Digital technologies and precision livestock farming (PLF) systems are transforming the livestock sector by enabling the monitoring and optimisation of farms and animal performance. However, the implications of integrating these systems into animal housing remain unclear, and an evaluation of how digitalisation is reshaping livestock facilities is lacking. To address this gap, a hybrid systematic and bibliometric review, combined with an exploratory analysis, was conducted without statistical inference or quantitative synthesis, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review investigated the relationship between digital technologies and livestock housing across the bovine, pig, and poultry species. A total of 1,242 scientific papers were analysed by technology category, required inputs, provided outcomes, application areas, data transmission, and the use of Artificial Intelligence (AI)/Generative Artificial Intelligence (GenAI) for data processing. Findings indicate a growing, consistent research interest across all species, driven primarily by themes related to animal monitoring, welfare, and housing. Topics such as “Engineering and Building”, “Herd Management”, and “Data Analysis and Modelling” emerged as recurrent throughout the analysed period. Animal-related technologies accounted for 21% of bovine papers, 6% of pig papers, and 1% of poultry papers, whereas software- and data-oriented tools were dominant in poultry systems. Data management and processing emerged as crucial components of PLF technologies, with cloud-based storage being predominant across all species (48% in bovine, 69% in pigs, and 63% in poultry). Artificial intelligence was used in approximately 30% of studies, with notable species variability: 29% in bovine, 63% in pigs, and 36% in poultry. GenAI remained limited, representing about 8% of AI-related papers, with applications focused on enhancing animal health and supporting farm management. The findings contribute to a deeper understanding of how digital technologies intersect with livestock housing, offering valuable insights into current developments and emerging trends in precision livestock farming.
Accurate mapping of tree species from satellite data remains challenging in heterogeneous mountain forests due to environmental gradients, mixed stands, and limited training labels. Geospatial foundation models (GFMs) learn rich representations from large multi-sensor archives, but their utility for species-level mapping remains unclear. Here, we evaluate two GFM embeddings, AlphaEarth and Tessera, for tree species classification in a demanding mountain landscape (Trentino, Italy; 18 species and groups), versus conventional Sentinel-1+2 composites, across experiments spanning classification accuracy, label efficiency, classifier complexity, environmental covariates, label impurity, and temporal transferability. GFM embeddings consistently outperform conventional baselines (weighted F1 = 0.83 vs. 0.80; macro F1 = 0.55 vs. 0.50), approaching saturation with only 5% of training parcels while organising species into ecologically meaningful taxonomic and functional groupings. Realising this advantage requires a nonlinear classifier: a compact neural network yields the largest single gain, whereas a linear classifier on GFM embeddings underperforms a neural network on conventional composites. Classification is robust to moderate label impurity, and training with parcel-level species proportions as soft labels improves minority-species discrimination (macro F1 = 0.586 –0.589) without purity filtering; adding terrain-derived covariates provides no further benefit. Temporal transfer across years degrades performance: weighted F1 falls from 0.84 to 0.77 (−9%) for Tessera and from 0.85 to 0.73 (−14%) for AlphaEarth, with disproportionate losses for rare species. These results show that GFMs shift the primary bottleneck in species mapping from feature engineering toward the availability, quality, and temporal alignment of reference data.
The wine microbiome is a key determinant in shaping wine terroir. To date, a comprehensive understanding of how microbial signatures influence wine metabolic profile remains poorly understood. To address this, in the present study an integrated shotgun metagenomics and untargeted metabolomic approach was employed to investigate the wine metabolome and connect the composition and functions of microbiomes involved in wine fermentation of Muscat grapes harvested in Italy and Greece. Beta diversity highlighted the dissimilarity between Italian and Greek fungal terroirs. A marked reduction in diversity during fermentation underscored the dominance of the inoculated Saccharomyces cerevisiae starter culture. The LEfSe analysis revealed an enrichment of Torulaspora delbrueckii in Greek samples, while Kluyveromyces marxianus and lactis were more abundant in Italian samples. Functional analysis revealed geographic differences in nucleotide, fatty acids and lysine metabolisms. Significant shifts were observed in energy, carbohydrate, and amino acid metabolisms, reflecting terroir-specific microbial activity. The metabolomics data highlighted regional differences in oligosaccharides, glycosylated phenolics, peptide and amino acid turnover, and central redox metabolites, suggesting divergent microbial responses and metabolic trajectories shaped by terroir and fermentation conditions. Obtained results highlight the effectiveness of this multi-omics approach in identifying product-specific fungal communities and wine metabolite signatures, providing new tools that could be used to ensure wine authenticity and quality control.
