Manual Counting Method Research Articles

Automated segmentation and quantification of retained austenite in AISI 4140 using image processing techniques

The mechanical properties of a material directly influenced by its microstructural phases and chemical composition. This study aimed to identify and quantify the retained austenite in AISI 4140 steel, a microstructural phase that can impact component durability and dimensional stability. A special etchant with sodium metabisulfite was used successfully to reveal retained austenite in microscopic images, as revealing it using typical etchants almost impossible. Image processing techniques, including the KNN supervised machine learning algorithm and segmentation MATLAB function, were employed to quantify retained austenite, with results compared to manual point counting method and XRD tests. A sequence of metallurgical and heat treatment processes was conducted to create the microstructural image dataset. Results showed that the specimens with no retained austenite in XRD tests did not reveal any in the microscopic images also, with only four cases out of sixteen showed retained austenite presence. The comparison showed reasonable and relatively close percentages to those calculated from XRD tests, with a maximum 2.37% difference with the MATLAB function, 4.91% with the KNN algorithm, and 2.76% with the manual method. The results suggest the potential for using this approach to confirm retained austenite presence and estimate its fraction without the need for XRD testing.

Multi-Scale Attention Network for Vertical Seed Distribution in Soybean Breeding Fields

The increase in the global population is leading to a doubling of the demand for protein. Soybean ( Glycine max ), a key contributor to global plant-based protein supplies, requires ongoing yield enhancements to keep pace with increasing demand. Precise, on-plant seed counting and localization may catalyze breeding selection of shoot architectures and seed localization patterns related to superior performance in high planting density and contribute to increased yield. Traditional manual counting and localization methods are labor-intensive and prone to error, necessitating more efficient approaches for yield prediction and seed distribution analysis. To solve this, we propose MSANet: a novel deep learning framework tailored for counting and localization of soybean seeds on mature field-grown soy plants. A multi-scale attention map mechanism was applied to maximize model performance in seed counting and localization in soybean breeding fields. We compared our model with a previous state-of-the-art model using the benchmark dataset and an enlarged dataset, including various soybean genotypes. Our model outperforms previous state-of-the-art methods on all datasets across various soybean genotypes on both counting and localization tasks. Furthermore, our model also performed well on in-canopy 360° video, dramatically increasing data collection efficiency. We also propose a technique that enables previously inaccessible insights into the phenotypic and genetic diversity of single plant vertical seed distribution, which may accelerate the breeding process. To accelerate further research in this domain, we have made our dataset and software publicly available: https://github.com/UTokyo-FieldPhenomics-Lab/MSANet .

Aerial visual data-driven approach for berthing capacity estimation in restricted waters

Coastal ship berth planning and management necessitate accurate identification of ship position and size, yet this is complicated by the presence of diverse ship types and mixed distribution patterns. Traditional survey-based and position-tracking ship monitoring methods have limitations in reflecting the precise spatial information of coastal ships and their surroundings. This paper introduces a novel aerial visual data-driven methodology to estimate the berthing capacity of sheltered space for stationary ships in geographically dispersed water areas. To measure the berthing capacity, image data from an unmanned aerial vehicle (UAV) survey is collected to reflect the spatial information of all types of ships. A deep learning-based computer vision algorithm is used to automatically detect, identify, and classify ships in sample images. Additionally, we introduce the occupancy factor to empirically quantify the spatial proportional correlation between the ship size and the requisite berthing capacity. A validation test of the proposed method is conducted across all sheltered spaces in Hong Kong, and the UAV survey yields over 7,000 informative images of all types of local ships. The ship identification algorithm achieves an average identification rate of 98.6%, demonstrating higher accuracy and reduced labor and time consumption than observational survey and manual counting methods. Findings indicate that the suggested sheltered space for individual ships is 7.75 times the ship size. The approach provides greater flexibility in estimating berthing capacity for mixed berthing forms in extensive or geographically dispersed water bodies, thereby having the potential to support the design of berths, anchorage areas, and typhoon shelter facilities in coastal regions.

