13004 research outputs found
Sort by
Establishment and parasitism levels of Ganaspis kimorum on Drosophila suzukii in Northeastern Italy: insights from a 4-yr release program
No full textThe management of the invasive fruit fly Drosophila suzukii (Diptera: Drosophilidae) still relies on chemical control, despite concerns about pesticide resistance and environmental impact. Integrated pest management strategies are being explored, but current options remain limited and often insufficient. In invaded regions, local natural enemies are mostly generalist pupal parasitoids, which have proven inadequate for controlling this rapidly spreading pest. Conversely, in its native range, D. suzukii is host to more specialized larval parasitoids that may mitigate its impact on fruit crops. Extensive foreign exploration and risk assessments identified the larval endoparasitoid Ganaspis kimorum (Hymenoptera: Figitidae) as the most suitable candidate for classical biological control. In 2021, Italy launched a propagative biocontrol program utilizing a Japanese population of G. kimorum. Here, we present the results from the first 4 yr of releases conducted in Northeast Italy, where the parasitoid was released at 20 different locations. We provide an overview of the monitoring activities at these sites to determine whether G. kimorum has successfully established and to assess its parasitism levels on both the target pest and nontarget species. Results confirmed that G. kimorum is a specialist on D. suzukii and likely does not compete with local parasitoids for reproductive resources. Collected data also suggest that the parasitoid has become established in several locations, and it is gradually expanding its range from the initial release sites. Four years after the initial release of G. kimorum, the data gathered offers valuable insights into the efficacy and ecological implications of this biocontrol strateg
xcms in peak form: now anchoring a complete metabolomics data preprocessing and analysis software ecosystem
Full text linkHigh-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms maintains long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics researc
Decoding microbial volatile signals in host–microbiome crosstalk
No full textThe human gut microbiome is a complex microbial ecosystem which has a profound impact on host health and disease. The research focus in this area is rapidly moving from taxonomy to functionality, elucidating the biological role of small molecules produced by the gut microbiome in regulating host metabolism. Among these, microbial volatile organic compounds (mVOCs) play several roles in bacterial communication and microbe-host signaling. Volatilomics, the comprehensive study of volatile metabolites, is emerging as a powerful tool for discovering and investigating these interactions. In this review we examine the current understanding of mVOCs in the gut and highlight how dedicated in vitro and ex vivo volatilomics experiments, alongside in vivo studies, can uncover the biological roles for these emerging small molecule
Bacterial transmission within social groups shapes the underexplored gut microbiome in the lemur Indri indri
Full text linkThe Indri indri is a critically endangered lemur species that has not successfully been maintained or bred under human care. Investigating this lemur's virtually unexplored gut microbiome will deepen our understanding of the species' health determinants and inform conservation efforts. Through metagenomic assembly and integration into an updated reference database, we found the I. indri faecal microbiome remains largely uncultivated (cultivated species representing <0.1% relative abundance) and is largely specific to this primate species. After reconstructing 342 metagenome-assembled genomes encompassing 48 candidate species from a total of 22 samples (18 of which newly sequenced), we substantially improved microbiome mappability to 85% on average and found evidence for a proportionally large core microbiome. Social group membership emerged as the main determinant of both their taxonomic and functional gut microbiome composition. Using strain-level profiling, we detected extensive microbiome transmission within social groups, suggesting physical interaction is key in promoting microbiome acquisition. Strain sharing rates were highest between mothers and their offspring. Intergroup strain sharing was minimal and inversely correlated with geographical distance, aligning with the rare intergroup interactions and stable territory occupancy coupled with ongoing habitat fragmentation. No evidence of microbiome acquisition through geophagy was detected. These findings underscore the profound influence of social structure on microbiome transmission and composition in I. indri, and highlight the importance of considering social dynamics into research and conservation strategies
Postprandial metabolism, inflammation, and plasma bile acid kinetics in a rat model: implications for translational research
Full text linkThe postprandial period is an opportunity window to assess metabolic phenotype, and its study is gaining popularity due to the wealth of information that can be uncovered when a dietary challenge is associated with the application of metabolomics approaches. Bile acids (BA) were recently identified as signaling molecules that display major changes in circulating levels following food intake. In this regard, a gap of information remains linking BA postprandial kinetics with their possible metabolic effects. This study aimed to characterizing a murine model for investigating postprandial metabolism and inflammation. Changes in plasma and hepatic markers of metabolism, inflammation and BA levels were assessed in male Sprague-Dawley rats before and after the ingestion of an energy-dense meal. Rats display postprandial alterations in circulating BA levels, with cholic acid constituting the predominant species (36%). These changes are accompanied by shifts in intermediates of energy metabolism and inflammatory markers, as demonstrated by a four-fold increase in hepatic NF-κB protein content, a key inflammatory transcription factor, two hours after food intake. Despite inherent species-specific differences, this murine model represents a promising tool for studying postprandial modulation energy metabolism, establishing a pioneering framework for future investigations into the role of BA in postprandial metabolic response
Improving spectral similarity and molecular network reliability through noise signal filtering in MS/MS spectras
