Resultados de investigación

The original raw data from Illumina platform were transformed to Sequenced Reads, known as Raw Data or RAW Reads, by base calling. Raw data are recorded in a FASTQ file, which contains sequencing reads and corresponding sequencing quality. Every read in FASTQ format is stored in four lines, as indicated below: @HWI-ST1276:71:C1162ACXX:1:1101:1208:2458 1:N:0:CGATGT NAAGAACACGTTCGGTCACCTCAGCACACTTGTGAATGTCATGGGATCCAT + #55???BBBBB?BA@DEEFFCFFHHFFCFFHHHHHHHFAE0ECFFD/AEHH Line 1 begins with a '@' character and is followed by the Illumina Sequence Identifiers and an optional description. The details of Illumina sequence identifier are listed in the table below. Identifier Meaning HWI-ST1276 Instrument – unique identifier of the sequencer 71 Run number – Run number on instrument C1162ACXX Flow Cell ID – ID of flow cell 1 Lane Number – positive integer 1101 Tile Number – positive integer 1208 X – x coordinate of the spot. Integer which can be negative 2458 Y – y coordinate of the spot. Integer which can be negative 1 Read number. 1 can be single read or Read 2 of paired-end. N Y if the read is filtered (did not pass), N otherwise. 0 Control number - 0 when none of the control bits are on, otherwise it is an even number CGATGT Illumina index sequences Line 2 is the raw sequence of the read. Line 3 begins with a '+' character and is optionally followed by the same sequence identifiers and descriptions as in Line 1. Line 4 encodes the quality values for the bases in Line 2 and contains the same number of characters as the bases in the read., The objective of this study was to disentangle the genes behind co-localized QTLs for increased yield and stem susceptibility to the maize stem borer, Sesamia nonagrioides Lef. (Mediterranean stem borer, MSB) in the long arm of chromosome 8 of maize. Fine-mapping of QTLs for both traits was performed in a mapping population derived from two near isogenic lines (NILs), NIL16 and NIL95, and RNA-seq has been used to facilitate the identification of QTL candidate genes. RNA-seq analysis was done with RNA extracted from the stem pith of NIL16 and NIL95 collected 30 days after infestation in infested and control plants using four biological replicates for each NIL-treatment combination. Samples were barcoded and prepared for sequencing at Novogene Co., Ltd, where 150 bp paired-end (PE) reads were obtained on an Illumina 1.9., RTI2018-096776-B-C21 and PID2021-122196OB-C21 Projects funded by MCIU/AEI/FEDER, Sequences of 150 bp cDNA paired-end recorded in FASTQ format and contained in files fq.gz. Files (=reads) for each sample are saved in the same folder: Folder S_16_1: Infestation with Sesamia nonagrioides, NIL16, biological replicate 1: S_16_1_MKRN240010851-1A_22C2L2LT4_L4_1.fq.gz S_16_1_MKRN240010851-1A_22C2L2LT4_L4_2.fq.gz S_16_1_MKRN240010851-1A_22CHMWLT4_L8_1.fq.gz S_16_1_MKRN240010851-1A_22CHMWLT4_L8_2.fq.gz Folder S_16_2: Infestation with Sesamia nonagrioides, NIL16, biological replicate 2: S_16_2_MKRN240010852-1A_22F27GLT4_L8_1.fq.gz S_16_2_MKRN240010852-1A_22F27GLT4_L8_2.fq.gz Folder S_16_3: Infestation with Sesamia nonagrioides, NIL16, biological replicate 3: S_16_3_MKRN240010853-1A_22C2L2LT4_L4_1.fq.gz S_16_3_MKRN240010853-1A_22C2L2LT4_L4_2.fq.gz S_16_3_MKRN240010853-1A_22CHMWLT4_L8_1.fq.gz S_16_3_MKRN240010853-1A_22CHMWLT4_L8_2.fq.gz Folder S_16_4: Infestation with Sesamia nonagrioides, NIL16, biological replicate 4: S_16_4_MKRN240010854-1A_22F27GLT4_L8_1.fq.gz S_16_4_MKRN240010854-1A_22F27GLT4_L8_2.fq.gz Folder S_95_1: Infestation with Sesamia nonagrioides, NIL95, biological replicate 1: S_95_1_MKRN240010855-1A_22F27GLT4_L8_1.fq.gz S_95_1_MKRN240010855-1A_22F27GLT4_L8_2.fq.gz Folder S_95_2: Infestation with Sesamia nonagrioides, NIL95, biological replicate 2: S_95_2_MKRN240010856-1A_22F27GLT4_L8_1.fq.gz S_95_2_MKRN240010856-1A_22F27GLT4_L8_2.fq.gz Folder S_95_3: Infestation with Sesamia nonagrioides, NIL95, biological replicate 3: S_95_3_MKRN240010857-1A_22F27GLT4_L8_1.fq.gz S_95_3_MKRN240010857-1A_22F27GLT4_L8_2.fq.gz Folder S_95_5: Infestation with Sesamia nonagrioides, NIL95, biological replicate 4: S_95_5_MKRN240010858-1A_22F27GLT4_L8_1.fq.gz S_95_5_MKRN240010858-1A_22F27GLT4_L8_2.fq.gz Folder C_16_1: Control, NIL16, biological replicate 1: C_16_1_MKRN240010839-1A_22C2L2LT4_L4_1.fq.gz C_16_1_MKRN240010839-1A_22C2L2LT4_L4_2.fq.gz Folder C_16_2: Control, NIL16, biological replicate 2: C_16_2_MKRN240010840-1A_22C2LCLT4_L8_1.fq.gz C_16_2_MKRN240010840-1A_22C2LCLT4_L8_2.fq.gz Folder C_16_3: Control, NIL16, biological replicate 3: C_16_3_MKRN240010841-1A_22C2LCLT4_L8_1.fq.gz C_16_3_MKRN240010841-1A_22C2LCLT4_L8_2.fq.gz Folder C_16_4: Control, NIL16, biological replicate 4: C_16_4_MKRN240010842-1A_22C2LCLT4_L8_1.fq.gz C_16_4_MKRN240010842-1A_22C2LCLT4_L8_2.fq.gz Folder C_95_1: Control, NIL95, biological replicate 1: C_95_1_MKRN240010843-1A_22C2LCLT4_L8_1.fq.gz C_95_1_MKRN240010843-1A_22C2LCLT4_L8_2.fq.gz Folder C_95_2: Control, NIL95, biological replicate 2: C_95_2_MKRN240010844-1A_22F27GLT4_L7_1.fq.gz C_95_2_MKRN240010844-1A_22F27GLT4_L7_2.fq.gz Folder C_95_3: Control, NIL95, biological replicate 3: C_95_3_MKRN240010845-1A_22F27GLT4_L8_1.fq.gz C_95_3_MKRN240010845-1A_22F27GLT4_L8_2.fq.gz Folder C_95_4: Control, NIL95, biological replicate 4: C_95_4_MKRN240010846-1A_22F27GLT4_L8_1.fq.gz C_95_4_MKRN240010846-1A_22F27GLT4_L8_2.fq.gz, No