Go-Game Image Recognition Based on Improved Pix2pix.

Go is a game that can be won or lost based on the number of intersections surrounded by black or white pieces. The traditional method is a manual counting method, which is time-consuming and error-prone. In addition, the generalization of the current Go-image-recognition methods is poor, and accuracy needs to be further improved. To solve these problems, a Go-game image recognition based on an improved pix2pix was proposed. Firstly, a channel-coordinate mixed-attention (CCMA) mechanism was designed by combining channel attention and coordinate attention effectively; therefore, the model could learn the target feature information. Secondly, in order to obtain the long-distance contextual information, a deep dilated-convolution (DDC) module was proposed, which densely linked the dilated convolution with different dilated rates. The experimental results showed that compared with other existing Go-image-recognition methods, such as DenseNet, VGG-16, and Yolo v5, the proposed method could effectively improve the generalization ability and accuracy of a Go-image-recognition model, and the average accuracy rate was over 99.99%.

Recommendations for Validation of Automated Viable Cell Counting Methods (Review)

Introduction. The quality of viable cell-based products (such as biomedical cell products and advanced therapy medicinal products) must be maintained during the full production cycle to ensure their safety and efficacy for patients. The minimum required number of viable cells is one of the quality control criteria in the final product release specifications. This study looks into the process of validation of automated viable cell counting methods.Text. The study reviewed the latest data on specific validation characteristics for automated cell counters as compared to manual counting methods. We identified the main problems with the validation methods. Based on the review of scientific and regulatory literature, we identified the key validation parameters, methods of their evaluation and measurement, and reporting of results. We described the validation algorithm for an automated cell counter, including such steps as the selection of reference standards, selection of the number of experimental points, experimental design, mathematical evaluation of the obtained results, and determination of the acceptance criteria.Conclusion. Based on the data reviewed, the authors developed recommendations for the validation of automated viable cell counting procedures.

Neural Degeneration in Normal-Aging Human Cochleas: Machine-Learning Counts and 3D Mapping in Archival Sections.

Quantifying the survival patterns of spiral ganglion cells (SGCs), the cell bodies of auditory-nerve fibers, is critical to studies of sensorineural hearing loss, especially in human temporal bones. The classic method of manual counting is tedious, and, although stereology approaches can be faster, they can only be used to estimate total cell numbers per cochlea. Here, a machine-learning algorithm that automatically identifies, counts, and maps the SGCs in digitized images of semi-serial human temporal-bone sections not only speeds the analysis, with no loss of accuracy, but also allows 3D visualization of the SGCs and fine-grained mapping to cochlear frequency. Applying the algorithm to 62 normal-aging human ears shows significantly faster degeneration of SGCs in the basal than the apical half of the cochlea. Comparison to fiber counts in the same ears shows that the fraction of surviving SGCs lacking a peripheral axon steadily increases with age, reaching more than 50% in the apical cochlea and almost 66% in basal regions.

Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream.

Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.

A Machine Learning-Based Image Segmentation Method to Quantify In Vitro Osteoclast Culture Endpoints

Quantification of in vitro osteoclast cultures (e.g. cell number) often relies on manual counting methods. These approaches are labour intensive, time consuming and result in substantial inter- and intra-user variability. This study aimed to develop and validate an automated workflow to robustly quantify in vitro osteoclast cultures. Using ilastik, a machine learning-based image analysis software, images of tartrate resistant acid phosphatase-stained mouse osteoclasts cultured on dentine discs were used to train the ilastik-based algorithm. Assessment of algorithm training showed that osteoclast numbers strongly correlated between manual- and automatically quantified values (r = 0.87). Osteoclasts were consistently faithfully segmented by the model when visually compared to the original reflective light images. The ability of this method to detect changes in osteoclast number in response to different treatments was validated using zoledronate, ticagrelor, and co-culture with MCF7 breast cancer cells. Manual and automated counting methods detected a 70% reduction (p < 0.05) in osteoclast number, when cultured with 10 nM zoledronate and a dose-dependent decrease with 1–10 μM ticagrelor (p < 0.05). Co-culture with MCF7 cells increased osteoclast number by ≥ 50% irrespective of quantification method. Overall, an automated image segmentation and analysis workflow, which consistently and sensitively identified in vitro osteoclasts, was developed. Advantages of this workflow are (1) significantly reduction in user variability of endpoint measurements (93%) and analysis time (80%); (2) detection of osteoclasts cultured on different substrates from different species; and (3) easy to use and freely available to use along with tutorial resources.

