BUSCADOR RECOLECTA

7 files, Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understanding their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Significant vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps towards incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasising the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements, Peer reviewed

Subcellular localisations of transsulphuration and glutathione synthesis genes., Peer reviewed

Supplementary Figures (1-2) and Tables (1-3)., Peer reviewed

Table S1. Statistics of the co-registration statistics between the external DEMs and the ICESat-2 snow-off DEM.-- Table S2. Statistics of the snow depth residuals for different snow-off Digital Elevation Models (DEMs) on 12 March 2019. The ICESat-2-ASO product is shown in detail on Figure 3. All residuals are shown in Figure 5 and S4.-- Table S3. Statistics of the co-registration statistics between the Pléiades and Copernicus DEMs to the ASO DEM at15 m. All these DEMs were previously co-registered to the ICESat-2 snow-off points.-- Figure S1. Map of the mean annual snow cover duration in the upper Tuolumne basin calculated from a time series of MODIS images (MOD10A1).-- Figure S2. Optimization of the kappa index to determine the photon count threshold defining snow-on and snow-off points based on MODIS snow cover area data.-- Figure S3. Vertical uncertainty of the ATL06 elevation (sigma_h_mean) against along-track slope (dh_fit_dx). The along-track slope (dh_fit_dx) is directly provided in ATL06.-- Figure S4. Same figure as Figure 5 but with snow depth derived from ICESat-2 and the ASO DEM (green, identical to Fig. 5) and ICESat-2 and the Copernicus DEM (purple). Note the different y-scale compared to Figure 5.-- Figure S5. Same as Figure 5 but selecting ICESat-2 ATL06 snow-on points of the strong beams only.-- Figure S6. Same as Figure 5 but selecting ICESat-2 ATL06 snow-on points of the weak beams only.-- Figure S7. Same as Figure 5 but calculating the relative snow depth error (ICESat-2 derived snow depth minus ASO snow depth divided by ASO snow depth) rather than the absolute snow depth error (ICESat-2 derived snow depth minus ASO snow depth).-- Figure S8. Distribution of the snow-off residual (ICESat-2 minus DEM) before (black line) and after coregistration (color) for the ASO DTM (left), the Pléiades DEM (middle) and the Copernicus DEM (right). The vertical lines show the median plus/minus the NMAD after coregistration.-- Figure S9. Snow depth (colored boxes) and snow-off (white boxes) residuals for ICESat-2 – ASO (left) and ICESat-2 – Pléiades (right). Transparent boxplots show the data where less than 100 points were available.-- Figure S10. Elevation difference of the snow-off DEMs, the difference between the Copernicus 30 DEM and the ASO DEM (a) and the Pléiades DEM and the ASO DEM (b). All DEMs were individually co-registered on the ICESat-2 snow-off points. © Author(s) 2023. CC BY 4.0 License. The copyright of individual parts of the supplement might differ from the article licence., Peer reviewed

