BUSCADOR RECOLECTA

Additional File 2: Figure S1. A β pathology in the temporal pole cortex. Description: Immunohistochemistry for Aβ (mouse monoclonal antibody, clone 6F/3D, Agilent, #M0872, 1:600) with peroxidase/DAB was performed in nearly-adjacent sections to those used for cyclic multiplex fluorescent immunohistochemistry in a Leica BOND-III automated stainer. Sections were counterstained with hematoxylin. Scale bars: 5 mm, insets 200 μm. Figure S2. Phospho-tau pathology in the temporal pole cortex. Description: Immunohistochemsitry for phospho-tauSer202/Thr205(mouse monoclonal antibody, clone AT8, Thermo-Scientific, #MN1020, 1:10,000) with peroxidase/DAB was performed in nearly-adjacent sections to those used for cyclic multiplex fluorescent immunohistochemistry in a Leica BOND-III automated stainer. Sections were counterstained with hematoxylin. Scale bars: 5 mm, insets 200 μm. Figure S3. Expression levels of selected markers across astrocytic and microglial subclusters from public single-nuclei RNA-seq studies. Description: Bubble plots illustrate the percent of nuclei (bubble size) and the gene expression levels (z-scores, color bar) of the astrocytic and microglial markers used in our cyclic multiplex fluorescent immunohistochemistry protocol across the astrocytic and microglial subclusters rendered by several published single-nuclei RNA-seq data sets. Note that our set of markers discriminates some of these transcriptomic subclusters. Figure S4. Characterization of astrocytes and microglia in AD vs. CTRL by cortical layer. Description: Box and whisker plots illustrate the distribution (box: median and interquartile range [IQR]; whiskers: 1.5 × IQR) of mean gray intensity (MGI) z-scores for (a) each astrocytic marker and (b) each microglial marker across the CTRL and AD groups by cortical layer. Only layers II to VI were included in this study. Figure S5. Characterization of astrocytic and microglial states by cortical layer. Description: Box and whisker plots show the distribution (box: median and interquartile range [IQR]; whiskers: 1.5 × IQR) of mean gray intensity (MGI) z-scores for each astrocytic (a) or microglial (b) marker across the three phenotypes by cortical layer. Only layers II to VI were included in this study. Figure S6. Effects of proximity to AD neuropathological changes on astrocytic and microglial phenotypes from two CTRL subjects with abundant Aβ plaques. Description: (a) Representative high-plex image of astrocytes from a CTRL subject with abundant Aβ plaques; note the differences with AD astrocytes in Fig. 5a. For clarity, only ALDH1L1, EAAT2, and GFAP markers are shown together with Aβ. Scale bar: 100 µm, insets a1–a3: 10 µm. (b) Histograms show the proportion of each astrocyte phenotype in n=2 CTRL subjects with abundant Aβ plaques relative to all their astrocytes as a function of their distance (µm, x axis) to the nearest Aβ plaque. Note that there are equal numbers of astrocytes within 25 µm from the nearest Aβ plaque classified as homeostatic, intermediate, or reactive. (c) Representative high-plex image of microglia from the same field of the same CTRL with abundant Aβ plaques; note the differences when compared to AD microglia in Fig. 5c. For clarity, only IBA1, TMEM119, and CD68 markers are shown together with Aβ. Scale bar: 100 µm, insets c1–c3: 10 µm. (d) Histograms indicate the proportion of each microglial phenotype in n=2 CTRL subjects with abundant Aβ plaques relative to all their microglial profiles as a function of their distance (µm, x axis) to the nearest Aβ plaque. Note that most microglia in the vicinity of Aβ plaques were classified as homeostatic, suggesting that their phenotypic transition to intermediate and reactive had not yet occurred. Figure S7. Differences in neuritic component of Aβ plaques from CTRL and AD subjects. Description: Representative images of Aβ and phospho-tau (PHF1) immunohistochemistry corresponding to the same fields of the AD and CTRL subjects shown in Fig. 5 and Fig. S6, respectively. Note the differences in the PHF1+ neuritic changes between CTRL and AD Aβ plaques. Scale bar: 100 µm, insets a1 and b1: 10 µm. Figure S8. Gradient boosting machine models accurately discriminate between glial phenotypes. Description: Receiver operating characteristic (ROC) curves demonstrate the high discriminative power of the gradient boosting machine (GBM) models to discern between states (i.e., homeostatic vs. intermediate vs. reactive) of (a) astrocytes and (b) microglia based on mean gray intensity (MGI) data from thousands of high-plex single-cell profiles. Rankings of the variable importance scores shown in the horizontal bar plots reveal the most relevant markers for each classification task, respectively. Figure S9. Application of deep learning model interpretability functions to astrocytes with extreme classification probabilities. Description: Examples of the convolutional neural network (CNN) model interpretability functions applied to astrocytes with extreme classification probabilities (i.e., confident and correct predictions). Columns 1 and 5 show DAPI and all astrocyte markers of the high-plex image of a single astrocyte cell body from a CTRL and an AD subject, respectively, after performing the CNN normalization steps described (i.e., segmentation, interpolation, channel-level z-score). Hence, the signal intensity is represented by dynamic range rather than by pixel intensity. Columns 2–4 and 6–8 show the saliency (2 and 6), integrated gradient (3 and 7), and GradCAM (4 and 8) maps, which illustrate the pixels of each marker that the CNN considered most important for the classification of these two astrocytes as CTRL or AD. Figure S10. Application of deep learning model interpretability functions to microglia with extreme classification probabilities. Description: Examples of the convolutional neural network (CNN) model interpretability functions applied to microglia with extreme classification probabilities (i.e., confident and correct predictions). Columns 1 and 5 show DAPI and all microglial markers of the high-plex image of a single microglial cell from a CTRL and an AD subject, respectively, after performing the CNN normalization steps described (i.e., segmentation, interpolation, channel-level z-score). Hence, the signal intensity is represented by dynamic range rather than by pixel intensity. Columns 2–4 and 6–8 show the saliency (2 and 6), integrated gradient (3 and 7), and GradCAM (4 and 8) maps, which illustrate the pixels of each marker that the CNN considered most important for the classification of these two microglia as CTRL or AD., Ministerio de Ciencia, Innovación y Universidades Real Colegio Complutense National Institute on Aging Alzheimer's Association., Peer reviewed

