BUSCADOR RECOLECTA

We investigated the extent to which adherence to the Mediterranean diet (MD) in combination with Mediterranean lifestyle factors influenced students’ perceptions of subjective well-being (SWB) and distress. 939 undergraduates completed a survey to assess sociodemographic and lifestyle characteristics, including adherence to the MD, depression, anxiety, stress, and SWB. Data were analysed with correlation, logistic, and multiple linear regression models. Higher adherence to MD correlated with better SWB. Fruit, red meat, sweet and caffeinated beverages contributed significantly. However, it was the combination of adherence to MD with other factors, including quality of social relationships, income, smoking, sleep, and physical activity that better predicted SWB. Our results confirm the positive influence of MD on SWB. However, they also suggest the need to consider perceptions of well-being by a more holistic approach that considers physical and social factors simultaneously to improve the development of more effective educational and motivational programmes, Materials and Methods: Study Design and Ethics Sample population description Statistical methods Table 1. Participants’ sociodemographic characteristics, lifestyle habits, and health status Table 2. Participants’ dietary preferences and eating habits. Table 3. Distribution of additional food and drink preferences of the students sample population with regards to the MD adherence classification Table 4: Correlations between sociodemographic factors, health status and lifestyle with adherence to MD Table 5. Multiple linear regression model to assess the relationship between stress, anxiety, depression, Mediterranean diet adherence, sociodemographic and lifestyle factors Table 6. Correlations between food choices and SWB, Peer reviewed

3-D habitat suitability maps (HSM) or probability of occurrence maps, built using Shape-Constrained Generalized Additive Models (SC-GAMs) for the 30 main commercial species of the Atlantic region. Predictor variables for each species were selected from: sea water temperature, salinity, nitrate, net primary productivity, distance to seafloor, distance to coast, and relative position to mixed layer depth. Each species HSM contains 47 maps, one per depth level from 0 to 1000 m. Probability values of each map range from 0 (unsuitable habitat) to 1 (optimal habitat). For depth levels below the 0.99 quantile of the depth values found on the species occurrence data, NA values were assigned. Maps have been masked to species native range regions. See Valle et al. (2024) in Ecological Modelling 490:110632 (https://doi.org/10.1016/j.ecolmodel.2024.110632), for more details., Peer reviewed

Additional structures from Screening Steps 0 and 3; RDF of the backbone beads; average tube fraction and AP of all of the systems in Step 3; energetic terms of the different FF parametrizations; temperature differences analysis; and AP of the other dipeptides and tripeptides in the higher concentration system., Peer reviewed

Detailed sample preparation procedures, physicochemical properties of adsorptives, schematic of capillary evaporation process in nanopores, top-view FE-SEM micrographs, measured and generated spectroscopic ellipsometric parameters, refractive index values of samples, effective refractive index calculation, measured ellipsometric parameter shifts recorded during nonane adsorption on all samples, preadsorbed liquid multilayer calculations, and thickness–relative pressure relationships of adsorbed multilayers., Peer reviewed

The Direct Current-Electrical Penetration Graph (DC-EPG) technique was used to compare and describe the feeding behaviour on grapevine of four xylem sap-feeding species considered candidate vectors of X. fastidiosa and widespread in Europe: the meadow spittlebug Philaenus spumarius, the spittlebug Neophilaenus campestris, the rhododendron leafhopper Graphocephala fennahi and the green leafhopper Cicadella viridis. The four species were settled on potted grapevine plants for a period of 6 hours and the feeding activities performed by these insects, from stylet insertion into the plant to withdrawal were recorded by DC-EPG coupled with Stylet+d software. Characteristic waveforms were marked with Stylet+a software and analysed by the macro XylFeed. The raw data of the sequential and non-sequential EPG parameters generated by the XylFeed are reported in this database as part of the connected publication., Peer reviewed

Additional file contains: Figure S1-S7 and Table S1-S6., [Introduction] The microbiome inhabiting plant leaves is critical for plant health and productivity. Wild soybean (Glycine soja), which originated in China, is the progenitor of cultivated soybean (Glycine max). So far, the community structure and assembly mechanism of phyllosphere microbial community on G. soja were poorly understood., [Methods] Here, we combined a national-scale survey with high-throughput sequencing and microsatellite data to evaluate the contribution of host genotype vs. climate in explaining the foliar microbiome of G. soja, and the core foliar microbiota of G. soja were identified., [Results] Our findings revealed that both the host genotype and environmental factors (i.e., geographic location and climatic conditions) were important factors regulating foliar community assembly of G. soja. Host genotypes explained 0.4% and 3.6% variations of the foliar bacterial and fungal community composition, respectively, while environmental factors explained 25.8% and 19.9% variations, respectively. We further identified a core microbiome thriving on the foliage of all G. soja populations, including bacterial (dominated by Methylobacterium-Methylorubrum, Pantoea, Quadrisphaera, Pseudomonas, and Sphingomonas) and fungal (dominated by Cladosporium, Alternaria, and Penicillium) taxa., [Conclusion] Our study revealed the significant role of host genetic distance as a driver of the foliar microbiome of the wild progenitor of soya, as well as the effects of climatic changes on foliar microbiomes. These findings would increase our knowledge of assembly mechanisms in the phyllosphere of wild soybeans and suggest the potential to manage the phyllosphere of soya plantations by plant breeding and selecting specific genotypes under climate change., Peer reviewed