In high-elevation systems influenced by receding cryosphere, geomorphology and lithology can strongly influence the hydrology of river networks. During summer 2022-2023, we studied the water temperature, delta O-18, pH, major ions, and trace element concentrations at two headwater catchments in the Eastern Italian Alps. We investigated the main streams at the spring and below the confluences with tributaries from glaciers, intact and relict rock glaciers, young moraines, and till deposits. In the non-glacierized catchment (6.3 km(2)), water temperature increased from 1.6 degrees C at the intact rock glacier spring to 7.3 +/- 1.5 degrees C at the catchment outlet, despite the inputs from till and rock glacier springs with <3.0 degrees C waters. In the glacierized catchment (3.7 km(2)), the proglacial reaches had a water temperature of 6.9 +/- 2.6 degrees C and the inputs from cold rock glacier springs decreased the water temperatures by 2-4 degrees C along the stream. Due to predisposing lithology, at the glacierized catchment the concentrations of trace elements such as Ni, Al, Mn, Zn, Y, and Li were high along the entire river network except in till and the relict rock glacier springs, which are not influenced by the cryosphere. For both catchment outlets, end-member mixing models estimated 60-65 % contribution from rock glaciers to stream runoff. In both river systems, meltwater from snow and ice was the dominant runoff component, with rainwater accounting for 20-30 % of runoff in the non-glacierized catchment and for <10 % in the glacierized one.
BACKGROUND:West Nile virus (WNV) is among the most widespread arboviruses and has become a seasonal threat in temperate regions. Sustained in an enzootic bird-mosquito cycle, with humans and horses as incidental hosts, its geographic range has expanded in recent decades due to ongoing climatic and ecological changes. While most infections are asymptomatic or mild, a minority progress to neuroinvasive disease with high morbidity and long-term sequelae. This review summarizes current knowledge on epidemiology, pathogenesis, clinical spectrum, diagnostic challenges, therapeutic options, prevention, and research gaps. DISCUSSION:Lineages 1 and 2 co-circulate in Europe, where repeated large outbreaks highlight WNV adaptability to warmer summers, altered rainfall, and expanded mosquito habitats driven by recent ecological shifts. After inoculation, replication occurs in keratinocytes and dendritic cells, amplification in lymph nodes, and dissemination to visceral organs and the central nervous system. Neuroinvasion depends on viral proteins and host immune responses. Severe disease is associated with advanced age, immunosuppression, comorbidities, and genetic susceptibility. Clinical manifestations range from febrile illness to meningitis, encephalitis, or acute flaccid myelitis. Persistent neurological and functional sequelae are common, adding to disease burden. Diagnosis relies on molecular and serological tests, limited by short viremia and cross-reactivity with other flaviviruses. No approved antiviral therapy exists; management is supportive. Experimental antivirals, monoclonal antibodies, and interferon have shown mixed results. Vaccine candidates have progressed to phase 1-2 trials, but none are licensed for humans. Prevention relies on integrated vector control, veterinary surveillance, and donor screening, framed within a One Health approach. CONCLUSION:WNV exemplifies the impact of global ecological change on zoonotic diseases. Strengthening surveillance, refining diagnostics, and advancing antivirals and vaccines through multidisciplinary collaboration are essential to mitigate future outbreaks.
