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The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.8 (https://digital.csic.es/handle/10261/288226).-- What’s new in version 2.9: 1) Based on the CRU TS 4.07 dataset, spanning the period between January 1901 to December 2022. For more details on the SPEI visit http://sac.csic.es/spei, No

14 pages. -- PDF file includes: Details of Theoretical calculations. -- Figure S1. (a) SEM images of the Bi2Se3 nanosheets. (b) XRD patterns of Bi2Se3 nanosheets. (c) HRTEM images of the Bi2Se3 nanosheets and its corresponding power spectrum. (d) EELS chemical composition maps obtained from the red squared area of the STEM micrograph. -- Figure S2. Bi 4f and Se 3d high-resolution XPS spectra. -- Figure S3. XRD pattern of I-Bi2Se3/S. -- Figure S4. TGA curve of I-Bi2Se3/S composite measured in N2 with a sulfur loading ratio of 70.2 wt%. -- Figure S5. Nitrogen adsorption-desorption isotherms of as synthesized I-Bi2Se3 and IBi2Se3/S composites. -- Figure S6. DFT calculation results of optimized geometrical configurations of the surface (110) of Bi2Se3 with LiPS (Li2S, Li2S2, Li2S4, Li2S6, Li2S8 and S8). -- Figure S7. DFT calculation results of optimized geometrical configurations of the surface (110) of I-Bi2Se3 with LiPS (Li2S, Li2S2, Li2S4, Li2S6, Li2S8 and S8). -- Figure S8. Optimized adsorption configuration of Li2S decomposition on Bi2Se3. -- Figure S9. First five cycles of CV curves of (a) I-Bi2Se3/S, (b) Bi2Se3/S and (c) Super P/S performed at a scan rate of 0.1 mV s−1. -- Figure S10. Differential CV curves of (a) I-Bi2Se3/S, (c) Bi2Se3/S and (e) Super P/S. The baseline voltage and current density are defined as the value before the redox peak, where the variation on current density is the smallest, named as dI/dV=0. -- Figure S11. CV curves of (a) Bi2Se3/S, (b) Super P/S and (c) Plot of CV peak current for peaks C1, C2, and A versus the square root of the scan rates. -- Figure S12. The CV curve of I-Bi2Se3 as electrode measured in symmetric coin cell using an electrolyte without Li2S6. -- Figure S13. (a) Charge, and (b) discharge profiles of I-Bi2Se3/S, Bi2Se3/S, and Super P/S electrodes showing the overpotentials for conversion between soluble LiPS and insoluble Li2S2/Li2S. -- Figure S14. Galvanostatic charge−discharge profiles of (a) Bi2Se3/S and (b) Super P/S at different current densities range from 0.1C to 4C. -- Figure S15. (a,b) EIS spectra of (a) Bi2Se3/S and (b) Super P/S coin cells before and after cycling. The solid line corresponding to the fitting result from the equivalent circuit (c) and (d), and the Rs, Rin, Rct, and Zw stand for the resistance of the electrolyte, insoluble Li2S2/Li2S layer, interfacial charge-transportation, and semi-infinite Warburg diffusion, respectively; and CPE stands for the corresponding capacitance. (e) Different resistances of three coin cells were obtained from the equivalent circuit. -- Figure S16. XRD patterns of electrode materials after 100 cycles at 1C. -- Figure S17. Galvanostatic charge/discharge profiles of I-Bi2Se3/S at 0.5C under a lean electrolyte condition with a high sulfur loading of 5.2 mg cm-2. -- Figure S18. (a) SEM image of the Li-anode after cycling; (b) EDX mapping image of Lianode showing sulfur signal after cycling. -- Figure S19. SEM image of the cathode material after cycling, EDX spectra and EDX elemental maps for S, Se, Bi and I. -- Figure S20. I-Bi2Se3 optimized configuration as calculated by DFT. The distance between I and Bi is 3.15 Å, which is similar values than the bond lengths in bulk BiI3. -- Table S1 Summary of the comparison of I-Bi2Se3 electrochemical performance as host cathode for LSBs with state-of-the-art Bi-based or Se-based materials., Peer reviewed

