BUSCADOR RECOLECTA

Table 1. Notation and biological meaning of data, latent states and parameters.1. The "data.Rdata" file contains the clean data of the study to run the model. -- 2. "Best_model_code.txt" file contains the best selected JAGS model. -- 3. "model_run.R" contains the script to run the model. -- 4. "Best_model_code.txt" contains the best model used in the analysis. -- TablaS2.2. Table that collects the iformation of the systematic review of survival and growth characteristics of different insular and mainland populations of Podarcis spp., Best_model_code.txt, data.Rdata, functions.R, model_run.R, Table_parameters.docx, TableS2.2.csv, Peer reviewed

Almond [Prunus dulcis Miller (D. A. Webb), syn. Prunus amygdalus L.)] is the major tree nut crop worldwide in terms of production and cultivated area. Almond domestication was enabled by the selection of individuals bearing sweet kernels, which do not accumulate high levels of the toxic cyanogenic glucoside amygdalin. Previously, we showed that the Sweet kernel (Sk) gene, controlling the kernel taste in almond, encodes a basic helix loop helix (bHLH) transcription factor regulating the amygdalin biosynthetic pathway. In addition, we characterized a dominant allele of this gene, further referred to as Sk-1, which originates from a C1036→T missense mutation and confers the sweet kernel phenotype. Here we provide evidence indicating that the allele further referred to as Sk-2, originally detected in the cultivar “Atocha” and arising from a T989→G missense mutation, is also dominantly inherited and confers the sweet kernel phenotype in almond cultivated germplasm. The use of single nucleotide polymorphism (SNP) data from genotyping by sequencing (GBS) for population structure and hierarchical clustering analyses indicated that Sk-2 occurs in a group of related genotypes, including the widespread cultivar “Texas”, descending from the same ancestral population. KASP and dual label functional markers were developed for the accurate and high-throughput selection of the Sk-1 and Sk-2 alleles, and the genotyping of a panel of 134 almond cultivars. Overall, our results provide further insights on the understanding of the almond cultivation history. In addition, molecular marker assays and genotypic data presented in this study are expected to be of major interest for the conduction of almond breeding programs, which often need to select sweet kernel individuals in segregant populations, Supplementary Figures and Tables. Supplementary Figure 1. ADMIXTURE cross-validation (CV) error estimates for a number of ancestral populations (K) ranging from 1 to 10. The red triangle indicates the lowest CV error detected for K=4 Supplementary Table 1. Results from genotyping 134 almond cultivars with the marker assays developed in this study. T1036 and G989 are indicative of the Sk-1 and Sk-2 alleles, respectively. Genotypic calls from the KASP and dual label assays were fully consistent, thus they are reported as a single column for each nucleotide position. Genotyping of cultivars indicated in bold was confirmed by Sanger sequencing, Peer reviewed

