BUSCADOR RECOLECTA

Supplementary Table S1: Patients characteristics, Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches., Peer reviewed

10 pages. -- List of contents: Supplementary text S1. Measuring the refined SEEA EA environmental income. -- Supplementary text S2. AAS and rSEEA environmental income comparison. -- Supplementary text S3. Measuring the standard SNA final products and values added in the pine-forest-farm case studies., Peer reviewed

Supplementary Table S2: Important features identified by One-way ANOVA and post-hoc analysis (Fisher’s LSD) comparing the expression of different signatures between IMMETCOLS Clusters., Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches., Peer reviewed

[EN] 1. Compressed folder ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip'): 40 folders with the graphic description of each event, numbered from 1 to 40 and with their respective start and end dates (e.g. '19_1973_oct-1974_feb'). In each of them, three folders: 'maps' with the integral maps of duration and intensity (.png format), 'seq_ano' with the maps of the spatial spread (.png format) and 'ts' with the averages of SPI01 and SPI12 and the area affected by the drought (SPI12 =< -0.84) (.png format). Map legends (.png format). .xlsx file showing the identification and characterization of the events. 2. Compressed folder ('Drought_events_characterization_code.zip'): project, script and folder with the data necessary to generate the descriptive analysis material in R. 3. netCDF file ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc'). Variables (6): 'SPI-01' (1-month Standardized Precipitation Index), 'SPI-03' (3-month Standardized Precipitation Index), 'SPI-06' (6-month Standardized Precipitation Index), 'SPI-12' (12-month Standardized Precipitation Index), 'SPI-36' (36-month Standardized Precipitation Index) and 'evnt' (Drought episodes). Temporal extent: 1272 (months from 1915-01 to 2020-01). Spatial extent: longitude = 125, latitude: 77, units = 0.1 degrees. Reference system: EPSG 4326, WGS84 projection, geographic coordinates. Open Data Commons Attribution (ODC-By 1.0)., [EN] The database consists of a netCDF ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc') and two zipped folders ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip' and 'Drought_events_characterization_code.zip'). The first folder includes a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =< -0.84) and area affected (>20 % of the grid area). For each episode, the time series of the SPI01 and SPI12 average of the whole grid (expressed in anomalies); the area of the grid under drought conditions (SPI12 =< -0.84) (expressed in percent per one); the integral maps of the episode according to its duration (expressed in number of months) and intensity (average of the cells under drought conditions); and the maps representing the spatial propagation of the episode are included. The second folder includes the code necessary to generate the descriptive analysis material. The netCDF consists of six variables. The first five are the precipitation anomalies (Standardized Precipitation Index at 1 month, 3, 6, 12 and 36 time scales: 'SPI-01', 'SPI-03', 'SPI-06', 'SPI-12' and 'SPI-36'), these have been obtained from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). The sixth variable is the drought events themselves ('evnt'), numbered from 1 to 40. These six variables refer to the Spanish peninsular territory and the time period 1915/01-2020/01. This record corresponds to version 1.0.0 of the dataset. The database is distributed under an open license (Open Data Commons Attribution, ODC-By)., [ES] La base de datos consta de un netCDF ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc') y dos carpetas comprimidas ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip' y 'Drought_events_characterization_code.zip'). En la primera carpeta se incluye un análisis descriptivo de los 40 episodios de sequía identificados según los criterios de intensidad de la sequía (SPI12 =< -0.84) y superficie afectada (>20 % de la superficie de la malla). Para cada episodio se incluyen las series temporales del SPI01 y SPI12 promedio de toda la malla (expresadas en anomalías); el área de la malla en condiciones de sequía (SPI12 =< -0.84) (expresada en tanto por uno); los mapas integrales del episodio atendiendo a su duración (expresada en número de meses) e intensidad (promedio de las celdas en condiciones de sequía); y los mapas que representan la propagación espacial del episodio. En la segunda carpeta se incluye el código necesario para generar el material del análisis descriptivo. El netCDF consta de seis variables. Las cinco primeras son las anomalías de precipitación (Standardized Precipitation Index a escala temporal de 1 mes, 3, 6, 12 y 36: ‘SPI-01’, ‘SPI-03’, ‘SPI-06’, ‘SPI-12’ y ‘SPI-36’), estas han sido obtenidas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). La sexta variable son los eventos de sequía propiamente dichos (‘evnt’), numerados del 1 al 40. Estas seis variables están referidas al territorio peninsular español y el periodo temporal 1915/01-2020/01. Este registro se corresponde con la versión 1.0.0 del conjunto de datos. La base de datos se distribuye bajo una licencia abierta (Open Data Commons Attribution, ODC-By)., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., No