18 pages. -- PDF file includes: materials and methods: Production of biotinylated detection antibodies (bd-MAb). -- Figure S-1. MP functionalization with c-MAb. -- Figure S-2. S-2. Scheme and protocol of the reference sandwich ELISA for Pf-LDH detection. -- Figure S-3. Two-step magneto-immunoassay for Pf-LDH detection. -- Figure S-4. Single-step magneto-immunoassay for Pf-LDH detection: comparison of colorimetric and fluorescent detection. -- Figure S-5. Performance of the 9 membranes studied. -- Figure S-6. Optimization of MP washing on-chip. -- Figure S-7. Optimization of the paper-based detection strategy. -- Figure S-8. Detection of malaria in positive blood samples using a commercial RDT. -- Table S-1. Examples of magneto immunoassays reported before for malaria diagnosis. -- Table S-2. Characteristics of the 9 paper-like membranes tested. -- Table S-3. Summary of results obtained in malaria-positive clinical samples using the paperbased fluorescence POC and reference ELISA, microscopy and RDT methodologies. -- Table S-4. Estimated production cost for each paper-based magneto-immunoassay test., Peer reviewed

This dataset contains the results of the characterisation of the prokaryotic community by flow cytometry and tritiated leucine incorporation from the MAFIA cruise (Migrants and Active Flux In the Atlantic ocean). Samples were collected in the tropical and subtropical Atlantic during the MAFIA cruise (April 2015) on board the BIO Hespérides. Seawater samples were collected at 13 stations (from the Brazilian coast to the Canary Islands), from the surface down to 3500 m, using a General Oceanics oceanographic rosette equipped with 24 l PVC Niskin bottles. Abundance and cell characteristics (high nucleic acid content fraction, cell volume, viability) were based on measurements performed with a FACSCalibur (Becton-Dickinson) flow cytometer. Leucine incorporation rates were estimated with tritiated leucine (Kirchman et al. 1985) using centrifugation and filtration methods (Smith and Azam 1992). The aim of this dataset was to estimate the influence of surface productivity on the standing stock, characteristics and activity (as leucine incorporation) of prokaryotes across the water column, Horizon 2020 (H2020), grant/award no. 817806: Sustainable management of mesopelagic resources; Ministerio de Ciencia e Innovación (MICINN), grant/award no. CTM2012-39587-C04: Migrants and Active Flux In the Atlantic Ocean; Ministerio de Ciencia e Innovación (MICINN), grant/award no. CTM2015-69392-C3: Constraining organic carbon fluxes in an eastern boundary upwelling ecosystem (NW Africa): the role of non-sinking carbon in the context of the biological pump; Ministerio de Ciencia e Innovación (MICINN), grant/award no. CTM2017-83362-R: INTERES: Papel de las interacciones fitoplancton-bacterias en la respuesta del plancton microbiano a la entrada de nutrientes alóctonos; Ministerio de Ciencia e Innovación (MICINN), grant/award no. PID2019-109084RB-C21: Biogeochemical impact of mesoscale and sub-mesoscale processes along the life history of cyclonic and anticyclonic eddies: plankton variability and productivity, No

A comma-delimited file containing summary data for 101 scientific articles grouped according to 29 market drivers for cephalopods. The dataset is organised according to 8 variables (column headers; row 1) and 101 rows of data (rows 2-102). Each row of data relates to one unique scientific study (citation) included in our global literature review (101 studies were included in total). The dataset can be used to explore relationships between individual market drivers and other key variables and to examine underlying data by accessing the citations listed, 1. Aquatic food systems are important contributors to global food security to satisfy an intensifying demand for protein-based diets, but global economic growth threatens marine systems. Cephalopod (octopus, squid, cuttlefish) fisheries can contribute to food security; however their sustainable exploitation requires understanding connections between nature’s contributions to people (NCP), food system policies and human wellbeing. 