dataesr/scientific-paragraphs-categorization huggingface dataset:
Haematoxylin and eosin staining UCX® spheroids were resuspended in Tissue Tek® O. C. T. ™ (Sakura, Zoeterwoede, The Netherlands) for preparing cryosections of 10 μm. Slides were first stained with Harris's haematoxylin (Sigma-Aldrich) for 10 minutes, followed by an incubation step with HCl 1% (v/v) in 70% EtOH, and by Eosin Y (Sigma-Aldrich) staining for 2 minutes. Slides were then submitted to increasing concentrations of ethanol and finally incubated in xylene (EMD Chemicals, Inc. , Gibbstown, NJ, USA). Samples were mounted with Entellan® (Merck, Darmstadt, Germany). Images were acquired on an Olympus CK30 inverted microscope and processed using Motic Images Version 2. 0 software.
Growth factor quantification The concentrations of hepatocyte growth factor (HGF), fibroblast growth factor (FGF)-2, vascular endothelial growth factor (VEGF)-A, interleukin (IL)-6, TGF-β1, keratinocyte growth factor (KGF) and C-GSF in the CM2D, CM3D and control sample were evaluated by means of a Fluorescent Bead Immunoassay kit (Flow-Cytomix™, eBioscience) and an enzyme-linked immunosorbent assay (ELISA) kit (Quantikine®, R&D Systems, Minneapolis, MI, USA), for KGF quantification. Protocols were performed as per manufacturer's recommendations. All samples were acquired on a Gallios (Beckman Coulter) and the results were obtained using FlowCytomix™ Pro 3. 0 Software and expressed as pg/mL of total protein, normalized in relation to the control. Results from three independent experiments are shown as fold increase of CM3D relative to CM2D.
The 40-hour time corresponds to the time period of incubation immediately before the complete scratch closure, and where cells were under the fastest migrating condition. Photographs were taken at an amplification of 40× on a Motic AE2000 inverted microscope. Cellular migration was analysed in the Motic Images Version 2. 0 program by calculation of scratch closure, given as the total area occupied by the cells after contact with CM, which was calculated in relation to the initial scratch area at 0 hours. At least nine and six independent experiments in triplicates were performed in HaCaT and HDF, respectively.
To prevent skin contraction, a donut-shaped splint was fashioned from a 0. 5-mm thick silicone sheet (Molecular Probes, Carlsbad, CA, USA). Each animal carried four wounds to which 100 μL of each sample 10× concentrated was applied via subcutaneous injection between the wound margin and the silicone splint of the respective wound, as follows: 1) CM2D; 2) CM3D; 3) control (UCX® medium that was never in contact with cells); and 4) sham (natural wound resolution). Sample administration was repeated after 24 hours, in a total of three applications. Wounds were not covered by any dressing but left to open air. Wound closure was defined as the time at which the wound bed was completely filled with new tissue and re-epithelialized. Wound area was calculated as a percent area of the original wound so that percentage of wound closure was defined as follows: (area of original woundarea of actual wound)/area of original wound × 100, being the wound area measured by tracing the wound margin and calculated using an image analysis program (ImageJ).
Statistical analysis Data analysis and graphs were plotted using GraphPad Prism® software (GraphPad Software, San Diego, CA, USA). Values presented in the text and figures are as mean ± standard deviations of at least three independent experiments, except otherwise specified. To estimate the significance of the differences of the growth factor quantification and of the data obtained in the in vivo assay, the parametric students t test was used. The one way analysis of variance test using Tukey's post-hoc test for correction of multiple comparisons was also performed. P < 0. 05 was considered significant.
