BUSCADOR RECOLECTA

Suppl. Fig S1. Comparison of length and number of substitutions versus p-value in the calculation of the Ka/Ks ratio. Genes have been colored according to the group to which they belong. A regression line has been added, together with its correlation coefficient and associated p-value. Suppl. Fig S2. Distribution of Ka/Ks along the length of genes S, M, N and E (black line). The normalized Shannon entropy obtained from Nextstrain database is shown for comparison (https://nextstrain.org/ncov/gisaid/global/6m). Pfam domains have been included (below): S → bCovS1N (PF16451, Betacoronavirus-like spike glycoprotein S1, N-terminal), bCoV_S1_RBD (PF09408, Betacoronavirus spike glycoprotein S1, receptor binding), CoV_S1_C (PF19209, Coronavirus spike glycoprotein S1, C-terminal), CoV_S2 (PF01601, Coronavirus spike glycoprotein S2); M → CoVM (PF01635, Coronavirus M matrix/glycoprotein); N → bCoV_lipid_BD (PF09399, Betacoronavirus lipid binding protein), bCoV_Orf14 (PF17635, Betacoronavirus uncharacterised protein 14), CoV_nucleocap (PF00937, Coronavirus nucleocapsid); E → CoVE (PF02723, Coronavirus small envelope protein E). The blue line marks the Ka/Ks value of 1. Suppl. Table S1. Genomes used in this work. Suppl. Table S2. Ka/Ks ratio obtained for each SARS-CoV-2 gene, together with the associated p-value. Blue color highlights structural genes, red color highlights non-structural genes, and gray color highlights accessory factors., The SARS-CoV-2 virus pandemic that emerged in 2019 has been an unprecedented event in international science, as it has been possible to sequence millions of genomes, tracking their evolution very closely. This has enabled various types of secondary analyses of these genomes, including the measurement of their sequence selection pressure. In this work we have been able to measure the selective pressure of all the described SARS-CoV-2 genes, even analyzed by sequence regions, and we show how this type of analysis allows us to separate the genes between those subject to positive selection (usually those that code for surface proteins or those exposed to the host immune system) and those subject to negative selection because they require greater conservation of their structure and function. We have also seen that when another gene with an overlapping reading frame appears within a gene sequence, the overlapping sequence between the two genes evolves under a stronger purifying selection than the average of the non-overlapping regions of the main gene. We propose this type of analysis as a useful tool for locating and analyzing all the genes of a viral genome, when an adequate number of sequences are available., Peer reviewed

Additional file 1: Table S1. Table of number of genes, and genes in KEGG signaling pathways contained in HiPathia R package sharing an increasing number of RP-HPO terms., Consejería de Salud y Consumo, Junta de Andalucía, H2020 Marie Skłodowska-Curie Actions, Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III, Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana, Peer reviewed

[Description of methods used for collection/generation of data] For the heat budget analysis, we utilized temperature tendency terms available in the Modular Ocean Model version 4p1 (MOM4p1). Heat flux anomalies (in W m-2) for each term were averaged over a time step at a given ocean grid cell, following Griffies et al. (2015). Subsequently, we calculated anomalies for each heat term relative to their seasonal cycles. These anoalies were then averaged separately over the days corresponding to the onset phase (i.e., heat build-up) and the decay phase (i.e., heat dissipation) of the marine heatwaves (MHWs). To study causal interactions between the physical variables (Max. SSTA, SIC, and MLD) and net primary production (NPP) rates, we applied Dynamic Empirical Modelling (EDM). Specifically, we utilised the Convergent Cross Mapping (CCM) method, as defined by Sugihara et al. (2012). Following a sensitivity test, we adjusted the time delays (τ) and embedding dimension (E) to 3 and 6, respectively., File List: MOM4p1; CCM., Peer reviewed