2. Experimental runs An exemplary for an entire isothermal experiment is given in Fig. 2. It shows the initial conditioning, consisting of ten redox cycles, followed by the actual experiment, consisting of seven isothermal plateaus. These isothermal plateaus each consist of four redox cylces employing the following H2 volume fractions: 50, 50, 30, 16.66 vol%. The 50 vol% run was repeated to possibly detect inconsistencies introduced through the preceding temperature change. Between reduction and oxidation or when changing from one temperature to another, the system was purged with nitrogen. 3. Reproducibility In order to reach the kinetic regime small sample masses (< 2 mg) and high gas flow rates (> 300 ml · min−1) were required. The reproducibility of the measurements was assessed at these conditions. All isothermal runs were repeated six times, all on different days using fresh solid samples and the same TGA programs. Fig. 3 shows the conversion rate dX/dt. for the individual runs during reduction at T = 900 ◦C. Even though, there were noticeable deviations between individual sets, the overall reproducibility of results was convenient. 4. Gas conversion The hydrogen conversion XH2 during the TGA experiments was estimated to ensure sufficient gas supply and negligible impact of steam produced. Low gas conversion signifies that enough gas was present and the atmosphere did not change significantly during the experiments. The mass balance ˙V | in · ci{nz· yH2,i}n n˙ H2,in − (m0 − m∞) MO · (dX/dt)max = ˙V| out · cou{tz· yH2,ou}t n˙ H2,out (1) was used calculate the molar flow n˙ H2,out of unreacted H2 leaving the reactor at maximum reactivity. This gives the highest possible gas conversion for a specific case, i.e. the worst case scenario for reaching the kinetic regime. The n˙ H2,out was used to calculate the conversion XH2 = n˙ H2,in − n˙ H2,out n˙ H2 in at different temperatures and H2 contents. The results are summarized in Table 1. As can be seen, the hydrogen conversion XH2 was low (< 2 %) for all cases, which means that neither gas starvation nor limitations due to steam generation should have played a significant role. 5. Isoconversional methods The additional plots for the isoconversional methods (i.e. evaluation of linear regression and R2) which were referenced in our main work are given in this section. Figure 4 shows the differential isoconversional method for one exemplary experiment with higher mass at 50 vol%H2. Except for the first point (X=0.1) a reasonably high R2 was again achieved. Figure 5 depicts the analysis of the nonisothermal reductions with the differential isoconversional method. Figure 6 shows the results for the integral isoconversional KAS method [1]. For both methods high R2 values were achieved.-- Under a Creative Commons license CC-BY 4.0 Deed., 1. TGA setup.-- 2. Experimental runs.-- 3. Reproducibility.-- 4. Gas conversion.-- 5. Isoconversional methods.-- 6. CFD study.-- 1. TGA setup: A schematic of the experimental setup is given in Fig. 1. The TGA experiments were performed with a NETZSCH STA 449 F3 Jupiter. It consisted of an inner reactor and an outer shell, both made of Al2O3. Different sample holders (crucible, plate, basket) could be mounted on top of a vertical sample carrier at the center of the inner reactor. If not stated differently, the experiments were conducted with the plate (NETZSCH alumina slipon plate, diameter 17 mm). The solid sample was placed on this sample holder. A type S thermocouple within the vertical sample carrier was used for temperature measurements. It had been calibrated with calcium oxalate and pure metal melting points (In, Sn, Bi, Zn, Al, Ag) prior to the experiments. Two different gas flows were relevant in the TGA setup. Firstly, a protective gas flow (20 ml · min−1 N2), which was always switched on, entered the reactor below the sample carrier. Secondly, the main gas flow (H2, O2, N2 and mixtures thereof) entered a water vapor generator, where a predefined mass flow of H2O could be added. The gas mixture was preheated to 180 ◦C and then entered the TGA system from the side. It was heated to the desired temperature during the upwards flow in the outer shell. After reaching the top of the reactor, the gases flowed downwards into the inner reaction chamber, which contained the solid sample, before leaving the reactor via the gas outlet. Both, the main and protective gas flows, were controlled by Bronkhorst mass flow controllers (MFC, 0-250 ml · min−1). 6. CFD study A small, transient CFD study of the TGA was conducted to gauge the temporal evolution of the hydrogen concentration above the solid sample. The TGA geometry of Fig. 1 was simplified (mostly at the inlet and the outlet). Simulations were performed in ANSYS Fluent 2023 R1 using the lamiar flow model and a fixed hydrogen diffusivity of 5·10−4 m2 · s−1. Heat transfer was considered via conduction, convection and radiation (Discrete Ordinate model). A homogeneous velocity distribution was assumed at the inlet (T = 200 ◦C, yH2 = 0.5). The heating was simulated via isothermal walls (T = 800 ◦C) which quickly heated the gases to the operating temperature. At the outlet, a simple pressure outlet condition was chosen. The discretization methods for momentum, mass and heat transfer were set to second order (only Discrete Ordinate was first order), and a first order implicit time solver (time step of 0.1 s) was chosen. Fig. 7 (a) shows the simplified TGA model, Fig. 7 (b) shows the velocity profile in steadystate and Fig. 7 (c) shows the H2 concentration profile at t = 7 s for one exemplary case. As can be seen, the flow around the plate leads to nearly zero velocity at the sample, which could possibly lead to diffusion limitations. More importantly, Fig. 7 (c) suggests an appreciable spatial gradient of hydrogen due to axial dispersion. The temporal evolution of the hydrogen concentration above the plate was given in our main work., Peer reviewed

This PDF file includes: Supplementary Fig. 1 to 5 and Supplementary Table 1., Peer reviewed

HMT correlations to transcription factor activity in GTEx., Peer reviewed

PDF file contains: Suplemmentary Figures S1-S11 and Suplemmentary Tables S1-S2. © Author(s) 2023. CC BY 4.0 License. The copyright of individual parts of the supplement might differ from the article licence., Peer reviewed

The N-terminal tails of eukaryotic histones are frequently posttranslationally modified. The role of these modifications in transcriptional regulation is well-documented. However, the extent to which the enzymatic processes of histone posttranslational modification might affect metabolic regulation is less clear. Here, we investigated how histone methylation might affect metabolism using metabolomics, proteomics, and RNA-seq data from cancer cell lines, primary tumour samples and healthy tissue samples. In cancer, the expression of histone methyltransferases (HMTs) was inversely correlated to the activity of NNMT, an enzyme previously characterised as a methyl sink that disposes of excess methyl groups carried by the universal methyl donor S-adenosyl methionine (SAM or AdoMet). In healthy tissues, histone methylation was inversely correlated to the levels of an alternative methyl sink, PEMT. These associations affected the levels of multiple histone marks on chromatin genome-wide but had no detectable impact on transcriptional regulation. We show that HMTs with a variety of different associations to transcription are co-regulated by the Retinoblastoma (Rb) tumour suppressor in human cells. Rb-mutant cancers show increased total HMT activity and down-regulation of NNMT. Together, our results suggest that the total activity of HMTs affects SAM metabolism, independent of transcriptional regulation., Peer reviewed

This dataset contains estimates of Live Fuel Moisture Content (LFMC) in the Western United States. LFMC is the percentage of vegetation water mass over the dry biomass of the plants. Here, LFMC is retrieved by isolating the water component of the passive microwaves vegetation optical depth (VOD) signal at three frequencies: L-band (1.4 GHz), X-band (10.65 GHz) and Ku-band (18.7 GHz). Each frequency represents a different canopy sensing depth. To isolate LFMC from VOD, auxiliary information to account for the biomass and structure of the vegetation has been used: radar backscatter data from Sentinel-1 and canopy height data from GEDI/Sentinel-2. The dataset spans between April 2015 and December 2018 for L- and X-bands, and between April 2015 and July 2018 for Ku-band retrievals., Peer reviewed