Resultados de investigación

Peer reviewed

Supporting Movie 1: Animation showing nano-FTIR images and LDOS images as a function of frequency., Details on the numerical calculation of LDOS and the band structure. Fabrication of the PC. Description of the model of s-SNOM and its connection to LDOS. Analysis of additional slices of the data cube. Analysis of the amplitude and phase of selected spectra. Additional discussion of the calculated LDOS. Details on Supporting Movie 1., Peer reviewed

Supplementary Figure 1. Geographic distribution of retrieved CTA studies. Searches were only conducted in English, so there is a bias for English-speaking countries in the number of HWC related CTA studies. Yet, substantial variation is apparent even among English-speaking countries. Studies were retrieved via systematic search and ad hoc retrievals (see Methodology). Supplementary Figure 2. Relative use of illness-inducing subtances across 256 CTA experiments. Substances were either applied to the food resource or directly to the subject following consumption of the food resource. ANTU, Alpha-naphtyl-thiourea; APT, Antimony potassium tartrate; Bufa, Bufadienolide (natural cane toad toxin); Carba, Carbachol; Cinna, Cinnamamide; CupSu, Cupric sulfate; CyPP, Cyclophosphamide; DiAnt, Dimethyl anthranilate; E and L, Emetine + Lithium chloride; EE, Ethinyl estradiol; Emet, Emetine; FBZ, Fenbendazole; FCZ, Fluconazole; FLR, Fluralaner; HyCy, Hyoscyamine; JJB, Jojoba; Leva, Levamisole; LiCl, Lithium chloride; Lnd, Landrin; Met, Methiocarb; Parac, Paracetamol; SodCa, Sodium carbonate; SodMo, Sodium monofluoroacetate; TandM, Thiram + Methiocarb; TBZ, Thiabendazole; Thir, Thiram; Ziram, Ziram; ?, unknown substance. Studies were retrieved via systematic search and ad hoc retrievals (see Methodology). Supplementary Table 1. Web of Science search string. Supplementary Table 2. Scopus search string. Supplementary Data 1. Conditioned taste aversion in conservation literature data sheets., Peer reviewed

Table S1. Markers associated with inflammation and oxidative stress; Figure S1. Chromatograms of lemon and orange extracts at 340 nm using HPLC-DAD-MS/MS; Table S2. Differences between postprandial and baseline values after consuming a high-fat-high-sugar meal and citrus extracts; Table S3. Oxylipins tentatively identified in plasma using UPLC-ESI-QTOF-MS; Table S4. Peak integration area values from the EICs of the identified plasma oxylipins; Figure S2. Total metabolite excretion (sum of urine fractions F1, F2, F3 and F4) of M8, M14, M6 and M10 after consuming lemon or orange extracts; Figure S3. Maximum plasma concentration (Cmax) of M8, M14, M2, and M13 after lemon or orange extracts intake; Figure S4. Interindividual differences between low and high metabolite producers in the pharmacokinetics of M8 and M2., Peer reviewed

