University of La Laguna Showcase

Raw results from cell and animal experiments

Research hypothesis: grazing intensity and stage of secondary succession after deforestation affect richness, diversity, plant composition of herbaceous and tree - shrub layer and forest structural traits (density, biomass, basal area an others) of tropical dry forest. Main findings: Grazing intensity, succession stage and interaction between both showed significant effects on most of variables. Grazing intensity did not disrupt natural succession process.

Chromosomal analysis of products of conception using targeted direct embryo biopsy by operative hysteroscopy

BICI−ULL (Bioclimatic Indicators in the Canary Islands by the ULL−GOTA) includes 53 biovariables derived from monthly temperature, precipitation, radiation, humidity, and wind speed averages over different periods calculated through dynamic regionalisation models. This project aims to improve our understanding of climate and its impacts on small, isolated regions with diverse ecosystems, providing valuable insights for the scientific community. The dataset can be found in the ‘Bioclimatic indicators’ folder. It consists of 795 files in NetCDF4 format with a grid cell resolution of 3×3 km, covering the region of the Canary Islands. The files represent 53 bioclimatic indicators calculated in 30-year intervals for the periods: 1980−2009, 2030−2059, and 2070−2099, under the Representative Concentration Pathway (RCP) scenarios RCPs 4.5 and 8.5, for future. The non-hydrostatic WRF model, with boundary conditions from three global models (GFDL−ESM2M, IPSL−CM5A−MR, and MIROC−ESM), was used to perform the simulations. The grid dimensions are (186×77), with a total of 14322 cell points. It covers a longitude range of 18.3◦W to 13.3◦W and a latitude range of 27.5◦N to 29.6◦N, although the biovariables are only computed for land cells. The files comply with the CF conventions for climate and forecast metadata designed to promote the processing and exchange of files created with the netCDF4 Application Programmer Interface. The names associated with each file follow the format: BIOx_model_rcp_yini_yend.nc The raw data from which the biovariables are generated are also available in this repository, in the ‘Raw Data’ folder. These include the standard variables: Mean, maximum, and minimum temperature (TG, TX, TN), precipitation (PR), shortwave downwelling flux (SR), relative humidity (RH), and wind speed (WS). Each file contains 12 timesteps, one for each month (January, February, …). The content of the variables is the mean over the 30-year periods. A specific bias adjustment technique known as the Scaled Distribution Mapping (SDM) algorithm, designed to preserve trends, was used (Switanek et al., 2017). File names follow the format: model_yini_yend_rcp_variable.nc x represents the number of the climatic indicator model makes reference to the driving Global Circulation Model used for the regionalsation simulations. It can be GFDL, IPSL, or MIROC. rcp indicates the forcing scenario, RCP 4.5 or RCP 8.5, or it will be defined as ‘HIST’ in the case of the historical period. yind and yend represent the years of the period, with ini identifying the first year of the period and end identifying the last year. variable refers to the standard variable, which can be TG, TX, TN, PR, SR, RH, WS The main folders contain, in turn, subfolders corresponding to each driving global model, and to the respective period and scenario. The definition of the 53 BICI-ULL bioindicators have been also summarised in the BICI-ULL_bioindicators_definitions.pdf file.

Dataset of Computer Aided Drug Design (CADD) and Molecular Dynamics (MD) operations for article, titled "NEW DYNAMIC SCORING METHOD FOR DEEP EVALUATION OF NALOXEGOL AS Β-TUBULIN BINDING INHIBITOR"

This repository contains the database used for the development of the study "Evolution and Trends in Research on Family Digital Leisure: A Bibliometric Analysis" followed the PRISMA 2020 Protocol. This study aims to analyze the evolution of the field of research on family digital leisure from 2012 to 2022. The analysis, conducted through a bibliometric approach, seeks to identify trends, changes, and emerging patterns in scientific literature. To achieve this purpose, two specific objectives were established. First, the temporal evolution of the field was explored, evaluating the variation in scientific production and academic attention over the last decade. Second, emerging themes and thematic groups that have gained relevance in scientific literature were identified. On January 31, 2023, a search was conducted in the topic section (abstract, title, and keywords) of the Web of Science (WoS), Clarivate Analytics, and Scopus Main Collection. The descriptors used were: (leisure OR game OR entertainment) AND (digital OR internet OR online OR cyber OR technolg* OR ICT OR ICTs) AND (family). This search was limited to the years 2012-2022. The initial search generated 2953 results, which, after removing duplicates, were reduced to 1843 documents. These were subjected to the following inclusion criteria: (1) being articles or reviews and (2) having been published between 2012 and 2022. Consequently, chapters of books, conference papers, and other formats were excluded, as well as scientific production accepted for publication in 2023 (early or advance access). Finally, 1381 documents were obtained to carry out the bibliometric study.

This is the self-running application that allows you to compare the graphical performance of your PC when using photogrammetric 3D models, NANITE models and models under mesh retopology. The development of this application is part of the work published in the article "Nanite as a Disruptive Technology for the Interactive Visualisation of Cultural Heritage 3D Models: A Case Study". The instructions for use are inside the file.

This is the self-running application that allows you to compare the graphical performance of your PC when using photogrammetric 3D models and models under mesh retopology. The development of this application is part of the work published in the article "Retopología de mallas aplicada a modelos 3D de patrimonio cultural para la mejora de la visualización interactiva en realidad virtual y realidad aumentada". The instructions for use are inside the file.

Thrips of the subtropical genus Scirtothrips are emerging as important pests in several crops. Scirtothrips dorsalis has been increasingly invading new areas outside of its native region of South and East Asia causing economic damage to several crops. Scirtothrips inermis is another polyphagous species with worldwide distribution. Both species are polyphagous, and in recent years have emerged as key pests in strawberry. In this study, we first evaluated the predation and oviposition rate of commercially available phytoseiid predatory mites Amblyseius swirskii, Amblydromalus limonicus, Transeius montdorensis, and Neoseiulus cucumeris on larval stages of both Scirtothrips species, and oviposition rates of predatory mites on the supplementary food source Artemia franciscana cysts were also assessed. Predatory mites equally accepted both thrips species as prey and showed stable oviposition rates on these diets. Amblyseius swirskii and A. limonicus were the most voracious, also exhibiting the highest oviposition rate of the predators tested. We further evaluated the biological control potential of predatory mites and anthocorid predators Orius laevigatus and Orius limbatus in a greenhouse experiment. Predators were released preventively and supported with Artemia cysts before the introduction of S. inermis . Both Orius predators achieved good control of the pest, with O. limbatus developing higher numbers than O. laevigatus . Regarding phytoseiids, A. swirskii and A. limonicus both controlled the pest and built higher populations than T. montdorensis and N. cucumeris . Our results show that a preventive strategy based on phytoseiid or anthocorid predators in strawberry can be effective in suppressing S. inermis .

Randomly generated CVRP intances with these characteristics: - TSPLIB format - node coordinates in [0, 100] x [0, 100] - demands in [1, 25] - Q = 100