bioRxiv Science⌕ Search

Biology subjects

Arcos, J.

Publications and source records attributed to Arcos, J..

2 recordsLinked to original sources

Optimized AAV to express the unfolded protein response transcription factor XBP1s ameliorates Alzheimer's disease features in mouse models

Proteostasis impairment at the level of the endoplasmic reticulum (ER) is a salient feature of Alzheimers disease (AD). The unfolded protein response (UPR) is the main pathway to cope with ER stress, where the expression of the transcription factor X-Box binding protein 1 (XBP1) is central to establish repair programs. To artificially enforce the adaptive capacity of the UPR in the AD brain, we recently reported the protective effects of overexpressing active XBP1 in the brain using adeno-associated vectors (AAVs) of AD mice, in addition to aged animals. Here we have generated a next generation vector suitable for clinical testing by (i) expressing codon-optimized human XBP1s without artificial tags, (ii) the use of the synapsin promoter to restrict expression to neurons, and (iii) incorporating a novel variant of AAV2 (AAV-TT) with greater biodistribution (here termed Proteostaser-1). Treatment of 5xFAD mice with Proteostaser-1 improved spatial learning and synaptic plasticity, and reduced the deposition of amyloid plaques in the brain. Proteostaser-1 administration also improved cognition in a model of sporadic AD based on the intracerebral injection of amyloid {beta} oligomers. Our results further support the therapeutic potential of the UPR as a strategy to ameliorate AD features and sustain synaptic function.

cell biology↗

From citizen to scientist: evaluating ant observation accuracy on a Mediterranean archipelago

Citizen science platforms, such as iNaturalist, have become invaluable tools for biodiversity monitoring, allowing non-experts to contribute photo-based observations that are then identified by the user community. However, one of the major challenges of these platforms concerns the accuracy of these identifications, especially for taxa requiring specialized knowledge or more sophisticated means of identification. In this study, I assess the reliability of ant identifications in the Balearic Islands by comparing community-generated identifications with expert validations across multiple taxonomic levels. Based on 300 iNaturalist observations, I analyze the influence of user experience, AI-generated suggestions, image quality, and community participation on identification accuracy. The results indicate that species-level accuracy was 69.91%, increasing to 90.91% at the genus level and 99% at the family level. Experienced users significantly improved accuracy, while early-stage identifications at finer taxonomic resolutions increased the likelihood of correct consensus. AI-assisted identifications performed well for frequently recorded species but struggled with underrepresented taxa. Surprisingly, photo quality had minimal impact, as common species were often identifiable even from low-resolution images. Additionally, the dataset documented exotic species, including the first record of the Formica rufibarbis complex in Mallorca. These findings highlight both the strengths and limitations of citizen science in taxonomic research and emphasize the need for strategies to enhance data reliability for conservation and biodiversity monitoring.

zoology↗