Resultados de investigación

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

Here, we provide an improved version of the PN40024 genome assembly, called PN40024.v4, which combines the top-quality Sanger contigs from the 12X version with Pacific Biosciences long reads (Sequel SMRT). Along with this new assembly, we also provide a new version of the gene annotation, called PN40024.v4.1 based on a newly developed annotation workflow, RNA-Seq datasets and manual curation of a set of genes of functional interest to the community. English (2022-10-28), Peer reviewed

Supplemental files are provided with the manuscript. Supplementary File 1 contains additional figures and Supplementary File 2 additional tables. Raw sequencing data and the PN40024.v4 genome assembly are available at ENA under BioProject PRJEB45423. Also, the PN40024.v4 genome assembly with structural and functional gene annotation is available on the INTEGRAPE website (https://integrape.eu/resources/genes-genomes/genome-accessions), on the Grape Genomics Encyclopedia portal (http://grapedia.org/) and under the DOI number doi:10.57745/F9N2FZ (https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/F9N2FZ). A Sequence Server v2.0.0 interface (http://138.102.159.70:4567/) was set up to perform BLAST analyses. A JBrowse interface (http://138.102.159.70/jbrowse/) was set up to visualize PN40024.v4 assembly and PN40024.v4.1 and v4.2 annotations, but also some previous annotation versions that were transferred, some RNA-Seq alignments and miscellaneous tracks. An Apollo interface (http://138.102.159.70:8080/apollo; training and account mandatory) was set up to manually curate gene annotations according to the dedicated guidelines (https://integrape.eu/resources/data-management/). Code used to analyze GBS data can be found at https://forgemia.inra.fr/sophie.blanc/gbs and code used to generate the PN40024.v4.2 version can be found at https://gitlab.com/MSVteam/pn40024-visualization-tools/-/tree/master/update_gff3_script. Supplemental material available at G3 online., Peer reviewed

Figure S1: Structure and protein sequence analysis of APX family in Ricinus communis (Rc), Manihot esculenta (Me), Jatropha curcas (Jc), Hevea brasiliensis (Hb), Arabidopsis thaliana (At) and Oryza sativa (Os); Figure S2: Sequence logos for the conserved motifs of APX family from Ricinus communis, Arabidopsis thaliana and Oryza sativa; Figure S3: Structure and protein sequence analysis of MDAR family in Ricinus communis (Rc), Manihot esculenta (Me), Jatropha curcas (Jc), Hevea brasiliensis (Hb), Arabidopsis thaliana (At) and Oryza sativa (Os); Figure S4: Sequence logos for the conserved motifs of MDAR from Ricinus communis, Arabidopsis thaliana and Oryza sativa; Figure S5: Structure and protein sequence analysis of DHAR family in Ricinus communis (Rc), Manihot esculenta (Me), Jatropha curcas (Jc), Hevea brasiliensis (Hb), Arabidopsis thaliana (At) and Oryza sativa (Os); Figure S6: Sequence logos for the conserved motifs of DHAR from Ricinus communis, Arabidopsis thaliana and Oryza sativa; Figure S7: Structure and protein sequence analysis of GR family in Ricinus communis (Rc), Manihot esculenta (Me), Jatropha curcas (Jc), Hevea brasiliensis (Hb), Arabidopsis thaliana (At) and Oryza sativa (Os); Figure S8: Sequence logos for the conserved motifs of GR from Ricinus communis, Arabidopsis thaliana and Oryza sativa; Figure S9: Cis-regulatory elements in the APX, MDAR, DHAR, and GR promoter regions from Ricinus communis, Manihot esculenta, Jatropha curcas, and Hevea brasiliensis; Figure S10: miRNA targeting RcAPX, RcMDAR, RcDHAR, and RcGR genes; Table S1: APX, MDAR, DHAR and GR sequences from rice and arabidopsis used as bait to BLASTp analysis; Table S2: Sequences of primers used in RT-qPCR experiments; Table S3: Physicochemical parameters and subcellular predictions from APX in Ricinus communis, Manihot esculenta, Jatropha curcas, Hevea brasiliensis; Table S4: Physicochemical parameters and subcellular predictions from MDAR in Ricinus communis, Manihot esculenta, Jatropha curcas, Hevea brasiliensis; Table S5: Physicochemical parameters and subcellular predictions from DHAR in Ricinus communis, Manihot esculenta, Jatropha curcas, Hevea brasiliensis; Table S6: Physicochemical parameters and subcellular predictions from GR in Ricinus communis, Manihot esculenta, Jatropha curcas, Hevea brasiliensis; Table S7: Conserved miRNAs targeting AsA-GSH genes in castor bean., Peer reviewed