Experimental and computational details; additional computational results; possible cation rotations considered within the initial data set; benchmark of regular k-point grid used in DFT calculations; theoretical INS spectra; structural transformation upon symmetrization of selected structural models; phonon band structure; results of NpT AIMD simulations; schematic representation of rotational angles describing cation orientations; close contacts and structural distortions in the representative models., IKUR-PVP-1 data set., Peer reviewed

Crystallographic model information, band structure calculations, single-crystal diffraction precession images, and property measurements (Raman spectroscopy, X-ray photoemission spectroscopy, heat capacity)., Peer reviewed

Figures S1–S5: Calibration curves for determination of the UV-Vis molar extinction coefficient of 4-nitrophenol, 4-aminophenol, nitrobenzene, cis-azobenzene and aniline; Figure S6: Apparent kinetic constant (kapp) of the reduction of nitrobenzene to aniline catalyzed by Au-NCs/SCNPs; Figures S7–S9: Calibration curves for determination of the UV-Vis molar extinction coefficient of 3-(4-nitrophenyl)-1,3-oxazolidin-2-one, (Z)-3,3′-(diazene-1,2-diylbis(4,1-phenylene))bis(oxazolidin-2-one) and 3-(4-aminophenyl)-1,3-oxazolidin-2-one; Figures S10–S13: 1H MNR spectra after isolation via preparative TCL of cis-azobenzene, aniline, (Z)-3,3′-(diazene-1,2-diylbis(4,1-phenylene))bis(oxazolidin-2-one) and 3-(4-aminophenyl)-1,3-oxazolidin-2-one., Peer reviewed