Full text linkIn mass spectrometry, fragmentation spectra play a central role in compound identification. However, noise in MS/MS spectra can significantly impact similarity scores and molecular network (MN) reliability, leading to inaccurate compound annotation in untargeted metabolomics. This work investigates the influence of noise on MS/MS similarity scores and molecular network structure. Noise elimination increased similarity scores for homologous spectra, enhancing match affordability. In MNs, effective noise management improved network structure, resulting in more interpretable networks with fewer edges and enhanced clustering, decreasing false-positive connections. To quantitatively assess these improvements, a minimum spanning tree (MST) analysis was performed, revealing denser regions in the denoised MNs. An increasing cutoff of noise threshold can lead to an overlay between two or more different compound spectra. A data-specific workflow was developed to identify the optimal threshold for denoising, balancing spectra quality and network integrity during noise elimination, by incorporating statistics calculated on the distribution of the MST distances and the number of fragment ions, which could be explained by an in-silico fragmentation algorithm. Finally, a faster-tailored denoising method, based solely on the intensity of individual spectral ions, demonstrated performance comparable to the previously cited fixed threshold approache
On the use of TabPFN on mass spectrometry analysis of volatile organic compounds
Full text linkVolatile organic compounds (VOCs) are key markers in applications ranging from food quality assessment to medical diagnostics that can be profiled, for example, by gas chromatography–mass spectrometry (GC-MS) or by direct injection mass spectrometry (e.g. proton transfer reaction mass spectrometry). The common practice in both cases is to construct a tabular dataset from the raw measurements by performing peak extraction across samples and use statistical or machine learning methods to analyze it. However, modeling VOC profiles is particularly challenging due to high dimensionality, noise, and small sample sizes. In this study, we evaluate the Tabular Prior-data Fitted Network (TabPFN), a foundation model recently introduced for tabular data, across diverse VOC datasets. Without requiring task-specific training, TabPFN achieves state-of-the-art performance in both classification and regression tasks, outperforming classical machine learning methods for most datasets. We further explore new strategies to enhance TabPFN’s performance, including ensembling and fine-tuning, finding that a plain ensemble seems to be the best option in this setting. Our results demonstrate that TabPFN is a highly effective modeling tool for VOC profiles obtained with different analytical approaches. It offers robust predictions even in the data-scarce, high-variability scenarios typical of real-world workflow
Cryosphere and lithology influence the hydrological gradients of high elevation Alpine catchments
Full text linkIn 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, δ18O, 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 km2), water temperature increased from 1.6 °C at the intact rock glacier spring to 7.3 ± 1.5 °C at the catchment outlet, despite the inputs from till and rock glacier springs with <3.0 °C waters. In the glacierized catchment (3.7 km2), the proglacial reaches had a water temperature of 6.9 ± 2.6 °C and the inputs from cold rock glacier springs decreased the water temperatures by 2–4 °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 on
Enhancing “Ewiss” cheese production using autochthonous lactic acid bacteria and Propioniciclava flava as an alternative to commercial propionic acid bacteria
Full text linkThis study explored enhancing Ewiss cheese's regional identity by using native starter cultures to develop a “regional cultured Ewiss cheese” (RCEC). Lactic acid bacteria (LAB) were previously isolated from local cheeses and selected from the University of Palermo culture collection (Streptococcus thermophilus strains RC-UNIPASAAFM00189, RC-UNIPASAAFM00233, RC-UNIPASAAFM00239, and RC-UNIPASAAFM00249), while Propioniciclava flava (RC-UNIPASAAFM01343) was isolated during this study. LAB were added as defined whey starters post-pasteurization; Pc. flava was introduced as an adjunct culture at pH 6.4. Microbial dynamics were monitored throughout cheese ripening. Thermophilic LAB dominated early stages; propionic acid bacteria increased after 9 months. Metataxonomics analysis revealed Streptococcus and Lactobacillus as dominant. Thirty-one volatile organic compounds were identified, with propionic acid most abundant. A control cheese using commercial cultures was produced for sensory comparison. Sensory analysis revealed that RCEC made with autochthonous cultures scored higher than the control in terms of overall acceptability. These results support Sicilian dairy diversification and valorisatio
Viscoelastic characterisation of high protein ice cream: predicting tactile sensory properties via time–concentration superposition and large amplitude oscillatory shear (LAOS) rheology
No full textIn the present work we report on the exploration of time concentration superposition principle (TCS) and non-linear dynamic rheology (LAOS) as useful instrumental tools for predicting tactile sensory modalities of ice cream at serving temperature (−14 °C). Three common tactile sensory properties of ice cream i.e., resistance to scooping (scoopability), creaminess and gumminess were assessed in high protein formulations differing in their protein to fat ratio (φP/F = 0.9 to 4) and protein source (milk protein concentrate (MPC) vs whey protein isolate-sodium caseinate (WPICAS) 1:1 blend). The complex viscosity – angular frequency data obeyed the TCS principle with the calculated shift factors reflecting effectively the compositional profile of ice creams i.e., ac ∝ φP/F1.16 and φP/F2.23, bc ∝ φP/F−1.27 and φP/F−1.75 for MPC and WPICAS fortified systems. LAOS assessment revealed a clear impact of protein type and φP/F on the shearing deformation of ice creams. MPC fortification and decrease in the φP/F enhanced the shear flowing ability of the ice creams. In all cases, the onset of shear stiffening and thickening behaviour was observed at shear stresses below the flow point, which indicates gel-like or colloid glass-like structures. According to partial least squares regression analysis, the TCS parameters (ac and bc), damping factor (tanδ) and the shear strain (γf) and elastic modulus (log ) at flow point were determined as the most important parameters predicting tactile sensory modalities on large deformation (spooning) such as scoopability, creaminess and gummines