Byte-by-byte Description of file: table1.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 3 I3 --- HCG [1/100] HCG number (Cat. VII/213) 5 I1 --- Nmemb [2/8] Number of galaxies in group 7- 15 F9.5 deg RAdeg Group right ascension (J2000) 17- 25 F9.5 deg DEdeg Group declination (J2000) 27- 31 I5 km/s HRV Group heliocentric radial velocity 33- 35 I3 Mpc Dist [0/160] Group distance (1) 37- 41 A5 --- VLAConfig VLA configuration(s) the group was observed with 43- 46 F4.1 km/s DVChan Channel width of reduced VLA HI observations 48- 51 F4.2 mJy/beam rms RMS noise in reduced VLA HI cube 53- 56 F4.1 arcsec beamMaj Major axis diameter of VLA synthesized beam 58- 61 F4.1 arcsec beamMin Minor axis diameter VLA synthesized beam 63- 66 F4.1 10+19cm-2 NHI 4-sigma HI column density sensitivity (over 20km/s) -------------------------------------------------------------------------------- Note (1): group distance calculated via the Cosmicflows-3 model (Tully et al., 2016AJ....152...50T, Cat. J/AJ/152/50; Kourkchi et al.. 2020AJ....159...67K), which have uncertainties of 3Mpc, -------------------------------------------------------------------------------- Byte-by-byte Description of file: table2.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 3 I3 --- HCG [2/100] HCG number (Cat. VII/213) 5 A1 --- l_logMHI-VLA [<] Upper limit flag for HI mass from VLA 7- 11 F5.2 [Msun] logMHI-VLA Logarithm of total group HI mass from VLA 13- 16 F4.2 [Msun] e_logMHI-VLA ?=- Uncertainty in HI mass from VLA 18- 22 F5.2 [Msun] logMHI-GBT ?=- Logarithm of total group HI mass from GBT observations (Borthakur et al., 2010ApJ...710..385B) 24- 28 F5.2 [Msun] logMHI-pred Logarithm of predicted total group HI mass based on B-band luminosity (Jones et al., 2016MNRAS.457.4393J) 30 A1 --- l_HIdef-VLA [>] Lower limit flag for HI deficiency from the VLA 32- 36 F5.2 --- HIdef-VLA HI deficiency of group from VLA HI mass 38- 42 F5.2 --- HIdef-GBT ?=- HI deficiency of group from GBT HI mass 44- 48 F5.2 [Msun] logMHI-gals ?=- Total HI mass in galaxies (VLA) 50- 53 F4.2 [Msun] logMHI-exfs ?=- Total HI mass in extended features (VLA) 55- 58 F4.2 --- fexfs ?=- Fraction of HI mass in extended features (VLA) 60- 61 A2 --- HIphase HI morphological phase (cf. Verdes-Montenegro et al., 2001A&A...377..812V) -------------------------------------------------------------------------------- Byte-by-byte Description of file: tablec1.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 3 I3 --- HCG [2/100] HCG number (Cat. VII/213) 5- 14 A10 --- Name Galaxy name 16- 24 F9.5 deg RAdeg Galaxy right ascension (J2000) 26- 34 F9.5 deg DEdeg Galaxy declination (J2000) 36- 39 A4 --- MType Galaxy morphological type (Hickson et al., 1989ApJS...70..687H, Cat. VII/213) 41- 49 A9 --- IRclass IR activity classification (Zucker et al., 2016ApJ...821..113Z, Cat. J/ApJ/821/113) 51- 55 I5 km/s HRV Galaxy heliocentric radial velocity (Hickson et al., 1992ApJ...399..353H, Cat. VII/213) 57- 61 F5.2 mag Bmag Galaxy B-band apparent magnitude (Hickson et al., 1989ApJS...70..687H, Cat. VII/213) 63- 66 F4.2 mag e_Bmag Uncertainty in B-band apparent magnitude 68- 72 F5.2 [Msun] logMHI-pred Logarithm of predicted HI mass (Jones et al., 2018A&A...609A..17J, Cat. J/A+A/609/A17) 74- 77 F4.2 [Msun] e_logMHI-pred Uncertainty in predicted HI mass 79- 83 F5.2 [Msun] logMHI ?=- Logarithm of HI mass (VLA) 85- 89 F5.2 --- HIdef ?=- Galaxy HI deficiency (VLA) -------------------------------------------------------------------------------- Byte-by-byte Description of file: list.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 3 I3 --- HCG [2/100] HCG number (Cat. VII/213) 5- 13 F9.5 deg RAdeg Right Ascension of center (J2000) 14- 22 F9.5 deg DEdeg Declination of center (J2000) 24- 27 I4 --- Nx Number of pixels along X-axis 29- 32 I4 --- Ny Number of pixels along Y-axis 34- 36 I3 --- Nz Number of pixels along Z-axis 38- 63 A26 "datime" Obs.date Observation date 65- 75 E11.6 m/s bVRAD Lower value of VRAD interval 77- 87 E11.6 m/s BVRAD Upper value of VRAD interval 89- 96 F8.2 m/s dVRAD VRAD resolution 98-103 I6 Kibyte size Size of FITS file 105-124 A20 --- FileName Name of FITS file, in subdirectory fits 126-149 A24 --- Title Title of the FITS file --------------------------------------------------------------------------------, Hickson compact groups (HCGs) are dense configurations of four to ten galaxies, whose HI morphology appears to follow an evolutionary sequence of three phases, with gas initially confined to galaxies, then significant amounts spread throughout the intra-group medium, and finally with almost no gas remaining in the galaxies themselves. It has also been suggested that several groups may harbour a diffuse HI component that is resolved out by interferometric observations. The HI deficiency of HCGs is expected to increase as the HI morphological phase progresses along the evolutionary sequence. If this is the case, HI deficiency would be a rough proxy for the age and evolutionary state of a HCG. We aim to test this hypothesis for the first time using a large sample of HCGs and to investigate the evidence for diffuse HI in HCGs. We performed a uniform reduction of all publicly available VLA HI observations (38 HCGs) with a purpose-built pipeline that also maximises the reproducibility of this study. The resulting HI data cubes were then analysed with the latest software tools to perform a manual separation of emission features into those belonging to galaxies and those extending into the intra-group medium. We thereby classified the HI morphological phase of each group as well as quantified their HI deficiency compared to galaxies in isolation. We find little evidence that HI deficiency can be used as a proxy for the evolutionary phase of a compact group in either of the first two phases, with the distribution of HI deficiency being consistent in both. However, for the final phase, the distribution clearly shifts to high HI deficiencies, with more than 90% of the expected HI content typically missing. Across all HCGs studied, we identify a few cases where there is strong evidence for a diffuse gas component in the intra-group medium, which might be detectable with improved observations. We also classify a new sub-phase where groups contain a lone HI-bearing galaxy, but are otherwise devoid of gas. The new morphological phase we have identified is likely the result of an evolved, gas-poor group acquiring a new, gas-rich member. The large spread of HI deficiencies in the first two morphological phases suggests that there is a broad range of initial HI content in HCGs, which is perhaps influenced by large-scale environment, and that the timescale for morphological changes is, in general, considerably shorter than the timescale for the destruction or consumption of neutral gas in these systems., Peer reviewed