Data processing: Computed columns: By parsing the input data it is possible to increase the amount of columns in the database without changing the source format. The following columns are computed (or updated) in the back-end when the database is loaded from disk to memory.-- Assumptions for calculations: A number of parameters are (re)calculated based on provided metadata for publications: the residence time (1), the SEI in J L-1 (2), energy efficiency of CO2 conv. % (5) but also the frequency range column (section 8.1). Each of these calculations rely on data that potentially is given as a range of values, see section 1. By convention, the mean of the range is taken for each of the parameters. All entries for which a (re)calculation has been performed on either the x or y data, have the Calculated field set to ‘Calculated’ (rather than ‘Original’). Filtering the database on only Original data, this calculated data can be replaced with calculations by the user based on other aggregation, if desired. As mentioned for equation (2), the calculation that converts power (in W) to SEI (J L-1) simply relies on the gas flow rate Φ (in sccm or mLmin-1) and therefore does not take into account if Φ is defined w.r.t. standard conditions or the actual conditions in the discharge regarding pressure and temperature. Since generally in this case both the power and gas flux are know, the SEI can be calculated (except for batch reactors where there is no flow), which allows for a comparison between a larger body of experiments. Contrary to this, the calculations for the residence time (1) and energy efficiency of CO2 conversion both take into account the discharge conditions. This discrepancy is a conscious one: the SEI (in J L-1) is often reported as a macroscopic process parameter that is calculated from the power and fluxes put into the reactor. For τ res taking into account the conditions inside the plasma is paramount, since it can provide the information on how long a particle is exposed to plasma conditions. Do note that in lieu of the plasma –or active zone– volume, for some experiments an approximation such as the reactor volume is used, see column relevant volume in section 4. Likewise for the efficiency, it is important to account for the actual measurement conditions to establish the number of particles into which energy has been channelled. For in situ versus effluent conditions this can be very different. Overestimating the number density favourably improves efficiency, whilst an underestimation similarly negatively impacts it. While the way in which the PDB is structured and reports metadata is not without caveats (section 4.3 of the paper), the distinction between in- versus post-plasma dissociation measurements can be partially addressed by tailored filtering and aggregation of the data contained within.-- Normalisations: Several normalisation functions are available in the interface of the database. As described in the main text, the aim to provide a tool for easy calculation of normalised data within the same interface and compare with different data sets. For flexibility sake, a wide set of normalisations are provided, without restriction on whether they are sensible in a given context. The purpose of each normalised function is described briefly and summarised in table 1 along with the equations utilised.-- Under a Creative Commons license CC BY 4.0., The Pioneer database (PDB) is divided into two parts: performance data and metadata. The performance data originate from measurements reported in literature, where they are typically provided in form of plots or tables. The PDB and its dedicated online tool allow to compare large amounts of performance data to derive trends leading eventually to process optimisation. Performance data is provided in form of plain text files with only two comma-separated columns of numbers with a point (.) as decimal separator, without a header (as it is inferred from the metadata). The first column contains what is henceforth called process parameters. These are the independent variables of the experiments, i.e. the x-values like power, pressure etc., see section 7. The second column contains the so-called performance parameters. These are the dependent variables of the experiment, i.e. the y-values like conversion, selectivity etc., see section 7. The metadata contain additional information about the measurements that are crucial for their interpretation. Metadata are provided in table format following the template discussed subsequently. Before elaborating on the actual data input, the structure of the PDB metadata is discussed. The metadata is grouped thematically in categories. Within each category, information is entered into fields, i.e. the columns of the table. Essential and conditional fields are distinguished. Essential fields contain crucial information for the assessment of the plasma-catalytic process. In the best case scenario, all of them are given in the respective publication. When fields are listed in the description of a category from section 2 onward, essential fields are indicated by a regular bullet point (•). Conditional fields are by no means less important than essential ones, but can rather be left empty depending on other fields. For instance, most fields in the catalyst category are left empty, when no catalyst is used. Thus, conditional fields are meant to save time. Listed in the following, they are indicated by a plus (+). In conclusion, all fields are strongly recommended as data useful for valuable comparison with other work from the community. A subgroup of essential as well as conditional fields are those fields that contain the process parameters defined in the first paragraph of this section. Generally speaking, process parameters are the experimental settings in the pursuit of highest performance. The user of the PDB thus encounters process parameters on two occasions: on the one hand as typical x-values in the performance data and on the other hand as input to fields of the metadata. Hereafter, fields that contain process parameters and parameters are used synonymously. The total of fields belonging to the same measurement make up what is hereafter called a data set, corresponding to a row of the table. Note that here the input of data is addressed. In the back end, a data set is broken up into (x,y)-pairs for more flexibility in data handling. Data is exported also in that format. To ensure comparability, a template is used for inputting information into the PDB metadata. With respect to information entered, fields can be divided in numerical and textual fields. They are filled with numbers or text, respectively. For example, parameters are usually numerical fields. A numerical field contains either (i) one number x if the numerical value is known and does not change during the experiment; (ii) a range of values between xmin and xmax –given as array-like notation rxmin; xmaxs– if the numerical value is not exactly known, or when it changes in the course of the measurement; or (iii) NA if the numerical value is not known. For further use, an aggregate function is applied to array-like data to obtain a single number, by convention the mean. A textual field contains a string of text. There are a few instances where text can be entered freely as long as some format is followed. However, usually the field is filled by selecting from a pre-defined list of options in the template. Most of these lists are fixed but some might be extended in the future depending on the experimental data provided. This paragraph just gives a general overview. In the in-depth discussion of the fields of each category, it gets more clear what exactly is supposed to be filled in each field. The metadata of the PDB are divided into six categories • data identification • gas mixture • plasma source • catalyst • separation unit • output data, 1. General 2. Data Identification 3. Gas Mixture 4. Plasma Source 5. Catalyst 5.1 Catalyst Coupling . 5.2 Catalyst Composition 5.3 Catalyst Pre-treatment Before Reaction 5.4 Catalyst Conditions 5.5 Catalyst Characterization 6. Separation Unit 7. Output Data 8. Data processing 8.1 Computed columns 8.2 Assumptions for calculations 8.3 Normalisations, When data is reported according to the specified scheme above, the combined data and metadata can be read from disk and processed with some scripting. Most notably this performs data type coercion and extraction from a more flexible ‘human-readable’ format to a consistent, ‘machine-usable’ scheme. Some of the computed columns are redundant to some extent –the authyear column for instance is just a concatenation of the first author name and publication year (yyyy) columns– but these are provided for ease of filtering or grouping data, avoiding frequent (re)computation., This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 813393, Peer reviewed

Table 1. Notation and biological meaning of data, latent states and parameters.1. The "data.Rdata" file contains the clean data of the study to run the model. -- 2. "Best_model_code.txt" file contains the best selected JAGS model. -- 3. "model_run.R" contains the script to run the model., Peer reviewed

