Resultados totales (Incluyendo duplicados): 34260
Encontrada(s) 3426 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331457
Dataset. 2022

COORDINATED CHANGES IN GENE EXPRESSION, H1 VARIANT DISTRIBUTION AND GENOME 3D CONFORMATION IN RESPONSE TO H1 DEPLETION [DATASET]

  • Serna, Núria
  • Salinas Pena, Mónica
  • Mugianesi, F.
  • Dily, François Le
  • Marti-Renom, Marc A.
  • Jordan, Albert
Supplementary Table 1. Hi-C experimental statistics. Statistics shown separated by replicates (top table) and merged datasets (middle table for valid pairs and bottom table for filtered reads). Supplementary Table 2. Mass-spectrometry analysis of histone H1 peptides after immunoprecipitation with H1 variant specific antibodies., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/331457
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331457
HANDLE: http://hdl.handle.net/10261/331457
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331457
PMID: http://hdl.handle.net/10261/331457
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331457
Ver en: http://hdl.handle.net/10261/331457
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331457

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331459
Dataset. 2022

SUPPLEMENTAL MATERIAL LEU22_LEU23 DUPLICATION AT THE SIGNAL PEPTIDE OF PCSK9 PROMOTES INTRACELLULAR DEGRADATION OF LDLR AND AUTOSOMAL DOMINANT HYPERCHOLESTEROLEMIA

  • Benito-Vicente, Asier
  • Uribe, Kepa B.
  • Larrea, Asier
  • Palacios, Lourdes
  • Cenarro, Ana
  • Calle, Xabier
  • Galicia-García, Unai
  • Jebari Benslaiman, Shifa
  • Sánchez-Hernández, Rosa M.
  • Stef, Marianne
  • Lambert, Gilles
  • Civeira, Fernando
  • Martín, César
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

Proyecto: //
DOI: http://hdl.handle.net/10261/331459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331459
HANDLE: http://hdl.handle.net/10261/331459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331459
PMID: http://hdl.handle.net/10261/331459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331459
Ver en: http://hdl.handle.net/10261/331459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331459

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331464
Dataset. 2022

SUPPLEMENTAL MATERIAL: COMPARISON OF THE NUCLEAR MODIFICATION OF B+ CVS. OPEN AND HIDDEN HEAVY FLAVOR MESONS

  • CMS Collaboration
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

Proyecto: //
DOI: http://hdl.handle.net/10261/331464
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331464
HANDLE: http://hdl.handle.net/10261/331464
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331464
PMID: http://hdl.handle.net/10261/331464
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331464
Ver en: http://hdl.handle.net/10261/331464
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331464

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331467
Dataset. 2022

MACHINE LEARNING STUDY OF METABOLIC NETWORKS VS CHEMBL DATA OF ANTIBACTERIAL COMPOUNDS [DATASET]

  • Diéguez, Karel
  • Casañola, Gerardo
  • Torres, Roldán
  • Rasulev, Bakhtiyor
  • Green, James R.
  • González-Díaz, Humberto
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

Proyecto: //
DOI: http://hdl.handle.net/10261/331467
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331467
HANDLE: http://hdl.handle.net/10261/331467
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331467
PMID: http://hdl.handle.net/10261/331467
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331467
Ver en: http://hdl.handle.net/10261/331467
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331467

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331468
Dataset. 2022

CRISPR INTERFERENCE INTERROGATION OF COPD GWAS GENES REVEALS THE FUNCTIONAL SIGNIFICANCE OF DESMOPLAKIN IN IPSC-DERIVED ALVEOLAR EPITHELIAL CELLS [DATASET]

  • Werder, Rhiannon B.
  • Liu, Tao
  • Abo, Kristine M.
  • Lindstrom-Vautrin, Jonathan
  • Villacorta-Martin, Carlos
  • Huang, Jessie
  • Hinds, Anne
  • Boyer, Nathan
  • Bullitt, Esther
  • Liesa, Marc
  • Silverman, Edwin K.
  • Kotton, Darrell N.
  • Cho, Michael H.
  • Zhou, Xiaobo
  • Wilson, Andrew A.
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/331468
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331468
HANDLE: http://hdl.handle.net/10261/331468
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331468
PMID: http://hdl.handle.net/10261/331468
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331468
Ver en: http://hdl.handle.net/10261/331468
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331468

