discussed, and, together with complementary morphological studies, our results have increased the number of records for marine epizoic diatoms to three genera with three species in China, including Pseudohimantidium and Pseudofalcula.

珪藻類の分類体系 (総説) : 現生珪藻の属ランクのチェックリスト

珪藻類の分類体系 (総説) : 現生珪藻の属ランクのチェックリスト

Lentification in Alpine rivers: patterns of diatom assemblages and functional traits

Hydrological alterations in Alpine rivers have been largely increasing due to the combined effects of global climate change and local impacts. Water flow intermittency starts with a process called lentification, which leads to the transformation of the aquatic ecosystem with severe consequences on biotic communities. These consequences have a greater impact in Alpine headwater streams, which are considered important and fragile hotspots of biodiversity and a source of threatened species; the new regime creates conditions to which native biota are poorly adapted. The main aim of our work was to evaluate taxonomical, functional and structural changes of diatom communities in Alpine rivers during the lentification process that precedes a supra-seasonal drought. Contrary to what was expected, communities exposed to river shrinking showed a level of homogeneity comparable to those collected in permanent sections. We therefore hypothesized that lentification created an intermediate disturbance that favoured heterogeneity of assemblages. Conversely, ecological guilds, growth forms and eco-morphological groups varied along temporal and spatial gradients driven by the lentification process. Small, motile and medium-sized low profile species were more abundant during summer, as well as adnate and stalked ones. Lentification limited the presence of high profile guilds and mucous-forming colonies. Droughts in Alpine streams are fairly recent processes, and the dynamics of aquatic communities facing water scarcity are largely unknown. The results we obtained provide important insights towards a more refined functional classification of diatoms for evaluating hydrological alterations.

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.

Automated Diatom Classification (Part B): A Deep Learning Approach

Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification.

Benthic diatoms on fluvial tufas of the Mesa River, Iberian Range, Spain

Background. The Mesa River (MR) in the Iberian Range (Spain) displays prominent, Pleistocene to present-day fluvial tufa deposits. Little of their associated microbiota has been studied to date despite the regional and historical relevance of these calcareous buildups. Goals. This paper is a preliminary exploration of the diatom (Bacillariophyta) genera associated with actively-growing tufa from 10 benthic environments along 24 km of the Mesa River. Methods. Brightfield microscopy, as well as consultation with specialists and specialized literature was used for taxonomic classification of diatoms. Results. We identified 25 diatom genera in three different types of sedimentary facies (porous and moss-algae rich, dense-laminated, and tufa-free gravel). Most diatoms were raphid pennate (class Bacillariophyceae), while a few were centric (class Coscinodiscophyceae) or araphid pennate (class Fragilariophyceae). They appeared as integral components of the tufa structure along with cyanobacteria and other algae and mosses. Conclusions. Together with previous studies on the hydrochemistry and sedimentology of the MR, our interpretations suggest that HCO3 -,pCO2, Ca2+, and TDIC negatively affect diatom richness and that their abundance is positively related to the presence of mosses and algae.

Diatom classification: clarifying the authority for the Cymbellaceae

The authority for the Cymbellaceae has been attributed to different authors in some diatom classifications. Different authorities can influence the priority of the name and, consequently, the stability of a taxon. In the most recent diatom classifications, the citation is erroneous attributed to Greville. These classifications are well known and cited and, therefore, this error can be easily propagated. Here I discuss the correct authority for this name in the light of the International Code of Nomenclature for algae, fungi, and plants, in order to maintain its stability.

Emendation of Fragilariophyceae classification (Bacillariophyta)

An historical review of some approaches to the classification of diatoms is presented. The class Fragilariophyceae was described by F. Round et al. (1990) and consisted of 54 genera of araphid diatoms. Over the past 25 years, 43 genera and two families within the class have been described. The systematic position of the family Plagiogrammaceae in the class of araphid diatoms was based on phylogenetic molecular studies. According to these data and unique morphological features, we propose a new order, Plagiogrammales ord. nov., with the type genus Plagiogramma. The name of the order Protoraphidales ord. nov. is validated, and the genus Podocystis is transferred from the family Fragilariaceae to the family Licmophoraceae. The systematic position of the genera Florella, Podocystis, Gato and Astrosyne is discussed.

