Resultados de investigación

Base de datos en formato SPSS

Datasets relacionados con el documento: http://hdl.handle.net/10396/24370

This dataset contains the experimental data associated to the publication "Identification of a missense variant in the porcine AGPAT gene family associated with intramuscular fat content through whole-genome sequencing" by Molinero et al. (2022; doi: https://doi.org/10.1111/age.13258). It includes a TXT file with the genotypes obtained using real-time PCR with High-Resolution Melt and the phenotypic data necessary for the validation of the effect of single nucleotide polymorphism rs196952262 on intramuscular fat in 1,034 pigs.

The dataset contains the chemical composition (water content, fiber, six carbohydrates, total proteins, organic matter, and lignin) of the subcortical tissues (phellem and phloem) of 8 Quercus suber trees, 4 infected with Coraebus undatus and 4 not infected.

Understanding the genetic basis of adaptation and plasticity in trees constitutes a knowledge gap. In this study we analysed 20-year tree-ring series of 130 trees, structured in 23 populations grown in a common garden experiment. We tested for genotype by environment interactions (G×E) of indexed ring width (RWI) and indexed early to latewood ratios (ELI) using factorial regression analyses, that describes G×E as differential gene sensitivity to climate. The data include raw tree-ring series, indexed ring-width and early to latewood ratio series, 20 SNPs, climate variables at site, populations sensitivity to particular climate factors, and climate at origin of populations.

Recent technological advancements in Artificial Intelligence make it easy to create deepfakes, hyper-realistic videos in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance and voice are used. As emotions are inherent in human communication, studying how deepfakes transfer emotional expressions from original to fakes is relevant. In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake database. First, we extracted the photograms from their videos. Then, we analyzed the emotional expression in both the original and the faked versions of the video recordings for all performers in the database. Results show that emotional expressions are not adequately transferred between original recordings and the deepfakes created from them. The high variability in emotions and performers detected between original and fake recordings indicates that performer emotion expressiveness should be considered for better deepfake generation or for detecting them.