4629 research outputs found
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Using transcriptomic data to improve the prediction of immunity traits in pigs
No full textConsidering health-related traits among breeding selection criteria has been proposed as a way to improve pig robustness. This study investigated the potential of whole blood RNA-sequencing data for predicting immunity-related traits, stress indicators and carcass weight, using data from 255 pigs belonging to a commercial Duroc population. The prediction performance of mixed models fitting either genomic (G), transcriptomic (T) or both effects as independent (GT) was evaluated and compared. Three additional models addressing the redundant information between G and T were also evaluated: the GTC model that subtracts the genetic effect from the transcriptome, the GTCi model that makes this correction based on the estimated heritability of T effects, and a multiomic model that weights G and T effects in a multiomics relationship matrix. The models including gene expression information captured a higher proportion of variance than the genomic model for all studied traits but carcass weight. Adding transcriptomic effects improved both model fit and phenotypic prediction of all immunity traits, particularly those with a high transcriptomic contribution such as the abundance of T helper and γδ T cells, the haptoglobin concentration and the leukocyte counts. Considering the interaction between genomic and transcriptomic effects led to greater prediction accuracies, with the GTCi model performing the best. Our work demonstrates the value of considering gene expression data to predict immunity traits as well as the importance of adequately modelling the interaction between genomic and transcriptomic effects.This study was funded by grants PID2020-112677RB-C21 and PID2023-148961OB-C21 awarded by MCIN/AEI/10.13039/501100011033. Jové-Juncà, T. was supported by an IRTA fellowship (CPI1221).info:eu-repo/semantics/publishedVersio
Esperant l’inici de la collita de l’oliva
No full textTots els olivicultors van seguint l’aspecte de les olives per decidir quan és el moment òptim per iniciar el llarg procés de la recollida d’olivesinfo:eu-repo/semantics/publishedVersio
Predicting EU orchard farmers' adoption intentions for pesticide-reducing innovations using an extended theory of planned behavior
No full textReducing synthetic pesticide use is central to the EU Green Deal. However, with legislative progress stalled, farmers increasingly bear the responsibility for implementing pesticide reduction. This highlights the need to understand how decisions are made and what shapes farmers' intentions to adopt technologies that reduced pesticide use, particularly in orchard systems where pesticide dependence remains high despite the availability of promising alternatives. This study examines stakeholder roles in orchard pest management, identifies key drivers of farmers’ adoption intentions, and develops a tool to predict adoption likelihood. A mixed-methods approach was applied, combining expert interviews (n = 16) and a structured survey of orchard farmers (n = 354). We extended the Theory of Planned Behavior (TPB) by incorporating three additional constructs: perceived cost, relative advantage of current practices, and perceived ease of use, all based on established behavioral theories. Results show that decision-making involves multiple stakeholders and is influenced by factors such as farm size, ownership structure, and external pressures. Meanwhile, structural equation modeling shows that attitudes and perceived ease of use significantly boost adoption intentions. In contrast, perceived cost and the relative advantage of current practices serve as barriers. Subjective norms and perceived behavioral control were not significant predictors. Based on these findings, a web-based adoption intention predictive tool was developed to support stakeholders in assessing adoption likelihood. The study offers practical insights for reducing adoption barriers, strengthening positive attitudes toward innovation, and supporting sustainable pest management in orchard systems.This study was supported by the NOVATERRA project under the research grant agreement number: 101000554. The NOVATERRA project has received funding from the European Union's Horizon 2020. We sincerely appreciate the valuable administrative and technical support provided by Cristina Poyato Santiago and Filippo Alfonso Baldaro to the NOVATERRA project.info:eu-repo/semantics/publishedVersio
Rothia nasimurium: un bacteri que entrena macròfags alveolars porcins per defensar-se millor
No full textinfo:eu-repo/semantics/publishedVersio
Tracking invasion events: phylogeography of Hyalomma marginatum in the Mediterranean basin with a focus on Southern France
No full textBackground Hyalomma marginatum is a hard tick vector of various pathogens, including Crimean-Congo Hem‑
orrhagic fever virus, recently detected in French specimens. This species has a wide distribution from North Africa
to Eastern Europe and has only recently been considered established in Southern France. These changes in species
distribution led us to explore the genetic structure of tick populations in the Mediterranean basin and attempt to infer
the origin of French populations.
Methods We used two mitochondrial markers (12S rRNA and Cytochrome Oxidase 1) and genotyped ticks from nine
Mediterranean countries. We compared genetic indices and haplotypic composition between these countries
and the various French geographical populations.
