BUSCADOR RECOLECTA

Additional file 1: Table S1. Summary statistics of the Baikal 1600 m long-read sequencing and metagenomic assembly. Fig. S1. A Principal component analysis (PCA) between deep Lake Baikal metagenomes based on a Bray-Curtis similarity k-mer profile frequencies of sequencing reads. Red and blue dots represent summer and winter Illumina metagenomes, respectively, while the green dot is the sample retrieved in this study and sequenced with PacBio Sequel II. B Phylum-level composition based on 16S rRNA gene fragments (Illumina and PacBio CCS5 reads) of the different metagenomes. The single metagenome highlighted in green corresponds to the PacBio sequencing, whilst the rest of datasets belong to previous Illumina sequencing. The phylum Proteobacteria was divided into its class-level classification. Only those groups with abundance values larger than 1% in any of the metagenomes are shown. C Classification of the 1600 m PacBio CCS5 16S rRNA reads at a higher taxonomic resolution. Only sequences larger than 1000 nucleotides were considered. Sequences ascribed to the Ca. Patescibacteria phylum are highlighted in green. Table S2. Genomic parameters of LAGs recovered in this study. Table S3. Genomic parameters of LAGs recovered in this study with ANI > 99.5% to MAGs retrieved from Lake Baikal 1250 and 1350 m deep. Fig. S2. Alignment of two LAGs that are complete in a single contig and the respective MAG from the Illumina assembly. Table S4. Genomic parameters of the resulting bins from the Baikal 1600 m CCS sequences. The four Baikalibacteria bins are highlighted in yellow. Fig. S3. A Maximum likelihood phylogenetic tree of the Baikalibacteria 16S rRNA genes. Sequences outside the deep branch coming from Figure 1 were used as an outgroup for the tree. The reads from the four read bins are colored in the figure. B Diversity of 16S rRNA sequences of Baikalibacteria bins. Linear representation of selected CCS5 reads (indicated with a red circle in the left panel) containing a 16S rRNA gene. A pairwise blastn comparison among reads was performed to detect orthologous genes. Fig. S4. A Average nucleotide identity based on metagenomic reads (ANIr) of LAGs and the four Baikalibacteria Bins. B ANIr of ten randomly selected sequences of each Baikalibacteria bin. Fig. S5. Metagenomic recruitment of the largest fragment of Baikalibacteria RBin09 on Lake Thun 180 m deep. Fig. S6. Maximum likelihood phylogenetic tree of the a phytoene elongase (LyeJ), b carotenoid 3,4-desaturase (CrtD), and c bisanhydrobacterioruberin hydratase (CruF) proteins., Peer reviewed

Au_filtered_dadi.vcf - used to produce SFS file for dadi analysis, Au_EuSicily_hybridsim; Au_EuSierra_hybridsim - used to simulate hybrids with HYBRIDLAB, Peer reviewed

Au_filtered_PCA.vcf - used as input for PCA, Au_STRUCTURE.str - used as input for STRUCTURE analysis, Au_IQTREE.phy - used as input for phylogenetic inference in IQTREE, Au_PhyloNetworks.loci - used as input to create gene trees for subsequent use in PhyloNetworks analysis, Au_filtered_TreeMix.vcf - used as input for TreeMix analysis, Au_TASSEL_western.vcf; Au_TASSEL_eastern.vcf; Au_TASSEL_central.vcf - used as input for genetic diversity analysis in TASSEL, Peer reviewed

(A) cbk1-5E-E164S cbk1-aid cdc15-2 (YMF3905), (B) cbk1-5E-E251S cbk1-aid cdc15-2 (YMF3910), (C) cbk1-5E-E409S cbk1-aid cdc15-2 (YMF3995), (D) cbk1-5E-E615T cbk1-aid cdc15-2 (YMF3906), and (E) cbk1-5E-E711S cbk1-aid cdc15-2 (YMF3907) cells were grown in YPD and arrested in late anaphase by shifting the temperature to 37 °C before the addition of rapamycin to half of the culture for 20 min. To allow progression through the cell cycle, cells were released in the absence (i) or presence (ii) of rapamycin. Samples were taken at the indicated times to determine cell-cycle progression by flow cytometry. (F) 3D Cbk1 structure (PDB 4LQS [48]) in which different protein domains are highlighted. Residue T574 in the activation loop, together with residues D475 and S409 in the kinase domain are denoted. (G) cbk1-T574A cbk1-aid cdc15-2 cells (YMF4191) were grown as above. FACS graphs can be found in the supplementary FACS file (S1 File)., pbio.3002263.s005.pdf, Peer reviewed

