Resultados totales (Incluyendo duplicados): 34260
Encontrada(s) 3426 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331318
Dataset. 2022

APPENDIX A. SUPPLEMENTARY MATERIAL: COMPLEMENTARY ROLES OF WILD BOARD AND RED DEER TO ANIMAL TUBERCULOSIS MAINTENANCE IN MULTI-HOST COMMUNITIES

  • Santos, Nuno
  • Ferreras-Colino, Elisa
  • Arnal, Maria Cruz
  • Fernández de Luco, Daniel
  • Sevilla, Iker A.
  • Garrido, Joseba M.
  • Fonseca, Eliana
  • Valente, Ana M.
  • Balseiro, Ana
  • Queirós, João
  • Almeida, Virgílio
  • Vicente, Joaquín
  • Gortázar, Christian
  • Alves, Paulo C.
The datasets are reported in the supplementary material Tables S1-S4., Table S1. Summary of the distributions of the proportion of the population harvested each year and supporting data. Figure S1. Histogram and probability density of the estimated proportion of the population harvested each year, autocorrelation and trace plots. Table S2. Summary of the estimated distributions of abundance and supporting data at each site. The Geweke test was -0.557 (P=0.577) for the wild boar and 1.085 (P=0.278) for the red deer models. Figure S2. Histogram and probability density of the abundance estimated at each site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S3. Summary of the estimated distributions and supporting references for the sensitivity and specificity of the diagnostic tests. Figure S3. Histogram and probability density of the estimated sensitivity and specificity of the diagnostic tests employed, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S4. Summary of the estimated distributions of the true prevalence by host species and supporting data at each study site. Figure S4. Histogram and probability density of the estimated true prevalence at each site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Figure S5. Histogram and probability density of the estimated probability of Mycobacterium tuberculosis complex excretion from infected wild boar and red deer, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S5. Summary of the estimated distributions of the R0 by host species at each study site. Figure S6. Histogram and probability density of the estimated R0 by host species at each study site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. For intelligibility, only R0 estimates <200 were plotted. Table S6. Probability of animal tuberculosis being a single-host or multi-host disease at each site. Probabilities derived from the 100,000 estimates of the R0 of Mycobacterium tuberculosis complex in each host species. Single maintenance host: only one species with R0>1; Obligatory multi-host: R0 both species<1; Facultative multi-host: R0 both species >1. Highest probability for each site is highlighted in bold. Figure S7. Diagnostic plots of the generalised linear models. Diagnostic plots for the wild boar, red deer, and multi-host models with the whole dataset and after removing one influential outlier (site 15). Table S7. Summary of the generalised linear model selection. Models with ΔAICc<2 from the most supported model. Figure S8. Basic reproduction number estimated for the wild boar and red deer in each of the study sites. Kernel density of 100 estimates of the R0 of Mycobacterium tuberculosis complex in the wild boar and red deer drawn randomly from the 100,000 estimates at each site. Axes in square root scale, R0=1 as blue horizontal and vertical lines., Peer reviewed

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DOI: http://hdl.handle.net/10261/331318
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331318
HANDLE: http://hdl.handle.net/10261/331318
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331318
PMID: http://hdl.handle.net/10261/331318
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331318
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oai:digital.csic.es:10261/331318