This package contains three Excel workbooks plus six accompanying R scripts documenting field and laboratory data and analyses on multicomponent nestling begging displays in spotless starlings. The core raw datasets are (i) "Natural_broods.xlsx", a parental visit-level table from natural nests (4 188 rows) in which each row represents a nestling’s behaviour and feeding outcome during a parental visit; and (ii) "Laboratory_nestlings.xlsx", which includes a bout-level postural dataset (300 rows; one row per chick × deprivation treatment × stimulus) and a call-level acoustic dataset (1 904 rows; one row per call with within-bout sequencing and bout identifiers).The file "Data_figures.xlsx" provides processed, figure-ready tables used to plot the manuscript figures and is intended for direct reuse in visualisation or verification. The six R scripts correspond to (1) the three analysis streams: (a) mixed-model effect sizes (R²-based)(scripts "R2_Models_/), (b) information-theoretic measures of coding effectiveness (scripts "Information_/"), and (c) receiver-based decoding via random-forest classification (script "Random_forest_laboratory_nestlings"; plus (2) One script for figure plotting. All scripts assume that the Excel data files are available in the working directory (or that paths in the scripts are edited accordingly)., [Description of methods used for collection/generation of data] Data collected to quantify how multicomponent begging displays encode nestling hunger (food deprivation) in spotless starlings, using (i) observations of natural broods at nest boxes and (ii) a controlled laboratory “hunger experiment”. 1 Experimental efforts and data collection 1.1 Study system and timing Fieldwork was conducted on a nest-box population of spotless starlings in central Spain. Begging behaviour was recorded when nestlings were 6 days old, a stage of rapid growth when nestlings obtain virtually all food through begging and physical interference among nestmates is still limited. 1.2 Observations at natural nests (field component) Sample: 24 first broods containing 4–5 six-day-old chicks. Recording context: early-morning feeding peak, using infrared video cameras. Pre-recording standardisation: nestlings were fed to satiation with mealworms, weighed, and individually marked. Behavioural scoring: a trained observer (blind to the study aims) reviewed the first three hours of footage after the first parental visit. Key variables recorded in the field included: timing of parental visits, identity of fed chicks, maximum postural begging intensity, begging rank order, time since previous feeding (deprivation time), and the size of the last prey item (relative to bill length). 1.3 Laboratory hunger experiment (controlled component) Sample: 25 first-brood nests; two intermediate-mass-ranked chicks per brood were transported to the laboratory the day before trials. Housing: artificial nest cups under controlled temperature and humidity. Conditioning: nestlings were habituated to a standard begging stimulus (playback of a parental feeding call from the same population) and fed on demand during multiple conditioning trials. Feeding schedule before tests: four routine feeding trials at 45-min intervals (ration adjusted to nestling mass). Hunger manipulation and recording: after the last meal, begging was recorded at 30, 60, and 90 min. Each trial comprised two playback stimuli, separated by sufficient time for begging to extinguish. Recording equipment: synchronized video (posture) and close-range audio (vocalisations). Post-trial handling: nestlings were fed ad libitum, blood-sampled for sexing, and returned to their original nests. 1.4 Behavioural and acoustic measurements Postural begging was scored on an ordinal 0–4 scale: 0 = no begging 1 = gaping, neck flexed, body horizontal 2 = gaping, neck extended, body horizontal 3 = gaping, neck extended, body raised, back reclining 4 = fully stretched, vertical back (sometimes with wing flapping) Laboratory videos were additionally used to quantify display fine structure (e.g., time spent at each postural level), from which total begging duration, mean postural intensity, maximum postural intensity, time at maximum intensity, and latency to beg were derived. Begging vocalisations were analysed from spectrograms to obtain temporal measures (e.g., bout duration, number of calls, calling latency) and call-level spectral/entropy measures. Some spectral traits were summarised via PCA to produce single composite variables. Files and variables Data Three Excel workbooks (.xlsx). Each workbook includes a sheet named Metadata containing the authoritative variable definitions (and, where relevant, abbreviations and units). Below, I summarise the contents of each file and list all variables used., [Methods for processing the data] Accompanying R code., This package contains three Excel workbooks plus six accompanying R scripts documenting field and laboratory data on multicomponent nestling begging displays in spotless starlings. The core raw datasets are (i) Natural_broods.xlsx, a visit-level table from natural nests (4 188 rows) in which each row represents a nestling’s behaviour and feeding outcome during a parental visit; and (ii) Laboratory_nestlings.xlsx, which includes a bout-level postural dataset (300 rows; one row per chick × deprivation treatment × stimulus) and a call-level acoustic dataset (1 904 rows; one row per call with within-bout sequencing and bout identifiers). Variables include identifiers (nest, chick, parent), deprivation (time since last feeding or controlled deprivation), ordinal maximum postural intensity (0–4), timing traits (latency, bout duration), feeding outcome (0/1), and multiple acoustic descriptors (temporal measures and frequency/entropy metrics, including PCA-derived composites). Data_figures.xlsx provides processed, figure-ready tables used to plot the manuscript figures and is intended for direct reuse in visualisation or verification. The R code for statistical analyses focuses on (a) mixed-model effect sizes (R²-based), (b) information-theoretic measures of coding effectiveness, and (c) receiver-based decoding via random-forest classification., Ministerio de Ciencia, Innovación y Universidades, PGC2018-099596-B-I00 Agencia Estatal de Investigación, MCIN/ AEI/10.13039/501100011033 Ministerio de Ciencia y Universidades, CGL2017-83843-C2-1-P Ministerio de Ciencia y Universidades, PID2021-126673NB-I00, File List: #Data All data are provided as three Excel workbooks (.xlsx): "Natural_broods.xlsx" "Laboratory_nestlings.xlsx" "Data_figures.xlsx" #Code Code is provided as six R scripts (.R): "R2_Models_laboratory_nestlings.R" "R2_Models_natural_broods.R"" "Information_laboratory_nestlings.R" "Information_natural_broods.R" "Random_forest_laboratory_nestlings.R" "Figures.R", Peer reviewed

