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Replication Data for Immunofluorescences for "Magnesium Influences Membrane Fusion during Myogenesis by Modulating Oxidative Stress in C2C12 Myoblasts"
No full textReplication Data for immunofluorescence
Replication Data for Wound width for "The different effect of pharmacological or low-doses of IFN-γ in endothelial cells are mediated by different intracellular signaling pathways"
No full textReplication Data for: wound widt
Replication Data for "Tumor Accumulation and Off-Target Biodistribution of an Indocyanine-Green Fluorescent Nanotracer: An Ex Vivo Study on an Orthotopic Murine Model of Breast Cancer"
No full textContains the results published in Sevieri M. et al. 2021, Int. J Mol. Science
Replication data for: "Investigating the growth kinetics in sourdough microbial associations"
No full textThis dataset contains graphs obtained with three models based on Gompertz’s, Baranyi and Roberts ’, and Schiraldi’s functions using using experimental data from growth tests of microbial consortia consisting of Fructilactobacillus sanfranciscensis strains and Kazachstania humilis strains
Replication Data for Metabolic Profiling of Type 2 Diabetes Patients after Bariatric Surgery by Raman Spectroscopy
No full textRaw data contained in "Metabolic profiling of Type 2 Diabetes patients after bariatric surgery by Raman spectroscopy" by Bonizzi A. et al
Replication Data for ELISA for "High Magnesium and Sirolimus on Rabbit Vascular Cells-An In Vitro Proof of Concept"
No full textReplication data for ELIS
Replication Data for Zonulin levels for "Bacterial DNAemia is associated with serum zonulin levels in older subjects"
No full textData including: list of Subjects, Sex, Age, BMI, Bacterial load (16S rRNA g.c./μl) and Zonulin level (ng/ml), for sample sets 1 and 2
Replication Data for "A conditional linear Gaussian network to assess the impact of several agronomic settings on the quality of Tuscan Sangiovese grapes"
No full textIn this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-
year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed
