Resultados de investigación

Proteomic analysis was performed on left hippocampal tissue from C57BL/6J mice subjected to different juvenile and adult stress paradigms and compared with control animals (CTRL). Experimental groups included mice exposed to juvenile stress (JS), adult stress (AS), or combined JS+AS, together with a control group. Hippocampi were collected at postnatal day 90 (PD90). Left hippocampi were homogenized in RIPA buffer, protein concentration was determined using the BCA assay, and samples were digested via gel-assisted proteolysis with trypsin. Peptide mixtures were analyzed by ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) using a Q-Exactive HF-X instrument (SCAI, Universidad de Málaga), acquiring MS/MS spectra for protein identification. Raw data were processed using Proteome Discoverer 2.5 and searched against the Mus musculus UniProt database (release 2025-02). A total of 3,850 high-confidence proteins were identified (FDR < 1%, ≥2 peptides, master proteins). Label-free quantification was performed using precursor ion intensities and normalized by total peptide amount. ANOVA identified upregulated proteins (p < 0.05 and log2 FC ≥ 1) and downregulated proteins (p < 0.05 and log2 FC ≤ −1) in the following comparisons: JS vs CTRL, AS vs CTRL, JS+AS vs CTRL, JS vs JS+AS, and AS vs JS+AS. Male and female datasets were analyzed separately. Raw proteomic data and volcano plots are available in RIUMA (Universidad de Málaga).

These data has been partially published in: GEMEZ-MATA J, P MORENO, D ÁLVAREZ-TORRES, E GARCIA-ROSADO, J BEJAR, MC ALONSO (2024) Comparative analysis of immune gene transcription in sea bream (Sparus aurata) challenged with RGNNV or RGNNV/SJNNV betanodaviruses. Pathogens, 13(6), 478 (https://doi.org/10.3390/pathogens13060478), Immunogene transcription after NNV injection in sea bass and sea bream.

DATASET. Purpose: Prior research indicates that problematic social media use (PSMU) is associated with cyberbullying perpetration (CBP) in adolescents. PSMU has been linked to sleep disturbances, and existing studies show that sleep difficulties are also associated with cyberbullying perpetration. Nevertheless, the interplay among these three variables remains unexplored. The present study examines whether PSMU is directly associated with later CBP and indirectly through poor sleep quality in adolescents. Methods: A sample of 335 students aged 12-17 years from four schools completed standardized and validated measures of PSMU, sleep quality, and CBP in a two‑wave longitudinal design with a 9‑month interval. A mediation analysis was conducted following the guidelines of the PROCESS macro for SPSS. Results: Problematic social media use was associated with subsequent cyberbullying perpetration both directly and indirectly via sleep quality. These findings underscore the relevance of considering sleep quality alongside problematic social media use in cyberbullying prevention efforts for adolescents, with implications for both boys and girls. Conclusions: The results suggest that interventions targeting both problematic social media use and sleep quality may help reduce cyberbullying perpetration among adolescents., Parte del proyecto I+D+i PID2020-117006RB-I00, financiado por MCIN/AEI/10.13039/501100011033, Ayuda FPU23/03374, financiada por MICIU/AEI /10.13039/501100011033 y por el FSE+ (V. Sánchez-Moreno)

The experimental data are organized into the following folders: 1.- FEA Simulations; Finite element analysis model files. 2.- Measurements; Experimental measurements of the system: 2.a.- EMI; Tests evaluating the influence of electromagnetic noise. 2.b.- Force; Tests characterizing the response to applied force. 2.c.- Proximity; Tests evaluating the response to non‑contact approaching objects. 2.d.- Proximity and Force; Combined tests in which objects first approach and then make contact to apply force. 2.e.- Videos; Recordings showing the sensor response to approaching objects., This dataset contains the data obtained from the experiments described in the article: J. Castellanos‑Ramos et al., “Adding Proximity Sensing Capability to a Tactile Array Based on Off‑the‑Shelf FSR and PSoC,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 4238–4250, July 2020. DOI: 10.1109/TIM.2019.2944555 URI: https://hdl.handle.net/10630/32690 URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8853265&isnumber=9112796

This dataset contains time series data representing cell-level interference generated from a dynamic system-level mobile network simulator. The data captures the temporal evolution of interference conditions across different cells under simulated network scenarios. Additionally, the dataset includes a file containing the spatial positions of the simulated base stations, expressed as Cartesian coordinates (x, y) in kilometers. This positional information enables spatial analysis of interference patterns and supports reproducibility of the simulation environment.