S0.1.1. Simulations with Molecular Dynamics: In the corresponding simulation box, the NVT ensemble was used to calculate a fixed number of N atoms. To maintain this fixed number, the isobaric ensemble was also considered. During the simulations, a time step of 0.25 ×10−15 s was implemented. The ReaxFF methodology incorporates a reactive force field (FF) to describe the chemical interactions in each system. In the present work, the FF developed by Kim et al [1] was applied, in which the interactions of the lignocellulosic components, namely carbon, hydrogen, and oxygen are considered. This FF was implemented due to its suitability for analogous lignocellulosic systems [2–4]. The MD calculations were performed with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [5] computational code and the Reax-module. The interactions among carbon atoms and the possible reactivity with the rest of the components were studied with a distance-corrected Morse potential [6]. This potential incorporates the Van der Waals (vdW) dispersion description and the computation of the bond orders (BO) to evaluate chemical interactions through the pyrolysis process. In the computational simulation models, the total energy of the system was calculated by Eq. 1: Esystem = Ebond + Eover + Eunder + Elp + Eval + Etor + EvdW + ECoulomb (1). In Eq. 1, Ebond corresponds to the bonding energy, Eover refers to the overcoordination stability, Eunder includes the energy to regulate the energy of undercoordinated atoms, and the Elp term is the lone pair energy. The Eval is termed the valence energy. The torsional energy among the atomic components inside the system is Etor, while EvdW includes the description of the vdW interactions. Additionally, the Coulomb energy is given by the ECoulomb term in Eq. 1. Those atomic interactions with non-bonding nature are fully described with a seventh-order function [7]. The morphology of the porous structure was theoretically modeled by computing the pore size distribution (PSD) of the final carbon materials resulting from the simulated pyrolysis. The calculations were performed with the Zeo++ computational code [8], which is intended to map the geometry of the pores via the Voronoi decomposition. The method incorporates a probe molecule with a radius size of 1.79˚A to evaluate the volume accessible to this probe. This radius corresponds to the approximate size of a nitrogen molecule, which is used in the physisorption experiments. The cellulose and hemicellulose precursors were studied as polymeric models in accordance to Martínez-Casillas et al [4]. Such molecular components were incorporated in the present work due to their successful incorporation into analogous biomass systems. The massive model systems were formed by tracking the specific rate contents for each lignocellulosic component with the Packmol code [9]. It produces random distributions in a closed simulation box with a side size of 140˚A, which is shown in Fig. 2 of the main text. The model systems of the agave fibers BA and BAC are based upon the experimental data, in which the following contents were found: 17% lignin, 38% cellulose, and 24% hemicellulose for BA, while for the latter, 13%, 44% and 10% for lignin, cellulose and hemicellulose respectively for BAC. The remaining components are byproducts, mainly ashes, which are not considered in the present calculations. Such compositions were based on the NREL characterization method implemented in the experiments [10]. To fulfill the total content of the sample, the molecular models were further normalized. The molecular weights of each model system were introduced to build the massive molecular system depicted in Fig. 2 (a), depicted in the main text. The lignin Adler’s model was set as the reference system, with a molecular weight of 2407.10 g/mol. The simulation box comprised 40 macromolecules and an accumulated molecular weight of 96,284 g/mol. It was then fixed as 24% of the complete molecular weight of the lignocellulosic components, after normalization. The polymer models for cellulose and hemicellulose corresponded to normalized contents of 45% and 31% [11]. Consequently, molecular weights of 180,532.5 g/mol and 124,366.83 g/mol for these two components, respectively, were considered for the BA model. In terms of polymer units, 69 hemicellulose and 109 cellulose units were introduced. The BAC model was also normalized by using the same criteria. The pyrolysis processes were evaluated with two different heating rates; namely, at 0.027 K/fs, and also at a slower rate of 0.005 K/fs for selected cases. The top temperature at which. the pyrolysis simulations were performed corresponds to the temperatures of 500 °C and 700 °C, which were reached with the experimental procedure [3, 4]. These temperatures remained constant upon annealing during 100,000 steps with a step size of 0.25 fs. When this stage of the simulation is finalized, a quenching process was applied to meet the initial room temperature of 300 K. A subsequent series of 50,000 MD steps at this fixed temperature evolved the system to reach a thermal equilibrium. It is worth mentioning that a methodology to compute the pressure values in annealing conditions, as that developed byWang et al [12] was impossible to incorporate in the simulations, since the vdW equation of state was applied. S0.1.2. Radial distribution function computation The X-ray diffraction data obtained between 2θ of 1° to 120° was used to compute the radial distribution function (RDF) that is aimed to elucidate the crystal structure of solids or amorphous materials [13]. The RDF is given in accordance with the eq. 2: G(r) = 4πrρ0[g(r) − 1] (2) in which r stands for the radial distance, and ρ0 is the atomic number density in average. Finally, the term g(r) corresponds to the macroscopic atomic pair density [13]. The physical significance of eq. 2 corresponds to the distribution of probability to find atomic pairs at a certain distance r. Moreover, G(r) is further assessed with a sine Fourier transform. It refers to the reciprocal space of the structure factor S(Q), containing the scattering structure distribution that comes from the experimental XRD data. Using the structure factor, Eq. 2 is re-written as: G(r) = 2/π Z ∞ 0 Q[S(Q) − 1] sin (Qr)dQ (3) Upon combination of eq. 2 and 3, the RDF can be obtained as given by g(r). Such values were found with the aid of the PDFGETX2 computational code [14]. The G(r) function was assessed via a Fourier transform using the structure factor S(Q) as input. A value of 16.0˚A−1 was used as the Qmax parameter [1] to apply the Fourier transform. As a final statistical treatment, the PDFGUI code was implemented to adjust the experimental RDF. The PDFGUI is a software with a user graphical interface based upon the PDFFIT2 computational code [15]. The software requires a starting guess to refine the structural data such as atomic positions, lattice constants, correlated atomic motion, and anisotropic atomic displacement. S0.1.3. Envelope Density assessment The envelope density methodology [16] was used to evaluate the densities of the final carbon materials. This study was intended to compare the densities given in the experiment with those computed with the theoretical approach. The ratio of the carbon material to the total volume is defined as the envelope density, and it was used in the experimental analysis. It is worth mentioning that a volume of 0.2mL was considered in the evaluation, which is given by Corning Inc. PCR R®tubes.-- Under a Creative Commons license BY-NC-ND 4.0, A full description of computational details related to molecular dynamics, radial distribution function computation, and Envelope Density assessment, along with experimental results concerning SEM images of group I and group II carbon materials.-- S0.1. Computational Details: S0.1.1. Simulations with Molecular Dynamics. S0.1.2. Radial distribution function computation. S0.1.3. Envelope Density assessment. S0.2. Complementary experimental results, The financial support for this work was provided by DGAPA-PAPIIT-UNAM through the projects IG100217, IG100923, IN106122 and IA102522. [...]. D.R. Lobato-Peralta and A. Ayala-Cortés received Ph.D. fellowships from the Consejo Nacional de Humanidades, Ciencias Tecnologías (CONAHCYT). J.M. thanksthe financial support of Fondo Sectorial de Investigación para la Educación-CONAHCYT under project No. A1-S-13294, and Fronteras de la Ciencia-CONAHCYT under Project No. 21077. J.M. also acknowledges the computational infrastructure provided by Laboratorio Nacional de Conversión Almacenamiento de Energía (CONAHCYT) under project No. 270810, as well as the UNAM Supercomputing Department for the computing resources under Project No. LANCAD-UNAM-DGTIC- 370 and LANCAD-UNAM- DGTIC-310. The computing time in an AWS EC2 instance is also acknowledged, which was granted under Project No. 8 of the DGTIC-AWS initiative., Peer reviewed