Assessing the suitability of cell counting methods during different stages of a cell processing workflow using an ISO 20391-2 guided study design and analysis.

Cell counting is a fundamental measurement for determining viable cell numbers in biomanufacturing processes. The properties of different cell types and the range of intended uses for cell counts within a biomanufacturing process can lead to challenges in identifying suitable counting methods for each potential application. This is further amplified by user subjectivity in identifying the cells of interest and further identifying viable cells. Replacement of traditionally used manual counting methods with automated systems has alleviated some of these issues. However, a single cell type can exhibit different physical properties at various stages of cell processing which is further compounded by process impurities such as cell debris or magnetic beads. These factors make it challenging to develop a robust cell counting method that offers a high level of confidence in the results. Several initiatives from standards development organizations have attempted to address this critical need for standardization in cell counting. This study utilizes flow-based and image-based methods for the quantitative measurement of cell concentration and viability in the absence of a reference material, based on the tools and guidance provided by the International of Standards (ISO) and the US National Institute of Standards and Technology (NIST). Primary cells were examined at different stages of cell processing in a cell therapy workflow. Results from this study define a systematic approach that enables the identification of counting methods and parameters that are best suited for specific cell types and workflows to ensure accuracy and consistency. Cell counting is a foundational method used extensively along various steps of cell and gene therapy. The standard used in this study may be applied to other cell and gene therapy processes to enable accurate measurement of parameters required to guide critical decisions throughout the development and production process. Using a framework that confirms the suitability of the cell counting method used can minimize variability in the process and final product.

Segmentation and counting of wheat spike grains based on deep learning and textural feature

BackgroundGrain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: ‘Bainong 307’, ‘Xinmai 26’, and ‘Jimai 336’, and it has achieved unprecedented predictive counting accuracy.MethodThe images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains.ResultsThe CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R2 of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R2 of 0.92, an MAE) of 1.15, and an MRE) of 2.09%.ConclusionsImage segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation.

Real-time missing seedling counting in paddy fields based on lightweight network and tracking-by-detection algorithm

The rate of missing rice seedlings is key information in the early stages of rice cultivation, which affects farmers' decision-making process, such as replanting. Due to the low efficiency and poor accuracy of traditional manual seedling counting methods, an automated method for accurately detecting missing rice seedlings is needed. Therefore, this study proposed a real-time missing rice seedling counting method based on object detection and tracking-by-detection algorithm, including improved YOLOv5s and ByteTrack. The study focused on two aspects: improving the detection accuracy of tiny and overlapping seedlings and constructing lightweight models. The detection accuracy was improved by adjusting the structure of the detection head and introducing Transformer encoders. Meanwhile, the methods of channel pruning and compact network construction were used for developing lightweight models. The experimental results showed that the improved YOLOv5s network had a mAP@0.5:0.95 of 72.3% and an FPS of 71.6f/s, significantly higher than the original YOLOv5s model (the mAP@0.5:0.95 was 71.8% and the FPS was 56.1f/s). The accuracy of the missing seedlings counting method was 93.2%, and the counting time was five times less than the manual counting time. Therefore, the proposed method is considered both practical and efficient in detecting and counting missing seedlings, providing a new solution for real-time counting of missing rice seedlings in paddy fields.