This database, based on archaeological remains of fish, seabirds and pinnipeds from the Holocene in the Southern Hemisphere, was compiled using a systematic review. The systematic review literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021). The search was performed in English and Spanish in 6th February 2023 in two databases: (a) Scopus and (b) Web of Science, by applying a combination of keywords within the documents’ title, abstract and keywords. Each search string contained four categories connected by the Boolean operator “AND”: study area (countries and provinces), selected species, period and archaeological terms. Each search term was connected by the Boolean operator “OR” within each category. Data from publications meeting all inclusion criteria were used to create a database in Microsoft Excel. The collected data were based on NISP (Number of Identified Specimens) and MNI (Minimum Number of Individuals) from marine fishes, seabirds and pinnipeds associated with archaeological sites (Grayson, 1984; Lyman, 1994). Both are quantitative metrics for analysing faunal assemblages. NISP is the count of identified specimens, allowing for an examination of the diversity and abundance of faunal remains, while MNI refers to the minimum number of individuals per taxonomic category present in the assemblages (Grayson, 1984; Lyman, 1994). We also collected data from the location (latitude and longitude) and radiocarbon dating of each archaeological site. In some cases, coordinates were not provided, but all publications included a map, allowing us to infer the coordinates using Google Earth Pro (version 7.3.6.9345). The Holocene is divided into three periods: the boundary between the Early and Middle Holocene is set around 8,200 years BP, and the Middle-Late Holocene around 4,200 years BP (Walker et al., 2012). Many publications provided multiple radiocarbon dates with their standard deviations; in these cases, we computed an estimated average of the combined dates and the precision of that estimate following Borenstein et al., (2010). Radiocarbon dates were reported either as years BP (Before Present) or years cal. BP (calibrated Before Present). In the context of marine ecosystems in the Southern Hemisphere, radiocarbon dating can be influenced by regional variations in atmospheric ¹⁴C levels and ocean reservoir effects, making calibration essential for accurate chronological reconstructions (Hogg et al., 2020). Calibration involves using region-specific curves. Accordingly, we used SHCal20 (Hogg et al., 2020) with the Calib v8.2 program (Stuiver & Reimer, 1993). This database contains two main parts: the README, where you can find all the data collected in each column along with an explanation of each item and the complete database, This work was supported by MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR, Grant no. FJC2020-043762-I to M.B. This research was also supported by the project ProOceans (PID2020-118097RB-I00). This research contributes to the objectives of Q-MARE (a PAGES working group). Acknowledgement to “Severo Ochoa Center of Excellence” grant CEX2019-000928-S. This research is also part of the Integrated Marine Ecosystem Assessments (iMARES) research group funded by Agència de Gestió d’Ajuts Universitaris i de Recerca (Generalitat de Catalunya) Grant no. 2021 SGR 00435, Peer reviewed