2. Our global literature review methodology examined what is known about cephalopod food systems, value chains and supply chains and associated market drivers. For analysis, we followed the IPBES conceptual framework to build a map of the links between cephalopod market drivers, NCP and good quality of life (GQL). Then we mapped cephalopod food system dynamics onto IPBES (in)direct drivers of change relating to catch, trade and consumption. 3. This research contributes knowledge about key factors relating to cephalopods that can support transitions towards increased food security: the value of new aquatic food species; food safety and authenticity systems; place-based innovations and empowerment of communities; and consumer behaviour, lifestyle and motivations for better health and environmental sustainability along the food value chain. We outline requirements for a sustainable, equitable cephalopod food system policy landscape that values nature’s contributions to people, considers UN Sustainable Development Goals and emphasises the role of seven overlapping IPBES (in)direct drivers of change: Economic, Governance, Sociocultural and Socio-psychological, Technological, Direct Exploitation, Natural Processes and Pollution. We present a novel market-based adaptation of the IPBES conceptual framework – our ‘cephalopod food system framework’, to represent how the cephalopod food system functions and how it can inform processes to improve sustainability and equity of the cephalopod food system. 4. This synthesised knowledge provides the basis for diagnosing opportunities (e.g. high demand for products) and constraints (e.g. lack of data about how supply chain drivers link to cephalopod NCP) to be considered regarding the role of cephalopods in transformations towards a resilient and more diversified seafood production system. This social-ecological systems approach could apply to other wild harvest commodities with implications for diverse marine species and ecosystems and can inform those working to deliver marine and terrestrial food security while preserving biodiversity, CEECIND; Styrelsen för Internationellt Utvecklingssamarbete; Xunta de Galicia, Award: 1400 ED481B2018/017; Grupo de Referencia Competitiva GI-2060 AEMI, Award: 1401 ED431C2019/11; Fundação para a Ciência e a Tecnologia, Award: LA/P/0094/2020; Ministério da Ciência, Tecnologia e Ensino Superior, Award: LA/P/0094/2020; Fundação para a Ciência e a Tecnologia, Award: UIDP/50017/2020; Ministério da Ciência, Tecnologia e Ensino Superior, Award: UIDP/50017/2020; Fundação para a Ciência e a Tecnologia, Award: UIDB/50017/2020; Ministério da Ciência, Tecnologia e Ensino Superior, Award: UIDB/50017/2020; Fundação para a Ciência e a Tecnologia, Award: 2020.02510; European Regional Development Fund, Award: EAPA_282/2016; Interreg Atlantic Area Programme, Award: EAPA_282/2016, No

Sample processing: Peripheral blood was collected into sodium heparin tubes and centrifuged for 10 min at 8 2500 rpm at room temperature (RT) to separate the cellular fraction from the plasma. The plasma was removed from the cell pellet and stored at -80 ºC for posterior use. The cell pellet was diluted 1:1 in RPMI 1640 medium and carefully added to Histopaque-1077 (Sigma) for peripheral blood mononuclear cell (PBMC) isolation by centrifugation at 2500 rpm at RT for 10 min. PBMC layer was collected, washed with RPMI 1640, counted and aliquoted for staining and flow cytometry analysis. All flow cytometry analyses were performed using fresh PBMCs. Flow cytometry: For the surface staining, PMBCs were resuspended in 50 μl PBS with 5 % FCS and stained with the different antibody cocktails for 20 min at 4 ºC in dark, washed twice with PBS + 5 % FCS and then fixed for 30 min at 4 ºC in the dark using 2% PFA. Following surface staining, cells that required intracellular staining were fixed/permeabilized for 30 min at 4 ºC in the dark using the FoxP3 transcription factor buffer kit (Miltenyi). Following fixation/permeabilization, cells were washed twice with permeabilization buffer, resuspended in 50 μl permeabilization buffer and stained with intracellular antibodies for 30 min at 4 ºC in the dark. Samples