8 pages. -- Fig S1. Top-view SEM images of Au nanodome array half-embedded into photocurable silicone Scale bar = 1 µm. -- Fig S2. Top-view SEM images of Al nanodome array half-embedded into PDMS cured at 100ºC. Scale bar = 1 µm. -- Fig S3. Wrinkled pattern dimensions (wavelength and skin layer thickness) for the PDMS films cured on a flat Au film as a function of the curing temperature. -- Fig S4. Experimental reflection spectra of the gold nanodomes array transferred to a cured flat PDMS substrate as a function of the applied strain. -- Fig S5. a) Reflectance spectrum of gold nanodomes transferred on top of the cured PDMS surface. b) Reflectance spectrum of gold nanodomes partially embedded into the PDMS cured by UV light. c) Reflectance spectrum of the gold nanodomes embedded inside the PDMS forming a FP cavity enhanced by the plasmon resonance of the gold semi-shells for 0% and 13% strains. d) Reflectance change of a wrinkled surface without gold semishells as a function of the applied strain. -- Fig S6. a) SEM top-view image of gold nanodomes on a silicon substrate. Scale bar = 500 nm. b) Comparison of the FDTD simulations and experimental measurement of the gold nanodome array transferred to a flat PDMS substrate. -- Fig S7. Optomechanical response of the swallowed array of gold nanodomes inside wrinkled PDMS to increasing strain. Incident light is polarized in (a) parallel and (b) perpendicular to the stretching direction. -- Fig S8. Evolution of the full width half maximum (FWHM) of the resonant peak initially located at 790 nm as a function of the strain for the sample cured at 140ºC. -- Fig S9. Schematic of the opto-mechanic compression set-up., Peer reviewed

[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., [EN] ECTACI database contains four statistical parameters (climatology, coefficient of variation, slope, and significant trend) from 125 standard climate indices for the whole Europe at 0.25° grid intervals from 1979 to 2017 at various temporal scales (monthly, seasonal, and annual)., [ES] La base de datos ECTACI proporciona cuatro parámetros estadísticos (climatología, coeficiente de variación, pendiente y significatividad de tendencia) de 125 índices climáticos para toda Europa con una resolución espacial de 0.25 grados desde 1979 a 2017 a tres escalas temporales (mensual, estacional y anual)., Spanish Commission of Science and Technology and FEDER by the research projects CGL2017‐82216‐R, CGL2017‐83866‐C3‐1‐R and PCI2019‐103631, the AXIS (Assessment of Cross(X) ‐ sectorial climate Impacts and pathways for Sustainable transformation), JPI‐Climate cofunded call of the European Commission by the research projects CROSSDRO, FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR) with cofunding by the European Union (grant 690462) by the research projects INDECIS which is part of ERA4CS, an ERA‐NET, Peer reviewed

Additional file 12 Figure S7. Scheme of the effect of Myosin over F-actin. (1), Myosin movement in parallel f-Actin. (2) F-actin sliding. (3) Tension building in the filaments. (4) Release from cortex after threshold tension is reached. After release, tension (illustrated in red) is redistributed to other F-actin in the network., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

15 pages. -- PDF file includes: 1. SEM-EDS characterization. -- 2. TEM characterization. -- 3. XPS characterization. -- 4. Electrochemical measurement. -- 5. IC measurement. -- 6. DFT calculations., Peer reviewed