The escalating threat of antimicrobial resistance calls for novel therapeutic agents. This study employed a ligand-based design approach to develop three series of N-arylpyrrole derivatives (Va–e, VIa–e, and VIIa–e), refined through molecular modeling. Synthesized compounds were evaluated against ESKAPE pathogens, MRSA, and Mycobacterium phlei. Series Va–e showed the most promise, with compounds Vb, Vc, and Ve outperforming levofloxacin against MRSA (MIC = 4 μg/mL vs. 8 μg/mL). Vc also exhibited activity against E. coli, K. pneumoniae, and A. baumannii, and showed significant inhibition against M. phlei (MIC = 8 μg/mL). Compounds were evaluated for antibiofilm and antivirulence properties, targeting resistance mechanisms linked to infection persistence and dissemination. Most exhibited broad-spectrum biofilm inhibition and antivirulence activity. Cytotoxicity studies revealed selectivity for bacterial cells. ADMET studies supported drug-like properties. Docking studies suggested UPPP inhibition as the potential mechanism. SAR analysis was conducted to support future optimizations., The researchers did not obtain any funding for the work they submitted. The publishing fee is covered under the CSIC agreement with Taylor & Francis. GY was supported by AYUDAS JUAN DE LA CIERVA-INCORPORACIÓN (IJC2020-044166-I). This work is also supported by the Ministerio de Ciencia, Innovación y Universidades Spain., Peer reviewed

This dataset includes metabolic data and different functional traits of two different pine species including trees in decline and healthy trees. One dataset includes all different shared metabolites identified in the two species and individuals sampled under peak drought stress. The second dataset includes functional traits from gas exchange, hydraulics and osmotic relationships. These functional traits were assessed during two study years with contrasting summer drought., MCIN/AEI /10.13039/501100011033: PID2019-110273RB-I00 MCIN/AEI /10.13039/501100011033: PID2022-142746NB-I00 EU NextGenerationEU/PRTR: TED2021-131947B-100, Peer reviewed

[Data availability] The Argania spinosa genome project is available in the NCBI BioProject database under accession PRJNA294096, and the biological sample used in this study is registered in the BioSample database under accession SAMN04014715. The nuclear genome assembly used for all analyses is available in the NCBI Genome/GenBank database under accession GCA_003260245.2. All annotation data and derived resources are provided through a single Zenodo repository (https://doi.org/10.5281/zenodo.17901083). This archive includes the soft-masked reference assembly, unified genome annotation GFF3, predicted proteome FASTA, repeat and ncRNA annotation files, and transcription factor list. When working with genomic coordinates, users should reference the NCBI genome assembly accession GCA_003260245.2, which defines the scaffold names and coordinate system used in all files. For most downstream analyses, the scaffold-level assembly and unified GFF3 are the recommended starting point, as they provide the most up-to-date and internally consistent view of the genome., [Code availability] No custom code was developed specifically for this study. All analyses were performed using publicly available software tools described in the Methods section, with version numbers and key parameters reported to ensure reproducibility. Users can reproduce or adapt the annotation and validation steps using these tools together with the data hosted in the Zenodo repository (https://doi.org/10.5281/zenodo.17901083)., We present a comprehensive annotation dataset for the scaffold-level nuclear genome assembly of the argan tree (Argania spinosa (L.) Skeels, Sapotaceae). Using Illumina whole-genome shotgun reads that we previously generated for the "Argan Amghar" individual and deposited under BioProject PRJNA294096, together with the corresponding GenBank assembly GCA_003260245.2, we re-assembled and curated a 690 Mbp draft with scaffold N50 of 25 Mbp and L50 of 11 large macro-scaffolds. Ab initio gene prediction with AUGUSTUS and GeneMark-ES, integrated by EVidenceModeler, produced 51,078 protein-coding genes and 2,081 non-coding RNA genes, while repeat annotation covers 53.0% of the assembly. Functional annotation combined eggNOG-mapper, InterProScan and BLASTp searches against UniProtKB/Swiss-Prot to assign curated functions, domains and Gene Ontology terms to 32,785 genes and to support 25,484 proteins with UniProt evidence. BUSCO analyses indicate high completeness of the assembly gene space and completeness of the predicted proteome (74.6%). All primary data products, including a unified GFF3 file and the predicted proteome FASTA, are openly available via NCBI and Zenodo ( https://doi.org/10.5281/zenodo.17901083 )., The computational resources of HPC-MARWAN are provided by the National Center for Scientific and Technical Research (CNRST), Rabat, Morocco, Peer reviewed