Document S1. Figures S1–S5. Table S1. RNA-seq quality and mapping data for B16 and CT26 cells, see STAR Methods - cDNA library preparation and illumina RNA sequencing. Table S2. Normalized read counts (FPKM) for each gene in each RNA-seq assay, see STAR Methods - Gene expression quantitation and differential expression analysis. Table S3. List of 50 top upregulated genes in B16 resistant clones relative to treatment-naïve cells, related to Figure 4A. Table S4. List of 50 top downregulated genes in B16 resistant clones relative to treatment-naïve cells, related to Figure 4A. Table S5. List of the top 500 upregulated genes in B16 resistant clones (relative to naïve B16 cells) and their corresponding log2 fold change in CT26 C1 and C4 clones (relative to naïve CT26 cells), related to Figure 7E. NA: not applicable., Peer reviewed

Table S1: Bayes Factor between treatment and control of VOCs from P. chlamydosporia in rice cultures on different DAI, Table S2: Bayes Factor between treatment and control of VOCs from P. chlamydosporia in Czapek–Dox broth cultures after different times of exposure to chitosan, Figure S1: Line plots with the median values and standard deviations of the peak heights of VOCs produced by P. chlamydosporia in rice cultures, Figure S2: GC-MS chromatogram of headspace volatile compounds produced by P. chlamydosporia 15 days after inoculation with control buffer solution (BS) and with chitosan solution (CH), Figure S3: Line plots with the median values and standard deviations of the peak heights of VOCs produced by P. chlamydosporia in modified Czapek–Dox broth cultures, Figure S4: GC-MS chromatogram of headspace volatile compounds produced by P. chlamydosporia cultured in modified Czapek–Dox broth for 5 days after 24 h with control buffer solution (BS) and 24 h of exposure to chitosan (CH)., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

El libro de códigos está disponible en español y en traducción al inglés., [ES] El objetivo del Ecobarómetro de Andalucía (EBA) es analizar la conciencia ambiental de los andaluces y cómo se relacionan con el medio ambiente. Para ello se elabora un sistema de indicadores a partir de los resultados proporcionados por una encuesta anual dirigida a la población andaluza mayor de 18 años. La encuesta tiene por finalidad medir las distintas dimensiones de la conciencia ambiental (afectiva, cognitiva, activa y conativa), analizando las percepciones, actitudes, conocimiento y comportamiento de los andaluces respecto a diversas cuestiones ambientales. Este dataset corresponde a los resultados obtenidos en la encuesta realizada a una muestra representativa de la población andaluza mayor de 18 años durante los meses de abril y mayo de 2009. El EBA presenta su novena edición desde que se iniciara en el año 2001. La estabilidad del contenido del cuestionario, así como su comparabilidad con barómetros similares empleados en estudios de ámbito estatal o internacional, lo configuran como un valioso instrumento para el estudio de la opinión pública andaluza en temas de medio ambiente, así como su evolución en el tiempo y sus peculiaridades en el contexto más amplio de las sociedades europeas., [EN] The objective of Andalusian Barometer (EBA) is to analyze the environmental awareness of Andalusians and how they relate to the environment. For this purpose, a system of indicators is elaborated from the results provide by an annual survey directed to the Andalusian population over 18 years of age. The survey aims to measure the different dimensions of environmental awareness (affective, cognitive, active and conative), analyzing the perceptions, attitudes, knowledge and behavior of Andalusians respect different environmental issues. This dataset corresponds to the results obtained in the survey carried out on a representative sample of the Andalusian population over 18 years of age during the months of April and May 2009. The EBA is in its ninth edition since it began in the year 2001. The stability of the content of questionnaire, as well as its comparability with similar barometers used in national or international studies, make it a valuable instrument for the study of Andalusian public opinion on environmental issues, as well as its evolution over time and its peculiarities in the broader context of the European societies., Investigación realizada en el marco de un convenio de colaboración suscrito entre la Consejería de Medio Ambiente y Ordenación del Territorio de la Junta de Andalucía y el Instituto de Estudios Sociales Avanzados del Consejo Superior de Investigaciones Científicas (IESA-CSIC)., BAROMETER2009_Datafile.csv BAROMETER2009_Datafile.sav BAROMETER2009_Codebook_EN.pdf BAROMETER2009_Codebook_SP.pdf BAROMETER2009_Readme_EN.pdf BAROMETER2009_Readme_SP.pdf BAROMETER2009_Survey.pdf, Peer reviewed

