Resultados de investigación

This study evaluated the immunomodulatory role of somatostatin-6 (SST6) in gilthead seabream (Sparus aurata) using a validated inflammation model induced by λ-carrageenan and a comprehensive panel of serum and skin mucus biomarkers. SST6 was administered at 1 nM and 2 nM, and its effects were assessed in these matrices. The λ-carrageenan model triggered a local inflammatory response with reduced peroxidase, esterase, and protease activities in skin mucus, consistent with the resolution phase of innate immunity. SST6 at 1 nM enhanced serum complement activity without affecting acute-phase proteins, suggesting reinforcement of basal immune surveillance in the host. When combined with λ-carrageenan, SST6 promoted an increase in serum immunoglobulin levels and a selective shift in proteinogram fractions (α2-globulin), while further decreasing the oxidative and proteolytic activities in skin mucus. Correlation analysis revealed the coordinated regulation of key mucosal enzymes, reinforcing the functional integration of local immune responses. These results indicate that SST6 facilitates an early transition from innate to adaptive immunity while preserving the integrity of the mucosal barrier. Furthermore, this study provides the first evidence of complement activation by SST6 in fish and highlights the value of integrating biomarker-based tools to monitor immunophysiological responses and test bioactive compounds in aquaculture research.

This dataset contains network traffic data captured from a real Industrial Control System (ICS) testbed, designed to support research on cybersecurity, intrusion detection, and anomaly detection in industrial environments. The dataset has been generated from a laboratory-scale industrial installation that emulates a real operational scenario, including Programmable Logic Controllers (PLCs), industrial communication protocols, and controlled cyber-attack scenarios. Traffic includes both normal operation and malicious behavior introduced through Man-in-the-Middle (MitM) attacks. The dataset is intended for academic and research purposes, particularly for the evaluation of Machine Learning and Deep Learning techniques applied to ICS cybersecurity.

Background The molecular effects involved in the cellular response to non-ioninzing electromagnetic radiation on marine and freshwater microalgae and fish cell lines are incompletely understood. Objectives The direct impact of non-ionizing electromagnetic radiation on marine and freshwater microalgae and fish cell lines was investigated. Methods Six microalgae species from different taxonomic groups were exposed to extremely low-frequency electromagnetic fields (ELF-EMF) for five days. Four fish cell lines (SAF-1, DLB-1, DLEC, and Fugu eye) were exposed to ultraviolet (UV) radiation, with SAF-1 demonstrating the highest sensitivity. In addition, three commercial antioxidants (2101, 3600, and 4004) were tested for their possible radioprotective effects. Results Although no statistically significant differences in cell density were observed, certain taxa (Chlorotylium sp., Mayamea sp., and Scenedesmus sp.) exhibited a declining trend compared to the controls, suggesting species-specific sensitivity to ELF-EMF. Principal component analysis revealed significant differences in physiological responses, particularly in photosynthetic variables such as electron transport rate and dissolved carbon. The total antioxidant capacity did not differ significantly between the control and exposed microalgae, and Chlorotylium sp. showed the highest basal levels. The photosystem II productivity index remained largely unaffected by ELF-EMF exposure, except for an increase in irradiated Scenedesmus sp. Low antioxidant concentrations (≤125 µg/ml) and short incubation times (6 h) of 2101 and 3600 induced proliferation or increased cell viability, particularly under irradiation. However, prolonged incubation time (24 h) or high antioxidant concentrations (≥250 µg/ml) resulted in significant cytotoxicity in all cell lines.

1.Description of the methodology used to generate the dataset. Basic descriptive methods (standard deviation, range, etc.) were used for descriptive statistical analysis of the measured variables. Cronbach’s alpha was applied to assess the reliability of the scales, with values of 0.70 for the IFIS scale and 0.79 for the PACES scale. Because the sample size was over 50, the Kolmogorov–Smirnov test was performed to verify a normal distribution of the data for each variable. A mean analysis of independent samples was performed for the gender variable through a student’s t-test and for the weight percentile category variable through an ANOVA. Bivariate correlations between numerical variables were established through the Pearson test to determine possible interactions between variables. 2. Software or tools needed to interpret the data. [Include the version of the software. If specific software with restricted access is required, explain how to obtain it. Assess whether it is possible to change the dataset to an open format (recommended)]. Data analysis was conducted using the Statistical Package for Social Science® software, version 28 (SPSS®, Chicago, IL, USA). 3. Procedures followed for data quality assurance. With a significance level of α = 0.05 and a perfect correlation coefficient when r = |1|, very strong when |0.8| ≤ r < |1|, strong when |0.5| ≤ r < |0.8|, weak when |0.3| ≤ r < 0 and null when r = 0.

1.Description of the methodology used to generate the dataset. Qualitative variables were described by absolute (n) and relative (%) frequencies; mean and standard deviation (SD) for quantitative variables. For the study of the evolution of children’s psychomotor skills according to gender, a two-factor ANOVA test with repeated measures in one of them was carried out using the General Linear Model (GLM) procedure. The dimensions of performance were studied using the Wilcoxon test. The logistic regression model was used to determine the effect of the improvement in the dimensions of psychomotor skills on the improvement in each of the performance dimensions: SA, LG, and KE. 2. Software or tools needed to interpret the data. [Include the version of the software. If specific software with restricted access is required, explain how to obtain it. Assess whether it is possible to change the dataset to an open format (recommended)]. Statistical analysis was performed with SPSS 25.0 for Windows. 3. Procedures followed for data quality assurance. The differences considered statistically significant are those whose p < 0.05.