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331469
Dataset. 2022

SEARCH FOR HIGGS BOSON PAIR PRODUCTION IN THE FOUR B QUARK FINAL STATE. SUPPLEMENTAL MATERIAL

  • CMS Collaboration
Details on simulation and systematic uncertainties., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/331469
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331469
HANDLE: http://hdl.handle.net/10261/331469
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331469
PMID: http://hdl.handle.net/10261/331469
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331469
Ver en: http://hdl.handle.net/10261/331469
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331469

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331471
Dataset. 2022

SEARCH FOR INVISIBLE DECAYS OF THE HIGGS BOSON PRODUCED VIA VECTOR BOSON FUSION IN PROTON-PROTON COLLISIONS AT √S = 13 TEV. SUPPLEMENTAL MATERIAL

  • CMS Collaboration
Additional figures and tables., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/331471
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331471
HANDLE: http://hdl.handle.net/10261/331471
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331471
PMID: http://hdl.handle.net/10261/331471
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331471
Ver en: http://hdl.handle.net/10261/331471
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331471

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331472
Dataset. 2022

SUPPLEMENTARY TABLE OF THE ARTICLE MACHINE LEARNING STUDY OF METABOLIC NETWORKS VS CHEMBL DATA OF ANTIBACTERIAL COMPOUNDS [DATASET]

  • Diéguez, Karel
  • Casañola, Gerardo
  • Torres, Roldán
  • Rasulev, Bakhtiyor
  • Green, James R.
  • González-Díaz, Humberto
22 pages. -- Table S01. Statistics for multiple types of biological activity parameters in ChEMBL dataset. -- Table S02. Details of the metabolic networks of >40 organisms. -- Table S03. Average values of fk for the metabolic networks of >40 organisms. -- Table S04. Conditions includes in ChEMBL Dataset of Antibacterial Drugs vs MRN analysis. -- Table S05. Linear index based on atoms descriptors included in the model., 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

Proyecto: //
DOI: http://hdl.handle.net/10261/331472
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331472
HANDLE: http://hdl.handle.net/10261/331472
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331472
PMID: http://hdl.handle.net/10261/331472
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331472
Ver en: http://hdl.handle.net/10261/331472
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331472

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331474
Dataset. 2022

SUPPLEMENT OF ADDED VALUE OF EURO-CORDEX HIGH-RESOLUTION DOWNSCALING OVER THE IBERIAN PENINSULA REVISITED - PART 1: PRECIPITATION

  • Martins Careto, João António
  • Matos Soares, Pedro
  • Cardoso, Rita M.
  • Herrera, Sixto
  • Gutiérrez, José M.
Table S1. Regional models driven by the ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts, for the 1989-2008 period. Table S2. EURO-CORDEX Regional models driven by the CMIP5 GCMs. Also shown the approximate spatial resolution from each GCM taken from https://portal.enes.org/data/enes-model-data/cmip5/resolution. Figure S1. Daily precipitation Intensity (left) and frequency (right) distributions taken from the hindcast EURO-CORDEX RCMs and ERA-Interim reanalysis (1989-2008) for the Iberian Peninsula. Also shown the Iberian Gridded Dataset distribution for the same domain and period. All RCM data was previously interpolated into the IGD 0.1o regular resolution. As for Era-Interim, two PDFs are shown, one for the original resolution of the low-resolution and other interpolated into the IGD resolution. The dash point and the value written refers to the 95th percentile of the observations for NGD on the original resolution (blue) and interpolated into the ERA-Interim resolution (red). The time periods are (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S2. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the ERA-Interim reanalysis for the 1989-2008 period, taken from the Hindcast EURO-CORDEX simulations, with the NGD regular dataset as reference for (a) daily precipitation intensity, considering the whole PDF shown in the left panels of Figure S1, (b) daily precipitation frequency considering the whole PDF shown in the right panels of Figure S1, (c) daily precipitation intensity extremes, only considering the values above the observational 95th percentile shown in Figure S1 left side and (d) daily precipitation frequency extremes, only considering the values above the observational 95th percentile shown in the right side of Figure S1. All model data were previously interpolated to 0.1oregular resolution from the observations. Figure S3. Daily precipitation Intensity (left) and frequency (right) for the historical driving CMIP5 GCMs and EURO-CORDEX RCMs for the Iberian Peninsula, considering the 1971-2005 period, where all results were previously interpolated into the observational grid. The dash point and the value written refers to the 95th percentile of the bservations. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S4. Daily precipitation Intensity (left) and frequency (right) for the historical driving CMIP5 GCMs and NGD observations interpolated into each GCM resolution for the Iberian Peninsula, considering the 1971-2005 period. Also shown the PDF from the NGD observations at the original resolution. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S5. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the CMIP5 GCMs for the 1989-2008 period, taken from the Historical EURO-CORDEX simulations, with the NGD regular dataset as reference for (a) daily precipitation intensity, considering the whole PDF shown in the left panels of Figure S3, (b) daily precipitation frequency considering the whole PDF shown in the right panels of Figure S3, (c) daily precipitation intensity extremes, only considering the values above the observational 95th percentile shown in Figure S3 left side and (d) daily precipitation frequency extremes, only considering the values above the observational 95th percentile shown in the right side of Figure S1. All model data were previously interpolated to 0.1o regular resolution from the observations. The thick blue lines separate the RCMs driven by different GCM., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/331474
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331474
HANDLE: http://hdl.handle.net/10261/331474
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331474
PMID: http://hdl.handle.net/10261/331474
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331474
Ver en: http://hdl.handle.net/10261/331474
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331474