Results Across all countries, we showed significant genetic differentiation, with a certain proximity between neigh‑
boring countries. We found very different genetic compositions among the French geographic populations: some
exhibited signs of recent expansion, while others suggested the presence of ancient populations.
Conclusions It is possible that small populations of H. marginatum were already present in France and are now
more abundant. This recent change in population structure could be owing to increased human activity and climate
change. These factors, combined with a potentially high level of phenotypic plasticity, could facilitate H. marginatum
conquest of more northerly latitudes in France and other European countries.The work was funded by the Holistique project (défi clé RIVOC Occitanie region, University of Montpellier): “Hyalomma marginatum in Occitanie region: analysis of biological invasion and associated risks.” It was also funded by the French Ministry of Agriculture-General Directorate for Food (DGAl, grant agreement: SPA17 number 0079-E).info:eu-repo/semantics/publishedVersio
Almond yield prediction at orchard scale using satellite-derived biophysical traits and crop evapotranspiration combined with machine learning
No full textAccurate almond yield prediction is essential for supporting decision-making across multiple scales, from individual growers to international markets. This is crucial in the Mediterranean region, where diminishing water resources pose significant challenges to the almond industry. In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. The almond prediction models were calibrated and validated using data provided by commercial growers, along with meteorological reanalysis and remote sensing products. The remote sensing products included: i) spectral indices, ii) vegetation biophysical traits retrieved from Sentinel-2, and iii) actual evapotranspiration (ETa) estimated using the Priestley-Taylor two-source energy balance (TSEB-PT) model driven by Copernicus-based data. Almond yield data were collected from commercial orchards located in Spain’s Ebro and Guadalquivir basins from 2017 to 2022. Data collected from growers enables the establishment of almond water production functions at the orchard scale, yielding results comparable to those reported in experimental study sites. Almond yield prediction models calibrated with remote sensing data demonstrated predictive accuracy comparable to that of models relying on ground-truth variables provided by farmers, such as irrigation, orchard age, tree density, and cultivar. Among them, the PMCRS model—which integrates the fraction of absorbed photosynthetically active radiation (fAPAR), the normalized difference moisture index (NDMI), canopy chlorophyll content (Cab), ETa, and meteorological data—achieved a RMSE of 399.1 kg ha-¹ in July. These findings highlight the potential of remote sensing-based models for accurately estimating almond yield. Furthermore, the PMCRS model proved scalable and effective when applied across four almond-producing regions in the Ebro basin. Future improvements may be realized through enhanced ETa retrieval using upcoming thermal satellite missions, integration of irrigation estimates, and the adoption of advanced machine learning and deep learning algorithms.The author(s) declare financial support was received for the research and/or publication of this article. This research was funded by the DIGISPAC project (TED2021-131237B-C21), from by the Ministry of Science, Innovation and Universities of the Spanish government and by the internal IRTA’s scholarship. The IRTA team is also supported by the CERCA Program, Government of Catalonia. The authors would also like to thank the Horizon 2020 Research and Innovation Program (H2020) of the European Commission, in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action and ACCWA project: grant agreement No.: 823965.info:eu-repo/semantics/publishedVersio
Noves plantes per formar part dels marges florals en horta
No full textEls marges florals són infraestructures vegetals que es situen vora els cultius i on s’hi planten diferents espècies amb flors per tal de proporcionar aliment i refugi als insectes que actuen com a enemics naturals de les plagues i pol·linitzadors dels cultius. Entre 2022 i 2024 es va fer el seguiment de diverses parcel·les monoespecífiques instal·lades en explotacions comercials d’ hortícoles. D’aquestes parcel·les es va fer el seguiment de la fenologia de l’espècie vegetal i es va mostrejar el nombre d’enemics naturals que s’hi allotjaven, les possibles espècies plaga que les afecten, així com dels pol·linitzadors que les visitaven.info:eu-repo/semantics/publishedVersio
Dynamics of Reinfection by Neobenedenia sp. (Monogenea, Capsalidae) in Almaco Jack, Seriola rivoliana, Kept in a Cultivation System