(C) Graphs represent the histological scores (top to bottom) of wound margin distance, re-epithelialization, granulation tissue and vascularization, evaluated in CM2D-, CM3D-, control and sham-treated wounds at days 7 (n = 4), 9 (n = 5) and 14 (n = 6). Data are shown as mean ± SEM. *P < 0. 05; **P < 0. 01. GT, granulation tissue; Ep, epithelium; HF, hair follicle; Gl, gland. may also be at the basis of the CM3D-mediated keratinocyte/fibroblast migration observed in our in vitro migration and proliferation assays. MMP-9 has been implicated in enhanced invasive potential of keratinocytes in response to EGF and HGF  [55]. The secretion of both MMP-2 and MMP-9 has been associated with MSC recruitment and infiltration into injured tissues in vivo  [56]. Thus, the higher production of both MMP-9 and MMP-2 by UCX® spheroids may reflect the requirement to surmount the complex ECM mesh formed within the spheroid when compared to the plane basement membrane formed in monolayers.
Acknowledgements We acknowledge  Conceição Peleteiro ,  Marta Lemos  and  Sandra Carvalho  from  Faculdade de Medicina Veterinária -Universidade de Lisboa  ( FMV-ULisboa ) for excellent assistance with histological analyses. This work was supported by  FCT-Fundação para a Ciência e a Tecnologia  [ EXPL/DTP-FTO/0308/2013 ,  SFRH/BPD/ 96719/2013  and  Ciência2008  to JPM,  SFRH/BPD/42254/2007  and  ON. 2(NORTE-07-0124-FEDER-000005 ) to DSN and  SFRH/BD/87508/2012  to MC]; by ECBio; and by the project on Biomedical Engineering for Regenerative Therapies and Cancer cofunded by " ON. 2 -O Novo Norte" (Programa Operacional Regional do Norte   2014-2020 ), by  Fundo Europeu de Desenvolvimento Regional-FEDER ,  Programa Operacional Factores de Competitividade-COMPETE  and by  QREN. The work herein presented was performed at iMed.
Earth's life-support systems are in rapid decline, yet we have few metrics or indicators with which to track these changes. The world's governments are calling for biodiversity and ecosystem-service monitoring to guide and evaluate international conservation policy as well as to incorporate natural capital into their national accounts. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has been tasked with setting up this monitoring system. Here we explore the immediate feasibility of creating a global ecosystem-service monitoring platform under the GEO BON framework through combining data from national statistics, global vegetation models, and production function models. We found that nine ecosystem services could be annually reported at a national scale in the short term: carbon sequestration, water supply for hydropower, and non-fisheries marine products, crop, livestock, game meat, fisheries, mariculture, and timber production.
, 2012). Countries struggle to report on progress towards environmental goals such as the Convention on Biological Diversity's (CBD) targets. These CBD targets now explicitly include ecosystem services, but tracking the ecosystem services targets for 2010 failed in part because necessary data were not globally available for calculating indicators and reporting progress  (Walpole et al. , 2009; CBD, 2014). Furthermore, efforts are underway by The World Bank and other international organizations to measure natural capital and more holistically account for the wealth of nations. Such accounting will require regular monitoring to assess changes in natural capital (stocks) and ecosystem services (flows) over time  (The World Bank, 2011; UNSD EEA and The World Bank, 2011; UNU-IHDP and UNEP, 2014) , but often the relevant data are lacking.
Overview of reported services We compiled indicators of the supply, delivery, and monetary value of ecosystem services at national scales from 1996 to 2005, focusing only on services that are annually reported or can be modeled at that time step for a large number of countries worldwide. We have chosen this time frame to evaluate annual changes in ecosystem services over an exemplary decade, which can be regarded as a starting point for monitoring as well as a call for regularly updating datasets. We found adequate temporal and spatial coverage for eight provisioning services (water supply for hydropower, non-fisheries marine products, crop, livestock, game meat, fisheries, mariculture, and timber production), and one regulating service (carbon sequestration). Data were not sufficient to include any cultural services at this stage, although their relevance is well recognized.
When possible, we calculated the supply, delivery, and monetary value for each service; however, data gaps precluded reporting of all three metrics for each service. In several cases, we combined datasets to be able to report indicators for the different components of ecosystem service supply, delivery, and value. Indicators for these nine ecosystem services were extracted from three primary sources: (1) national statistics derived from census data, primarily reported through the Food and Agriculture Organization (FAO) of the United Nations, (2) a global dynamic vegetation model (LPJmL), and (3) an ecosystemservice production function model (InVEST).
