Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy.

Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.

Unravelling the hidden diversity of cave mycobiota in Thailand's Satun Geopark.

Karst caves are distinctive ecosystems that have limited nutrients, darkness, low to moderate temperatures, and high moisture levels, which allow for a diverse range of fungal communities to thrive. Despite their significance, little is understood about the fungi found in karst caves in Thailand. In 2019, we studied the cultured mycobiota from five substrate types (air, water, rock, soil/sediment, and organic debris) in two karst caves (Le Stegodon and Phu Pha Phet Caves) of the Satun UNESCO Global Geopark, southern Thailand. A cumulative count of 829 distinct fungal morphological types was identified, encompassing 319 fungal culturable were observed. Based on preliminary analyses of the internal transcribed spacer (ITS) sequence using BLAST searches, the most common phylum among the fungal morphotypes was Ascomycota, harboring 282 species in 91 genera, 93.4% of which were distributed in the classes Eurotiomycetes, Sordariomycetes, and Dothideomycetes. The most common fungal genera identified in the two karst caves were Aspergillus, Penicillium, Cladosporium, Talaromyces, Xylaria, and Trichoderma, with 45, 41, 24, 14, 14, and 6 species identified, respectively. Discovering fungi in Thai karst caves highlights the extensive fungal diversity in the Satun UNESCO Global Geopark, implying undiscovered species, and emphasizing the need for comprehensive investigations in other unexplored Thai karst caves.

Two Novel Species of Talaromyces Discovered in a Karst Cave in the Satun UNESCO Global Geopark of Southern Thailand.

Karst caves are oligotrophic environments that appear to support a high diversity of fungi. Studies of fungi in Thailand's caves are limited. During a 2019 exploration of the mycobiota associated with soil samples from a karst cave, namely, Phu Pha Phet in the Satun UNESCO Global Geopark in Satun Province, southern Thailand, two previously undescribed fungi belonging to Talaromyces (Trichocomaceae, Eurotiales, Eurotiomycetes) were studied using a polyphasic approach combining phenotypic and molecular data. Based on datasets of four loci (ITS, BenA, CaM, and RPB2), phylogenetic trees of the section Trachyspermi were constructed, and two new species-Talaromyces phuphaphetensis sp. nov. and T. satunensis sp. nov.-phylogenetically related to T. subericola, T. resinae, and T. brasiliensis, are described. Detailed descriptions and illustrations of the new species are provided. This study increases the number of cave-dwelling soil fungi discovered in Thailand's Satun UNESCO Global Geopark, which appears to be a unique environment with a high potential for discovering fungal species previously undescribed.

Identification, pathogenicity and effects of plant extracts on Neopestalotiopsis and Pseudopestalotiopsis causing fruit diseases.

Pestalotiopsis and related genera, including Neopestalotiopsis and Pseudopestalotiopsis have damaged many plants for many decades; however, there is little available information about the fungi on tropical fruit in Thailand. This study isolated and characterized pestalotioid fungi on tropical fruit, investigated host specificity, and screened whether plant extracts could control the fungi. In total, 53 diseased fruit samples were sampled from eight types of fruit trees (jackfruit, rose apple, mangosteen, plum, snake fruit, rambutan, strawberry, and avocado). Based on morphological characteristics, 44 isolates were classified as belonging to pestalotioid taxa. Of these isolates, seven with distinct characteristics were selected for identification using molecular analysis, and six isolates were identified as Neopestalotiopsis and one as Pseudopestalotiopsis. In the cross-inoculation experiment, the isolates exhibited nonhost specificity and could infect at least two host plants. The isolates were used to screen for a potential biocontrol resource using six crude plant extracts (clove, ginger, lemongrass, mangosteen, roselle, and turmeric). All crude extracts except mangosteen could inhibit the growth of Neopestalotiopsis and Pseudopestalotiopsis. Application of crude plant extracts could be a potential treatment to control these diseases on tropical fruit.