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- Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy
- Haozhong Ma , Jinshan Yang , Xiaolu Chen , Xinyu Jiang , Yimin Su , Shanlei Qiao , Guowei Zhong
- J. Microbiol. 2021;59(6):563-572. Published online March 29, 2021
- DOI: https://doi.org/10.1007/s12275-021-1013-z
- 51 View
- 0 Download
- 11 Web of Science
- 12 Crossref
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Abstract
-
Fungi of the genus Aspergillus are ubiquitously distributed
in nature, and some cause invasive aspergillosis (IA) infections
in immunosuppressed individuals and contamination
in agricultural products. Because microscopic observation
and molecular detection of Aspergillus species represent the
most operator-dependent and time-intensive activities, automated
and cost-effective approaches are needed. To address
this challenge, a deep convolutional neural network (CNN)
was used to investigate the ability to classify various Aspergillus
species. Using a dissecting microscopy (DM)/stereomicroscopy
platform, colonies on plates were scanned with
a 35× objective, generating images of sufficient resolution for
classification. A total of 8,995 original colony images from
seven Aspergillus species cultured in enrichment medium
were gathered and autocut to generate 17,142 image crops
as training and test datasets containing the typical representative
morphology of conidiophores or colonies of each strain.
Encouragingly, the Xception model exhibited a classification
accuracy of 99.8% on the training image set. After training,
our CNN model achieved a classification accuracy of
99.7% on the test image set. Based on the Xception performance
during training and testing, this classification algorithm
was further applied to recognize and validate a new
set of raw images of these strains, showing a detection accuracy
of 98.2%. Thus, our study demonstrated a novel concept
for an artificial-intelligence-based and cost-effective detection
method
ology for Aspergillus organisms, which also has the potential to improve the public’s understanding of the fungal kingdom. -
Citations
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Computers and Electronics in Agriculture.2024; 224: 109104. CrossRef - Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review
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Diagnostics.2024; 14(5): 484. CrossRef - Current status and new experimental diagnostic methods of invasive fungal infections after hematopoietic stem cell transplantation
Zhenhua Tang, HaiTao Wang, Yuankai Liu, Chen Wang, Xinye Li, Qiong Yang
Archives of Microbiology.2024;[Epub] CrossRef - Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology
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Experimental and Applied Medical Science.2024; 5(2): 119. CrossRef - Attention-Guided Transfer Learning for Identification of Filamentous Fungi Encountered in the Clinical Laboratory
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Computational Intelligence and Neuroscience.2022; 2022: 1. CrossRef - Morphologic identification of clinically encountered moulds using a residual neural network
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Frontiers in Microbiology.2022;[Epub] CrossRef
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- Biotransformation of (-)-α-pinene and geraniol to α-terpineol and p-menthane-3,8-diol by the white rot fungus, Polyporus brumalis
- Su-Yeon Lee , Seon-Hong Kim , Chang-Young Hong , Se-Yeong Park , In-Gyu Choi
- J. Microbiol. 2015;53(7):462-467. Published online June 27, 2015
- DOI: https://doi.org/10.1007/s12275-015-5081-9
- 49 View
- 0 Download
- 16 Crossref
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Abstract
- In this study, the monoterpenes, α-pinene and geraniol, were biotransformed to synthesize monoterpene alcohol compounds. Polyporus brumalis which is classified as a white rot fungus was used as a biocatalyst. Consequently α-terpineol was synthesized from α-pinene by P. brumalis mycelium, after three days. Moreover, another substrate, the acyclic monoterpenoids geraniol was transformed into the cyclic compound, p-menthane-3, 8-diol (PMD). The main metabolites, i.e., α-terpineol and PMD, are known to be bioactive monoterpene alcohol compounds. This study highlights the potential of fungal biocatalysts for monoterpene transformation.