La cartografía temática del Proyecto PID2021-126283NB-I00 recoge la representación geoespacial de los datos tomados y procesados en el transcurso del Proyecto con relación a la identidad de los arrendadores mayores de rentas regias ordinarias de la Corona de Castilla y los volúmenes de arrendamiento formalizados por el conjunto de los arrendadores mayores avecindados en cada localidad para cuatro cortes cronológicos: 1454, 1482, 1494 y 1503. Los datos de base correspondientes a los años 1454, 1482 y 1494 proceden de fuentes documentales inéditas del Archivo General de Simancas (sección Escribanía Mayor de Rentas, legajos 1-100). Los datos correspondientes a 1503 proceden del vaciado de información de la obra de J.M. Carretero Zamora, D. Alonso García, Hacienda y negocio financiero en tiempos de Isabel La Católica: el libro de Hacienda de 1503 (Madrid: Editorial Complutense, 2003).

README – SPAREMETAL DATASET 1 Title: Experimental and predictive data from sustainable machining of light alloys (SPAREMETAL project) Project: PID2021-125988OB-I00 Sistema experto para la mejora de la integridad superficial en el mecanizado sostenible de aleaciones ligeras Institution: University of Malaga (Universidad de Málaga) Principal Investigator: Lorenzo Sevilla Hurtado (ORCID: 0000-0002-2236-5807) Contributors: Francisco Javier Trujillo Vilches Carolina Bermudo Gamboa Sergio Martín Béjar Manuel Herrera Fernández Tobias Andersson Daniel Svensson Yezika Sánchez Hernández Description: This dataset contains experimental and processed data obtained during dry machining processes of light alloys (UNS A97075 aluminum alloy and Ti6Al4V titanium alloy). The data were collected within the SPAREMETAL research project and include both online monitoring data and offline characterization results. This is dataset 1 (there are 4 in total for this project), which analyzes the effect of cutting parameters on the wear of the tool used and on the chip geometry resulting from the machining process. Data include: - Online monitoring data: cutting forces, temperature, vibrations, energy consumption - Offline measurements: surface roughness (2D/3D), microhardness, geometrical deviations - Derived datasets: aggregated databases linking input machining parameters and output variables - Predictive models: artificial neural networks (ANN) developed in MATLAB/Simulink Folder structure: 01. RAW DATA: Folder containing the raw data for the output variables; 02. PROCESSED DATA: Folder with the processed databases of the output variables; 03. MODELS: Developed mathematical models for the output variables as a function of the input variables; 04. Documentation: Documentation associated with the Project; File formats: - CSV, XLSX, FEL, DWD, ATS, DXD (data) - MAT, M, SLX, MALAPP, MLX, EXE (models) - PDF, DOCX (documentation) - PNG, TIFF, JPG (images) Methodology: The dataset was generated through controlled machining experiments using CNC turning equipment. Input parameters include cutting speed, feed rate and depth of cut. Output variables were measured using dynamometers, thermographic cameras, accelerometers and optical metrology systems. Units: All variables are expressed in SI units unless otherwise specified in metadata files. Reuse: Data can be reused for: - Validation of machining models - Development of predictive algorithms - Comparative studies in manufacturing engineering License: Creative Commons Attribution 4.0 (CC BY 4.0) Funding: Spanish State Research Agency (AEI) – Projects of Knowledge Generation 2021 Contact: Lorenzo Sevilla Hurtado University of Malaga Email: (lsevilla@uma.es) Date of publication: 2026 Version: v2.0, Este dataset contiene datos experimentales y procesados obtenidos durante procesos de mecanizado en seco de aleaciones ligeras (aleación de aluminio UNS A97075 y aleación de titanio Ti6Al4V). Los datos se recopilaron en el marco del proyecto de investigación SPAREMETAL e incluyen tanto datos de monitorización en tiempo real como resultados de caracterización fuera de línea. Se trata del dataset 1 (existen 4 para este proyecto), en el que se analiza el efecto de los parámetros de corte sobre el desgaste herramienta empleada y sobre la geometría viruta resultante del proceso de mecanizado de ambas aleaciones. Datos generados/recogidos: En el proyecto SPAREMETAL se han generado datos experimentales derivados de procesos de mecanizado en seco de aleaciones ligeras (UNS A97075 y Ti6Al4V), incluyendo: • Datos de monitorización online (fuerzas de corte, temperatura, vibraciones, consumo energético) • Datos de monitorización offline (rugosidad, desviaciones geométricas, microdureza, fatiga) • Bases de datos integradas de parámetros de corte y variables de salida • Modelos predictivos basados en redes neuronales artificiales • Archivos de simulación (MATLAB/Simulink) • Documentación metodológica y protocolos experimentales Formatos y estándares utilizados: • CSV, XLSX, FEL, DWD, ATS, DXD (data) • MAT, M, SLX, MALAPP, MLX, EXE (models) • Documentación: PDF, DOCX • Imágenes: TIFF, PNG Valor para otros investigadores: Los datos permiten: • Reproducir experimentos de mecanizado sostenible • Validar modelos predictivos • Desarrollar nuevos algoritmos de optimización, Ministerio de Ciencia e Innovación