Supplemental information: Document S1. Figures S1–S7 and Table S5. Table S1. General features of the 46 isolated bacteriophages. Phage ID, phylogenetic classification in 13 groups, genome size and circularity, sequencing depth, GC %, CDS, putative lysogeny genes, taxonomic family, subfamily, and genus according to IGS values with the closest phage are provided. Additionally, concentration of phages for the spot assay, formation of plaque haloes, susceptible CLTs, RBP system and number of depolymerase domains (Dpos) are also shown. REP: transcription repressor/regulator, PAR: parAB genes, REC: recombinases. TSP: Tail spike protein. CBM: carbohydrate-binding module. Taxonomy ranks marked with a star can be reclassified based on relationships showed in this study. CLTs marked with a star indicate that bacteria were acapsular. Dpos marked with a star indicate putative soluble Dpos. Table S2. General features of the 138 Klebsiella clinical strains. The strain, sequencing details, virulence and antibiotic resistance scores, wzi allele, CLT and OLT inference, number of prophages, CRISPR loci, and presence of defense systems and secondary receptors are provided. The presence of a capsule was determined by visual inspection of colonies using light microscopy. Table S3. Phage-bacteria positive combinations. The strain used for phage isolation is indicated. Spotting: positive by the spot-assay. Virulent: positive by the killing planktonic or progeny assay. Table S4. Clustering of phage RBDs with putative depolymerase activity from this work and the literature. RBDs are identified by the phage name followed by the phage protein in which the domain was detected, and the positions considered after the removal of the anchor domain. Document S2. Article plus supplemental information., Peer reviewed

Bacteriophages play key roles in bacterial ecology and evolution and are potential antimicrobials. However, the determinants of phage-host specificity remain elusive. Here, we isolate 46 phages to challenge 138 representative clinical isolates of Klebsiella pneumoniae, a widespread opportunistic pathogen. Spot tests show a narrow host range for most phages, with <2% of 6,319 phage-host combinations tested yielding detectable interactions. Bacterial capsule diversity is the main factor restricting phage host range. Consequently, phage-encoded depolymerases are key determinants of host tropism, and depolymerase sequence types are associated with the ability to infect specific capsular types across phage families. However, all phages with a broader host range found do not encode canonical depolymerases, suggesting alternative modes of entry. These findings expand our knowledge of the complex interactions between bacteria and their viruses and point out the feasibility of predicting the first steps of phage infection using bacterial and phage genome sequences., Peer reviewed

Table S1: Sampling information of the Salmonella enterica isolates included in this study. Table S2: Summary of sequence read quality. Table S3: Summary of assembly results. Table S4: Serotypes and STs. Table S5: Isolates included in HSC and their features. Figure S1: Sequence and structure of the contig containing the mcr-1 gene with the Incx4 plasmid., Peer reviewed