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Citations
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Sarunpron Khruengsai, Teerapong Sripahco, Pavaret Sivapornnukul, Patcharee Pripdeevech
Process Biochemistry.2024; 136: 221. CrossRef - Biotransformation of Geraniol to Geranic Acid Using Fungus Mucor irregularis IIIMF4011
Haseena Shafeeq, Bashir Ahmad Lone, Ananta Ganjoo, Nargis Ayoub, Hema Kumari, Sumeet Gairola, Prasoon Gupta, Vikash Babu, Zabeer Ahmed
ACS Omega.2024; 9(40): 41314. CrossRef - Comparative Genome-Wide Analysis of Two Caryopteris x Clandonensis Cultivars: Insights on the Biosynthesis of Volatile Terpenoids
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Plants.2023; 12(3): 632. CrossRef - Fungal biotransformation of limonene and pinene for aroma production
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Brazilian Journal of Chemical Engineering.2023; 40(1): 1. CrossRef - Análisis de la mezcla de alcoholes en motor diésel
Hector Riojas González, Indira Reta Heredia, Liborio Jesús Bortoni Anzures, Juan Julián Martínez Torres
Revista Colombiana de Química.2023;[Epub] CrossRef - The pinene scaffold: its occurrence, chemistry, synthetic utility, and pharmacological importance
Rogers J. Nyamwihura, Ifedayo Victor Ogungbe
RSC Advances.2022; 12(18): 11346. CrossRef - Biotransformation: A Novel Approach of Modulating and Synthesizing Compounds
Proloy Sankar Dev Roy, Brajeshwar Singh, Vikas Sharma, Chandan Thappa
Journal for Research in Applied Sciences and Biotechnology.2022; 1(2): 68. CrossRef - An update on the progress of microbial biotransformation of commercial monoterpenes
Ruchika Mittal, Gauri Srivastava, Deepak Ganjewala
Zeitschrift für Naturforschung C.2022; 77(5-6): 225. CrossRef - Plant-microbial remediation of chlorpyrifos contaminated soil
Xin Wang, Jia-wen Hou, Wen-rui Liu, Jia Bao
Journal of Environmental Science and Health, Part B.2021; 56(10): 925. CrossRef - Analgesic Potential of Terpenes Derived fromCannabis sativa
Erika Liktor-Busa, Attila Keresztes, Justin LaVigne, John M. Streicher, Tally M. Largent-Milnes, Eric Barker
Pharmacological Reviews.2021; 73(4): 1269. CrossRef - Grape and Wine Composition in Vitis vinifera L. cv. Cannonau Explored by GC-MS and Sensory Analysis
Giacomo L. Petretto, Luca Mercenaro, Pietro Paolo Urgeghe, Costantino Fadda, Antonio Valentoni, Alessandra Del Caro
Foods.2021; 10(1): 101. CrossRef - Production, Properties, and Applications of α-Terpineol
Adones Sales, Lorena de Oliveira Felipe, Juliano Lemos Bicas
Food and Bioprocess Technology.2020; 13(8): 1261. CrossRef - Biotransformation of terpene and terpenoid derivatives by Aspergillus niger NRRL 326
Cengiz Çorbacı
Biologia.2020; 75(9): 1473. CrossRef - Optimization of limonene biotransformation for the production of bulk amounts of α-terpineol
Gustavo Molina, Marina G. Pessôa, Juliano L. Bicas, Pierre Fontanille, Christian Larroche, Gláucia M. Pastore
Bioresource Technology.2019; 294: 122180. CrossRef - Biogeneration of aroma compounds
Adones Sales, Bruno Nicolau Paulino, Glaucia Maria Pastore, Juliano Lemos Bicas
Current Opinion in Food Science.2018; 19: 77. CrossRef - Transcriptomic analysis of the white rot fungus Polyporus brumalis provides insight into sesquiterpene biosynthesis
Su-Yeon Lee, Myungkil Kim, Seon-Hong Kim, Chang-Young Hong, Sun-Hwa Ryu, In-Gyu Choi
Microbiological Research.2016; 182: 141. CrossRef
- Biotransformation of essential oil composition of Zanthoxylum limonella by the fungus Pleopunctum pseudoellipsoideum provides the products with enhanced antimicrobial activities
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