Supplementary Note 1 : Melanin chemistry- background. Supplementary Note 2: Geological setting. Supplementary Note 3: Details of sulfur oxidation. Supplementary Figure 1. XRD analysis of the lateral fossil. Sedimentary matrix (brown profile) compared to the body of the fossil (black profile). Supplementary Figure 2. Correlation maps. Supplementary Figure 2. Correlation maps (cont.). Supplementary Figure 3. Individual SRS-XRF maps of selected elements in the lateral fossil. Supplementary Figure 4. SRS-XRF maps of the dorsal fossil. Supplementary Figure 5. Fits to the EXAFS data. Supplementary Figure 5 (continued). Fits to the EXAFS data. Supplementary Figure 6. Melanin in extant tissue. Supplementary Figure 7. Red human hair analyses. Supplementary Figure 8. SRS-XRF point analyses. Supplementary Figure 8 (continued). SRS-XRF point analyses. Supplementary Figure 9. SRS-XRF map of normally pigmented Mus musculus. Supplementary Figure 10. FTIR analyses. Supplementary Figure 11. ESEM analyses. Supplementary Figure 11 (continued). ESEM analyses. Supplementary Figure 12A. Optical images to show details of fossil separation. Supplementary Figure 12B (continued). Optical images to show details of fossil separation. Supplementary Figure 12C (continued). Optical images to show details of fossil separation. 25 Supplementary Figure 12C (continued). Optical images to show details of fossil separation. Optical photograph of the dorsal fossil [GZG.W.17393a, left] compared to its counterpart [GZG.W.17393b, right]. Scale bar in cm. A fourth region is highlighted for comparison to check whether uneven separation of soft tissue between part and counterpart could explain the lack of chemical residue in the scanned images. (Copyright for these photos: GZG Museum / G. Hundertmark.) Supplementary Figure 12D (continued). Optical images to show details of fossil separation. Supplementary Table 1. Sulfur speciation and quantification. Supplementary Table 2. Calibrated energies for dominant sulfur absorption peaks. Supplementary Table 3. ESEM EDS analyses of A. apodemus lateral fossil., Peer reviewed

8 pages. -- SI1: Non exhaustive compilation of news about the Tonga volcano eruption. -- SI2: Zoom of the pressure anomaly maps. -- SI3: Effects of the vulcano induced tsunami in the Pacific Ocean. -- SI4: Sea level and atmospheric pressure records in the Balearic Islands (Western Mediterranean). -- SI5: Map of maximum SLO amplitudes observed during the different passages. -- SI6: Analysis of the increase of SLO amplitudes during the atmospheric wave passages., Peer reviewed

26 pages. -- Table A1. General information of the study farms. -- Table A2. Sampling completeness (SC) values for each study farm. -- Table A3. Number of virgin flowers used for each pollination treatment. -- Table A4. Results of Variance Inflation Factor (VIF) analysis for the quantitative predictor variables of the full model. -- Table A5. Subset of alternative models (∆AICc < 4) obtained by automated model selection (MuMIn package). -- Table A6. Results of the GLMMs conducted for each study area (RC and VT). -- Table A7. Pollinators at each farm accordingly to crop species. -- Table A8. Plant species found at crop edges across the 14 study farms. -- Table A9. Plant species found at crop edges in each of the study farms. -- Table A10. Output of the best GLMM (after model selection, see main text) with binomial error distribution and logit link function. -- Table A11. Output of the GLMM with binomial error distribution and logit link function. -- Fig. A1. Quantitative plant-pollinator interaction networks. -- Fig. A2 Relationship between crop seed set and centrality for a) RC area, and b) VT area, using a mixed-effects logistic regression. -- Fig. A3 Relationship between crop seed set and a) crop dependence on pollinators (Dependence), b) richness of crop pollinators (P richness), as found using a mixed-effects logistic regression. -- Fig. A4 Relationship between crop weighted closeness centrality (closeness centrality) and a) the richness of floral attraction units (FAU) at crop edges, and b) edges’ FAU abundance (log-transformed), using a mixed-effects logistic regression., Peer reviewed

Data S1. Genetically determined differences between sP and sNP rats in voluntary alcohol drinking, and the effects on orexin and ppDyn in RNA levels in the lateral hypothalamus, and plasma prolactin levels, with methods., Peer reviewed

13 pages. -- The PDF file includes: Supporting text. -- Figs. S1 to S1. -- Legends for Movies S1 to S4., Self_organized_appendix.pdf, pnas.2216024120.sm01.mp4, pnas.2216024120.sm02.mp4, pnas.2216024120.sm03.mp4, pnas.2216024120.sm04.mp4, Peer reviewed

44 pages. -- Table S1. Classification of the 415 studies in the database based on application and related Sustainable Development Goal target., Peer reviewed