were washed twice with permeabilization buffer following staining and fixed. All samples were acquired on a Gallios (Beckman Coulter) Flow Cytometer. The list of the antibodies used for immune cell phenotyping was: Miltenyi Biotec, CD14-VioBlue (130-110-524), CD16-FITC (130-25 113-392), CD25-APC (130-113-280), CD3-VioGreen (130-113-134), CD38-FITC (130-113-426), Treg detection kit CD4/CD25/CD127 (130-096-082), CD56-PerCP Vio700 (130-100-681), CD57-27 APC-Vio770 (130-111-813), CD8-PerCP Vio700 (130-110-682), FoxP3 Staining Buffer Set(130-28 093-142), GzmB-PE (130-116-486), HLADR-APC-VIO770 (130-111-792), LAG3-APC (130-105-29 453), NKG2A-PE-VIO615 (130-120-035), NKG2C-PE (130-103-635), NKP46-APC (130-092-609), TIM3-PE vio770 (130-121-334); Biolegend, NKp30-PE/Cy7 (325214), PD1-Alexa Fluor700 31 (329952), CD45-Brilliant Violet 421 (304032); BD, NKG2D-BV421 (743558). High dimensional flow cytometry data analysis: viSNE and FlowSOM (Self-organizing map) analyses were performed using Cytobank (https://cytobank.org). We used t-distributed stochastic neighbouring embedding (t-SNE) to reduce the dimensionality of the cell marker datasets generated using the antibody panels indicated above. FlowSOM clustering analysis compared expression of cell markers was used to identify each cluster and perform an unbiased analysis of the PBMC immunophenotyping data. CD56+ cells, CD56+ or CD14+ cells and CD3+CD8+ cells from FACS panels 2, 3 and 4 respectively, were analysed separately. SOM was generated using equal sampling of at least 1000 cells from each FCS file and hierarchical consensus clustering by the following markers: CD3, CD16, CD57, NKp30, NKp46, NKG2C, NKG2D and NKG2A for panel 2 analysis; CD14, CD3, HLA-DR, CD16, GZMB, TIM3, LAG3, PD1 or CD56,CD3, HLA-DR, CD16, GZMB, TIM3, LAG3, PD1 for panel 3 analysis and GzmB, CD38, HLA-DR, TIM3, LAG3, PD1 for panel 3 analysis. For each SOM, 100 clusters and 5, 8 or 10 metaclusters (MTs) were identified for panel 2, panel 3 and 4, which were represented in Minimum Spanning Trees (MTS). Multiplex plasma protein analyses: Luminex assay was run according to manufacturer’s instructions in 100 μl of plasma, using a custom human cytokine panel (RD Systems, catalogue no. LXSAHM). The next proteins were included: IFNα, IFNβ, IFN, IL28A/IFNλ2, IL28B/IFNλ3, IL2, IL1β,IL18/IL1F4,IL1RA,IL33, IL36b/IL1F8, IL7, IL10, IL31, IL6, IL12/IL23 p40, IL15, IL17E/IL25, IL8/CXCL8, CXCL10/IP10, CCL2/MCP1, CCL8/MCP2, CXCL9/MIG, CXCL2/MIP2α, MICA, MICB, ULBP-1, ULBP-2/5/6, ULBP-52 3, TNFα, GzmA and GzmB. Supernatants were mixed with beads coated with capture antibodies and incubated on a 96 well filter plate for 2 hours. Beads were washed and incubated with biotin-labelled detection antibodies for 1 hour, followed by a final incubation with streptavidin-PE. Assay plates were measured using a Luminex 200 instrument (ThermoFisher, catalogue no. APX10031). Data acquisition and analysis were performed using xPONENT software. The standard curve for each analyte had a five-parameter R2 value > 0.95 with or without minor fitting using xPONENT software. Granzyme activity assay in serum: Serum samples were used to evaluate the activity of both GzmA and GzmB using specific quenching FRET fluorescent substrates (FAM-VANRSAS-DABCYL and FAM-IEPDNLV-DABCYL peptides, respectively). In a nutshell, 40 μl of 100 mM Tris-HCl pH 8.5 or 100 mM Tris-HCl 50 mM NaCl pH 7.8 (buffers for GzmA or GzmB respectively) were added to flat bottom, black plates, with 10 μl of the serum samples. 