No full textFish susceptibility to parasitic infection is a crucial issue in aquaculture, where the density of captive fish increases the intensity of parasitic infections. Monogeneans are a group of parasitic flatworms that include pathogenic species for fish, among them Neobenedenia spp., which pose significant challenges for fish health in farming systems. Understanding the dynamics of parasitism in fish and how they may vary according to host susceptibility or environmental conditions is essential for the development of effective management strategies in aquaculture. Therefore, the aim of this study was to investigate the individual susceptibility of Almaco jack (Seriola rivoliana) to infections by Neobenedenia sp. and examine how reinfection affects parasite load in an aquaculture setting. Our findings unveiled an aggregated distribution of parasites in the fish population, indicating a non-random pattern influenced by specific host factors. Furthermore, our results revealed that even minor temperature variations, such as an increase of just 1°C, were associated with a noticeable rise in parasite abundance. These results underscore the importance of regular monitoring in S. rivoliana tank-maintained systems, as even minor temperature fluctuations can cause a substantial increase in Neobenedenia sp. infections, particularly in more susceptible individuals.We thank Wyatt Delgado Zambrano and Pamela Rodríguez Bailón for their help with the work in the laboratory. We thank Dr. Stanislaus Sonnenholzner and Centro Nacional de Acuicultura e Investigaciones Marinas (CENAIM-ESPOL) for the donation of the fish. This research was partially funded by the Instituto Superior Tecnológico Luis Arboleda Martínez through two research projects: “Diversidad de parásitos metazoarios en peces marinos de importancia económica y potencial acuícola en Ecuador (ISTLAM CI-PI-003-2019)” and “Parásitos metazoarios en peces: implicaciones y control en la interacción patógeno-hospedero (ISTLAM-OCS-RES.2023-288)".info:eu-repo/semantics/acceptedVersio
Transforming hyperspectral images into chemical maps: a novel End‐to‐End Deep Learning approach
No full textCurrent approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. The U-Net is compared with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error of between 9% and 13% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.53% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0%–100%, U-Net learns to stay inside this range. Thus, the findings of this study indicate that U-Net is superior to PLS for chemical map generation.This work was supported by The Innovation Fund Denmark and FOSS Analytical A/S (grant number 1044-00108B); FEDER and MICIU/AEI/10.13039/501100011033/ (grant number RTI2018-096993-B-I00, 2019–2022); and the Spanish National Institute of Agricultural Research (INIA) (grant number PRE2019-089669, 2020–2024).info:eu-repo/semantics/publishedVersio
Evaluation of the efficiency of cyclodextrin polymers as sustainable sampling material for catching palytoxin-like compounds in seawater
Get PDFPalytoxin-like compounds, including ovatoxins, are potent emerging toxins responsible for human respiratory poisonings following inhalation of contaminated marine aerosols. Periodic massive proliferations of the ovatoxin-producing organism (Ostreopsis cf. ovata) worldwide, particularly in the Mediterranean, have caused severe toxic outbreaks, drawing the attention of health authorities. At present, an efficient and sustainable sampling system for monitoring ovatoxins in seawater remains unavailable. Herein, different cyclodextrin (CD) polymers were investigated as a green and effective alternative to conventional and low-performing resins to detect ovatoxins in seawater. Spiking experiments using different concentrations of palytoxin or ovatoxins (namely 200 and 3.3 ng PLTX/mL or 200 ng OVTX-a/mL) were conducted and LC-HRMS was used to evaluate the suitability of CD polymers in capturing palytoxin-like compounds. Several conditions were tested for extracting polymer materials, including different extraction times (1.5 to 4 h), various solvent mixtures (acidic or alkaline), and organic modifiers (methanol or acetonitrile) at different ratios. Among the tested polymers, γ-CD-hexamethylene diisocyanate (HDI) resulted to be the most promising one, providing ovatoxin recoveries in the range 82–108% at a spiking level of 200 ng OVTX-a per mL. The best extracting condition was alkaline pH methanol:water 8:2 mixture, which showed the best palytoxin recovery in both high and low concentration spiking experiments. Finally, a time-dependent increase in the amount of ovatoxins captured by γ-CD-HDI disks deployed in O. cf. ovata cultures was observed. These findings provide valuable insights on the efficiency of passive sampling using CD polymers for capturing ovatoxins during O. cf. ovata bloom events.Open access funding provided by Università degli Studi di Napoli Federico II within the CRUI-CARE Agreement. This research was funded by the European Union (Project BlueShellfish grant no. 101086234, https://doi.org/10.3030/101086234). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. The research was also funded by the Defense Threat Reduction Agency Project CB10396 (Service Contract W81XWH20C0135), and by the Ministerio de Ciencia e Innovación (MICIN) and the Agencia Estatal de Investigación (AEI) through the CELLECTRA project (PID2020-112976RB-C21 and PID2020-112976RB-C22). Contribution of C. Melchiorre was funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4—Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP E63C22000990007, Project title “National Biodiversity Future Center—NBFC”.info:eu-repo/semantics/publishedVersio