Code and data for paper "Discovery of senolytics using machine learning" by Smer-Barreto et al, 2023. Files contain the datasets employed for model training and computational screening, as well as a jupyter notebook with code for model training, feature selection and screening., Peer reviewed

Table A1. Table of astrometric binary systems, separated by their original survey. Table A2. Table of spectroscopic binary systems, separated by their original survey., Peer reviewed

Experimental Section: [Materials] 1-Ethyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([C2mpyr][TFSI]), 99 % was purchased form IoLiTec. 3,4-Ethylenedioxythiophene (EDOT), 99 % was supplied by Fisher Scientific. Iron (III) chloride hexahydrated (FeCl3·6H2O), 99% was acquired from Sigma Aldrich. All the reagents were used as received with no further purification, apart from [C2mpyr][TFSI], which was dried at 60 ºC under vacuum overnight before use. [Methods] Thermogravimetric analyses were performed under air (25 mL min-1 flow rate) using TGA 8000 Pekin Elmer. The samples were equilibrated at 100 ºC for 20 min and then heated at a rate of 10ºC min-1 in the range of 100-800 ºC. Scanning electron microscope (SEM) measurements were performed on a Hitachi Tabletop Microscope (TM3030 series) at a 15 kV force field, running in a point-by-point scanning mode. The samples were placed on an aluminum holder with doublesided carbon tape and introduced into the SEM chamber. ImageJ was used to measure the pore size distribution in the range of 80-100 pores. The textural properties were characterized by means of N2 adsorption–desorption at −196 °C in a Micromeritics ASAP2020 apparatus. Prior to the analysis, the materials were degassed at 70 °C during 8 h under vacuum at 10−4 mbar. From N2 adsorption–desorption isotherms, the BET area was calculated from the Brunauer–Emmett–Teller equation. Finally, the pore size distribution (PSD) was calculated using the method proposed by Barrett–Joyner–Halenda (BJH) method. The electrochemical characterization was carried out using a VMP-3 potentiostat (Biologic Science Instruments). Scaffolds of Ø=5 mm were employed for the ORR glued with 15 μL of PEDOT:PSS (Clevios PH1000) onto a glassy carbon electrode (Ø=4 mm) as working electrode against platinum wire as reference electrode. Cyclic voltammetry was employed in the range of -0.7 to 0.7 V vs Ag/AgCl at different scan rates to observe the electrochemical response of the different materials using 0.1M KOH electrolyte. Platinum wire was used as counter electrode and Ag/AgCl as reference electrode. The liquid electrolyte was saturated with oxygen bubbling O2 (99.5 %, Air Liquide) for 30 min before running the experiment. [3D scaffold synthesis and characterization] The 3D scaffolds were produced through a multistage process similar to previous reports and as shown in Figure 1.1,2 Sucrose and OIPC ([C2mpyr][TFSI]) were sifted through two sieves with mesh sizes of 250 and 100 μm sequentially. Thereafter, sucrose and OIPC grains in the middle fraction were collected ensuring grain sizes between 250-100 μm. Sieved sucrose (250 mg) and FeCl3·6H2O (20 mg) oxidant were mixed with the aid of a mortar and pestle in presence of 20 (7wt.%), 40 (13 wt.%) or 80 (23 wt.