Resultados de investigación

This dataset contains experimental data on the effects of cadmium and ciprofloxacin exposure in zebrafish (Danio rerio) larvae. The dataset is part of a study that integrates multiple biological levels, including phenotypic, behavioural, biochemical and molecular. Measured endpoints include mortality, development, locomotor activity, oxidative stress biomarkers (deposited in DUnAs repository) and gene expression and global DNA methylation (deposited herein at DIGITAL.CSIC). The dataset supports the identification of mechanistic toxicity pathways and contributes to environmental risk assessment and adverse outcome pathway (AOP) development. Date of the description: May, 2026, Project: EPIBOOST Grant Agency: European Union Grant Number: 101078991, Peer reviewed

The Supplemental Material includes a brief description of the Keldysh formalism, a detailed explanation of the calculation of the analytical unperturbed Green's function, and the expression of the time averaged mean square deviation. Likewise, it also presents numerical parameters and additional data, including a parameter-variation analysis and momentum-resolved Green's functions at different times., No

Experimental details of the samples, descriptions of the DSC and BDS techniques, a detailed account of the dielectric spectra analysis, the analysis of ρ(T) for fast-water relaxation, test of empirical temperature-shift scaling for H2O and D2O relaxation times, and DSC and BDS results for PVME solutions., No

METARAOR Project (Grant num. PID2022-139349OB-I00) funded by MCIN/AEI/10.13039/501100011033/FEDER, EU Vicenç Mut - CAIB postdoctoral grant (to MBS) FSE+ EU funds, project number PD-050-2023, Peer reviewed

Figure S1: Final frames of 1000 ns polyglycine hexapeptide simulations; Figure S2: Final frames of 1000 ns polyglycine decapeptide simulations; Figure S3: SASA measurements of polyglycines with different backbone polarity combinations; Figure S4: Final frames of 1000 ns homopeptide simulations; Figure S5: SASA measurements of homopeptides with different backbone polarities; Figure S6: Images of cluster analysis-derived AA conformations; Figure S7: Images of PEPFOLD3 structure predictions; Figure S8: LogP values and AP scores of hetero-hexapeptides (42mM) and decapeptides (25mM); Figure S9: Final frames of 1000 ns CG simulations of C-flag encoded hexa- and deca-heteropeptides; Figure S10: Final frames of 1000 ns CG simulations of E-flag encoded hexa- and deca-heteropeptides; Figure S11: Final frames of 1000 ns coarse-grained simulations with encoding based on cluster analyses; Figure S12: Final frames of 1000 ns coarse-grained simulations with encoding based on PEPFOLD3 fold predictions; Figure S13: SASA measurements for hetero-hexapeptide simulations; Figure S14: SASA measurements for hetero-decapeptide simulations; Figure S15: Simulations with increasing concentrations of peptides using Cluster encoding; Figure S16: SASA measurements for heteropeptides at increasing concentrations; Figure S17: Concentration impact on aggregation in hexapeptides using MARTINI3; Figure S18: Images of the final frames of MARTINI 3 simulations; Figure S19: SASA measurements using MARTINI 3; Table S1: Summary of key publications performing CG-MD simulations of peptide self-assembly; Table S2: Polygycine homopeptide SASA scores; Table S3: Average number of water contacts per residue in polyglycine hexapeptides; Table S4: Average number of water contacts per residue in polyglycine decapeptides; Table S5: LogP values and AP scores of homopeptide simulations; Table S6: Detected DSSP codes using GROMACS analysis of peptide conformations obtained from cluster analysis and PEPFOLD3 structure prediction; Table S7: Statistical tests RM-ANOVA and paired difference t-tests for AP scores of C-flag, E-flag, Cluster encoded, and PEPFOLD3 encoded simulation groups; Table S8: Average number of water contacts per residue in hetero-hexapeptide simulations with E-flag, PEPFOLD3, and cluster encoding; Table S9: APSASA for simulations using Cluster encoding; Table S10: Average number of water contacts per residue in Cluster encoded hetero-hexapeptide simulations at concentrations of 800 (166 mM) and 2400 (498 mM) peptides per box; Table S11: AP scores for hetero-hexapeptides using Cluster encoding at three different concentrations; Table S12: Average number of water contacts per residue in hetero-hexapeptide simulations using Cluster encoding at three different concentrations (200, 800, and 2400 peptides per box) performed with the MARTINI 3 force field., No

SI: Fig. S1. Residual plots of the kinetic models fitted against experimental data: (A) residuals for TC, (B) residuals for MB, and (C) residuals for RhB. Table S1. Parameters obtained after non-linear fitting of tested kinetic adsorption models., No

Black dots are viable r-k pairs found to be compatible with catch and CPUE data and the priors, with the most probable r-k pair indicted by the red cross (with CI). The dotted rectangle indicates the prior r-k range., Peer reviewed

Satellite observations are a key tool in this effort, employing various technologies to measure sea ice thickness. Altimetry satellites are sensitive to ice thicker than approximately 1 meter, while low-frequency passive microwave sensors such as SMOS can retrieve thinner sea ice by detecting its natural microwave emission. These two approaches therefore provide complementary information. However, there is still room to improve the quality of remotely sensed sea ice thickness products by refining the retrieval methodologies. Progress is limited mainly by the scarcity of in situ measurements, which are essential for developing empirical models and performing robust validation. The main objective of the project was to collect brightness temperatures from L-band radiometric measurements of sea ice using the ARIEL portable L-band radiometer, and simultaneously measure sea ice thickness with the SIMS instrument, as well as sea ice conditions (salinity, temperature, and snow properties). This dataset is key to improving the emission modelling required for the development of sea ice thickness products derived from remote sensing observations, We acknowledge the Spanish funding agency Agencia Estatal de Investigación (España) and Ministerio de Ciencia e Innovación (España) for its support through the ARCTIC-MON project (PID2021-125324OB-I00) enabled us to cover travel expenses, shipping, as well as the rental and purchase of some of the required equipment. We also acknowledge PONANT for providing us with berths on the icebreaker Le Commandant Charcot, for providing the SIMS and drilling instruments, and for the technical support from Daniel Cron, 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, There are three files, one for each cruise segment: - SIMSVAL: 21 March 2025 – 05 April 2025 - POLARIS: 05 April 2025 – 17 April 2025 - FANS: 17 April 2025 – 02 May 2025, Peer reviewed

MMC S1. The SM contains the dielectric functions and the size-dependent optical properties of cementitious oxides. It also includes a more detailed description of the Hapke model for studying their reflectance., No

Annual LBB fits (red curve) with indication of median (bold green) and annual (dashed green) Linf and Lopt estimates., Peer reviewed