Fungal Infection

MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection

Smartphone clip-on microscopes turn everyday devices into low-cost, portable imaging systems that can even reveal fungal structures at the microscopic level, enabling mold inspection beyond unaided visual checks. In this paper, we introduce MobileMold, an open smartphone-based microscopy dataset for food mold detection and food classification. MobileMold contains 4,941 handheld microscopy images s...

Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy

Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by artefacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based detection framework using the RT-DETR model architecture to achieve precise, query-driven localization of fungal structures in high-resolu...

Bootstrap resampling of mass spectral pairs with SpecReBoot reveals hidden molecular relationships

Mass spectral molecular networking (MN) has emerged as a key computational approach to organize and analyze the vast volumes of tandem mass spectromet...

NanoSimFormer: An end-to-end Transformer-based simulator for nanopore sequencing signal data

Nanopore sequencing has achieved a new standard of accuracy with the advent of R10.4.1 flow cell and high-performance Transformer-based basecalling mo...

Sequence constraints predispose Class D GPCRs to follow an atypical activation mechanism

The biophysical principles underlying distinct conformational changes in proteins with similar topologies remain poorly understood. Class D G Protein-...

HS-GC-IMS couples with convolutional neural network for Burkholderia gladioli pv. Cocovenenans detection in Auricularia Auricula.

The shortage in early detection methods for the pathogen Burkholderia gladioli pv. cocovenenans (BGC) and its toxin bongkrekic acid rises the risk for...

Novel natural vector with asymmetric covariance for classifying biological sequences.

The genome sequences of organisms form a large and complex landscape, presenting a significant challenge in bioinformatics: how to utilize mathematica...

Bioactive films with essential oils and machine learning for controlling Aspergillus niger growth and fumonisin B production in vitro.

Aspergillus niger is an important species in the fungal community of many foods and is one of the most significant microorganisms used in biotechnolog...

Identification of Fusarium sambucinum species complex by surface-enhanced Raman spectroscopy and XGBoost algorithm.

Rapid and reliable identification of Fusarium fungi is crucial, due to their role in food spoilage and potential toxicity. Traditional identification ...

Predicting Biological Activity from Biosynthetic Gene Clusters Using Neural Networks.

Microorganisms such as bacteria and fungi have been used for natural products that translate to drugs. However, assessing the bioactivity of extract f...

Deep Learning-Guided Discovery of Celestolide as a Natural Allosteric Inhibitor Targeting CYP51 and Its Application in Strawberry Preservation.

Most CYP51 inhibitors act competitively and are prone to resistance, whereas allosteric inhibitors hold promise but are difficult to develop. In this ...

An efficient plant disease detection using transfer learning approach

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to...

Harnessing advances in mechanisms, detection, and strategies to combat antimicrobial resistance.

Antimicrobial resistance (AMR) is a growing global health crisis, threatening the effectiveness of antibiotics and other antimicrobial agents, leading...

Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.

Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning.

Androgenetic alopecia (AGA), the most common form of hair loss, has been linked to dysbiosis of the scalp microbiome. In this study, we collected micr...

The Biogeography of Soil Bacteria in Australia Exhibits Greater Resistance to Climate Change Than Fungi.

Soil microorganisms are crucial to ecosystem health, and their composition and distribution are shaped by a range of environmental factors. However, t...

Artificial neural network-based fungal chitin production for submicron-chitosan synthesis: effects on bioremediation for heavy metal pollution.

This study focused on optimizing fungal chitin (CT) production from a newly identified Fusarium incarnatum (GenBank: OL314753) for subsequent synthesi...

Applying machine learning with MobileNetV2 model for rapid screening of vaginal discharge samples in vaginitis diagnosis.

Vaginitis is a prevalent gynecological condition that impacts women's quality of life, with most women likely to experience it at least once. Traditio...

An efficient plant disease detection using transfer learning approach.

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to m...

DeepMBEnzy: An AI-Driven Database of Mycotoxin Biotransformation Enzymes.

Mycotoxins are toxic fungal metabolites that pose significant health risks. Enzyme biotransformation is a promising option for detoxifying mycotoxins ...