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Overview of Fungi01:29

Overview of Fungi

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Fungi are a diverse group of eukaryotes more closely related to animals than other eukaryotes. Fungal cell walls comprise chitin, a polysaccharide that provides structural strength, and glucans, which contribute to flexibility and integrity. Other polysaccharides, such as mannans and galactosans, may supplement or replace chitin in some fungi. These adaptations, along with their preference for acidic environments and tolerance for high osmotic pressure, enable fungi to thrive in various...
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Basidiomycota is a diverse phylum of fungi that includes ecologically significant decomposers such as white rot fungi, symbionts like mycorrhizal fungi, plant pathogens such as rusts and smuts, and edible species like Agaricus bisporus (the common button mushroom). These fungi play crucial roles in nutrient cycling, symbiotic relationships, and even human health. Their defining feature is the basidium, a microscopic club-shaped structure responsible for producing basidiospores.Fruiting Bodies...
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Microsporidia are a group of obligate intracellular fungi that were initially classified as protists but were later reclassified based on phylogenetic, molecular, and structural evidence linking them to the Chytridiomycota. These unicellular, non-motile organisms are highly specialized parasites that infect a wide range of animal hosts, including humans. They have evolved extensive genomic and metabolic reductions, making them highly dependent on their hosts for survival.Morphology and Genomic...
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Zygomycota, previously classified as a distinct fungal group, are primarily terrestrial, saprophytic molds that play a crucial role as decomposers. Recent phylogenetic studies have revealed that these fungi are now divided into two major clades — Mucoromycota, which includes many symbiotic species, and Zoopagomycota, which primarily consists of parasitic and pathogenic fungi. These groups exhibit distinct ecological roles and reproductive strategies while sharing key structural and...
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Phylum Ascomycota, a major division within the subkingdom Dikarya, comprises a diverse range of fungal species, including both unicellular yeasts and filamentous molds such as Aspergillus and Penicillium. These fungi thrive in a variety of habitats, from aquatic ecosystems to terrestrial environments, playing crucial ecological and economic roles.Morphology and ReproductionThe defining characteristic of Ascomycetes, commonly referred to as sac fungi, is the ascus—a sac-like structure that...
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Related Experiment Video

Updated: Aug 28, 2025

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
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Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning.

Zhuo Liu1, Yanjie Li1

  • 1Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China.

Journal of Fungi (Basel, Switzerland)
|September 22, 2022
PubMed
Summary

SWIR spectroscopy can non-destructively classify dark septate endophytes (DSEs) fungi and detect their growth stages. This rapid method, using De-trending + first Derivative spectra and a support vector machine model, offers high accuracy for fungal studies.

Keywords:
dark septate endophytes (DSEs)fungi identificationpreprocessingsupport vector machine (SVM)variable selection

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Area of Science:

  • Agricultural Science
  • Mycology
  • Spectroscopy

Background:

  • Dark septate endophytes (DSEs) are beneficial fungi aiding plants against abiotic stress.
  • Accurate identification and growth stage monitoring of DSEs are crucial for agricultural applications.

Purpose of the Study:

  • To evaluate SWIR spectroscopy for non-destructive classification of DSEs fungi types.
  • To assess the capability of SWIR spectroscopy in detecting DSEs fungal growth stages.
  • To develop a rapid and efficient method for DSEs fungal analysis.

Main Methods:

  • Collected SWIR spectral data from five DSEs fungi across six growth stages.
  • Applied three spectral pre-processing methods and sensitivity analysis (SA) for variable selection.
  • Utilized a machine learning model, specifically support vector machine (SVM), for classification and detection.

Main Results:

  • The combination of De-trending + first Derivative (DET_FST) spectral processing and SVM model achieved high accuracy.
  • Mean accuracy for generic model fungi classification was 0.92, and for growth stage detection was 0.99.
  • Identified seven key spectral bands (1164, 1456, 2081, 2272, 2278, 2448, 2481 nm) crucial for SVM classification.

Conclusions:

  • SWIR spectroscopy, coupled with appropriate data processing and machine learning, provides an effective tool for DSEs fungi classification and growth stage determination.
  • This non-destructive, time-saving method can significantly aid in fungal research and agricultural management.
  • The study highlights the potential of spectral analysis for rapid fungal diagnostics.