Automatic Counting Method of Fry Based on Computer Vision

Nowadays, fish farming plays an essential role in aquaculture. In the process of fry sales, the traditional manual counting method takes a great deal of time. Because the fish are constantly moving in the water, they are difficult to count and are likely to be unnecessarily damaged. With the rise of computer vision, fry counting based on image processing is also developing. It has the advantages of having high accuracy and fast processing speed. The primary counting process is divided into detection, adhesion segmentation, and fry counting. First, we improved the maximum interclass variance threshold selection method, and we extracted the histogram features under different channels to detect the fry; then, a matching segmentation method based on concave and skeleton crossing points was used to segment the adherent fry to improve the counting accuracy, the concaves were detected by CNN. We tested the method on real fry data sets, and the average accuracy reached 97%. Our approach had better accuracy than any existing current fish counting methods. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

An integrated framework of mobile crowd estimation for the 2019, July 1st rally in Hong Kong.

Traditional approach of mobile crowd estimation involves counting a group of individuals at a specific place, manually, in real-time. It is a laborious exercise that can be physically and mentally demanding. In Hong Kong, a large rally can last more than six hours, making the manual count method susceptible to human errors. While crowd counting using object detection and tracking has been well-established in computer vision, such application has remained relatively small scale within a controlled indoor setting (e.g. counting people at fixed gateways in a mall). No attempt to date has applied the automatic crowd counting method to count hundreds of thousands of people along an open stretch of rally route within the complex urban outdoor landscape. This research proposed an integrated approach that combines the capture-recapture method in statistics and a Convolutional Neural Network (CNN) method in computer vision to count the mobile crowd. The research teams implemented the integrative approach and counted 276,970 people with a 95% confidence interval of 263,663 to 290,276 in the 2019, July 1st Rally in Hong Kong. This work counted the attendance of a large-scale rally as a proof of concept to fill in a gap in the empirical studies. The intellectual merits and research findings shed useful insights to improve mobile population estimation and leverage alternative data sources to support related scientific applications.

一种改进型YOLOv4的田间麦穗快速检测方法

The automatic detection of wheat ears in the field has important scientific research value in yield estimation, gene character expression and seed screening. The manual counting method of wheat ears commonly used by breeding experts has some problems, such as low efficiency and high influence of subjective factors. In order to accurately detect the number of wheat ears in the field, based on mobilenet series network model, deep separable convolution module and alpha channel technology, the yolov4 model is reconstructed and successfully applied to the task of wheat ear yield estimation in the field. The model can adapt to the accurate recognition and counting of wheat ear images in different light, viewing angle and growth period, At the same time, the model volume with different alpha parameters is more suitable for mobile terminal deployment. The results show that the parameters of the improved yolov4 model are five times smaller than the original model, the average detection accuracy is 76.45%, and the detection speed FPS is two times higher than the original model, which provides accurate technical support for rapid yield estimation of wheat in the field.

Application of image processing technology on testing blending ratio and blending irregularity of blended yarns

The blending ratio and blending irregularity of the blended yarn determine its property and quality. In this article, the image processing method was introduced to accurately and conveniently measure the blending ratio and blending irregularity of blended yarn by using images of the blended yarn cross sections. The Euclidean distance peeling method was applied to segment the adhered fibers to make them individual. Then, the fibers were classified according to characteristic parameters of fiber cross section such as color, shape, and area. The experiments were implemented to verify the accuracy and applicability of the image processing method to test the irregularity of blended bundles, slivers, and yarns by different fibers including polyester, viscose, and wool. The results show that the fiber identification error rate of the image processing method for blended bundles, compared with the designed, is less than 2%; the blending irregularity difference rate between image processing method and the manual counting method is less than 5% for blended sliver by sliver blending; the difference rate of the blending ratio of polyester/viscose blended yarn between designed and measured by image processing method is less than 5%, while manual counting method is less than 10%. The findings mean that the image processing method proposed in this article can greatly save labor and time on identifying and counting fibers, and it is a more accurate, convenient, and reliable method to test blending irregularity.

Scalp hair loss is not random across follicular units: A new insight into human hair ageing.

