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A) Representative confocal laser scanning microscope images of bacterial cells extracted from soil microcosms at 28 dpi. Cells were stained using the BacLight LIVE/DEAD bacterial viability kit. Green cells represent viable cells and red represents dead cells. Scale bar represents 50 μm. B) Quantification of dead cells at different time points of soil microcosms based in BacLight LIVE/DEAD staining. Cells were quantified using Fiji ImageJ. Detailed methods:0.5 g from soil microcosms experiments was resuspended with 1 mL MilliQ water, samples were vortexed and soil particles allowed to sediment. Supernatants were recovered, centrifuged at 1000 rpm for 5 min to eliminate soil and supernatant was transferred to a fresh tube and cells were pelleted by centrifugation at 10000 rpm, 5 min and finally, cells were resuspended in 300 μL 0.85% NaCl. Then, 50 μL of cells were stained with 0.2 μL of BacLight LIVE/DEAD bacterial viability kit (ThermoFisher), incubated for 5 min in the dark and visualized promptly with a confocal microscope. Green SYTO9 fluorescence was measured with excitation at 488 nm and emission at 500–520 nm and red PI fluorescence was measured by excitation at 561 nm and emission at 610–630 nm. The alive control was prepared by washing an overnight culture with 0.85% NaCl and the dead control was prepared by killing cells from the overnight culture with 70% isopropanol for 1 h, followed by centrifugation of cells and resuspension in 0.85% NaCl. The number of alive and dead cells was quantified using ImageJ by setting a “threshold method = Moments” and particles size = 0.5-Infinity., Peer reviewed

Figure S1: HPLC chromatograms for Lf standard solutions; Figure S2: HPLC calibration method for holoLf; Figure S3: Lf release profile applying the Peppas–Sahlin kinetic release profile; Figure S4: Chromatograms obtained for unprotected holoLf proteolysis; Figure S5: Chromatograms obtained for supernatants collected from the MPs kept 1 h in GF + E followed by 23 h of incubation in IF; Figure S6: Chromatograms obtained for supernatants collected from the MPs kept 1 h in GF followed by 23 h of incubation in IF; Figure S7: Chromatograms obtained for supernatants collected from the MPs kept 24 h in IF; Figure S8: Kinetics of iron released from holoLf depending on the pH of the simulated fluid., Peer reviewed

Contents: Table S1. Spectroscopic parameters of the most stable structures of furan…hexane calculated at the B3LYP-D3BJ/def2tzvp level of theory.-- Table S2. Experimental transition frequencies of furan…hexane.-- Table S3. DFT calculation results of TTT1, TTT2, and TTT3 at different levels of theory.-- Table S4. DFT coordinates of TTT1, TTT2, and TTT3 in their respective principal inertial axis systems at the B3LYP(D3BJ)/def2-TZVP level of theory.-- Point S1. PIO calculation details.-- Figure S1-S3. PIO plots of TTT1, TTT2, and TTT3.-- Table S5. SAPT calculation results of TTT1, TTT2, and TTT3., Peer reviewed