Use of Diatoms in River Health Assessment

We review the use of diatoms in monitoring and assessment of river health in the world. First, we summarize the use of diatoms as indicators of nutritional status, acidification, and organic and metal pollution. We then examine some problems that have been associated with diatoms and indices that were used in different regions around the world: 1) incomplete taxonomy; 2) effects of natural geographic differences on diatom distributions (e.g., climate, lithology); 3) effects of natural site differences on diatom distribution and abundance (e.g., substrate, depth, chemistry); 4) differences in sampling, sample processing, and analytical methods; and 5) difficulties in finding minimally disturbed reference sites. Based on these problems, we present the different selections and the research progress of diatoms and indices used in monitoring and assessing river health around the world including the Americas, Europe, Africa, Asia, and Australia. We recommend that future studies and uses of diatoms focus on the following aspects: 1) improve the classification and identification of diatoms; 2) increase the use of diatom predictive indices to monitor and assess water quality; 3) develop global standard methods for sampling diatoms in streams, rivers, and lakes; and 4) apply hind-casting predictive models.

Historical constraints, species concepts and the search for a natural classification of diatoms

Historical constraints, species concepts and the search for a natural classification of diatoms

A novel fuzzy based approach simple sequentially rejective multiple for inducing diatom habitat models and discovering diatom indicating properties

For a given physico-chemical parameter, diatoms have specific optimum and tolerance, thus they can be used as indicators. Many diatoms in the relevant ecological literature can be found as indicators for a certain water quality (WQ) or trophic state index (TSI) class, but for many other diatoms the indicating properties are unidentified. Noticeable progress in this direction has been made by classical decision trees. However, these methods have several drawbacks in diatom classification. To overcome this problem, in this paper we use a novel fuzzy approach for discovering the diatoms' indicating properties. The prediction accuracy of the diatom habitat models (DHMs) depends on the types of fuzzy operators, the similarity metric, and the number and shape of the membership functions (MFs). We use several MFs to describe the relationship between the diatoms and the abiotic factors. Two novel similarity metrics are introduced in order to increase the models' classification accuracy. We compare the description/prediction accuracy of our approach and several classical and fuzzy based classification algorithms. Also, we compare the proposed similarity metrics with an existing similarity metric used for inducing DHMs. The evaluation results show that the proposed approach has higher prediction accuracy than the other methods, and is less prone to over-fitting when the novel similarity metrics are used. The obtained models are verified with the known ecological references, and they were also used for adding new ecological references for some diatoms. Thus, the induced DHMs could serve as basis for establishing a new fresh-water indicator system.

Classification of marine diatoms using pigment ratio suites

Diatoms are widely distributed in many temperate areas and some species frequently form extensive blooms in spring. Hence, monitoring the variations of specific genera or species of diatoms is necessary for studying phytoplankton population dynamics in marine ecosystems. To test whether pigment ratios can be used to identify diatoms at a below-class taxonomic level, we analyzed 14 species/strains of diatoms isolated from Chinese seas using high performance liquid chromatography (HPLC). We normalized all pigment concentrations to total chlorophyll a to calculate the ratios of pigment to chlorophyll a, and calculated the ratios between accessory pigments (or pigment sums). Cluster analysis indicated that these diatoms could be classified into four clusters in terms of three accessory pigment ratios: chlorophyll c2: chlorophyll c1, fucoxanthin:total chlorophyll c and diadinoxanthin:diatoxanthin. The classification results matched well with those of biological taxonomy. To test the stability of the classification, pigment data from one species, cultured under different light intensities, and five new species/strains were calculated and used for discriminant analysis. The results show that the classification of diatom species using pigment ratio suites was stable for the variations of pigment ratios of species cultured in different light intensities. The introduction of new species, however, may confuse the classification within the current scheme. Classification of marine diatoms using pigment ratio suites is potentially valuable for the fine chemotaxonomy of phytoplankton at taxonomic levels below class and would advance studies on phytoplankton population dynamics and marine ecology.