%) mg of OIPC. Finally, 5 μL of Milli-Q water was added in the blend and subsequently mixed until a homogeneous wet material was obtained. The mixture was poured into a hollow plastic cylinder of (Ø=5 mm) in diameter and gently compacted from both sides to form cylindrical-shape templates of 2-3 mm in length. Then, the templates were hung by a thread inside a Schlenk flask. Afterwards, 0.5 mL of EDOT monomer was introduced at the bottom of the Schlenk flask and subsequently left under vacuum for 5 min. VPP was carried out in a bath of 140 ºC overnight. The temperature of the Schlenk flask at the sucrose- OIPC templates was monitored and did not exceed 55 ºC, which ensures the solid state of the OIPC ( = 90 ºC). Once the reaction was 𝑇𝑚 completed, the cylinders were immersed overnight into Milli-Q water to dissolve the sucrose and the excess of oxidant, resulting in self-standing and porous architectures with interconnected microchannels. The scaffolds were cleaned with water and isopropanol for five days in a Soxhlet system until complete removal of iron byproducts. The absence of iron was proved by TGA through the complete weight loss..-- Experimental Section; Figure S1: first derivative from the TGA curves of SC-20, SC-40, and SC-80; Table S2: porosity parameters extracted from physisorption experiments; SBET: specific surface area; VPORE: pore volume; DPORE: equivalent pore diameter (calculated as 4 PORE/SBET); Figure S2: differential scanning calorimetry of C2mpyrTFSI OIPC, SC-20, SC-40, and SC-80 scaffolds at 10 °C min–1; Figure S3: pore size distribution estimated by SEM using ImageJ for 80–100 pores; Figure S4: cyclic voltammograms of PEDOT:PSS, glassy carbon (GC), and platinum (Pt) in N2- and O2-saturated 0.1 M KOH solution; Figure S5: galvanostatic discharge of SC-40 at 0.05 mA cm–2 in 0.1 M KOH electrolyte; electrode mass loading: 47.8 mg cm–2; Figure S6: cyclic voltammograms of SC-40, SC-80, and VPP-CNT in O2-saturated 0.1 M KOH solution at 10 mV s–1.--Under a Cretative Commons license BY 4.0., Experimental Section; Figure S1: first derivative from the TGA curves of SC-20, SC-40, and SC-80; Table S2: porosity parameters extracted from physisorption experiments; SBET: specific surface area; VPORE: pore volume; DPORE: equivalent pore diameter (calculated as 4 PORE/SBET); Figure S2: differential scanning calorimetry of C2mpyrTFSI OIPC, SC-20, SC-40, and SC-80 scaffolds at 10 °C min–1; Figure S3: pore size distribution estimated by SEM using ImageJ for 80–100 pores; Figure S4: cyclic voltammograms of PEDOT:PSS, glassy carbon (GC), and platinum (Pt) in N2- and O2-saturated 0.1 M KOH solution; Figure S5: galvanostatic discharge of SC-40 at 0.05 mA cm–2 in 0.1 M KOH electrolyte; electrode mass loading: 47.8 mg cm–2; Figure S6: cyclic voltammograms of SC-40, SC-80, and VPP-CNT in O2-saturated 0.1 M KOH solution at 10 mV s–1, O.S. thanks the University of the Basque Country (projects COLLAB22/05 and GIU21/033). This research was partly supported by the Australian Research Council Training Centre for Future Energy Storage Technologies (IC180100049) and funded by the Australian Government. This work was supported by an Ikerbasque Research Fellowship from the Basque Government., Peer reviewed

