Latest Articles

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  Section: Ecology ; Topics: Ecology

  Tracking changes in birds' interaction milieu

  Rigal, Stanislas;   Devictor, Vincent; Dakos, Vasilis  

  10.24072/pcjournal.730 - Peer Community Journal, Volume 6 (2026), article no. e51

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  As biodiversity is declining, the dynamics of species interactions is a growing conservation concern. However, estimating and monitoring explicit species interactions across large spatial and temporal scales remain challenging. An alternative and yet under-explored approach is to track whether and how the interaction milieu, defined as the background of all realised interactions, is changing in space and time. Here, we assess changes in the interaction milieu of common bird communities in France. We estimate associated species pairs using spatial and temporal information for 109 species monitored across 1,969 sites during 17 years. We validate the ecological significance of associated species pairs by testing the relationship between the propensity to be associated and species functional proximity or shared habitat preference. We reconstruct association networks for these intra-guild bird communities and track temporal changes in network layout in terms of size, density of links, modularity and degree distribution. We show that, beyond changes usually documented based on species numbers and abundances, the interaction milieu is also changing non-randomly. Communities become smaller with a similar relative number of associations that becomes unevenly distributed through time. These structural changes vary among bird communities according to their habitat and may impact community functioning and how communities can cope with global change.

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  Section: Microbiology ; Topics: Microbiology, Ecology, Environmental sciences

  Consensus statement from the second RdRp Summit: towards a unified framework for RNA virus biology

  Lucaci, Alexander G;   Shaikh , Hisham;   Chong, Li Chong;   Tahzima, Rachid;   Forgia, Marco;   Mansour, Karima;   Sakaguchi, Shoichi;   Nakagawa, So;   Hou, Xin;   Demina, Tatiana;   Raj Jayaraj Mallika, Fhilmar;   Kupczok, Anne;   Lytras, Spyros;   Debat, Humberto;   Charon, Justine;   Urzo, Michael;   Raco, Milica;   Kim, Rachel;   Rivero, Ricardo;   Karapliafis, Dimitris;   Sirkinti, Leyla;   Luebbert, Laura;   Nishimura, Luca;   Chikhi, Rayan;   De Coninck, Lander;   Charriat, Florian;   Soufir, Emma;   Gajdov, Vladimir;   Krannich, Thomas;   Dudas, Gytis;   Lood, Cédric;   Rodríguez-Ramos, Josue;   Pecman, Anja;   Neri, Uri;   Werner, Almut;   Le, Mia;   Osundahunsi, Bolaji;   Petersen, Nils;   Maclot, François;   Gutierrez, Serafin;   Paraskevopoulou, Sofia;   Hillary, Luke;   Olendraite, Ingrida  

  10.24072/pcjournal.727 - Peer Community Journal, Volume 6 (2026), article no. e50

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  RNA-dependent RNA polymerase, or RdRp, remains the central molecular hallmark of RNA viruses. It serves as both a universal anchor for virus detection and a critical target for understanding the functional and evolutionary properties of RNA viruses. Since the inaugural RdRp summit in 2023, there have been significant advances in sequencing, structural prediction and artificial intelligence, all of which have accelerated the pace of RNA virus discovery and taxonomic annotation, revealing unprecedented levels of viral diversity, including novel phyla and unique genome architectures. Recent advances include the discovery of novel viral phyla such as Ambiviricota and the application of AI-driven models like LucaProt, highlighting both the rapid expansion of viral diversity and the growing role of machine learning in RNA virus research. The second RdRp summit, which was held in Lisbon in May 2025, gathered a group of research scientists from diverse subfields of virology to address emerging challenges in RNA virus biology. These challenges ranged from standardising annotation and data sharing to harnessing structure-guided phylogenetics and petabyte-scale computational tools. Here, our consensus statement outlines key progress, current and future challenges and community-driven initiatives, including benchmarking, virus-host inference, and ongoing knowledge exchange efforts - all of which are designed to unify the field. Importantly, this statement reflects a clear community consensus and provides concrete recommendations to prioritize standardized benchmarking, structure-informed evolutionary analysis, and reproducible virus–host inference as foundational pillars for advancing RNA virus research. By fostering an environment of sustained collaboration, our efforts aim to build a coherent framework for modern RNA virus biology and to accelerate the exploration of the hidden RNA virosphere.

