Machine Learning Method Reconstructs Hidden Muscular Forces in Bird Respiratory Systems
Researchers used Kolmogorov-Arnold networks (KANs) to infer the hidden muscular forces driving avian respiration using only air-sac pressure measurements. The method uncovered a two-phase muscle activation pattern within each breathing cycle that was not visible in the pressure signal itself. The findings demonstrate a general approach for recovering unobserved driving forces in partially observed dynamical systems, with potential applications across physics and biology.
A new study posted to arXiv presents a data-driven method for reconstructing latent forces in dynamical systems, applied here to avian respiratory mechanics. Using Kolmogorov-Arnold networks (KANs), an interpretable machine learning architecture, the researchers inferred governing equations directly from air-sac pressure recordings in birds. The pressure signal alone appeared consistent with a relaxation-like oscillation, but the reconstructed forcing revealed a more complex, nontrivial two-phase activation structure within each respiratory cycle. This predicted pattern—two distinct phases of expiratory muscle activation per breath—was independently validated through electromyographic (EMG) recordings of the relevant muscles, providing experimental corroboration of the model's output. The authors argue the approach establishes a broadly applicable route for uncovering hidden physical structure and unobserved driving variables in systems where only partial observations are available.
What's missing
The study is a preprint and has not yet undergone peer review. The scope of EMG validation (species, sample size, experimental conditions) is not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.