Università Cattolica del Sacro Cuore Showcase

We present here the Matlab code used for the numerical simulations of the paper "Informal networks and education choice: a dynamic macro model". The simulations present the properties of the equilibria of our model via bifurcation diagrams.

Heavy metal (HM) contamination in agricultural soils significantly threatens soil health and plant productivity. This study investigates cadmium (Cd) and zinc (Zn) stress impact on tomato plants (Solanum lycopersicum) while exploring the mitigation potential of microbial biostimulants (MBs)—arbuscular mycorrhizal fungi (AMF) and Pseudomonas fluorescens So_08 (PGPR)— employing multi-omics approaches. Specifically, the investigation delves deeply into soil-plant communication mechanisms mediated by root exudates and rhizosphere microbial communities. Root exudate profiling revealed distinct metabolic changes under HM stress, which compromised soil-plant interactions. Under Cd stress, key classes of metabolites, including phenylpropanoids, lipids, and isoprenoids, show reduced secretion. These metabolites play crucial roles in antioxidative defense, suggesting a shift in resource allocation mechanisms. Moreover, Cd negatively impacted rhizosphere fungal populations. Conversely, Zn stress prompted an increased exudation of lipids, including sphingolipids and sterols, reflecting an adaptive strategy to preserve membrane integrity and functionality. This stress also influenced rhizobacterial community structures. The MB application mitigated HM-induced stress by enhancing specialized metabolite syntheses, including cinnamic acids, terpenoids, and flavonoids, which promoted crop resilience. MBs also reshaped microbial diversity, fostering beneficial species like Portibacter spp., Alkalitalea saponilacus under Cd stress, and stimulating rhizobacteria like Aggregatilinea spp. under Zn stress. Multi-omics data integration combined with network analysis highlighted key features associated with improved nutrient availability and reduced HM toxicity under MB treatments, including metabolites and microbial taxa linked to sulfur cycling, nitrogen metabolism, and iron reduction pathways. These findings demonstrate that MBs can modulate plant metabolic responses and restore rhizosphere microbial communities under Cd and Zn stress, with PGPR showing broader metabolomic recovery effects and AMF influencing specific metabolite pathways. This study provides new insights into plant-microbe interactions in HM-contaminated environments, supporting the potential application of biostimulants for sustainable soil remediation and plant health improvement.

Supplemental Table S1. Excel file containing the following sheets: a) rumen metabolites annotated by UHPLC-HRMS analysis; b) milk metabolites annotated by UHPLC-HRMS analysis; c) Correlation Network resulting from data fusion of rumen and milk metabolites; d) Log2FC values of the VIP compounds discriminating the rumen metabolomic profile for the pairwise comparison 3-NOP vs CTR groups; e) Log2FC values of the VIP compounds discriminating the milk metabolomic profile for the pairwise comparison 3-NOP vs CTR group.

Supplementary data for the manuscript "Unleashing the nutritional potential of Brassica microgreens: A case study on seed priming with Vermicompost" published in the journal Food Chemistry. Supplementary Table S1. Composition of vermicompost biostimulant. Supplementary Table S2. Raw dataset on the untargeted UHPLC-QTOF/MS metabolomics of vermicompost seed-primed microgreens. Supplementary Table S3. DAMs derived from the Volcano analysis on cress microgreens. Supplementary Table S4. DAMs derived from the Volcano analysis on daikon microgreens. Supplementary Table S5. DAMs derived from the Volcano analysis on mustard microgreens. Supplementary Table S6. DAMs derived from the Volcano analysis on red cabbage microgreens. Supplementary Table S7. Pearson correlation coefficients (r cut-off = 0.57) on the rCCA-mediated integration of bioactive compounds and in vitro biological activities of vermicompost seed-primed microgreens. Supplementary Figure S1. Biomass (g) of 10-day grown Brassicaceae microgreens. Vertical bars indicate standard deviation (n = 4). From top to bottom: cress, daikon, mustard, and red cabbage.