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331478
Dataset. 2022

SUPPLEMENT OF ADDED VALUE OF EURO-CORDEX HIGH-RESOLUTION DOWNSCALING OVER THE IBERIAN PENINSULA REVISITED – PART 2: MAX AND MIN TEMPERATURE

  • Martins Careto, João António
  • Matos Soares, Pedro
  • Cardoso, Rita M.
  • Herrera, Sixto
  • Gutiérrez, José M.
Table S1. Regional models forced by the ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts, for the 1989-2008 period. Table S2. EURO-CORDEX Regional models driven by the CMIP5 GCMs. Also shown the approximate spatial resolution from each GCM taken from https://portal.enes.org/data/enes-model-data/cmip5/resolution. Figure S1. Maximum (left) and minimum (right) daily temperature distributions taken from the hindcast EURO-CORDEX RCMs and ERA-Interim reanalysis (1989-2008) for the Iberian Peninsula. Also shown the Iberian Gridded Dataset distribution for the same domain and period. All RCM data was previously interpolated into the IGD 0.1o regular resolution. As for Era-Interim, two PDFs are shown, one for the original resolution of the low-resolution and other interpolated into the IGD resolution. The dash point and the value written refers to either the 90th percentile for max temperature or the 10th percentile for min temperature of the observations for NGD on the original resolution (blue) and interpolated into the ERA-Interim resolution (red). The time periods are (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S2. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the ERA-Interim reanalysis for the 1989-2008 period, taken from the Hindcast EURO-CORDEX simulations, with the IGD regular dataset as a reference for (a) maximum daily temperature, considering the whole PDF, (b) minimum daily temperature considering the whole PDF. (c) maximum daily temperature extremes, only considering the values above the observational 90th percentile from maximum temperatures and (d) minimum temperature extremes only considering the values below the observational 10th percentile from minimum temperatures. All model data were previously interpolated to 0.1o regular resolution from the observations. Figure S3. Maximum (left) and minimum (right) daily temperature distribution for the historical EURO-CORDEX RCMs driven by the CMIP5 GCMs, for the Iberian Peninsula, considering the 1971-2005 period, where all results were previously interpolated into the observational grid. The dash point and the value written refers to the 90th percentile of the observations for the maximum temperature and to the 10th percentile of the observations for the minimum temperature. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S4. Maximum (left) and minimum (right) daily temperature distributions for the historical driving CMIP5 GCMs and IGD observations interpolated into each GCM resolution for the Iberian Peninsula, considering the 1971-2005 period. Also shown the PDF from the IGD observations at the original resolution. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S5. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the CMIP5 GCMs for the 1989-2008 period, taken from the Historical EURO-CORDEX simulations, with the IGD regular dataset as a reference for (a) maximum daily temperature, considering the whole PDF shown in the left panels of Figure S3, (b) minimum daily temperature considering the whole PDF shown in the right panels of Figure S3, (c) maximum daily temperature extremes, only considering the values above the observational 90th percentile from maximum temperatures and (d) minimum daily temperature extremes, only considering the values below the bservational 10th percentile from minimum temperatures. All model data were previously interpolated to 0.1o regular resolution from the observations. The thick blue lines separate the RCMs driven by different GCM., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/331478
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331478
HANDLE: http://hdl.handle.net/10261/331478
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331478
PMID: http://hdl.handle.net/10261/331478
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/331478
Ver en: http://hdl.handle.net/10261/331478
Digital.CSIC. Repositorio Institucional del CSIC
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