Resultados de investigación

[EN] It contains a netCDF file which needs specific data analysis software. [ES] Contiene un fichero netCDF que necesita software de análisis de datos específico., [ES] El dataset SEDIDatabase se actualiza periódicamente, se puede consultar y descargar en el siguiente enlace: https://sedi.csic.es/ [EN] The SEDIDatabase dataset is updated periodically, it can be consulted and downloaded at the following link: https://sedi.csic.es/, [EN] This dataset includes series of the Standardized Evapotranspiration Deficit at 0.25º spatial resolution and monthly time resolution at global scale from 1980., [ES] Esta base de datos proporciona el Standardized Evapotranspiration Deficit Index a escala global con una resolución espacial de 0.25 grados y una resolución temporal mensual desde 1980., This work was supported by the research projects PCIN-2015-220 and CGL2014-52135-C03-01 financed by the Spanish Commission of Science and Technology and FEDER. IMDROFLOOD financed by the Water Works 2014 co-funded all of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS(Sweden), DLR(Germany),BMWFW(Austria), IFD (Denmark), MINECO (Spain), and ANR (France), with co-funding by the European Union (Grant 690462)., Peer reviewed

The supplemental files include: the description of the methods, figures of the dipole patterns as a function of temperature for periodicities of n=8 and n=14, derivation of the structural order-parameter, and a discussion on the normalization of the helicity., Peer reviewed

The supplementary material provides (1) detailed derivations of formulae used in the main text: of the NS-subhalo encounter rate accounting for subhalo non-rigidity, and of the probabilities of observing neutron stars in certain temperature ranges, (2) brief discussion on tidal effects of neutron stars on subhalo accretion, (3) elaboration on the applicability of previous cosmic ray and paleo-detector limits to our scenario of clumped dark matter, (4) additional information on the sensitivities of and target regions for future telescopes., Peer reviewed

8 pages. -- Table 1. SAXS data collection and processing. -- Figure S1: Binding of nucleotides does not promote significant conformational changes in Apg2 and Apg2C tertiary structure. -- Figure S2: Characterization of the interaction of Apg2ΔC with Hsc70 in the absence of nucleotides by SPR. -- Fig. S3: NMR chemical shift perturbation analysis. -- Fig- S4: Apg2IDR interacts with Hsc70 with 2-fold reduced affinity in the presence of nucleotides., Peer reviewed

5 pages. -- Supplementary Figure S1 (previous page): Cell-entry inhibition by 10E8 Fab mutants. -- Supplementary Figure S2: Characterization of 10E8 mutant Fabs. -- Supplementary Figure S3: Representative images of POPC vesicle binding experiments in the absence (middle) and presence (right) of epitope, and in vesicles including the anionic phospholipid POPS without epitope (left). -- Supplementary Table S1: Kinetic parameters of binding of Fab 10E8 variants to MPER-TMD671-709 peptide reconstituted in SLBs., Peer reviewed

20 pages. -- Figure S1. Bifunctional lipid probes and workflow used for in vivo photoaffinity binding studies. -- Figure S2. Photolabeling experiments of non-lipid nanodomain marker (transferrin receptor) and lipid nanodomain marker (Cav-1) with bifunctional lipids and competition experiments between bifunctional analogues and cold lipids in living cells. -- Figure S3. Role of previously described chol-binding motif in IFN-γR2-chol interaction in vivo. -- Figure S4. Physiochemical analysis of IFN-γR2TMD wild type and mutants. -- Figure S5. Validation of the novel chol-binding domain localized within the IFN-γR2TMD. -- Figure S6. Characterization of IFN-γR1 and IFN-γR2 constructs for ID-PRIME and proof of principle. -- Figure S7. Chol binding is required for IFN-γR transmembrane signal activation and PDL1 cell surface exposure in response to IFN-γ stimulation. -- Figure S8. Subcellular localization of candidate chol-binding proteins. -- Supplementary Table 1. Single-and Multi-span motifs found using MOPRO. -- Supplementary Table 2. List of chol-binding protein candidates., Peer reviewed

Since the discovery of the Higgs boson in 2012, detailed studies of its properties have been ongoing. Besides its mass, its width - related to its lifetime - is an important parameter. One way to determine this quantity is by measuring its off-shell production, where the Higgs boson mass is far away from its nominal value, and relating it to its on-shell production, where the mass is close to the nominal value. Here, we report evidence for such off-shell contributions to the production cross section of two Z bosons with data from the CMS experiment at the CERN Large Hadron Collider. We constrain the total rate of the off-shell Higgs boson contribution beyond the Z boson pair production threshold, relative to its standard model expectation, to the interval [0.0061, 2.0] at 95% confidence level. The scenario with no off-shell contribution is excluded at a p-value of 0.0003 (3.6 standard deviations). We measure the width of the Higgs boson as Th = 3.2 (+2.4 / -1.7) MeV, in agreement with the standard model expectation of 4.1 MeV. In addition, we set constraints on anomalous Higgs boson couplings to W and Z boson pairs., Peer reviewed

62 pages. -- PDF file includes: Major Resources Table. -- Detailed Methods. -- Figure S1. PCSK9-LDLr binding curves showing the equation of the fitted curves, parameters used to calculate EC50 and R square of each curve. -- Figure S2. Real time PCR curves of LDLr mRNA expression in HEK293 cells transfected with wt, D374Y, L8 or L11 PCSK9 variants. -- Table S1. Detected peptides by LC-MS/MS in the higher molecular weight extra band from ER enriched extracts from HEK293 cells transfected with the L11 SP variant. -- Table S2. In silico prediction of peptide detectability using CONSeQuence, a consensus prediction system built around four independent machine learning algorithms. -- Table S3: List of proteins identified in the sample by LC-MS/MS in the higher molecular weight extra band from ER enriched extracts from HEK293 cells transfected with the L11 SP variant., Peer reviewed

FIG. 1. Nuclear modification factor of the B+c meson compared to that of light charged hadrons, and B+, Bs and D0mesons, as a function of the measured transverse momentum. The total uncertainty is shown for the B+c meson, whereasthe statistical (bars) and systematic (filled rectangles) uncertainties are shown for the other hadrons., FIG. 2. Nuclear modification factor of the B+c meson compared to that of the ground and first excited states of charmonia and bottomonia, as a function of the measured transverse momentum. The total uncertainty is shown for the B+c meson, whereas the statistical (bars) and systematic (filled rectangles) uncertainties are shown for the other hadrons., Peer reviewed

1 table., Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research., Peer reviewed