National statistics The FAO reports the trade, in-country production, and monetary value of several marketed provisioning services. The data are largely derived from national censuses, annually and voluntarily reported by countries to the United Nations. The data suffers from well-documented biases. First, nation-states do not always honestly report production data  (Watson and Pauly, 2001). Second, activities outside markets are undocumented (e. g. , selfconsumption, local trade, or black markets). Third, monitoring infrastructure varies, meaning reporting can be irregular and inaccurate, especially for developing countries. Therefore, the FAO is often forced to fill reporting gaps with estimated and modeled data. While these compounded error sources introduce significant uncertainty, FAO data is irreplaceable-it represents the only current platform that annually reports provisioning services with global coverage.
We used FAO data to report, by nation-state, the annual total production and monetary value for crop, livestock, game meat, timber, mariculture, non-fisheries marine products, and fisheries production from 1996 to 2005. Countries and/or specific products in a country (e. g. , maize production) were excluded if they did not regularly report both total production (tonnes) and monetary value (constant million 2004-2006 US$) each year. In all cases, production data were reported in tonnes. Some countries/specific products were excluded because production value was reported by the FAO only in constant million 2004-2006 international dollars. To estimate food production, we differentiated between crop, livestock, and game meat production. For crop production, we chose to report the summed total output (tonnes) of 12 crop types that could be validated against a global vegetation model (see below): (1) groundnuts, (2) maize, (3) pulses, (4) rapeseed, (5) rice, (6) soybean, (7) sugarcane, (8) sunflower, (9) temperate cereals (barley; wheat; rye), (10) temperate roots (sugarbeet), (11) tropical cereals (millet; sorghum), and (12) tropical roots (cassava).
We then multiplied estimated price by in-country production to calculate the value of in-country roundwood production. Production and value data for marine resources also came primarily from FAO statistics. As for the other services, FAO marine products data are invaluable but flawed. For example, fisheries data are know to suffer false reporting  (Watson and Pauly, 2001). Reporting errors are compounded by known limitations and unknown errors in the algorithm needed to spatially allocate FAO reported data to countries. Despite these limitations, we reported three categories of marine ecosystem services. First, we defined mariculture as the production of marine taxa from marine and brackish water environments as assigned by FAO, excluding seaweeds, which are treated as natural products (see below).
Mariculture production data (but not monetary value) were available over our target decade. For the three exclusive economic zones (EEZs) that fall within the China region (China, Macau, and Hong Kong), we combined the values by summing across these EEZs. Second, we reported six non-fisheries marine products (corals, fish oil, ornamental fish, seaweeds, sponges, and shells). Only export data were used, with commodity types and subcategories accessed as in  (Halpern et al. , 2012) , although some subcategories are no longer reported. Third, we reported production and value of wild-caught commercial fisheries. Data were derived from annual FAO fisheries statistics, which are reported in major fishing areas rather than EEZs or countries, and include total annual catch through 2005. The Sea Around Us Project at the University of British Colombia spatially allocated these FAO fishing areas into reporting regions associated with nation-states based on species range maps, fishing treaties and arrangements, and various other factors  (Watson et al.
LPJmL The Lund-Potsdam-Jena managed land model (LPJmL) is based on the dynamic global vegetation and water budget model LPJ  (Sitch et al. , 2003). We used LPJmL to model carbon sequestration, timber supply, and water supply globally, as well as to estimate the production of 12 crop functional groups to compare against FAO reported data. LPJmL simulates the productivity of the most important annual crops worldwide as well as potential natural vegetation of terrestrial ecosystems with a spatial resolution of 0. 5 longitude/latitude. LPJmL tracks 16 crop functional groups, including 12 arable crops, two types of managed grassland, and two bioenergy plants. Crop and grassland area is prescribed from a dataset combining global cropland extent in 2000  (Ramankutty et al. , 2008) , changes in historical cropland between  1700 and 2000 (Klein Goldewijk and van Drecht, 2006) , and harvested areas for rainfed and irrigated crops in 2000  (Fader et al.
