Resultados de investigación

Background Major depressive disorder affects more women than men, possibly due to sex-specific biological and environmental factors. Previous studies have shown that Galanin, as well as specific fragments of it, such as Galanin 1–15 [GAL(1–15)], play a crucial role in modulating depression in animal models by acting on brain regions like the hippocampus and dorsal raphe nuclei. Until now, its effects had only been studied in male rats, where GAL(1–15) produces depressive and anxiogenic behaviors in behavioral tests. This study analyzes, for the first time, the effects of GAL(1–15) on female rats and compares the expression of galaninergic and serotonergic genes between males and females to understand sex-dependent mechanisms in depression. Methods For this purpose, the effect of GAL(1–15) administered intranasally in female rats was assessed, using validated behavioral tests for depression: the Forced Swim Test (FST) and the Tail Suspension Test (TST), as well as the anxiety behavior test: the Open Field Test (OFT). Furthermore, the expression of galaninergic genes (GAL, GALR1, GALR2, GALR3) and the 5-HT1A receptor was compared in untreated male and female rats using qPCR in key brain regions implicated in depressive disorder: the dorsal raphe nucleus (DR), the dorsal hippocampus, and the prefrontal cortex (PFC). Results We demonstrated that GAL(1–15) intranasally administered in female rats strongly produced depressive behavior in the FST as well as in the TST. Moreover, in the anxiety test, intranasal GAL(1–15) at high doses produced anxiety behavior in the OFT. In addition, the qPCR study revealed that naïve female rats exhibited increased expression of the galaninergic system and 5-HT1A receptor compared to naïve male rats in the DR, dorsal hippocampus, and PFC, several nuclei implicated in depression. Conclusions.These results demonstrate the existence of sexual dimorphism in the galangergic system.These findings on sex-specific neurobiological variations are crucial for advancing toward more precise therapies tailored to the specific characteristics of each sex in the treatment of depression.

The scientific literature shows that without an adaptive management of cyberbullying situations cybervictims may be more likely to become cyberaggressors. Therefore, numerous studies have examined potential factors that contribute to mitigating the strong association between cybervictimisation and cyberaggression, particularly focusing on how adolescents cope with the experience of cybervictimisation. However, clear results regarding gender differences in coping strategy utilisation have not been identified. Consequently, it is necessary to continue delving deeper into this field to further elucidate these prior findings. Thus, this study aimed to examine the possible moderating role of adaptive and maladaptive cognitive coping strategies in the relationship between cybervictimisation and cyberaggression, as well as to analyse gender differences within it. A sample of 2,525 adolescents (12–18 years) participated by completing self-report measures which assess cybervictimisation, cyberaggression and cognitive emotion regulation strategies. After performing several moderation analyses, the results showed that both adaptive and maladaptive strategies independently moderated the link between cybervictimisation and cyberaggression in girls, but not in boys. These findings have contributed to a deeper understanding of the role that coping strategies used by cybervictimised adolescents can play. They also show evidence of the potential benefits of developing various coping strategies that help cybervictims to better manage these adverse situations and reduce cyberaggression., Proyecto I+D+i PID2020-117006RB-I00 financiado por MCIN/AEI/10.13039/501100011033/ y el grupo PAIDI Applied Positive Lab CTS-1048 (Junta de Andalucía) La uiversidad de Málaga/CBUA financió la tasa de acceso abierto del artículo

These data derived from the proyect PID2020-115954RB-I00: Interacción entre la virulencia de nodavirus y la respuesta inmune innata en dorada y lubina (INTERNODASS)

Emotion regulation (ER) is a core transdiagnostic mechanism implicated in the development and maintenance of anxiety and depression. Although insomnia also plays a key role in these internalizing conditions, it has received comparatively less empirical attention. Moreover, the extent to which insomnia, above and beyond ER, contributes to anxiety and depressive symptoms at different severity levels remains poorly understood. A cross-sectional sample of 2,352 adolescents (11–18 years old) completed validated measures assessing ER, insomnia, and symptoms of anxiety and depression. Based on their scores on the Depression, Anxiety and Stress Scales (DASS-21; Lovibond & Lovibond, 1995), participants were classified into four groups: clinical anxiety, subclinical anxiety, clinical depression, and subclinical depression. Within each severity group, hierarchical multiple regression analyses were conducted to determine the unique and incremental contribution of ER and insomnia to anxiety and depressive symptoms, after controlling for age and sex. ER significantly predicted anxiety and depression only within the clinical groups. Insomnia emerged as a significant predictor of anxiety in both subclinical and clinical groups, and of depression in the clinical group, over and above ER and demographic covariates. Across models, insomnia accounted for a greater proportion of explained variance compared to ER. These findings underscore the central role of sleep difficulties and ER as transdiagnostic mechanisms of internalizing psychopathology, particularly at higher symptom levels. They also highlight the importance of prioritizing insomnia-focused interventions, given the robust contribution of sleep disturbances to anxiety and depression across severity groups.