Supporting Information Section 1: Figure S1.1. Figure S1.2. Figure S1.3. Figure S1.4. Table S1.1. Table S1.2., Supporting Information Section 2: Figure S2.1. Table S2.1. Figure S2.2. Figure S2.3., Supporting Information Section 3: Figure S3.1. Figure S3.3., Supporting Information Section 4: Figure S4.1 Table S4.1.Figure S4.2, Supporting Information Section 5: Figure S5.1. Figure S5.2. Figure S5.3. Figure S5.4. Figure S5.5., Supporting Information Section 6: Figure S6.1. Figure S6.2. Figure S6.3., Supporting Information Section 7: Figure S7.1. Figure S7.2. Table S7.1. Figure S7.3., Peer reviewed

Supplementary Figures (S1A-B, S2, S3, S4A-C and Table S1 & S2., Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry–based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies., King's College Hospital Charity (King's College Hospital); Barts Charity, Peer reviewed

Data obtained in oceanographic surveys conducted north of the Catalan coast (from 41˚N to 42.3˚N and from 2.5˚E to 4˚E). Vessel Name: García del Cid, This dataset gives information on sardine eggs and larval diversity and abundances, integrated through the water column, off the Catalan coast in two oceanographic cruises in winter 2017 and 2018. The investigation showed the associations between larval fish assemblages and oceanographic conditions in the upper ocean (top 500 m). Furthermore, this dataset gives information on fish larvae and sardine eggs abundances at different layers of the water column and explores the diel vertical distribution of fish early stages in relation to oceanographic features, Role of vertical mixing processes and dense water formation in the spawning strategy and recruitment of winter breeding fishes in the NW Mediterranean. Ministerio de Economía y Competitividad CTM2015-68543-R, Peer reviewed

BASES DE DATOS Y CAPAS DE INFORMACIÓN GEOGRÁFICA (formato xlsx y shp)

Contenido: Dataset con las bases de datos (formato xlsx) y capas en formato geográfico (formato shp).

Formatos: bases de datos xlsx, capas de información geográfica shp comprimidas en zip, especificaciones de datos en pdf y txt.

Archivos totales: 36 (1 pdf + 1 txt + 13 xlsx + 21 zip).

ESPECIFICACIONES DE LOS DATOS (2 archivos)

1 archivo -formato pdf- con las especificaciones de contenidos, formatos y metodología de los datos publicados en e-cienciaDatos.

1 archivo -formato txt- con las especificaciones de los datos del dataset.

00_Especificaciones datos_BBVV-BBINM.pdf

00_Readme_00 BD y capas.txt

MUNICIPIOS (6 archivos)

1 archivo -formato xlsx- con los municipios analizados (151) en los años 1991, 2001, 2006 y/o 2011 y los indicadores básicos municipales (1_0).

5 archivos -formato shp comprimido en zip-, uno por año y Catálogo (1991, 2001, 2006 y 2011). Capas de información geográfica en formato SHP con los polígonos de los municipios estudiados (1_1 y 1_2).

1_0_BD_Municipios_1991_2001_2006_2011.xlsx

1_1_Capa_Municipios_BBVV_1991.zip

1_1_Capa_Municipios_BBVV_2001.zip

1_1_Capa_Municipios_BBVV_2011.zip

1_2_Capa_Municipios_BBINM_2006.zip

1_2_Capa_Municipios_BBINM_2011.zip

BARRIOS Y ÁREAS ESTADÍSTICAS (14 archivos)

5 archivos -formato xlsx-, uno por año y catálogo (1991, 2001, 2006 y 2011). Bases de datos de los Barrios y Áreas Estadísticas e indicadores (2_1_0 y 2_2_0).

5 archivos -formato shp comprimido en zip-, uno por año y catálogo (1991, 2001, 2006 y 2011). Capas de información geográfica en formato SHP con los polígonos de los Barrios Vulnerables y de los Barrios de Inmigración (2_1_1 y 2_2_1).

4 archivos -formato shp comprimido en zip- (2001, 2006, 2011). Capas de información geográfica en formato SHP con los polígonos de las Áreas Estadísticas detectadas (2_1_2 y 2_2_2).