This study investigates how Large Language Models (LLMs) transform business negotiations. Through an experiment with 120 senior executives, we examined negotiations with symmetric and asymmetric AI assistance. When only one side had access to LLMs, they gained substantial advantages-buyers achieved 48.2% better deals and sellers 40.6% better outcomes compared to their counterparts. However, symmetric LLM access yielded even more striking results, with 84.4% higher joint gains compared to non-assisted negotiations. This improvement came with increased information sharing (+28.7%), creative solution development (+58.5%), and value creation (+45.3%). Notably, when both sides used LLMs, they relied less on traditional trustbuilding approaches while maintaining fairness, with minimal gain differences between parties (2.2%). Based on these findings, we introduce 'technological equilibrium' to explain how equal AI access transforms negotiation dynamics. While early adopters showed clear advantages, our results suggest that symmetric access ultimately promotes both value creation and procedural fairness through technological parity, enabling integrative outcomes even when trust is limited.

The BEEHIVE dataset has been created for Precision Agriculture, Measurement Science, and Entomology research specifically dealing with Apis mellifera (common honeybee) image analysis. The contents of the dataset include data acquired from two cameras located at different observation points: the "Frame" dataset, acquired with a camera placed inside a frame of the beehive and depicting very close-range images of the bees (potentially a few with Varroa destructor mites on their backs), and the "Bottom" dataset, acquired with a camera positioned at the bottom of the beehive. In this case, a metallic grid partially occludes the view. The two datasets are already subdivided into training, validation, and test sub-datasets following a 70%-20%-10% splitting protocol. The "Frame" dataset includes 1.440 training images, 411 validation images, and 206 test images. The "Bottom" dataset includes 1044 training images, 303 validation images, and 147 test images. Each dataset includes annotations obtained in RoboFlow for the task of object detection, considering two classes: "bee" and "blurred_bee" for the "Frame" dataset, "bee" and "occluded_bee" for the "Bottom" dataset. The data is valuable for the field of Precision Agriculture, Entomology, Measurement Science, and Computer Vision, especially for the tasks of bees' monitoring, counting, and detection of potential parasites by training image-based Deep Learning models. It is also useful as a reference dataset for benchmarking models. If you use this dataset for your work, please cite the related papers: - https://doi.org/10.3390/s24165270

The empirical application of RBF in pediatric services at Lacor and Kalongo Hospital from 2018 to 2024 (before and after COVID-19 pandemic) documents the promotion of significant improvements in healthcare quality and outcomes.

The dataset encompasses comprehensive raw data pertaining to the antimicrobial, barrier, and mechanical properties of films. These films were meticulously crafted by blending carrot pomace with wheat gluten and polyglycerol-3 plasticizer in a mortar with pestle. The formulations were then enriched with different concentrations (0%, 3%, and 5%) of eugenol, a naturally occurring antimicrobial compound derived from essential oils. The final step involved the mixtures compression molding in a hot press at 90 °C for 5 minutes under 5 tons of pressure, resulting in the creation of the films for which detailed data is presented in this dataset.

Supplementary material of manuscript - A Pseudomonas Plant Growth Promoting Rhizobacterium and Arbuscular Mycorrhiza differentially modulate the growth, photosynthetic performance, nutrients allocation, and stress response mechanisms triggered by a mild Zinc and Cadmium stress in tomato

The aim of our study was to assess the gut microbiota composition in patients with FHA, compare it with that of a healthy population, and evaluate the effects of hormonal replacement therapy (HRT) on it. No statistical difference was found comparing the gut microbiota alpha diversity of FHA patients at baseline and post treatment. Also the overall gut microbial composition after HRT was similar to baseline. On the contrary, the relative abundance analysis revealed specific changes after HRT. At the phylum level, Fusobacteria were significantly increased after HRT, as well as Ruminococcus and Eubacterium genera. To identify the main factors having an impact on the gut microbiota components that showed a significant variation after HRT, we conducted a multiple linear regression where the main hormonal parameters, inflammatory variables and endometrial thickness were included. Fusobacteria, which were increased after HRT in FHA, were correlated with the reduction in some proinflammatory cytokines (i.e., IL1ra, IL4, GCSF, and CCL2; Supplemental Table S1). The increase in [Ruminococcus] gnavus group post-therapy was associated with changes in endometrial thickness, and several hormonal and cytokine mediators (Supplemental Table S2). Finally, some hormonal variables and endometrial thickness, rather than inflammatory parameters, were the most relevant associated factors with the variations of [Eubacterium] hallii group (Supplemental Table S3).