Fungal Infection

Integrating deep learning and field validation into a decision support system for Northern Corn Leaf Blight management in maize.

Northern Corn Leaf Blight (NCLB; also, Turcicum Leaf Blight, TLB), caused by Exserohilum turcicum (teleomorph: Setosphaeria turcica), is one of the most destructive foliar diseases of maize worldwide, often causing severe yield losses under favorable conditions. We developed a maize-specific, web-based Decision Support System (DSS) for real-time NCLB detection and management (https://maize-nclb.st...

Divergent fates: Polystyrene microplastics alter trifloxystrobin degradation and microbial ecology differently in agricultural soil vs. river sediment.

This study investigates the effects of polystyrene microplastics (PS-MPs) on trifloxystrobin (TRI) degradation behavior and microbial community structure in agricultural soil and river sediment. Degradation kinetics and machine learning modeling (R2 > 0.95) revealed that PS-MPs significantly inhibited TRI dissipation, with biological degradation identified as the primary driver. Specifically, the ...

A curated dataset and lightweight deep learning framework for tea leaf disease classification.

Tea (Camellia sinensis) is the world's second most consumed beverage, enjoyed daily by more than two billion people. In Bangladesh, it serves as a cor...

Exploration of soil microbial community: a comparative study between organic and conventional systems in perennial and annual crops.

​Soil microbiota is central to agroecosystem sustainability, influencing fertility, plant health, and resilience. Agricultural practices shape these c...

Spectrally optimised YOLOv10s-SeqOpt framework for real-time UAV-based early detection of avocado foliar diseases in indian orchards.

Avocado orchards in southern India experience annual yield losses of 20-40% due to foliar pathogens, including Anthracnose (Colletotrichum Gloeosporio...

An automated workflow for fungal cell counting, cell size and data analysis: enhancing throughput and accuracy with the cell analysis and counting tool using ilastik software (caactus).

BACKGROUND: The development of filamentous fungal cells from spore to mature mycelium is influenced by a myriad of environmental conditions. Tradition...

Integrating scanning electron microscopy, explainable deep learning, and ITS sequencing for accurate identification in some species Geastrum.

The differentiation of species within the genus Geastrum remains a challenging task due to the strong morphological similarity among taxa and the limi...

WormSpot: a machine learning-powered viability scoring platform in C. elegans for Candida pathogenicity studies.

UNLABELLED: Invasive Candida infections pose a critical health challenge, exacerbated by emerging antifungal resistance. Caenorhabditis elegans (C. el...

Breaking the culture habit: Complementing culture-based veterinary diagnostics with metagenomic data -A case study of feline and canine skin infections.

BACKGROUND: Skin infections have been described as the primary cause for veterinary small animal practice visits, frequently requiring topical and sys...

Artificial intelligence-assisted diagnosis and subtype differentiation of infectious keratitis.

BACKGROUND: Infectious keratitis (IK) is a major cause of corneal blindness world-wide, and prompt identification of IK and its etiologic subtype is e...

Multimodal artificial intelligence and the persistent challenge of early mycosis fungoides diagnosis.

Portable Electrochemiluminescence Microarray Sensor for Machine Learning-Assisted Quantitative Analysis of Zearalenone.

Zearalenone (ZEN) is an estrogenic mycotoxin produced by Fusarium fungi and is widely present in cereal-based foodstuffs including wheat, rice, and ma...

Determination of Fungiform Papilla Number Using Deep Learning Methods.

This study aimed to develop a deep learning-based method for the automatic detection and counting of fungiform papillae (FP) on the dorsal surface of ...

Fungal infections: Classification performance and detectability with machine learning models.

This study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants, a method that enhanc...

Microbial succession from nursery to vineyard highlights the role of beneficial and pathogenic microbes in young vineyard yield.

BACKGROUND: The grapevine microbiome plays a central role in shaping vineyard performance, yet the influence of nursery inherited microbes on vineyard...

Deep Learning-Assisted Room-Temperature Phosphorescence Sensor Array Based on Host-Guest Doping for Visual Discrimination of Triazole Fungicides.

Detection and discrimination of structurally similar triazole fungicide (TF) subtypes remain highly desirable yet challenging. This work presents a st...

Imaging Biomarkers for Early Differentiation of Candida and Aspergillus Endogenous Fungal Endophthalmitis: Multicenter Optical Coherence Tomography-Based Analysis.

PURPOSE: Endogenous fungal endophthalmitis (EFE) is a rare, sight-threatening intraocular infection with heterogeneous clinical presentations. Candida...

Support vector Regression-Optimized Ultrasonic-Assisted alkaline extraction of Chroogomphus rutilus Polysaccharides: Enhanced yield and preservation of helical conformations.

Polysaccharides from Chroogomphus rutilus (CRPs) exhibit promising bioactivities; however, conventional alkaline extraction (CAE) often results in low...

The Role of Artificial Intelligence Algorithms in Challenging Diagnostic Cases - Between Potential and Real Clinical Support.

INTRODUCTION: Extranodal natural killer/T-cell lymphoma, nasal type (ENKTL), is a rare EBV-associated malignancy characterized by destructive tumors i...

Investigating the toxicological impacts of thiabendazole exposure on uterine corpus endometrial carcinoma utilizing network toxicology, machine learning, and molecular docking.

Uterine corpus endometrial carcinomas (UCEC) are common cancers of the female reproductive system linked to environmental chemicals. Thiabendazole (TB...