2_1_0_BD_BBVV-AEV_1991.xlsx

2_1_0_BD_BBVV-AEV_2001.xlsx

2_1_0_BD_BBVV-AEV_2011.xlsx

2_1_1_Capa_BBVV_1991.zip

2_1_1_Capa_BBVV_2001.zip

2_1_1_Capa_BBVV_2011.zip

2_1_2_Capa_AEV_2001.zip

2_1_2_Capa_AEV_2011.zip

2_2_0_BD_BBINM-AEINM_2006.xlsx

2_2_0_BD_BBINM-AEINM_2011.xlsx

2_2_1_Capa_BBINM_2006.zip

2_2_1_Capa_BBINM_2011.zip

2_2_2_Capa_AEINM_2006.zip

2_2_2_Capa_AEINM_2011.zip

SECCIONES CENSALES PERTENECIENTES A ÁREAS ESTADÍSTICAS (8 archivos)

4 archivos -formato xlsx- (2001, 2006, 2011). Bases de datos de las secciones censales que conforman las Áreas Estadísticas detectadas e indicadores (3_1_BD y 3_2_BD).

4 archivos -formato shp comprimido en zip- (2001, 2006, 2011). Capas de información geográfica en formato SHP con los polígonos de las secciones censales que conforman las Áreas Estadísticas detectadas (3_1_Capa y 3_2_Capa).

3_1_BD_SSCC_AEV_2001.xlsx

3_1_BD_SSCC_AEV_2011.xlsx

3_1_Capa_SSCC_AEV_2001.zip

3_1_Capa_SSCC_AEV_2011.zip

3_2_BD_SSCC_AEINM_2006.xlsx

3_2_BD_SSCC_AEINM_2011.xlsx

3_2_Capa_SSCC_AEINM_2006.zip

3_2_Capa_SSCC_AEINM_2011.zip

SECCIONES CENSALES EXTERIORES A ÁREAS ESTADÍSTICAS (6 archivos)

3 archivos -formato xlsx- (2001 y 2011). Bases de datos de las secciones censales que aun superando el valor de referencia para alguno de los indicadores básicos de vulnerabilidad son exteriores a las áreas estadísticas vulnerables (4_1_BD y 4_2_BD).

3 archivos -formato shp comprimido en zip- (2001 y 2011). Capas de información geográfica en formato SHP con los polígonos de las secciones censales que aun superando el valor de referencia para alguno de los indicadores básicos de vulnerabilidad son exteriores a las áreas estadísticas vulnerables (4_1_Capa y 4_2_Capa).

4_1_BD_SSCCVV_Exteriores_2001.xlsx

4_1_BD_SSCCVV_Exteriores_2011.xlsx

4_1_Capa_SSCCVV_Exteriores_2001.zip

4_1_Capa_SSCCVV_Exteriores_2011.zip

4_2_BD_SSCCINM_Exteriores_2011.xlsx

4_2_Capa_SSCCINM_Exteriores_2011.zip

El dataset forma parte de los datos abiertos de los Catálogos de Barrios Vulnerables de España 1991, 2001 y 2011. La información puesta a disposición pública es de dos tipos: las bases de datos de indicadores y los polígonos de las delimitaciones de Barrios y Áreas Estadísticas delimitados; y el fichero completo de Barrios producido para las tres ediciones de los Catálogos. Los contenidos publicados son los siguientes:

- Bases de datos: formato xlsx (13 archivos).

- Capas: formato shp (21 archivos).

- Fichero: 395 archivos que contienen las 6.299 fichas del fichero. Formato pdf (248 archivos) y formato comprimido zip (147 archivos).

- Resúmenes nacionales y por Comunidades Autónomas (101 archivos pdf).

- Ficheros de ciudades (294 archivos: 147 pdf y 147 zip).

- Documento de especificación de contenidos y datos: formato pdf (21 archivos, aunque se trata del mismo documento replicado en cada uno de los datasets).

Para esta edición se ordenado y homogeneizado toda la información que se encontraba dispersa en cada una de las ediciones, y se han producido todas las fichas resumen (estatales, autonómicas y municipales) y los listados de Barrios y Áreas Estadísticas delimitados.

*** NOTAS

Las especificaciones pormenorizadas de los datos publicados y una síntesis metodológica de los trabajos puede consultarse en el documento 00_Especificaciones datos_BBVV-BBINM.pdf que se incluye en todos los datasets distribuidos.

This proteomic study was done in order to analyze the differential protein expression of neural stem cells during proliferation and induction of differentiation in vitro. All samples are human neural stem cells derived from fetal ventral mesencephalon (hVM1 clone 32 cell line). Experimental conditions included cells in proliferation and cells induced for dopaminergic differentiation.

Dataset: The dataset includes 5 files: a complete report on protein expression as well as functional analyses using the Qiagen Ingenuity Pathway Analysis including canonical pathways, diseases and disorders, biological functions, networks, and top upstream regulators, in proliferating and differentiated cells.