Fungal Infection

Enhancing the application of near-infrared spectroscopy in grain mycotoxin detection: An exploration of a transfer learning approach across contaminants and grains.

Cereals are a primary source of sustenance for humanity. Monitoring, controlling, and preventing mycotoxins in cereals are vital for ensuring the safety of the cereals and their derived products. This study introduces transfer learning strategies into chemometrics to improve deep learning models applied to spectral data from different grains or toxins. Three transfer learning methods were explored...

The dawn of the revolution that will allow us to precisely describe how microbiomes function.

The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and others - is known as the microbiota. A holobiont, in turn, refers to an integrated ecological unit where microbial communities function and interact with their host, thus is a more integrative concept. To understand the processes involved, the divers...

Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-...

Classification of Mycena and Species Using Deep Learning Models: An Ecological and Taxonomic Approach.

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed ...

Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.

Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad-spectrum activity and minimum resistance development again...

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies ofte...

Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therape...

Identification of potent phytochemicals against Magnaporthe oryzae through machine learning aided-virtual screening and molecular dynamics simulation approach.

Magnaporthe oryzae stands as a notorious fungal pathogen responsible for causing devastating blast disease in cereals, leading to substantial reductio...

Ultrafast on-site adulteration detection and quantification in Asian black truffle using smartphone-based computer vision.

Asian black truffle Tuber sinense (BT) is a premium edible fungus with medicinal value, but it is often prone to adulteration. This study aims to deve...

Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases.

Metagenomics, particularly genome-resolved metagenomics, have significantly deepened our understanding of microbes, illuminating their taxonomic and f...

Rapid metabolic profiling and authentication of Cordyceps using ambient ionization mass spectrometry and machine learning.

Cordyceps sinensis, a symbiotic organism formed between a fungus and an insect, is celebrated for its substantial medicinal benefits and economic sign...

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to th...

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsaturated fats and antioxidants, lowering the risk of ca...

Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.

Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. T...

Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of Fraxinus excelsior Tolerance to Ash Dieback in Europe.

Common ash (Fraxinus excelsior) is under intensive attack from the invasive alien pathogenic fungus Hymenoscyphus fraxineus, causing ash dieback at ep...

Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.

Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilitie...

Characterizing the microbiome recruited by the endangered plant in phosphorus-deficient acidic soil.

Phosphorus (P)-deficient soils serve as crucial habitats for endangered plant species. Microbiomes play pivotal roles in soil element cycling and in d...

Understanding the impacts of drought on peanuts L.): exploring physio-genetic mechanisms to develop drought-resilient peanut cultivars.

Peanut is a vital source of protein, particularly in the tropical regions of Asian and African countries. About three-quarters of peanut production oc...

Prediction of Deoxynivalenol contamination in wheat kernels and flour based on visible near-infrared spectroscopy, feature selection and machine learning modelling.

Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield an...

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and the Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because...