Comprises 3 sheets: 1. Leaf morphological traits. 2. Physiological traits. 3. Chl fluorescence traits 4. Final productivity. 5. Mineral content. Description of methods used for collection/generation of data: (a) ‘leaf morphological traits’: traits were determined at early milk grain development (growth stage 73 (Z73) according to the Zadoks scale (Zadoks et al., 1974). The mass of the dry-oven flag leaves was measured after their incubation in an oven at 60 ºC for 48 h. Leaf area was determined using an electronic planimeter Li-COR Model Li-3050A (Lincoln, ME, USA). (b) ‘physiological traits’: samples were collected during two consecutive days at Z73. Ψleaf was determined using a Schölander pressure chamber instrument Model 1000 (PMS Instrument Company, Albany OR), after maintaining the flag leaves in a humid and cold ambient for 25 min to achieve water equilibrium. RWC was calculated after determining fresh mass, dry mass and 24-h water-saturated mass of the flag leaves. The Chl content was measured using Dualex instrument (FORCE-A, Paris, France) (Goulas et al., 2004). Photosynthesis traits (Aatm, Eatm, gs, atm, Ci, atm, WUEins, atm) were determined with the portable photosynthesis system CIRAS-3 (PP System, Amesbury, MA) and parameter settings were as follows: 22 ºC; light intensity, 1500 μmol m−2 s−1 with a blue:green:red LED ratio of 38:37:25; cuvette flow of air, 250 cm3 min−1 and relative humidity, 50%. The flag leaves were powdered and stored at −80 ºC. Samples were extracted in ethanol/water to assess the content of NO3- and FAA. NO3- content was measured in the soluble ethanol/water extracts as described in the study by Cawse(1967) with minor modifications to accommodate it to a 96-well microplate reader. The total content of FAA was determined by the ninhydrin colorimetric method as described in the study by (Vicente et al., 2016). The content of Pro, AsA and GSH was extracted and spectrophotometrically determined as described in the study by Bendou et al.(2022). (c) ‘chl fluorescence components’: Chl fluorescence related traits (Fv/Fm, ΦPSII, ΦNPQ, ΦNO) were measured using a portable fluorometer PAM 2000 (Walz, Effeltrich, Germany). The whole plant remained in the dark for 30 min before starting a dark-light induction curve within the slow-kinetic window for 30 min (Kasajima et al., 2009). (d) ‘final productivity’: samples were collected at maturity and final harvest after senescence. The number of ears per plant, grain yield per ear (GYE), grain number per ear (GNE) and the oven-dry mass of the above- and underground components of the plants were computed after drying them in an oven at 60 ºC. (d) ‘mineral content’: Flag leaves and grains from final harvest at maturity were separated to measure their mineral composition. The content of C and N in powdered flag leaves and grains was determined by the Dumas combustion method using a C-N analyser Leco model CHN-628 (LECO Corporation, St. Joseph, MI) as described in the study by (Pereira et al., 2023). The content of P, K, Ca, Mg, S, Mn, Fe, Cu, Zn, Mo and Na was determined by inductively coupled plasma atomic emission spectroscopy using an ICP-OES model Varian 720-ES (Agilent, Santa Clara CA). All the samples were dried in an oven at 105 ºC for 12 h. An amount of 0.5 g of each sample was weighed and digested in a microwave digestion system model EthosTM Up (Milestone Srl, Italy) using a volume of 10 mL of 65% HNO3 and 30% H2O2 in a ratio of 8:2 (v:v) and a temperature of 200 ºC for 15 min. The digested samples were finally diluted with ultrapure water to 25 mL before ICP-OES analysis., In this research study, we evaluated the effect of mild water deficit on the physiological response of the flag leaf at the milky grain development stage and the final grain yield and mineral content at maturity of bread wheat (Tritticum aestivum cv Gazul). Three plants were grown in 5-L pots containing a mixture of perlite and vermiculite in a volumetric ratio of 3 to 1 as the soilless substrate and under greenhouse conditions from February 2023 to July 2023. After seed sowing, the potted plants were fertigated at full-holding capacity (100% FC) three days per week. After the unfolding of the fourth leaf, pots were divided into two blocks: one half of the pots followed the same fertigation pattern at 100% FC while fertigation was reduced to 65% FC in the other half until plant maturity and senescence. At the milky grain development stage, when plants water demand was highest, we assessed leaf morphological traits, photosynthesis related traits, content of proline, free amino acids, ascorbic acid, glutathione and nitrate at two consecutive days. At maturity, plants were harvested to determine final biomass, water use efficiency, grain yield, and mineral content of the flag leaves and the grains., Research was funded by MCIN/AEI/10.13039/501100011033 (Project nº PID2019-107154RB-100) and the regional government of Castilla y León (Project nº CSI260P20). The Project ‘CLU-2019–05—IRNASA/CSIC Unit of Excellence’ funded by the Junta de Castilla y León and co-financed by the European Union (ERDF ‘Europe drives our growth’) and the CSIC Interdisciplinary Thematic Platform (PTI) Optimization of Agricultural and Forestry Systems (PTI-AGROFOR) are also acknowledged., No

The introduction of non-indigenous species to aquatic ecosystems is one of the main threats to global biodiversity. This paper reports the occurrence of the blue swimmer crab Portunus segnis in southwestern European waters (i.e., the Gulf of Cadiz). We discuss the invasive potential of P. segnis and possible mechanisms of its expansion into this new region. The detection of this species also highlights the importance of involving citizen scientists in the reporting of non-indigenous species., This paper was prepared within the framework of the InvBlue project (PID2019-105978RA-I00) from the Spanish “Ministerio de Economía y Competitividad (MINECO), Plan Nacional I + D”. GF de C-S acknowledges support from the Spanish Ministry of Universities under “Margarita Salas program” (UCA/R155REC/2021), financed by Next Generation EU., Peer reviewed