50 μl of GzmA or GzmB substrates were added and the fluorescence of the plate was read at time zero and 1 h for GzmA and 24 h for GzmB using 475 nm excitation and 520 nm emission wavelenghts. Gzm activity was calculated based on a calibration curve with known concentrations of carboxyfluorescein. Statistics: To minimize inter-experimental variability and batch effects between patients, all PBMC samples were acquired, processed, and freshly analysed during four consecutive weeks from April to June 2020. Serum and plasma samples were frozen at -80ºC and later on all of them were thawed and analysed at the same time. Univariate and multivariate logistic regression models were developed using two different groups of variables, representing soluble and immunomodulatory factors (Group 1) or cell populations (Group 2) shown in Table S5. Age, sex and lymphocyte counts were included in all groups except for the comparison between HD and COVID19, since these variables were not known in HDs. First, a univariate logistic regression analysis was performed in the corresponding groups. Variables included in the multivariate discriminant analysis were those with a value of p < 0.1 in the univariate logistic regression analysis and / or with a value of p < 0.1 in the medians comparison tests. The univariate statistic test used has been chi-square or Fisher exact test for qualitative variant comparison and Mann-Whitney (comparison of two groups of variables) or Kruskal-Wallis (comparison of more than two groups of variables) for quantitative variant comparison. The post-test used was Benjamini, Krieger and Yekutieli test. Variable normality has been analysed with Kolmogorov-Smirnov test and Rho’s Spearman has been calculated as correlation coefficients. Statistical models were developed to predict COVID19 of diagnostic and severity. A multivariate logistic regression and discriminant analyses were performed to develop predictive models. Area Under the Curve (AUC), OR and CI95% values were reported for significant variables. Nagelkerke R2 was calculated to analyse sample variability and Hosmer-Lemeshow test was performed to analyse goodness of fit for the logistic regression model. Hosmer-Lemeshow p values higher than 0.05 indicate an adequate calibration of the predictive model. The statistics software used was GraphPad Software 7.0, (Inc. San Diego, CA) and SPSS 26.0 (IBM Corp., Armonk, NY).-- This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions., Supplementary materials and methods: sample processing, flow cytometry, high dimensional flow cytometry data analysis, multiplex plasma protein analyses, granzyme activity assay in serum, statistics. Supplementary figure legends (1-5). Supplementary table legends (1-7)., The authors would like to thank the Biobank of the Aragon Health System integrated in the Spanish National Biobanks Network and the Servicios Científico Técnicos de Citometria de Flujo del CIBA for their collaboration. Work in the JP laboratory is funded by the FEDER (Fondo Europeo de Desarrollo Regional, Gobierno de Aragón, Group B29_17R), Health National Institute Carlos III (COV20-00308), Aragón Government (Fondo COVID-19), Fundación Santander-Universidad de Zaragoza (Programa COVID-19), Agencia Estatal de Investigación (SAF2017-83120-C2-1-R; PID2020-113963RBI00), Fundación Inocente, ASPANOA and Carrera de la Mujer de Monzón. EMG is funded by Agencia Estatal de Investigación (SAF2017-83120-C2-1-R and PID2020-113963RB-I00). IUM and SH are supported by a PhD fellowship from Aragon Government, CP by a PhD fellowship from AECC, LS by a PhD fellowship (FPI) from the Ministry of Science, Innovation and Universities. DDM is supported by a postdoctoral fellowship 'Sara Borrell', and MA is supported by a postdoctoral fellowship 'Juan de la Cierva-incorporacion' from the Ministry of Science, Innovation and Universities. EM and BGT are supported by Rio Hortega contract. JP is supported by the ARAID Foundation., Peer reviewed

Dataset (in RData format) and Scripts corresponding to each of the analyses carried out for the manuscript entitled "Large-scale movement patterns in a social vulture are influenced by seasonality, sex, and breeding region"., Peer reviewed