Supplementary data 1: -Sheets: 20 -Tables: 10 -Figures: 1, Peer reviewed

Supplementary Table S3: Important features identified by One-way ANOVA and post-hoc analysis (Fisher’s LSD) comparing the expression of metabolic genes in the IMMETCOLS Clusters. (Part 1). Supplementary Table S3: Important features identified by One-way ANOVA and post-hoc analysis (Fisher’s LSD) comparing the expression of metabolic genes in the IMMETCOLS Clusters. (Part 2), Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches., Peer reviewed

9 pages. -- Figure S1: RGB (Red–Green–Blue) composite image on (a) 8, (b) 13, and (c) 18 August 2021 of the Sentinel-2 satellite (10 m spatial resolution). -- Figure S2: Turbidity (FNU) on (a) 3, (b) 8, (c) 13, and (d) 18, August 2021 of the Sentinel-2 satellite (10 m spatial resolution); (e–h) the same for chlorophyll-a concentration (Chl-a, mg/m3). -- Figure S3: Sentinel-2 and Landsat-8 RGB (Red–Green–Blue) composite image acquired on (a) 4 September 2021, (b) 7 September 2021, (c) 11 September 2021, (d) 12 September 2021, (e) 17 September 2021, and (f) 22 September 2021. -- Figure S4: Turbidity (FNU) from Sentinel-2 and Landsat-8 acquired on (a) 4 September 2021, (b) 7 September 2021, (c) 11 September 2021, (d) 12 September 2021, (e) 17 September 2021, and (f) 22 September 2021. -- Figure S5: Chlorophyll-a concentration (Chl-a, mg/m3) from Sentinel-2 and Landsat-8 acquired on (a) 4 September 2021, (b) 7 September 2021, (c) 11 September 2021, (d) 12 September 2021, (e) 17 September 2021, and (f) 22 September 2021. -- Figure S6: RGB (Red–Green–Blue) composite image from Sentinel-2 and Landsat-8 acquired on (a) 11, (b) 12, (c) 21, and (d) 28 March 2021. -- Figure S7: Turbidity (FNU) from Sentinel-2 and Landsat-8 acquired on (a) 11, (b) 12, (c) 21, and (d) 28 March 2021. -- Figure S8: (a) RGB (Red–Green–Blue) composite image, and (b) Turbidity (FNU) from Sentinel-2 on 21 March 2021 corresponding to the southeastern shore of Mar Menor., Peer reviewed

Supplementary material 1: Relationship between favorability and probability. Supplementary material 2: Testing the theory with empirical data., Peer reviewed

Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation., Peer reviewed

Supplementary Table S4: Important features identified by One-way ANOVA and post-hoc analysis (Fisher’s LSD) comparing the expression of immune signatures in the IMMETCOLS Clusters., Existing immune signatures and tumor mutational burden have only modest predictive capacity for the efficacy of immune check point inhibitors. In this study, we developed an immune-metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 77 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here, we reveal that the IMMETCOLS signature classifies tumors into three distinct immune-metabolic clusters. Cluster 1 displays markers of enhanced glycolisis, hexosamine byosinthesis and epithelial-to-mesenchymal transition. On multivariate analysis, cluster 1 tumors were enriched in pro-immune signature but not in immunophenoscore and were associated with the poorest median survival. Its predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic but also has a solid mitochondrial function, with concomitant upregulation of glutamine and essential amino acid transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together, these findings suggest that the IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches., Peer reviewed