Source data for the article entitle "Interchain Expanded Extra-Large Pore Zeolites", Nature 2024, including the detailed structural and physicochemical and chemical characterization of those zeolites, [EN] 1. Description of methods used for collection/generation of data: Diffraction (electron, synchrotron X-ray, neutron). Infrared spectroscopy multinuclear MAS NMR gas adsorption electron paramagnetic resonance DFT calculations Catalytic reaction 2. Methods for processing the data: Those needed depending on the type of data (SHELX, Rietveld refinement, fast Fourier Transform...). Please, check the paper., Spanish Ministry of Science Innovation, PID2019-105479RB-I00, TED2021-131223B-I00 projects, and RYC2018-024561-I and FJC2018-035697-I National Natural Science Foundation of China, 22371121, 22288101, 21920102005, 21835002, 22271115 and 52270111 National Key Research and Development Program of China, 2022YFA1503600, 2021YFA1501202, 2021YFA1501401 the 111 project, B17020 Fundamental Research Funds for the Central Universities of China, 020514380306 Consejería de Universidades, Investigación e Innovación, Junta de Andalucía, POSTDOC_21_00069 Knut and Alice Wallenberg Foundation, KAW, 2012-0112 European Union’s Horizon 2020 research and innovation program, 823717-ESTEEM3 regional government of Aragon, DGA E13_20R China Spallation Neutron Source, P1823060200002, Fig-2.xlsx; Ex-Fig1.xlsx; Ex-Fig. 3.xlsx; Ex-Fig. 6.xlsx; Ex-Fig. 7.xlsx; Ex-Fig. 8.xlsx; Fig-S8.xlsx; Fig-S9.xlss; Fig-S10.xlsx Fig-S11.xlsx Fig-S12.xlsx Fig-S13.xlsx Fig-S14.xlsx Fig-S15.xlsx Fig-S16a.csv Fig-S16b.csv Fig-S17.csv Fig-S18.xlsx; Fig-S19.csv; Fig-S20.xlsx; Fig-S21.xlsx; Fig-S22a.csv; Fig-S22b.csv; Fig-S23.xlsx; Fig-S24.xlsx; Fig-S25.xlsx, No

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Peer reviewed

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data. Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau / Third Pole Environment Data Center (http://data.tpdc.ac.cn)., 1. It is challenge to scale-up from simplified proxies to ecosystem functioning since the inherent complexity of natural ecosystems hinders such an approach. One way to address this complexity is to track ecosystem processes through the lens of plant functional traits. Elevational gradients with diverse biotic and abiotic conditions offer ideal settings for inferring functional trait responses to environmental gradients globally. However, most studies have focused on differences in mean trait values among species and little is known on how intraspecific traits vary along wide elevational gradients and how this variability reflects ecosystem productivity., 2. We measured functional traits of the sub-shrub Koenigia mollis (Basionym: Polygonum molle) (a widespread species) in 11 populations along a wide elevational gradient (1515-4216 m) considering from subtropical forest to alpine biomes treeline in the central Himalayas. After measuring different traits (plant height, specific leaf area, leaf area, length of flowering branches, leaf carbon isotope – δ 13C, leaf carbon and leaf nitrogen concentrations), we investigated drivers on changes of these traits and also characterized their relationships with elevation, climate and net primary productivity (NPP)., 3. All trait values decreased with increasing elevation, except for δ 13C that increased upwards. Likewise, most traits showed strong positive relationships with potential evapotranspiration (PET), while δ 13C exhibited a negative relationship. In this context, elevation-dependent water-energy dynamics is the primary driver of trait variations. Further, five key traits (plant height, specific leaf area, leaf carbon, leaf nitrogen and leaf δ 13C) explained 90.45% of variance in NPP., 4. Synthesis. Our study evidences how elevation-dependent climate variations affect ecosystem processes and functions. Intraspecific variability in functional traits is strongly driven by changes in water-energy dynamics, and reflects changes in community productivity over elevation. K. mollis, with one of the widest elevational ranges known to date, could be a model species to infer functional trait responses to environmental gradients globally. This study sheds new insight on how plants modify their basic ecological strategies to cope with changing environments., National Science and Technology Major Project of China: Second National Science and Technology and Research Programme (STEP)（2019QZKK0000）, Peer reviewed

List of Figures: S1 Graph of the half-period of the center (p. 2).-- S2 Time series for different diffusion values and distances to the bifurcation point (p. 3).-- S3 Transient times for initial populations with growing spatial regions below the ghost bottleneck (p. 4).-- S4 Effect of the different initial spatial distribution of cooperators on extinction times (p. 5).-- S5 Time to extinction from an initial spatial distribution with random gaps depending on gap frequency close to the bifurcation (p. 6).-- S6 Time to extinction from random gap distribution far from the bifurcation (p. 7).-- S7 Phase portraits for different values of diffusion before the bifurcation in the two-patch model (p. 8).-- S8 Phase portraits depending on diffusion after the bifurcation in the two-patch model (p. 9).-- S9 Times to extinction dependence on initial conditions in the two-patch model: homogeneous and heterogeneous extinctions (p. 10).-- S10 Comparison of ghost phenomena depending on diffusion in the two-patch model (p. 11)., No