- Document S1. Figures S1–S7. - Table S1. List of differentially expressed genes in WT vs. mutant samples at 12 dpa with statistical data and TPM for each sample, related to Figure 6C. - Table S2. List of differentially expressed genes in WT vs. mutant samples at 14 dpa with statistical data and TPM for each sample, related to Figure 6C. - Table S3. Overlapping genes from the intersection of 12 dpa and 14 dpa lists of differentially expressed genes with statistical data and TPM for each sample, related to Figure 6C. - Table S4. Functional annotation results from common DEGs at 12 and 14 dpa that were upregulated in the WT blastema, related to Figure 6D. - Table S5. Functional annotation results from common DEGs at 12 and 14 dpa that were upregulated in the mutant blastema, related to Figure 6E. - Document S2. Article plus supplemental information., Peer reviewed

DNA extraction protocol (Cobas® DNA Sample Preparation Kit) Table S1. Panel of 29 genes analyzed with next generation sequencing, and distinction between nephropathic and phenocopy genes Table S2. Total variants, silent and non-silent mutations in FSGS patients Table S3. List of variants based on the UnifiedGenotyper and ACMG score Tabla S4. Frequency of variants annotated found in our study ..., Peer reviewed

This dataset contains the data and scripts required to reproduce the analyses in Toro-Delgado et al. (J. Anim. Ecol.). It contains the temperature measurements from Catalonia, the R scripts to conduct the statistical analyses, and Python scripts to download the solar radiation data from the Copernicus Atmospheric Monitoring Service (CAMS). The temperature data from Great Britain was released in Bladon et al. (2020)., [Description of the data and file structure] The "Data" folder contains the file "Butterfly_Thermoregulation_Data.txt", a tab-separated file containing the data gathered from the field in Catalonia. The column meanings are as follows: Order: a numbered column, to be able to recover the original order of the rows. Time: Hour of the measurement (in CEST, 24-hour format). Species: the species of butterfly being measured (scientific name). Code: field code used to identify the specimen. GPS_Y (): the latitude, in decimal format. GPS_X(): the longitude, in decimal format. Slope: categorical variable classifying the slope of the terrain in ranges. Aspect: the orientation of the slope on a compass (direction of ascent). Cells containing N/A are those for which slope is of 0 degrees, i.e. flat areas, and so no aspect can be applied to these (there is no direction of ascent if the ground is flat). Veg.Type: categorical variable describing the vegetation type. Manage: variable indicating if there are signs of some type of human management. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts. Shelter: variable indicating if the spot in which the butterfly was detected was fully exposed to the wind (1) or fully sheltered (5), measured at chest height and 5m from the point. Sunny: variable indicating how sunny it was; either fully sunny (S), mostly sunny (SSN), half sunny (SN), mostly cloudy (SNN) or fully cloudy (N). Tair: temperature of the air, at waist height and in the shade, at the moment of capture of the butterfly. In degrees Celsius. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts. Tbody1: first measurement of butterfly thoracic temperature at the time of capture; in degrees Celsius. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts. Pbody1: position from which the thoracic temperature was measured; on the side of the thorax (L) or on the dorsal part (D). Tperch: temperature of the substrate the butterfly was on (measured only when it was not flying). Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded. Tair_perch: temperature of the air surrounding the substrate on which the butterfly was place (approximately 2cm above it). Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded. Activity: categorical variable indicating what the butterfly was doing when first sighted. Height(cm): the height at which the butterfly was. Plant: species of plant OR type of substrate on which the butterfly was found. Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded. Sp.Chase: if the butterfly was chasing/being chased by another, the species of the second butterfly. Empty cells correspond to observations of butterflies that were not chasing nor being chased by another, so this variable did not apply to these observations and hence it could not be recorded. As most butterflies were not not in a chase, this column consist mostly of empty cells. Sex: sex of the specimen being measured (if known). Empty cells correspond to specimens for which sex could not be determined. Wing_length: length of the forewing, measured from the base (“shoulder”) to the tip. In millimetres. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. Location: whether the locality is in the lowland or in the mountains (Pyrenees). Country: the country in which the butterfly was captured and measured. Locality: the locality in which the butterfly was captured and measured. Captured: whether the butterfly was captured and taken to the lab for other studies or released after measuring the temperature measure. Empty cells are left deliberately this way, instead of being filled with N/A, to ensure proper functioning with the provided scripts., [Sharing/Access information] Temperature data for the British butterflies is available at: https://datadryad.org/stash/dataset/doi:10.5061/dryad.z08kprr9n(opens in new window) The data provided here (temperature measurements of Spanish butterfly populations) was collected by the authors, [Code/Software] The “Scripts” folder contains: The “Analysis” folder, with several R scripts with the required code to reproduce all statistical analyses. This was originally done with R 4.1.3. The "worldclim_analysis.R" script can be used to reproduce the analysis of WorldClim data to characterise the climate of both Catalonia and Great Britain; the "GAM_models_buffering_mechanisms" can be used to reproduce the GAM models; the "Master_script.R" script contains the rest of analyses. The “CAMS_solar_radiation_data_download” folder, with multiple Python scripts used to automate the download of the solar radiation data from CAMS. Note that, to be able to use these, the reader will have to register on the CAMS website and obtain access credentials, and then replace the “credentials_file” variable at the beginning of each Python script with the path to the file containing the credentials. More details are available at: https://ads.atmosphere.copernicus.eu/api-how-to, Understanding how different organisms cope with changing temperatures is vital for predicting future species’ distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood. We investigated which factors influence thermoregulatory ability in a subset of a Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species’ demographic trends in Catalonia. The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour, from heat-seeking to heat avoidance, at approximately 22 °C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations. Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future., Spanish National Research Council, Award: JAEINT_20_00248, Departament de Recerca i Universitats, Award: FI-1 00556, Ministerio de Ciencia, Innovación, Award: PID2020-117739GA-I00 MCIN / AEI / 10.13039/501100011033, Isaac Newton Trust, Award: RG89529, Wellcome Trust, Award: RG89529, University of Cambridge, Award: RG89529, Natural Environment Research Council, Award: NE/V007173/1, Ministerio de Ciencia, Innovación y Universidades, Award: FPU22/02358, European Social Fund Plus, Award: FI-1 00556, Peer reviewed