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331322
Dataset. 2022

SUPPLEMENTARY MATERIALS OF THE ARTICLE CONFORMATIONAL REARRANGEMENTS REGULATING THE DNA REPAIR PROTEIN APE1

  • Komaniecka, Nina
  • Porras, Marta
  • Cairn, Louis
  • Santas, Jon Ander
  • Ferreiro, Nerea
  • Penelo, Juan Carlos
  • Bañuelos, Sonia
9 pages. -- Figure S1: SDS‐PAGE of the various APE1 variants. -- Figure S2: (A) Far‐UV spectra of the different APE1 variants: unlabelled (black), Cy3‐APE1 (magenta), Cy5‐APE1 (cyan) and doubly labelled (purple). The protein concentration was 4 mM in buffer 20 mM potassium phosphate pH 7.0, 50 mM NaCl, 5 mM MgCl2. (B) Thermal denaturation profiles as based on the change in ellipticity at 222 nm in the absence (solid line) and presence (broken line) of an equimolar amount of product DNA. The scan rate was 1ºC / min. -- Figure S3: Binding of labelled APE1 to the oligonucleotides mimicking the abasic product (P) and substrate (S). -- Figure S4: Incision kinetics of 2 mM substrate DNA by the various APE1 variants as followed by 18% polyacrylamide‐urea gel electrophoresis. -- Figure S5: Sequence of the oligonucleotides used as model of the abasic APE1 product (left) and substrate (right), highlighting the two labelled thymines. X stands for tetrahydrofurane. -- Figure S6: Original prediction as obtained with AlphaFold of human APE1 (UniProt entry P27695), represented as a cartoon, and aligned to the DNA part (grey sticks) of crystal structure 5DFF [17]. -- Figure S7: Emission spectra of doubly labelled APE1 (black line) and Cy5‐APE1 (cyan solid line) upon excitation at 547 nm. Spectrum of Cy5‐APE1 with exc 647 nm (cyan broken line). -- Figure S8: Charge distribution in full‐length APE1 bound to DNA., Peer reviewed

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DOI: http://hdl.handle.net/10261/331322
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331322
HANDLE: http://hdl.handle.net/10261/331322
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331322
PMID: http://hdl.handle.net/10261/331322
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331322
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oai:digital.csic.es:10261/331322

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331324
Dataset. 2022

SUPPLEMENTARY INFORMATION FOR DRIVER INTERACTIONS LEAD CHANGES IN THE DISTRIBUTION OF IMPERILED TERRESTRIAL CARNIVORES

  • Márquez, Carolina
  • Ferreira, Catarina
  • Acevedo, Pelayo
Supplementary contents: Appendix A: Additional results of carnivore environmental favourability in Andalusia. Appendix B: Data on drivers threatening terrestrial carnivores in Andalusia used in General Linear Modelling. Appendix C: Additional results of multiple drivers influence changes in carnivore environmental favourability in Andalusia. Supplementary references., Peer reviewed

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DOI: http://hdl.handle.net/10261/331324
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331324
HANDLE: http://hdl.handle.net/10261/331324
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331324
PMID: http://hdl.handle.net/10261/331324
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oai:digital.csic.es:10261/331324

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331332
Dataset. 2022

SUPPLEMENTARY MATERIAL: FACTORS INFLUENCING LEAD, MERCURY AND OTHER TRACE ELEMENT EXPOSURE IN BIRDS FROM METAL MINING AREAS

