Biology subjects

Veres, A.

Publications and source records attributed to Veres, A..

3 recordsLinked to original sources

bioRxiv2025

Edge computer vision produces microarthropod-based high-throughput biodiversity metrics

Soil fauna is crucial for carbon cycling, controlling organic matter decomposition and contributing to ecosystem services1-7. Soil microarthropods are top-down regulators in the decomposer food web and serve as fundamental indicators of soil health. Yet, routine field-level monitoring remains restricted to resource-intensive research, leaving a critical gap in arable land management and policy. To address this, we developed Edapholog(R) extractor, a fully automated laboratory device that detects and identifies live soil microarthropods via real-time, AI-based image analysis, enabling taxonomic identification. Here, we demonstrate its accuracy across 319 arable fields spanning ten European countries. We show that computer vision can support ecological interpretation in arable systems and long-term studies on conservation tillage and cover cropping, offering a scalable tool for integrating soil biodiversity metrics into regenerative agriculture and carbon farming. We validated the system against classical taxonomy and found that across [~]35,000 microarthropod individuals, the device achieved an overall accuracy of 86%, sensitivity of 75%, and specificity of 99% compared to manual identifications. Community composition analyses revealed high similarities (83%), with minimal richness differences (7%) and low species replacement (13%) across countries, indicating that the AI does not introduce taxonomic bias. When applied in a long-term field experiment, the system detected significant taxon-specific responses to conservation tillage, with effect sizes ranging from 0.5 to 4. Total abundance, richness, and a soil biological health index were 39%, 47%, and 150% higher, respectively, under conservation tillage compared to conventional ploughing. These effects were statistically consistent between the automated and classical methods. However, while manual microscopy required several hours per sample, the AI-based system delivered immediate results without the need for taxonomic expertise. Edapholog(R) extractor offers exciting opportunities for rapid, scalable soil biodiversity monitoring for future sustainable land management.

ecology↗

bioRxiv2018

Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cells expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we illustrate and enhance the use of matrix factorization as a solution to this problem. We show with simulations that a method that we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including the relative contribution of programs in each cell. Applied to published brain organoid and visual cortex scRNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected (e.g. cell cycle and hypoxia) and intriguing novel activity programs. We propose that one of the novel programs may reflect a neurosecretory phenotype and a second may underlie the formation of neuronal synapses. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types.

bioinformatics↗

bioRxiv2016

Population diversity jeopardizes the efficacy of antibiotic cycling

Treatment strategies that anticipate and respond to the evolution of pathogens are promising tools for combating the global rise of antibiotic resistance1-3. Mutations conferring resistance to one drug can confer positive or negative cross-resistance to other drugs4. The sequential use of drugs exhibiting negative cross-resistance has been proposed to prevent or slow down the evolution of resistance5-8, although factors affecting its efficacy have not been investigated. Here we show that population diversity can disrupt the efficacy of negative cross-resistance-based therapies. By testing 3317 resistant Staphylococcus aureus mutants against multiple antibiotics, we show that first-step mutants exhibit diverse cross-resistance profiles: even when the majority of mutants show negative cross-resistance, rare positive cross-resistant mutants can appear. Using a drug pair showing reciprocal negative cross-resistance, we found that selection for resistance to the first drug in small populations can decrease resistance to the second drug, but identical selection conditions in large populations can increases resistance to the second drug through the appearance of rare positive cross-resistant mutants. We further find that, even with small populations and strong bottlenecks, resistance to both drugs can increase through sequential steps of negative cross-resistance cycling. Thus, low diversity is necessary but not sufficient for effective cycling therapies. While evolutionary interventions are promising tools for controlling antibiotic resistance, they can be sensitive to population diversity and the accessibility of evolutionary paths, and so must be carefully designed to avoid harmful outcomes.

microbiology↗