The contribution of natural killer (NK) cells to tumor rejection in the context of programmed death-ligand 1/programmed death 1 (PD-L1/PD-1) blockade is a matter of intense debate. To elucidate the role of PD-L1 expression on tumor cells and the functional consequences of engaging PD-1 receptor on cytotoxic cells, PD-L1 expression was genetically inactivated and WT or PD-L1-deficient parental tumor cells were adoptively transferred intravenously into F1 recipients. The engraftment of PD-L1-deficient A20 tumor cells in the spleen and liver of F1 recipients was impaired compared with A20 PD-L1 WT tumor counterparts. To elucidate the mechanism responsible for this differential tumor engraftment and determine the relevance of the role of the PD-L1/PD-1 pathway in the interplay of tumor cells/NK cells, a short-term competitive tumor implantation assay in the peritoneal cavity of semiallogeneic F1 recipients was designed. The results presented herein showed that NK cells killed target tumor cells with similar efficiency regardless of PD-L1 expression, whereas PD-L1 expression on A20 tumor cells conferred significant tumor protection against rejection by CD8 T cells confirming the role of the co-inhibitory receptor PD-1 in the modulation of their cytotoxic activity. In summary, PD-L1 expression on A20 leukemia tumor cells modulates CD8 T-cell-mediated responses to tumor-specific antigens but does not contribute to inhibit NK cell-mediated hybrid resistance, which correlates with the inability to detect PD-1 expression on NK cells neither under steady-state conditions nor under inflammatory conditions., Peer reviewed

Figure 1S. Evolution over time of genetic study requests in Spain, LATAM and Turkey. Figure 2S. Evolution over time of requests for genetic studies by LATAM countries. Figure 3S. Distribution of sample types by geographical area. Figure 4S. Time elapsed between different steps by geographical area. Figure 5S. Allele distribution according to the different genotyping reasons in different Latin American countries. Table 1S. Description of the allelic variants and associated alleles tested by A1AT Genotyping Test; Information about the activity of the A1AT protein expressed. Table 2S. Cases in which sequencing revealed additional mutation from direct genotyping., Grifols., Peer reviewed

7 pages. -- PDF file includes: S1. AC HAADF STEM images of Ge/Ge0.92Sn0.08 core/shell NWs. -- S2. Estimate the gate capacitance of NW field-effect transistor. -- S3. Electrical characteristics of single Ge field-effect transistor. -- S4. Optical characteristics of single Ge/Ge0.92Sn0.08 core/shell NW detector. -- S5. FDTD simulation, Group IV Ge1–xSnx semiconductors hold the premise of enabling broadband silicon-integrated infrared optoelectronics due to their tunable band gap energy and directness. Herein, we exploit these attributes along with the enhanced lattice strain relaxation in Ge/Ge0.92Sn0.08 core/shell nanowire heterostructures to implement highly responsive room-temperature short-wave infrared nanoscale photodetectors. Atomic-level studies confirm the uniform shell composition and its higher crystallinity with respect to thin films counterparts. The demonstrated Ge/Ge0.92Sn0.08 p-type field-effect nanowire transistors exhibit superior optoelectronic properties achieving simultaneously relatively high mobility, high ON/OFF ratio, and high responsivity, in addition to a broadband absorption in the short-wave infrared range. Indeed, the reduced band gap of the Ge0.92Sn0.08 shell yields an extended cutoff wavelength of 2.1 μm, with a room-temperature responsivity reaching 2.7 A/W at 1550 nm. These results highlight the potential of Ge/Ge1–xSnx core/shell nanowires as silicon-compatible building blocks for nanoscale-integrated infrared photonics., Peer reviewed

Additional file 1: Table 1. Supplementary data. HLA-A, -B, -C and -G alleles from IPD-IMGT/HLA Database with 100% identity with 9-mer UL401524 CMV found in this study., Instituto de Salud Carlos III., Peer reviewed

Series temporales mensuales de caudal., [EN] This dataset includes series of monthly streamflow of 3224 gauging stations throughout Europe with reconstructed data for the period 1962-2017., [ES] Base de datos de caudal mensual de 3224 estaciones de aforo a lo largo de Europa con datos reconstruidos para el periodo 1962-2017., Spanish Commission of Science and Technology and FEDER by the research projects CGL2017‐82216‐R and CGL2017‐83866‐C3‐1‐R and PCI2019‐103631, AXIS (Assessment of Cross(X) ‐ sectorial climate Impacts and pathways for Sustainable transformation), JPI‐Climate cofunded call of the European Commission by the research project CROSSDRO, FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR) with cofunding by the European Union (grant 690462) by the research project INDECIS which is part of ERA4CS, an ERA‐NET initiated by JPI Climate, Peer reviewed