Università Cattolica del Sacro Cuore Showcase

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).

Background: This study assessed the role of culture in the measurement of the Self-Curiosity Attitude-Interest scale (SCAI-M), a measure of attitude and interest in increasing one’s knowledge of self, adopting both a person-centered and variable-centered approach. Methods: The study was conducted on a Mexican sample composed of 484 adult participants who completed both the SCAI-M and a series of instruments that measure cultural dimensions thought Qualtrix. Data were collected between November 9, 2020 to December 18, 2020, and respondents were contacted using advertisements on social media platforms (Facebook and WhatsApp). Analyses included multigroup confirmatory factor analysis and latent profile analysis. Results: A latent profile analysis allowed for the generation of three groups featuring distinct cultural orientations that were similar to previously found cultural profiles (Consensus-oriented Egalitarians, Flexible Individualists, and Rules-based Competitors). Multigroup Confirmative Factor Analysis showed partial metric and scalar invariance for the SCAI-M between groups; moreover, we found proofs of convergent validity with other cultural dimensions besides the ones linked with the Hofstede model. Our results indicate that cultural profiles and cultural variables are associated with both the level and meaning of self-curiosity among Mexican citizens. Conclusions: Finally, the discussion includes considerations on self-curiosity divergence among minority cultures and relevant clinical applications; a field for which we propose future research.

Supplementary Table 4. Metabolomic dataset resulting from the untargeted UHPLC-HRMS analysis containing all the annotated mass features and key putatively identified metabolites, together with their identification score, relative abundance value, and composite isotopic mass spectrum.

The impact of four different particle sizes of polyethylene plastics, including microplastics and nanoplastics (MNPs) applied at four different concentrations was studied using lettuce (Lactuca sativa L.) as a model plant. The impact of MNPs was investigated with a focus on the tripartite interaction between the root metabolic process, exudation pattern, and rhizosphere microbial modulation using a multi-omics integrated approach. Our results showed that particle sizes and their concentrations significantly and distinctively modulated plant processes. Untargeted metabolomics suggested that the significantly affected pathways were fatty acids, amino acids, and hormones biosynthesis. Additionally, a related exudation of phenylpropanoids was observed. Rhizosphere bacterial α-diversity was reduced in a size-dependent trend for specific taxa. The omics data integration highlighted a correlation between Proteobacteria and Actinobacteria phyla and Bacillaceae family (Peribacillus simplex) and the exudation of flavonoids, phenolic acids, and lignans in lettuce exposed to increasing sizes of MNPs. This study provides a novel insight into the potential effects of different particle sizes and concentrations of MNPs on the soil-plant continuum, providing evidence about size- and concentration-dependent effects, suggesting the need for further investigation focused on medium- to long-term exposure.

RNA-seq raw count matrix from Orthotopic human immune system reconstituted (HIR) patient-derived xenograft (PDX) PDAC mouse models. metadata file match sample IDs and treatment conditions

Supplementary table 1. Diet ingredient formulation considering the different nutritional strategy-based clusters (Gallo et al., 2022).