0 climate dataset at the global scale to simulate transient changes  (Mitchell and Jones, 2005). Carbon sequestration was calculated as the sum of carbon contained in vegetation, litter, and soil. Potential annual timber biomass (timber supply) was calculated from carbon bound in wood, summed for each country. Only wood from non-protected areas was included, as wood within protected areas is not available for legal harvest. Protected areas were estimated from the UN List of Protected Areas (IUCN and UNEP-WCMC, 2015) for which a finer raster of 0. 125 was created from the vector data, thus subdividing each LPJmL grid cell into 4 Â 4 cells  (Chape et al. , 2013). If more than half of the area of a sub-cell was protected, the sub-cell was counted as protected. The protected areas accounted for in the analysis include only strict nature reserves-wilderness areas (IUCN categories 1a/b) and National Parks (category II).
The management factor was held constant over our target decade so that we could compare annual fluctuations in crop production for each country between LPJmL modeled data and FAO reported data. Therefore, simulated inter-annual variability of crop harvest for this study is a reflection of climate variability but not changes in farming practices. To account for possible multiple cropping events on the same areas within a year, the modeled country-based yields were multiplied by the average harvested area (per country across the target decade) for each group of crops as reported to the FAO. Again, harvested areas were held constant over the decade to facilitate FAO/LPJmL comparisons. To compare FAO/LPJmL crop production, we first calculated the total production of the 12 crop functional types in each year and country using LPJmL and FAO data.
To do so, we summed the total LPJmL predicted crop production for each crop functional type across all grid cells in each country. Then, for each country, we standardized crop production, subtracting the mean crop production across the 10 years and dividing by the standard deviation. Next, to compare FAO reported and LPJmL modeled crop production, we regressed FAO data against LPJmL data in each country, using years as replicates (N = 10).
InVEST Integrated Valuation of Environmental Services and Tradeoffs (InVEST) is a suite of spatial models that takes a simplified yet scientifically robust approach to quantifying, mapping, and valuing a variety of terrestrial and marine ecosystem services, including water supply for hydropower production  (Kareiva et al. , 2011; Tallis et al. , 2013). The InVEST models have components to estimate the supply, delivery, and value of included services. In this large-scale application, the more extensively validated LPJmL model was used to calculate water supply estimates, in the form of annual water discharge per grid cell. InVEST was then used to calculate the amount of energy that may be produced by reservoir hydropower plants from simulated water runoff at each facility. Water discharge grids for each modeled year were overlaid with the point locations of global reservoirs provided by the World Water Development Report II (WWDRII) Reservoirs database  (Vorosmarty et al.
We were able to collect the necessary hydropower facility information for 194 of the 668 dams represented in the WWDRII database, and so only calculated energy production at that subset of facilities. Dam height was given in the WWDRII database for each reservoir and efficiency data were derived from  (Vorosmarty et al. , 2003). We were unable, however, to acquire specific data on flow fractions and therefore delineated a value of 1 for all facilities (100% of available water is used for energy generation). Assuming that all water above the turbine in reservoirs was available for hydropower production likely created over-estimates of the amount of energy production per dam. Many reservoirs are multi-use, and some fraction of water is not used for hydropower generation. Since we only capture large dams, not tens of thousands of small dams, the overall estimate is still likely conservative.
Data source accuracy Any global monitoring system will rely on a combination of remotely sensed, directly observed, survey-based, and modeled data. Even when data sources meet the requirements for annual   (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005). Lines depict the percent increase or decrease from 1996 levels in the supply (blue), delivery (yellow), and value (red) of each ecosystem service. Data were derived from national statistics (e. g. , timber production) and models (e. g. , timber supply). Countries were only included if they regularly reported each stage of ecosystem service provision (supply, delivery, or value) that we were able to track (Supplementary material). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
) reporting and global coverage at a national scale, it is important to consider data accuracy. We provide three examples of how accuracy can be assessed. We do not attempt to be exhaustive in establishing the validity and accuracy of data sources used, but rather to provide demonstrative examples of how data sources could be assessed for accuracy as they become available and are added to a global monitoring program. For many ecosystem services, the most likely method for reaching global, consistent coverage in the near term is modeling. No global models can accurately reflect services everywhere, so clear communication about where models agree with observed data is essential. We applied the global vegetation and water model LPJmL to estimate river discharge and InVEST to estimate energy production from hydropower.