Table S1: Serologic results conducted on free-ranging pudu samples collected 2017–2023 in Chile; Table S2: Serum singles samples from captive pudus tested to determine the presence of livestock and zoonotic pathogens antibodies; Table S3: Serum samples from captive pudus tested to determine the presence of livestock and zoonotic pathogens antibodies in different years., Peer reviewed

(DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning, The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020., Peer reviewed

S1 Table. List of primers employed by SybrGreen for real-time PCR., Peer reviewed

S2 Table. Kinases employed in this study., Cellular senescence and low-grade inflammation favor the acceleration of aging. The liver is an essential metabolic organ because changes related to its function are related to age-related diseases. The objective of this study was to evaluate the effects of maraviroc (MVC) and/or rapamycin (RAPA) on liver tissue in an experimental model of frailty syndrome in mice, since MVC and RAPA are two molecules able to decrease CCR5 expression, which is overexpressed in patients with frailty. Methods: Eighty male homozygous IL10KO mice were randomly assigned to one of 4 groups (n = 20): i) IL10KO group; ii) MVC group, iii) RAPA group, and iv) MVC-RAPA group. Liver samples were analyzed. Gene expression quantification and western blotting were also performed. The proinflammatory cytokines IL-6 and IL-18 were decreased in MVC and MVC/RAPA groups, IL-12 was decreased in RAPA and MVC/RAPA groups and TNF-α was decreased in all therapeutic groups. P21 was decreased in RAPA and MVC/RAPA groups, Galactosidase beta-1, was also significantly reduced in all therapeutic groups, as were NF-kB1, NF-kB2 and STAT3. In all groups, mTOR and CCL5 were significantly reduced. CCR5 expression was decreased in the MVC and MVC/RAPA groups. Conclusion: MVC and RAPA may protect against some factors involved in liver aging. More studies will be necessary to verify their clinical applications., Peer reviewed

S1 Fig. Original blots of the western blot analysis of Fig 3., Peer reviewed

Data_files_1: Final data frames containing 111 records with information on the individual ID of the birds (id), the aviary where they stayed during the experiment (aviary), and their corresponding sex (sex), reproductive status: bred vs not bred (reproductive), body weight before releasing in the aviaries ingrams (body.mass), malondialdehyde acid values in uM (mda), identification of laboratory analysis session (mda.lab.session), glutathione values in mM (glutathione), identification of laboratory analysis session (glutathione.lab.session), telomere length values in T/S ratio (telomere.length), strength of the pair relationship log transformed (pair.bond.strength.log), strength of the relationship with non-pair members log transformed (Non.pair.sociability.log), and strength of the relationships with all individuals in the avirary (overall.sociability.log). (Romero-Haro et al individuals ddbb.xlsx). This data was used for the analysis at the individual level to run the two linear mixed-effect models (one for males and one for females) that include pre-breeding pair bond strength the response variable, and the physiological variables (MDA, glutathione, and telomere length) as predictor variables. Data_files_2: Final data frames containing50 records with information on the individual ID of the females (female.ID) and the males (male.ID), the aviary where they stayed during the experiment (aviary), malondialdehyde acid values in uM for females (female.oxidative.damage.mda) and males (male.oxidative.damage.mda), identification of laboratory analysis session for the female (female.mda.session) and the male (male.mda.session) sample, glutathione values in mM for the female (female.glutathione) and the male (male.glutathione), identification of laboratory analysis session for the female (female.glutathione.session) and the male (male.glutatione.session), telomere length values in T/S ratio for the female (female.telomere.length) and the male (male.telomere.length), latency to breed in days (latency.to.breed), strength of the pair relationship log transformed (pair.bond.strength.log), strength of the relationship with non-pair members log transformed for the female (non.pair.sociability.female.log) and the males (non.pair.sociability.male.log) (Romero-Haro et al pair ddbb.xlsx). This data was used to run the analysis at the dyadic level on assortative mating and the pair-focused linear mixed-effects model that includes the bond strength of the pair as the response variable, and the levels of oxidative damage of the female and the male of the pair, and their interaction, as predictor variables., Peer reviewed