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Nou-Plana, I.

Publications and source records attributed to Nou-Plana, I..

2 recordsLinked to original sources

Developmental genetic response of the zooplanktonic tunicate Oikopleura dioica to marine noise pollution.

BackgroundAnthropogenic noise is an emerging threat to marine ecosystems, yet its effects on marine invertebrates, particularly zooplanktonic species, remain poorly understood. Despite increasing evidence of behavioral and physiological impacts in invertebrates, the effects of noise on embryonic development and the molecular mechanisms underlying acoustic responses remain largely unexplored. Here, to address this gap, we investigated the impact of high-intensity underwater noise exposure on embryogenesis of the appendicularian tunicate Oikopleura dioica, a cosmopolitan zooplanktonic tunicate that plays important ecological roles in marine trophic webs and carbon cycling. Under lab-controlled conditions, we examined the effects of experimental noise exposure on early embryogenesis at both morphological and transcriptomic levels using RNA-seq in 8-cell (8c) and early tailbud (ETB) stages. ResultsNoise exposure produced no significant increase in embryo malformations compared to non-exposed controls, indicating substantial phenotypic resilience under laboratory conditions. Interestingly, transcriptomic analyses revealed a rapid molecular response of 70 differentially expressed genes (DEG) already detectable after only 30 minutes of exposure at the 8-cell stage, which became markedly amplified with 700 DEGs by the ETB stage. Together, differential expression, GO enrichment, and co-expression network analyses identified coordinated regulation of processes associated with membrane homeostasis, pyrimidine/CTP metabolism, extracellular matrix organization, cytoskeletal architecture, RNA regulation, translational control, proteostasis, mitochondrial metabolism, and developmental pathways. Importantly, both developmental stages precede the formation of differentiated mechanosensory structures, suggesting that the observed responses are unlikely to reflect conventional sound perception. ConclusionsThese findings provide the first molecular characterization of noise effects during O. dioica embryogenesis and reveal an unexpected molecular sensitivity of O. dioica embryos to underwater noise despite preserved morphological development. The transcriptional signatures support a mechanobiological framework in which acoustic exposure may directly perturb cellular mechanical homeostasis through membrane-and cytoskeleton-associated processes, triggering compensatory stress-adaptation responses involving proteostasis, RNA regulation, and metabolic reprogramming. Together, these findings establish O. dioica as a valuable emerging model for investigating the developmental and evolutionary consequences of acoustic pollution in marine ecosystems.

developmental biology↗

AI-Powered Acoustic Surveillance for Early Detection of Calf Respiratory Disease

Effective management of Bovine Respiratory Disease Complex (BRDC) requires timely, non-invasive diagnostic tools to protect calf health and welfare. Among early clinical signs, coughing stands out as both frequent and informative. To explore its potential for early BRDC detection, we deployed an artificial intelligence (AI)-driven acoustic monitoring system that recorded over 2,730 hours of audio during a 30-day period. Four experimental pens, each housing seven calves and stratified by infection status and antibiotic treatment, were equipped with a dedicated microphone to enable targeted acoustic surveillance. This configuration enabled pen-specific detection of cough events, which were subsequently classified using an AI HuBERT-based model trained on 1,045 labelled clips. The classifier achieved 92% accuracy. Temporal patterns in cough frequency aligned with infection dynamics, treatment responses, and circadian patterns. Notably, AI-detected coughs consistently preceded clinical scores by 1-2 days, confirming the systems sensitivity to early respiratory disorders. These findings support the use of acoustic surveillance as a valid, scalable, and autonomous tool for continuous monitoring and early warning of respiratory diseases in calves. ImplicationsThis study demonstrates that AI-powered acoustic monitoring enables real-time, non-invasive detection of coughs in calves for early warning of respiratory diseases, outperforming traditional veterinarian clinical scoring by 1-2 days. Its high accuracy and sensitivity to respiratory infection dynamics and treatment effects position it as a scalable tool for precision livestock farming.

zoology↗