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  Section: Genomics ; Topics: Genetics/genomics ; Conference: JOBIM

  rnaends: an R package to study exact RNA ends at nucleotide resolution

  Caetano, Tomas;   Redder, Peter;   Fichant, Gwennaele;   Barriot, Roland  

  10.24072/pcjournal.724 - Peer Community Journal, Volume 6 (2026), article no. e49

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  5’ and 3’ RNA-end sequencing protocols have unlocked new opportunities to study aspects of RNA metabolism such as synthesis, maturation and degradation, by enabling the quantification of exact ends of RNA molecules in vivo. From RNA-Seq data that have been generated with one of the specialized protocols, it is possible to identify transcription start sites (TSS) and/or endoribonucleolytic cleavage sites, and even, in some cases, co-translational 5’ to 3’ degradation dynamics. Furthermore, post-transcriptional addition of ribonucleotides at the 3’ end of RNA can be studied at the nucleotide resolution. While different RNA-end sequencing library protocols exist that have been adapted to a specific organism (prokaryote or eukaryote) or specific biological question, the generated RNA-Seq data are very similar and share common processing steps. Most importantly, the major aspect of RNA-end sequencing is that only the 5’ or 3’ end mapped location is of interest, contrary to conventional RNA sequencing that considers genomic ranges for gene expression analysis. This translates to a simple representation of the quantitative data as a count matrix of RNA-end location on the reference sequences. This representation seems under-exploited and is, to our knowledge, not available in a generic package focused on the analyses on the exact transcriptome ends. Here, we present the rnaends R package which is dedicated to RNA-end sequencing analysis. It offers functions for raw read pre-processing, RNA-end mapping and quantification, RNA-end count matrix post-processing, and further downstream count matrix analyses such as TSS identification, fast Fourier transform for signal periodic pattern analysis, or differential proportion of RNA-end analysis. The use of rnaends is illustrated here with applications in RNA metabolism studies through selected rnaends workflows on published RNA-end datasets: (i) TSS identification, (ii) ribosome translation speed and co-translational degradation, (iii) post-transcriptional modification analysis and differential proportion analysis.

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  Section: Archaeology ; Topics: Archaeology, Computer sciences, Engineering

  A multimodal approach to heritage preservation in the context of climate change

  Roqui, David; Cormier, Adèle; Grozavu, Nistor; Bourges, Ann

  10.24072/pcjournal.723 - Peer Community Journal, Volume 6 (2026), article no. e48

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  Cultural heritage sites face accelerating degradations due to climate change, yet traditional monitoring relies on unimodal analysis (visual inspection or environmental sensors alone) that fails to capture the complex interplay between environmental stressors and material deterioration. We propose a lightweight multimodal architecture that fuses sensor data (temperature, humidity) with visual imagery to predict degradation severity at heritage sites. Our approach adapts PerceiverIO with two key innovations: (1) simplified encoders (64D latent space) that prevent overfitting on small datasets (37 samples for training, 555 with data augmentation; 13 for validation, and 13 for testing), and (2) Adaptive Barlow Twins loss that encourages modality complementarity rather than redundancy. On data from Strasbourg Cathedral, our model achieves 76.9% accuracy and 77.0% weighted-F1 score on the test set, a 43% improvement over standard multimodal architectures (VisualBERT, Transformer) and 25% over vanilla PerceiverIO. Ablation studies reveal that sensor-only achieves 61.5% while image-only reaches 46.2%, confirming successful multimodal synergy. A systematic hyperparameter study identifies an optimal moderate correlation target (τ = 0.3) that balances alignment and complementarity, achieving 69.2% accuracy compared to other τ values (τ = 0.1/0.5/0.7: 53.8%, τ = 0.9: 61.5%). This work demonstrates that architectural simplicity combined with contrastive regularization enables effective multimodal learning in data-scarce heritage monitoring contexts, providing a foundation for AI-driven conservation decision support systems.