Scalp hair has the greatest number of hairs (typically 1-5) per follicular unit but is also the most susceptible body site to hair loss with age. Hence, we set-out to determine the degree to which scalp hair parameters change with age in women and men, any sex differences thereof and whether hair loss is random across follicular units. A retrospective cross-sectional study of 200 Chinese men and 200 Chinese women (30-69 years). Image analysis and manual counting methods were used to measure occipital located hair parameters from 6 × 8 mm shaved scalp photographs and plucked hair microscopy images. Of the five hair parameters, the number of hairs per follicular unit had the greatest (negative) correlation with age in both men and women. Men had a greater number of hairs and follicular units than women on average but had a greater decrease in the number of hairs per follicular unit with age, particularly for the loss of multi-hair (3+) follicular units. The loss of hairs with age was significantly different to that expected by a random loss of hairs across follicular units and better described by a model of increased hair loss risk the greater number of hairs per follicular unit. We have found evidence of hair loss preferentially occurring in multi-hair follicular units, which was more pronounced in men. These data suggest that part of the reason scalp hair is more susceptible to hair loss than on other body sites is due to the greater presence of multi-hair follicular units on the scalp.

Multi-Modal Portable Respiratory Rate Monitoring Device for Childhood Pneumonia Detection.

Accurate assessment of Respiratory Rate (RR) is the most important mechanism in detecting pneumonia in low-resource settings. Pneumonia is a disease with one of the highest mortality rates among young children under five. However, the diagnosis of pneumonia for infants remains challenging, especially in low- and middle-income countries (LMIC). In such situations, RR is most often measured manually with visual inspection. Accurate RR measurement requires the child to remain calm without any stress for a few minutes. The difficulty in achieving this with a sick child in a clinical environment can result in errors and misdiagnosis, even more so when the child is crying and non-cooperating around unfamiliar adults. Therefore, we propose an automated novel RR monitoring device built with textile glove and dry electrodes which can make use of the relaxed posture when the child is resting on the carer's lap. This portable system is non-invasive and made with affordable instrumentation integrated on customized textile glove. The glove has multi-modal automated RR detection mechanism that simultaneously uses bio-impedance and accelerometer data. This novel textile glove with dry electrodes can easily be worn by a parent/carer and is washable. The real-time display on a mobile app shows the raw data and the RR value, allowing a healthcare professional to monitor the results from afar. The prototype device has been tested on 10 volunteers with age variation of 3 years to 33 years, including male and female. The maximum variation of measured RR with the proposed system is ±2 compared to the traditional manual counting method. It does not create any discomfort for either the child or the carer and can be used up to 60 to 70 sessions/day before recharging.

Quality of Automatic Traffic Volume Counting by Cameras and Impact on the Qualitative Indicators of Traffic

This paper presents research on the collection, analysis, and evaluation of the fundamental data needed for road traffic systems. The basis for the research, analysis, planning and projections for traffic systems are traffic counts and data collection related to traffic volume and type. The quality and accuracy of this data are very important for traffic planning or optimization. Therefore, the purpose of this research is to apply advanced methods of automatic counting of motorized traffic and to evaluate the impact of this data on the measurement of important traffic indicators. The accuracy of measurements arising from the traditional method of data collection through manual counting will be compared with the most advanced methods of automatic counting through cameras. For this purpose, an analytical algorithm for the recognition and processing of data related to road users as a function of the time of day was applied. The program was written in the programming language Python, and the accuracy of the data and its effect on the results of qualitative traffic indicators were analyzed using the Synchro software model. The developed program is capable of recognizing and classifying different types of vehicles in traffic, such as motorbikes, motorcycles, cars, pick-ups, trucks, vans and buses, as well as counting the traffic volume over time. The results obtained from these two models show the advantages of applying advanced methods of data collection and processing related to dynamic traffic processes, as well as the quality in terms of the impact on the measurement of qualitative traffic indicators. A comparison of the quality of results for the different time intervals and varying levels of visibility in traffic is presented using tables and graphs. At nighttime, when visibility was poor, the discrepancy between the manual and automatic counting methods was around 9.5%. However, when visibility was good, the difference between manual counting and the automated program was 4.87% for the period 19:00–19:15 and 3.64% for the period 05:00–05:15. This discrepancy was especially noticeable when distinguishing between vehicle categories, due to the limitations in the accuracy in recognizing and measuring the dimensions of these vehicles. The difference between the two calculation models has a minor effect on qualitative traffic indicators such as: approach LOS, progression factor, v/s, v/c, clearance time, lane group flow, adj. flow, satd, and flow approach delay.