Validation of the scales of a questionnaire about pre-service science teachers’ perceptions of IBSE. It includes the dataset, the readme file and the R-code used in the validation process, that consisted of an exploratory analysis and refining, CFA and cluster analysis., R&D project B1-2020-23, funded by Universidad de Málaga R&D project PID2022-140001OA-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE and the FSE+ RYC2020- 029033-I/AEI/10.13039/501100011033, financed by the State Research Agency and the European Social Fund

The files contain the mobility traces generated by a group of 19 experimental subjects that emulated a set of predetermined ADL (Activities of Daily Life) and falls. The traces are aimed at evaluating fall detection algorithms. Several video clips describing the performed movements are also included., European FEDER funds, Spanish Ministry of Economy and Competitiveness Award Number, Universidad de Málaga (Spain), Andalucia Tech, TEC2013-42711-R

README – SPAREMETAL DATASET 3 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 3 (there are 4 in total for this project), which analyzes the effect of cutting parameters on slenderness tests. Cutting forces, Temperature, Macrogeometrical deviations, Surface Roughness and Energy consumption were analysed. 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 3 (existen 4 para este proyecto), en el que se analiza el efecto de los parámetros de corte en los ensayos de esbeltez de la aleacion UNS A97075. Se analizaron las fuerzas de corte, la temperatura, las desviaciones macrogeométricas, la rugosidad superficial y el consumo de energía para esta aleación. 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

Emotional intelligence (EI) is typically linked to higher subjective happiness scores in human service professionals. It is unknown which EI facets are more predictive in explaining subjective happiness beyond that accounted for by other key predictors such as perceived stress. This study investigated which EI facets were the most predictive in explaining subjective happiness above perceived stress in a relatively large sample of Spanish teachers. The sample was composed of 1323 Spanish teaching professionals (821 females and 529 secondary school teachers) from different educational centers located in Southern Spain. A student-recruited sampling technique was used, and the surveys included the Wong and Law Emotional Intelligence Scale, the Subjective Happiness Scale, and the Perceived Stress Scale. Predictive and incremental validity was assessed with SPSS, and hierarchical regression analysis was used to predict subjective happiness from EI facets beyond that accounted for by perceived stress. The results showed that all four EI facets correlated significantly with each other. Also, they all were positively and significantly associated with subjective happiness, whereas perceived stress was negatively associated with happiness scores. Moreover, self-emotion appraisal, use of emotions and regulation of emotions accounted for a significant amount of variance in the prediction of satisfaction with life beyond the effects of sociodemographic variables and perceived stress. This study extends the specific contribution of EI facets in predicting subjective happiness, rather than EI as a unified construct, in a relatively large sample of Spanish teachers. Self-focused dimensions involving appraisal, use and regulation of emotions appeared to be the most important predictors of happiness beyond stress experienced by teachers. Improved knowledge of the link between specific dimensions of EI and global subjective happiness might improve training in a well-being prevention..., Junta de Andalucía (UMA18-FEDERJA-147). Ministerio de Ciencia e Innovación (PID2020-117006RB-I00). Grupo PAIDI CTS-1048 de la Junta de Andalucía. Universidad de Málaga. Ministerio de Ciencia, Innovación y Universidades (FJC2019-038942-I/AEI/10.13039/501100011033)

This database is made up of EEG signals from 6 subjects listening to sentences in different languages and their answers to the questions: have you understood the meaning of the sentence?. The languages chosen are: english, german, italian, korean and spanish. These signals have been captured with the BrainVision actiCHAMP-PLUS system and consist of a total of 64 EEG channels. The BrainVision Recorder software was used to store the signals. The stimulus presentation software used to design the experiment is Eprime 3. For more detailed information on this database, the capture system used and its applications, see [1]. If these data are used for any publication, the following paper must be cited: [1] Isaac Ariza, Ana M. Barbancho, Lorenzo J. Tardón, Isabel Barbancho, Energy-based features and bi-LSTM neural network for EEG-based music and voice classification. Neural Comput & Applic 36, 791–802 (2024). https://doi.org/10.1007/s00521-023-09061-3, Funding for open access publishing: Universidad Málaga/CBUA. This publication is part of Project PID2021-123207NB-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE. This work was partially funded by Junta de Andalucía, Proyectos de I+D+i, in the framework of Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020), under Project No. PY20_00237. Funding for open access charge: Universidad de Málaga/CBUA. This work was done at Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech.

This paper presents a new synthetic dataset openly available at https://www.uma.es/robotics-and-mechatronics/info/132852/negs-ugv-dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released, This work was partially supported by the Andalusian project UMA18-FEDERJA-090 and by the Spanish project RTI2018-093421-B-I00.