Resultados de investigación

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. The data were analysed using non-parametric statistical tests because they did not conform to a normal distribution. For this purpose, the Kolmogorov–Smirnov normality test was used. A bivariate correlation was performed using Spearman’s Rho test between the different constructs and factors that made up the SCQ-S tool in the sample under study. Likewise, using the chi-square test, we attempted to determine the relationship between the different nominal categorical variables added to the tool. Independent samples mean analysis was also performed using the Mann–Whitney U-test when the number of groups was 2 and the Kruskal–Wallis test when the number of groups was greater than 2. In addition, exploratory factor analysis was performed using principal axis factoring and Vari-max rotation based on a fixed number of 9 factors to be extracted, determined by a previous thorough literature review. The adequacy of the sample and the reliability of the factor models were assessed using Kaiser–Meyer–Olkin and Cronbach’s alpha. Descriptive data on means and standard deviations of the numerical variables included in the study were also obtained. 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 carried out with Statistical Package for Social Science® software, version 28 (SPSS®, Chicago, IL, USA). 3. Procedures followed for data quality assurance. The level of statistical significance was p < 0.05. For those cases where significance was obtained, effect size and statistical power were analysed using G*Power 3.1.9.7.

The association between stress and immune response activations in different diseases has been analyzed in this paper based on salivary analytics. Moreover, a first attempt to discriminate between diseases was performed by machine learning. The salivary analytics consisted of the measurement of physical (cortisol) and psychological stress (salivary alpha-amylase) indicators, innate (acute phase proteins: C-reactive protein and haptoglobin), and adaptive immune (adenosine deaminase, Cu and Zn) markers and oxidative stress parameters (antioxidant capacity and oxidative status). A total of 107 commercial growing pigs in the field were divided into six groups according to the signs of disease after proper veterinary clinical examination, specifically, healthy pigs, pigs with rectal prolapse, tail-biting, diarrhea, lameness or dyspnea. Associations between stress and immune markers were observed with different intensities. The higher associations (r = 0.61) were observed between oxidative stress markers and adaptive immune markers. On the other hand, moderate associations (r = 0.31-0.48) between physical and psychological stress markers with both innate and adaptive immune markers were observed. All pathological conditions showed statistically significant differences in at least 4 out of the 11 salivary markers studied, with no individual marker dysregulated in all the diseases. Moreover, each disease condition showed differences in the degree of activation of the systems analyzed which could be used to create different salivary profiles. A total of two dimensions were selected according to the machine learning analysis to explain the 48,3% of the variance of our data. Lameness and prolapse were the two pathological conditions most distant from the healthy condition followed by dyspnea. Tail biting and diarrhea were also far from the other diseases but nearer healthy animals. There is still room for improvements, but these preliminary results showed a great power for disease detection and characterization using salivary biomarkers profiling in the near future.

Es un zip que incluye carpetas para los distintos objetivos del trabajo, en las cuales hay un archivo ".rds" que se lee con R., Bases de datos utilizadas para los análisis del artículo "Acoustic telemetry and accelerometers: a field comparison of different proxies for activity in the marine environment". Existen 5 bases de datos, una base de datos para los objetivos 1, 2 y 3, y dos bases de datos para el objetivo 4, correspondientes a las series temporales corta y larga descritas en el manuscrito.

Different cell lines (SAF-1, DLB-1, PLHC-1 and FuB-1) mitro stress (OCR and ECAR) raw data., Different cell lines (SAF-1, DLB-1, PLHC-1 and FuB-1) mitro stress (OCR and ECAR) raw data.

Ultra Performance Liquid Chromatography coupled with a high-resolution quadrupole-orbitrap mass spectrometry DATA