  • Durkalec, Maciej
  • Martínez-Haro, Mónica
  • Nawrocka, Agnieszka
  • Pareja-Carrera, Jennifer
  • Smits, Judit E.G.
  • Mateo, Rafael
Supplementary Table S1 Number of birds that were mist netted as a part of the study by species and site. Supplementary Table S2 Operating conditions of the Agilent 7700x ICP-MS spectrometer. Supplementary Table S3 Results of the analysis of TORT-3 (Lobster hepatopancreas), ERM-BB186 (Pig kidney) and BCR-185 (Bovine liver) certified reference materials. Units in mg kg-1 dry weight. Supplementary Table S4 Results of the analysis of SRM-1577c (Bovine liver) certified reference material in µg kg -1 (*) or mg kg-1 (**) d. w. Supplementary Table S5 Results of the analysis of SRM-1643f (Trace elements in water), in µg L-1. Supplementary Table S6 Analyzed concentrations of trace elements in feathers of birds (in μg g-1 of d. w.) by site and species. Supplementary Table S7 Mean (± SD) stable carbon (δ13C) and nitrogen (δ15N) isotope ratios (‰) of bird feathers by species, with sample size (N) and species characteristics (G – granivores, I – insectivores, O – omnivores, P – piscivores, M – migratory, R – resident). Supplementary Fig. S1 The results of the PCA: Scree plot (A) showing the proportion of variance in the principal components and graphs showing the proportion of each variable in PC1 (B), PC2 (C), and PC3 (D). Supplementary Fig. S2 Biplot showing stable isotope values in bird feathers (δ13C versus δ15N). Data points represent mean values and error bars represent standard error of the mean (SEM). Food groups are indicated by color: granivorous birds are dark red, omnivorous birds are yellow, insectivorous birds are green, and piscivorous birds are blue. Supplementary Table S8 Estimated marginal of trace elements by feeding habits of birds. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of study sites, age of feathers, and migration pattern. The intervals were back-transformed from the log scale and the tests were performed using Tukey method on the log scale. Supplementary Table S9 Estimated marginal means of trace elements by study site. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of feeding habits, age of feathers, and migration pattern. The intervals were back-transformed from the log scale and the tests were performed using Tukey method on the log scale. Supplementary Table S10 Estimated marginal means (± SEM) of trace in new and old feathers. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of feeding habits, study site, and migration pattern. The intervals were back-transformed from the log scale and the tests were performed on the log scale. Supplementary Table S11 Estimated marginal means (± SEM) of trace elements in feathers of birds by migration pattern. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of feeding habits, study site, and age of feathers. The intervals were back-transformed from the log scale and the tests were performed on the log scale., Peer reviewed

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DOI: http://hdl.handle.net/10261/331332
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331332
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331332
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331334
Dataset. 2022

SUPPLEMENTARY MATERIAL FOR GRIFFON VULTURES, LIVESTOCK AND FARMERS: UNRAVELING A COMPLEX SOCIO-ECONOMIC ECOLOGICAL CONFLICT FROM A CONSERVATION PERSPECTIVE

  • Oliva-Vidal, Pilar
  • Hernández-Matías, Antonio
  • García, Diego
  • Colomer, M. Àngels
  • Real, Joan
  • Margalida, Antoni
Appendix S1: Questionnaire used in this study. Farmer's perceptions of vulture attacks on livestock. Appendix S2: Number of farmers who have responded each question and number of responses obtained. Appendix S3. Livestock type and characteristics (percentages) involved in the 616 complaints reported from 2008–2020: cattle (n = 471), horses (n = 92) and sheep/goats (n = 53). Appendix S4: Number of farms for each livestock type managed by the 133 farmers interviewed, and number of farms reporting attacks and characteristics (%) of the livestock involved. Some farmers provided multiple responses regarding the characteristics of the livestock involved in the attacks. Appendix S5: Models (GLMs) determining the number of complaints for vulture attacks on livestock. We present the variables included in the set of models (model description); the number of estimated parameters (K); the Akaike Information Criteria corrected for small sample sizes (AICc); and the Akaike weight (wi) by the models evaluated (32 models; n = 110). Abbreviations relating to variables correspond to: ‘extensive livestock density’ (Ext); ‘distance to nearest landfill site’ (Dland); ‘number of griffon vulture pairs’ (Gpair); ‘griffon vulture abundance’ (Gabun); and ‘distance to nearest supplementary feeding station’ (DSFS). Selected models are shown in bold. Appendix S6: Relative importance of the variables estimated from the Akaike weight of the 32 models evaluated in the analysis of the determinants of the number of complaints for vulture attacks on livestock. Values close to 1 indicate variables that appear in most models and that show the highest likelihood given the data, i.e., those that show the highest Akaike weight values., Peer reviewed

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DOI: http://hdl.handle.net/10261/331334
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331334
HANDLE: http://hdl.handle.net/10261/331334
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oai:digital.csic.es:10261/331334
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331335
Dataset. 2022