Information about funding sources that supported the collection of the data (including research project reference/acronym): This study is part of project PID2022-137806OB-I00, funded by the Spanish Ministry of Science, Innovation and Universities: MICIN/AEI/10.13039/501100011033/FEDER, UE, Peer reviewed

This dataset contains the raw data supporting the findings of the study titled "Environmental drivers of abyssal benthic megafaunal biodiversity in the south-central Clarion-Clipperton Zone, Pacific Ocean", This research was supported by the Ministry of Oceans and Fisheries, Korea (No. 20160099). ESL received support from the Ramón y Cajal grant RyC2023-043275-I, funded by the MCIN/AEI/10.13039/501100011033 and the European Union Next Generation EU/PRTR, This work contributes to the Institut de Ciències del Mar "Severo Ochoa Centre of Excellence" accreditation CEX2024-001494-S funded by AEI 10.13039/501100011033 of the Spanish Ministry of Science and Innovation, Peer reviewed

This research was supported by the Ministry of Oceans and Fisheries, Korea (No. 20160099). ESL received support from the Ramón y Cajal grant RyC2023-043275-I, funded by the MCIN/AEI/10.13039/501100011033 and the European Union Next Generation EU/PRTR, This work contributes to the Institut de Ciències del Mar "Severo Ochoa Centre of Excellence" accreditation CEX2024-001494-S funded by AEI 10.13039/501100011033 of the Spanish Ministry of Science and Innovation, Peer reviewed

Movebank ID 4778411162.--This study is part of the long-term Studies in Ecology and Evolution (SEE-Life) program and the Zone Atelier Antarctique of the CNRS. This work is a contribution to the Integrated Science to Inform Antarctic and Southern Ocean Conservation (Ant-ICON) Scientific Research Programme of the Scientific Committee on Antarctic Research (SCAR), GLS tracking of non-breeding movements of adult Adélie penguins breeding at colony in Pointe Géologie Archipelago, Terre Adélie, in East Antarctica. Data include moulting and wintering locations over 5 years (2015, 2017-2020), The logistic and financial support was provided by the Institut Polaire Français Paul-Émile-Victor through the programme 1091. We received additional funding from WWF-UK, the Ecos Sud Program (project PU20B01/U20B03) and the Japan Society for the Promotion of Science (Kakenhi Grant-in-Aid #26840153 and #22K18432). ZZ was supported by the MOPGA post-doctoral grant 2020 from Campus France. This work acknowledges the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S funded by AEI 10.13039/501100011033, ICM-CSIC), the project SOSPEN (Spanish National Plan for Scientific and Technical Research and Innovation, 2021, PID2021-124831OA-I00) and the project SEASentinels (Spanish National Plan for Scientific and Technical Research and Innovation, 2023, CNS2022-135631), Peer reviewed

JBR was funded by the projects RYC2021–031331-I and PID2022-139422NB-I00, financed by MICIU/ AEI/10.13039/501100011033, FSE+, and the European Union Next Generation EU/PRTR. This work contributes to the Institut de Ciències del Mar "Severo Ochoa Centre of Excellence" accreditation CEX2024-001494-S funded by AEI 10.13039/501100011033 of the Spanish Ministry of Science and Innovation, Peer reviewed