In panel A, each line corresponds to the correlation between FAO and LMPmL data within one country, based on 10 data points (total crop production in each of 10 years). Black lines depict significant correlations (P < 0. 05); gray lines are not significant. Panel B depicts regression slopes spatially. cereals (millet; sorghum), and (12) tropical roots (cassava) (Fig. 3 ). Despite the fact LPJmL did not account for temporal changes in farm management practices, FAO and LPJmL crop production positively correlated over time in 77% of the 149 countries analyzed. Positive correlations were well distributed across the world, thereby increasing our confidence in both data sources. However, positive correlations were statistically significant in only 13% of countries, which could either be a result of incomplete or false reporting to FAO or problems with the LPJmL model such as (1) poor climate data not representing inter-annual variability correctly, (2) missing processes in the crop model, or (3) changes in agricultural management practices over time from agricultural policies, investments, or innovations.
Even regional ecosystemservice monitoring programs are constrained by land-use data; for example, the European Union commissioned effort relied heavily on a land-use dataset (Corine Land Cover) which is updated at a minimum of 5-yr time intervals  (Maes et al. , 2014). Advances in remote sensing technology or post-processing that allowed for regular updating, and hence accurate determination of global land-use change, could open the door to regular use of multiple ecosystem-service models at global scales (Table  1 ). Alternatively, models could be developed to estimate ecosystemservice supply and delivery directly from remotely sensed products, thus bypassing the need for land-use/land-cover classifications to be improved. Yet another alternative for some services would be to avoid models entirely through creative use of existing data.
1 and at NLO with POWHEG, as described in  [12]. The corresponding higher-order effects depend on the dilepton kinematic variables. Higher-order EW corrections are small in comparison to FSR corrections. They increase for invariant masses in the TeV region  [33] , but are insignificant compared to the experimental precision for the whole mass range under study. The NNLO QCD effects are most important in the low-mass region. The ef-fect of the correction factors on the acceptance ranges up to 50% in the low-mass region (below 40 GeV), but is almost negligible in the high-mass region (above 200 GeV). The main SM background processes are simulated with POWHEG (DY → τ + τ -, single top quark) and with MADGRAPH  [34]  (tt, diboson events WW/WZ/ZZ). Both POWHEG and MAD-GRAPH are interfaced with the TAUOLA package  [35] , which handles decays of τ leptons.
This sample is used to estimate the probability for a jet to pass the requirements of the electromagnetic trigger and to be falsely reconstructed as an electron. This probability is then applied to a sample of events with one electron and one jet to estimate the background contribution from an electron and a jet passing electron selection requirements. As the contribution from two jets passing the electron selections is considered twice in the previous method, the contribution from a sample with two jets multiplied by the square of the probability for jets passing the electron selection requirements is further subtracted. The QCD multijet background in the dimuon channel is evaluated by selecting a control data sample before the isolation and charge sign requirements are applied, following the method described in  [49].
The latter must be taken into account because the response matrix is determined from simulation. Background uncertainty. The background estimation uncertainties are evaluated in the same way in both the dielectron and dimuon channels. The uncertainty in the background is comprised of the Poissonian statistical uncertainty of predicted backgrounds and the difference between the predictions from the data and simulation. The two components are combined in quadrature. The uncertainty in the background is no larger than 3. 0% (1. 0%) at low mass, reaching 16. 3% (4. 6%) in the highest mass bin in the dielectron (dimuon) channel. γγ-initiated background uncertainty. The uncertainty in the correction for γγ-initiated processes is estimated using FEWZ 3. 1 with the NNPDF2. 3QED PDF and consists of the statistical and PDF uncertainty contributions combined in quadrature.