SUPPLEMENTAL INFORMATION DIFFUSION AND INTERACTION DYNAMICS OF THE CYTOSOLIC PEROXISOMAL IMPORT RECEPTOR PEX5

  • Galiani, Silvia
  • Reglinski, Katharina
  • Carravilla, Pablo
  • Barbotin, Aurélien
  • Urbančič, Iztok
  • Ott, Julia
  • Sehr, Jessica
  • Sezgin, Erdinç
  • Schneider, Falk
  • Waithe, Dominic
  • Hublitz, Philip
  • Schliebs, Wolfgang
  • Erdmann, Ralf
  • Eggeling, Christian
6 pages. -- Supplementary Figure 1: Endogenous PEX5 in WT cell lines had no influence on the diffusion of labelled PEX5L. -- Supplementary Figure 2: Schematic representation of the protein constructs used in this work with their calculated molecular weights. -- Supplementary figure 3: Disordered regions from HsPEX5 and TbPEX5 amino acid sequences were predicted using the prDOS server (http://prdos.hgc.jp)., Peer reviewed

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DOI: http://hdl.handle.net/10261/331335
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331335
HANDLE: http://hdl.handle.net/10261/331335
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oai:digital.csic.es:10261/331335
PMID: http://hdl.handle.net/10261/331335
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oai:digital.csic.es:10261/331335

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331340
Dataset. 2022

SUPPORTING INFORMATION: PERMEABILITY OF ARTIFICIAL BARRIERS (FENCES) FOR WILD BOAR (SUS SCROFA) IN MEDITERRANEAN MIXED LANDSCAPES

  • Laguna, Eduardo
  • Barasona, José A.
  • Carpio, Antonio J.
  • Vicente, Joaquín
  • Acevedo, Pelayo
Table S1. Data for the 21 wild boar monitored by GPS-collars in this study. Table S2. Questionnaire for the characterization of fences, boundaries and perimeters of the estates in the study area. Fig. S1. Main type of fences presents in our study area. Type I (simple [A] or reinforced livestock-type fence [B]), type II (poorly-maintained big game-proof fence), type III (moderately-maintained big game-proof fence) and type IV (well-maintained big game- proof fence). Fig. S2. Fences crosses by wild boar family groups (data from photo-trapping) and some images of holes in fences obtained during the walking tour to quantify the permeability index (number of holes per km of fence). Table S3. Type of fence by main land use in the study area. Each fence (n=189) featured one or two land use. We included: length in m and percentage of type of fence for each of the possible combinations between land use. Table S4. Model selection results from the analysis of the factors influencing wild boar crossing success across fences. The corrected Akaike Information Criterion (AICc) and delta AICc (ΔAICc), i.e. the difference in AICc score relative to the model with the lowest value (most parsimonious model), are showed. As predictors were used: Type of fence (I-IV), Sex (males, females), Period (FSP, Hunting, FAP; see text for details) and their interactions (Sex*Period, Sex*Type and Type*Period). Individual (ID) was considered as a random effect factor. Fig. S3. Average daily activity of the wild boar monitored in this study. The grey band represent the inactivity period which will be excluded for the estimation of the crossing success. Fig. S4. Temporal and seasonal patterns of the interactions between animals and fences. (A) Number of crosses and bounces per month (from 1=January to 12=December). (B) Number of crosses and bounces per period (FSP= food shortage period, Hunting= hunting season, FAP= food abundance period). Appendix 1- Fence Behaviour Analysis., Peer reviewed

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DOI: http://hdl.handle.net/10261/331340
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oai:digital.csic.es:10261/331340
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oai:digital.csic.es:10261/331340
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oai:digital.csic.es:10261/331340

