Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ

Li, Chenxi; Ma, Xiaoyu; Deng, Jing; Li, Jiajia; Liu, Yanjie; Zhu, Xudong; Liu, Jin; Zhang, Ping

doi:10.1002/elsc.202100055

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Further, it was placed on a haemocytometer and abundance of yeast cells concerning with different food and different stages viewed under LIECA DM 2000 LED light microscope (Figure S2). The yeast cells were counted from the four corner squares and from the central big square of haemocytometer according to (Li et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

Shifting of food sources affect abundance of yeast like symbionts in Lasioderma serricorne (F.)

Clinton,

Srinivasan,

Peñalver Cruz

et al. 2024

J Applied Entomology

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Host diet is one of the key factors that shapes insect associated microbial communities, and the impact of dietary shift on abundance of microbial communities and insect fitness is poorly understood. Cigarette beetle (Lasioderma serricorne (F.)) are in close association with yeast symbionts for nutritional and defence benefits. The present study highlights that dietary source determines the yeast abundance in cigarette beetle and any change in diet induces a shift in yeast abundance. A general trendline of increase in yeast abundance during larval and pupal stage followed by a drop in adult stages of cigarette beetle was observed among all diet sources. The yeast abundance varied among diet sources and was significantly higher on natal (wheat) and reverted colonies compared to other exposed dietary sources. Yeast abundance in reverted colonies of soybean and bengal gram increased to the levels of those that were originally feeding on wheat suggesting a similar microbial structure among those diet sources. The correlation between yeast abundance and beetle biomass among diet guilds suggest a predictable relationship between yeast symbionts and diet source. The investigation can provide further insights on yeast symbiont abundance and food source specificity in cigarette beetle biology and management.

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Section: Methodsmentioning

confidence: 99%

Shifting of food sources affect abundance of yeast like symbionts in Lasioderma serricorne (F.)

Clinton,

Srinivasan,

Peñalver Cruz

et al. 2024

J Applied Entomology

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“…The spore suspension was filtered through by 4 layers of muslin cloth to remove mycelium. spores were counted by using a hemocytometer, and the final count was maintained up to 1×106 spores/ml (Li et al, 2021). Inoculation and incubation bunches of Thompson Seedless grapes, each weighing 100 grams, underwent thorough surface sterilization process by immersion in 70% ethanol for 2 minutes.…”

Section: Pathogenicity and Temperature Optimizationmentioning

confidence: 99%

Bioprotective effects of different Plant Extracts and Essential Oils against Rhizopus stolonifer and Aspergillus niger causing Post Harvest decay in Grapes

Shamsullah,

Ibrahim,

Kakar

et al. 2024

Integr. Plant Biotechnol.

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The grapes (Vitis vinifera) are one of the largely consumed fruits in the world. Grape production in Pakistan has increased in the recent years. These are mainly consumed fresh or as dried raisins. Grapes are highly perishable, which makes them liable to rot during transport, storage, and marketing. The perishable nature of grapes leads to a considerable amount of post-harvest loss. The production and quality of grapes are adversely affected by several abiotic and biotic factors. Two different cities Pishin (Balochistan) and Faisalabad (Punjab) fruit market and stores were selected to collect rotten grapes. The fungal pathogens isolated were identified as A. niger and R. stolonifers. The pathogenicity of isolated pathogens was assessed at different temperatures (5, 20, and 30 °C) on the Thompson seedless grape variety. Infection and proliferation of fungi were found conducive at 30°C. As antifungal agents, three organic origin oils, neem, cinnamon, and clove oils with levels i.e., 500, 1000, 2000 ppm as well as marigold and neem extracts with diverse concentrations of like 12.5, 25, and 50% were employed. Among these organic compounds, cinnamon and clove oils at 2000 ppm exhibited 6.54% and 15.3% decay against A. niger, respectively. Meanwhile, against R. stolonifer, cinnamon and clove oils at 2000 ppm exhibited 12.22% and 6.74% decay, respectively. This study's findings focus on the effectiveness of both essential oils and plant extract in significantly delaying the decay process caused by fungal pathogens. The present study underscores the protective ability of the plant extracts and natural oils which could be a way forward for eco-safe management of post-harvest diseases of grapes before they reach the market.

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“…Machine learning based methods use statistical models to learn patterns and features from the data to classify and count cells. These methods require extensive feature engineering and parameter tuning, making them time-consuming and computationally expensive [ 61 , 62 ]. On the other hand, deep learning-based methods use artificial neural networks with multiple layers to automatically learn and extract features from the data.…”

Section: Literature Reviewmentioning

confidence: 99%

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

et al. 2023

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Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

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Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ

Cited by 7 publications

References 25 publications

Shifting of food sources affect abundance of yeast like symbionts in Lasioderma serricorne (F.)

Shifting of food sources affect abundance of yeast like symbionts in Lasioderma serricorne (F.)

Bioprotective effects of different Plant Extracts and Essential Oils against Rhizopus stolonifer and Aspergillus niger causing Post Harvest decay in Grapes

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

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