PetiteFinder: an automated computer vision tool to compute Petite colony frequencies in baker’s yeast

BackgroundMitochondrial respiration is central to cellular and organismal health in eukaryotes. In baker’s yeast, however, respiration is dispensable under fermentation conditions. Because yeast are tolerant of this mitochondrial dysfunction, yeast are widely used by biologists as a model organism to ask a variety of questions about the integrity of mitochondrial respiration. Fortunately, baker’s yeast also display a visually identifiable Petite colony phenotype that indicates when cells are incapable of respiration. Petite colonies are smaller than their Grande (wild-type) counterparts, and their frequency can be used to infer the integrity of mitochondrial respiration in populations of cells. Unfortunately, the computation of Petite colony frequencies currently relies on laborious manual colony counting methods which limit both experimental throughput and reproducibility.ResultsTo address these problems, we introduce a deep learning enabled tool, petiteFinder, that increases the throughput of the Petite frequency assay. This automated computer vision tool detects Grande and Petite colonies and computes Petite colony frequencies from scanned images of Petri dishes. It achieves accuracy comparable to human annotation but at up to 100 times the speed and outperforms semi-supervised Grande/Petite colony classification approaches. Combined with the detailed experimental protocols we provide, we believe this study can serve as a foundation to standardize this assay. Finally, we comment on how Petite colony detection as a computer vision problem highlights ongoing difficulties with small object detection in existing object detection architectures.ConclusionColony detection with petiteFinder results in high accuracy Petite and Grande detection in images in a completely automated fashion. It addresses issues in scalability and reproducibility of the Petite colony assay which currently relies on manual colony counting. By constructing this tool and providing details of experimental conditions, we hope this study will enable larger-scale experiments that rely on Petite colony frequencies to infer mitochondrial function in yeast.

Genotyping by sequencing for estimating relative abundances of diatom taxa in mock communities

BackgroundDiatoms are present in all waters and are highly sensitive to pollution gradients. Therefore, they are ideal bioindicators for water quality assessment. Current indices used in these applications are based on identifying diatom species and counting their abundances using traditional light microscopy. Several molecular techniques have been developed to help automate different steps of this process, but obtaining reliable estimates of diatom community composition and species abundance remains challenging.ResultsHere, we evaluated a recently developed quantification method based on Genotyping by Sequencing (GBS) for the first time in diatoms to estimate the relative abundances within a species complex. For this purpose, a reference database comprised of thousands of genomic DNA clusters was generated from cultures of Nitzschia palea. The sequencing reads from calibration and mock samples were mapped against this database for parallel quantification. We sequenced 25 mock diatom communities containing up to five taxa per sample in different abundances. Taxon abundances in these communities were also quantified by a diatom expert using manual counting of cells on light microscopic slides. The relative abundances of strains across mock samples were over- or under-estimated by the manual counting method, and a majority of mock samples had stronger correlations using GBS. Moreover, one previously recognized putative hybrid had the largest number of false positive detections demonstrating the limitation of the manual counting method when morphologically similar and/or phylogenetically close taxa are analyzed.ConclusionsOur results suggest that GBS is a reliable method to estimate the relative abundances of the N. palea taxa analyzed in this study and outperformed traditional light microscopy in terms of accuracy. GBS provides increased taxonomic resolution compared to currently available quantitative molecular approaches, and it is more scalable in the number of species that can be analyzed in a single run. Hence, this is a significant step forward in developing automated, high-throughput molecular methods specifically designed for the quantification of [diatom] communities for freshwater quality assessments.