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331351
Dataset. 2022

DATASHEET_3_A NOVEL GENE SIGNATURE UNVEILS THREE DISTINCT IMMUNE-METABOLIC REWIRING PATTERNS CONSERVED ACROSS DIVERSE TUMOR TYPES AND ASSOCIATED WITH OUTCOMES.DOCX

  • Pedrosa, Leire
  • Foguet, Carles
  • Oliveres, Helena
  • Archilla, Iván
  • García de Herreros, Marta
  • Rodríguez, Adela
  • Postigo, Antonio
  • Benítez-Ribas, Daniel
  • Camps, Jordi
  • Cuatrecasas, Miriam
  • Castells, Antoni
  • Prat, Aleix
  • Thomson, Timothy M.
  • Maurel, Joan
  • Cascante, Marta
Supplementary Figure S3: Heatmap (A) and statistic results (B) studding the expression of genes related with autophagy in IMMETCOLS Clusters. (A) Heatmap representing the average expression of genes related to autophagy in IMMETCOLS Cluster. Gene expression values are range-scaled between -1 and 1. In top the Cluster classification is showed with red, green or blue, for Cluster 1, Cluster 2 and Cluster 3 respectively. The genes of canonical autophagy are in orange circle and the LAP genes are in blue circles. (B) Important features identified by One-way ANOVA and post-hoc analysis (Fisher’s LSD) comparing the expression of genes related to autophagy 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

Proyecto: //
DOI: http://hdl.handle.net/10261/331351
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oai:digital.csic.es:10261/331351
HANDLE: http://hdl.handle.net/10261/331351
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oai:digital.csic.es:10261/331351
PMID: http://hdl.handle.net/10261/331351
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oai:digital.csic.es:10261/331351

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331352
Dataset. 2022

DATASHEET_4_A NOVEL GENE SIGNATURE UNVEILS THREE DISTINCT IMMUNE-METABOLIC REWIRING PATTERNS CONSERVED ACROSS DIVERSE TUMOR TYPES AND ASSOCIATED WITH OUTCOMES.DOCX

  • Pedrosa, Leire
  • Foguet, Carles
  • Oliveres, Helena
  • Archilla, Iván
  • García de Herreros, Marta
  • Rodríguez, Adela
  • Postigo, Antonio
  • Benítez-Ribas, Daniel
  • Camps, Jordi
  • Cuatrecasas, Miriam
  • Castells, Antoni
  • Prat, Aleix
  • Thomson, Timothy M.
  • Maurel, Joan
  • Cascante, Marta
Supplementary Figure S4. Network integration metabolic pathways. Major activated pathways are highlighted in red in cluster 1, green in cluster 2 and in blue in cluster 3, 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

Proyecto: //
DOI: http://hdl.handle.net/10261/331352
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331352
HANDLE: http://hdl.handle.net/10261/331352
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oai:digital.csic.es:10261/331352
PMID: http://hdl.handle.net/10261/331352
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Ver en: http://hdl.handle.net/10261/331352
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331356
Dataset. 2022

SUPPLEMENTARY MATERIAL OF THE ARTICLE MESOSCALE ASSESSMENT OF SEDENTARY COASTAL FISH DENSITY USING VERTICAL UNDERWATER CAMERAS

  • Follana-Berná, Guillermo
  • Arechavala-López, Pablo
  • Ramírez-Romero, Eduardo
  • Koleva, Elka
  • Grau, Amàlia
  • Palmer, Miquel
4 pages. -- PDF file includes supplementary information about the main article. -- 1. Estimating fishing exposure at the sampled sites. -- 2. Detailed results of the statistical analysis. -- Table 1: Effects size for all the explanatory variables considered. -- Table 2: Fish density (fish/km2) at each of the 15 sampling sites, as estimated from the model above., Peer reviewed

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DOI: http://hdl.handle.net/10261/331356
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331356
HANDLE: http://hdl.handle.net/10261/331356
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oai:digital.csic.es:10261/331356
PMID: http://hdl.handle.net/10261/331356
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