Fungal Infection

AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface.

Adapted plant pathogens from various microbial kingdoms produce hundreds of unrelated small secreted proteins (SSPs) with elusive roles. Here, we used AlphaFold-Multimer (AFM) to screen 1879 SSPs of seven tomato pathogens for interacting with six defence-related hydrolases of tomato. This screen of 11,274 protein pairs identified 15 non-annotated SSPs that are predicted to obstruct the active site...

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars and is essential for reliable production in maize. Identifying different gene expression patterns can deepen our perception of maize resistance to dise...

Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling.

Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which...

Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography.

OBJECTIVES: This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification...

Diagnosis of invasive fungal infections: challenges and recent developments.

BACKGROUND: The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised pa...

Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images.

Infectious keratitis refers to a group of corneal disorders in which corneal tissues suffer inflammation and damage caused by pathogenic infections. A...

Application of deep learning algorithm in the recognition of cryptococcosis and talaromycosis skin lesions.

BACKGROUND: Cryptococcosis and talaromycosis are known as 'neglected epidemics' due to their high case fatality rates and low concern. Clinically, the...

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative.

Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistan...

Engineering growth phenotypes of Aspergillus oryzae for L-malate production.

Improving the growth status of Aspergillus oryzae is an efficient way to enhance L-malate production. However, the growth mechanism of filamentous fun...

Automatic real-time monitoring of fungal spores: the case of spp.

We present the first implementation of the monitoring of airborne fungal spores in real-time using digital holography. To obtain observations of spp....

Deep learning as a new tool in the diagnosis of mycosis fungoides.

Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differen...

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review.

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the cur...

Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors.

Although Alternaria spores are well-known allergenic fungal spores, automatic bioaerosol recognition systems have not been trained to recognize these ...

Imaging and Deep Learning Based Approach to Leaf Wetness Detection in Strawberry.

The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need ...

The hiatus between organism and machine evolution: Contrasting mixed microbial communities with robots.

Mixed microbial communities, usually composed of various bacterial and fungal species, are fundamental in a plethora of environments, from soil to hum...

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albica...

Synthetic growth by self-lubricated photopolymerization and extrusion inspired by plants and fungi.

Many natural organisms, such as fungal hyphae and plant roots, grow at their tips, enabling the generation of complex bodies composed of natural mater...

Impact of ANN in Revealing of Viral Peptides.

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to s...

Data-Driven Deep Supervision for Medical Image Segmentation.

Medical image segmentation plays a vital role in disease diagnosis and analysis. However, data-dependent difficulties such as low image contrast, nois...

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics.

Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the prima...