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

The origin of the FtsZ/tubulin protein family was extremely relevant for life since these proteins are present in nearly all organisms, carrying out essential functions such as cell division or forming a major part of the cytoskeleton in eukaryotes. Therefore, investigating the early evolution of the FtsZ/tubulin protein family could reveal crucial aspects of the diversification of the three domains of life. In this study, we revisited the phylogenies of the FtsZ/tubulin protein family in an extensive prokaryotic diversity, focusing on the main evolutionary events that occurred during its evolution. We found evidence of its early origin in the last universal common ancestor since FtsZ was present in the last common ancestor of Bacteria and Archaea. In bacteria, ftsZ genes are genomically associated with the bacterial division gene cluster, while in archaea, ftsZ duplicated prior to archaeal diversification, and one of the copies is associated with protein biosynthesis genes. Archaea have expanded the FtsZ/tubulin protein family with sequences closely related to eukaryotic tubulins. In addition, we report novel CetZ-like groups in Halobacterota and Asgardarchaeota. Investigating the C-termini of prokaryotic paralogs basal to eukaryotic tubulins, we show that archaeal CetZ, as well as the plasmidic TubZ from Firmicutes, most likely originated from archaeal FtsZ. Finally, prokaryotic tubulins are restricted to Odinarchaeaota and Prosthecobacter species, and they seem to belong to different molecular systems. However, their phylogenies suggest that they are closely related to α/β-tubulins pointing to a potential ancestrality of these eukaryotic paralogs of tubulins, Peer reviewed

18 pages, Supporting information for the article https://doi.org/10.1016/j.scitotenv.2021.145020, Peer reviewed

40 pages, Supplementary information for the article https://doi.org/10.1038/s41598-021-02328-6, Approach.-- Standardization of indicators,-- Calculation of index values.-- Socioeconomic indicators and factors.-- Institutional Indicators and Factors.-- Resilience factors summary.-- Index performance.-- Additional analyses.-- Bibliography, 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

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

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