Both fuel and air reactors were considered fluidized beds in a turbulent or rapid fluidization regime, according to the terminology developed by Kunii and Levenspiel. For their modeling, the fluid-dynamic model presented by Pallarès and Johnsson was adapted to the specific conditions of CH4 combustion by CLC. The fluid dynamic model can be considered 1.5D. The axial direction was the main dimensional parameter, but the existence of gas and solid diffusion in the radial direction was also considered. Both reactors were divided into two regions in the axial direction, each one with different characteristics regarding the distribution and mixing of the oxygen carrier particles. A dense zone was considered in the lower part, with an approximately constant concentration of solids, and a diluted zone above it with a decay of the solids concentration with the height of the reactor. The mass balance in each reactor was carried out considering the main reactions that take place between the oxygen carriers and the reacting gas. The model included the corresponding reaction kinetics as well as the relevant gas phase reactions. The mass balance was developed for every reacting compound in each phase described in the fluid dynamic model. A detailed description of the model along with the equations that govern the fluid dynamic and the mass balance modules are included in previous works. The model was solved using Fortran® code as a high-level programming language. The used algorithm solved simultaneously the air and fuel reactors until the convergence of the solids conversion was reached. This implied that the following essential requirement had to be met at steady state: the amount of oxygen that reacted in the air reactor was the same as that transferred in the fuel reactor. The calculation was done for a given value of the solids circulation rate and, consequently, the oxygen carrier-to-fuel ratio (Φ parameter). For the design of the CLC unit, it was considered the inlet gas velocity at the inlet of every reactor. The gas velocity at the inlet of the air reactor, Ug,AR, was set at 6 m/s to reduce the cross sectional area of the reactor and to allow a high solids entrainment rate in the fast fluidization regime. Likewise, the fuel reactor operated in the turbulent fluidization regime and it was assumed that the gas velocity at the inlet of the reactor, Ug,FR, was equal to the terminal velocity, Ut, of the oxygen carrier particles. This parameter was different for each oxygen carrier since it depended on the operating temperature of the reactor and the density of the material. Figure S1 graphically illustrates the fluidization regime of the fuel reactor and the air reactor in the flow regime map.-- Under a Creative Commons license BY-NC-ND 4.0, S.1. Brief description of the CLC model: Figure S1. Fluidization regime for the fuel reactor and air reactor in the flow regime map., This work was supported by the SWINELOOP (PID2019-106441RB-I00/AEI/10.13039/501100011033) and CSIC 202180I016 projects. A. Cabello is also grateful for Grant IJC2019-038908-I funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

La inscripción recorre un friso situado en la reja de la capilla de la inmaculada, en el lado de la nave. La reja es obra de Juan Francés, rejero de la Catedral de Toledo. El texto latino recoge la última perícopa del Ave Regina Coelorum., CSIC. Proyecto intramural: “Epigrafía latina inédita de los siglos XV al XVIII en monumentos civiles y eclesiásticos de la provincia de Cuenca (2006-2007). Referencia: 2006 | 0| 011., Peer reviewed

Texto en latín grabado en la orla del escudo elíptico que corona la puerta de entrada a la capilla de San Pedro Y San Pablo desde la nave. El texto se lee en la dirección de las agujas del reloj, comienza en la cruz superior, en el escudo situado sobre la puerta de entrada a la capilla. No habla este escudo Andújar cuando trata de la Capilla de San Pedro y San Pablo. El escudo pertenecía a los León. El texto forma parte de una oración popular medieval que se encuentra en la leyenda Dorada, "De Sancto Andrea Apostolo", de Santiago de la Vorágine., CSIC. Proyecto intramural: “Epigrafía latina inédita de los siglos XV al XVIII en monumentos civiles y eclesiásticos de la provincia de Cuenca (2006-2007). Referencia: 2006 | 0| 011., Peer reviewed