dataesr/scientific-paragraphs-categorization huggingface dataset:
Observed data at the point scale: description of site-level data The observed data at the point scale are forest-census-based plot measurements across the Amazon region, in which all plots that were subject to anthropogenic disturbances, including selective logging, were excluded  (Brienen et al. , 2015). The average plot size is ∼ 1. 2 ha  (Brienen et al. , 2015)  so that the plots incorporate most size classes of natural gaps, particularly as the plot data were averaged across sites occurring within the same pixel. Across the plot network, the biases introduced into the estimate of carbon balance by 1 ha plots not sampling the very largest and rarest natural gaps are in fact very small  (Espírito-Santo et al. , 2014). We use datasets of AGB  (Lopez-Gonzalez et al. , 2011 , 2014; Mitchard et al.
(2012). Wood density is estimated for each stem using the mean value for the species in the Global Wood Density Database  (Chave et al. , 2009; Zanne et al. , 2009) , or the mean for the genus using congeneric taxa from Mexico, Central America and tropical South America if no data were available for that species  (Mitchard et al. , 2014). We here evaluate the principal AGB dataset (K DHρ ) from  Mitchard et al. (2014)  in more detail (the other allometric equations are presented in the Supplement). 2. 2. 2 Simulated data at the pixel scale: description of DGVM simulations We use outputs from four state-of-the-art DGVMs, namely the Lund-Potsdam-Jena model with managed Land (LPJmL,  Bondeau et al. , 2007; Gerten et al. , 2004; Sitch et al. , 2003) , the Joint UK Land Environment Simulator (JULES), v.
Comparing inventory and simulation results In our application, dataset X corresponds to the inventory measurements at the point scale (Fig. 1a ). For this dataset, we have to derive the within-pixel variability (Fig. 1b ). Dataset Y corresponds to the simulated pixel values (Fig. 1c ). Hence, the pixel scale is defined by the resolution of the model simulation (1 • ×1 • , approximately 12 200 km 2 , Fig. 1c ). We calculate the three metrics (Sect. 2. 1. 3) from the observed and simulated ecosystem variables AGB, WP and τ.
, 2016). Recent efforts aiming at improving simulated Amazon forest biomass and productivity by including spatial variation in biophysical parameters (such as τ and Vc max ) have found that using single values for key parameters limits simulation accuracy  (Castanho et al. , 2013). Thus, we conclude that a more mechanistic representation of the processes driving the spatial variability in carbon stocks and fluxes, forest structure, and tree demographic dynamics is necessary to improve simulation accuracy  (Rödig et al. , 2018). 5 Future applications of the methodological approach and outlook In general, we assume that the basic concept of our method is applicable to any comparison between two datasets that are characterized by differences in spatial scale. If the process that causes small-scale variability can be approximated as white noise, corrected statistics can be computed.
Acknowledgements. We acknowledge funding from the  European Union 's  Seventh Framework Programme AMAZALERT project  ( 282664 ), the  Helmholtz Alliance "Remote Sensing and Earth System Dynamics ", and the  Belmont Forum/BMBF  funded project CLIMAX. We acknowledge the efforts of the TEAM, RAINFOR and ATDM projects making the observational datasets available. This work was supported by the  German Research Foundation (DFG)  and the  Technical University of Munich (TUM)  in the framework of the  Open Access Publishing Program. Edited by: Carlos Sierra Reviewed by: two anonymous referees.
Code and data availability. All models are described in more detail in the Supplement. The model code for LPJmL is available at https://github. com/PIK-LPJmL/LPJmL (last access: 17 December 2018) and archived under https://doi. org/10. 5880/pik. 2018. 002. The model code for ORCHIDEE is available at https://doi. org/10. 14768/06337394-73A9-407C-9997-0E380DAC5597. The model code for JULES is available from the JULES FCM repository: https: //code. metoffice. gov. uk/trac/jules (registration required, last access: 17 December 2018). The model code for INLAND is available at http://www. ccst. inpe. br/wp-content/uploads/inland/inland2. 0. tar. gz (last access: 17 December 2018). The permanent archive of the observational data from Mitchard et al. (  2014 ) can be accessed at https://doi. org/10.
| GPS locations CatTracks were set up to record latitude and longitude every 30 min. Distance travelled (m) and speed (m/h) were calculated from GPS coordinates and time (GMT) of recorded positions. Mean distances per day were calculated excluding data from the day of capture and the day of recapture, to remove bias from anesthesia and capture. F I G U R E 1 Aggregation of molting female southern elephant seals (Mirounga leonina) in a wallow TA B L E 1 Dates when counts were undertaken and the number of female elephant seals equipped with GPS loggers, along with the mean number of days of observation   Boeuf, 2001) , we considered that points with speed > 1000 m/h could possibly correspond to movements at sea. Therefore, measures when speed was > 300 m/h and < 1000 m/h were considered to be due to position errors (i.
The objective was to measure and monitor precipitation not only in terms of quantity, but also of falling snow particle characteristics and microphysics. The range of instruments included a profiling K-band and a polarimetric scanning X-band radar, a multi-angle snowflake camera (MASC), an OTT Pluvio2 weighing gauge, and a Biral VPF-730 disdrometer. A weather station reporting temperature, moisture and wind conditions near the instruments was also deployed. Finally, a depolarization lidar was tentatively operated but had problems and is not further mentioned here. All instruments were removed at the end of January 2016 except the K-band radar, which remained in operation throughout 2016 and beyond. Grazioli et al. (2017a)  provide a comprehensive description of the data and analysis techniques and discuss scientific outcomes.
Further work is ongoing to address the calibration and validation of meteorological and climate models and of satellite remote sensing techniques with the data (snowfall occurrences and rates, but also vertical profiles). Meanwhile, because this is a unique dataset, dissemination to the wider community for similar use with other models and remote sensing processing approaches or other research purposes is considered timely. This paper provides a compact description of the dataset and dissemination. Grazioli et al. (2017a)  provide ample information on the observation site, most instruments and methods. A summary and complementary information are provided below.
However, the raw Doppler spectra can be reprocessed using  Maahn and Kollias (2012) 's improved and innovative processing chain for data collected in snow to retrieve Doppler radar moments such as reflectivity Z and Doppler velocity. Most Z-S relations for radars have been derived for 10, 35 or 94 GHz and therefore the measured equivalent radar reflectivity at 24 GHz is first converted to X-band. Once mapped to Xband reflectivity this can be converted to snowfall rate S by means of a Z-S power law fitted to the local conditions using the weighing gauge information or parameterizations from the existing literature (for more details, see  Grazioli et al. , 2017a). The MRR was used with a 100 m vertical resolution. The second instrument, a mobile X-band polarimetric radar (MXPol), for which extensive experience with the measurement of snow is available  (Schneebeli et al.
Disdrometer and MASC The Biral VPF 730 disdrometer is also a non-capture instrument, which estimates the size and speed of airborne particles from the diffusion and diffraction of an infrared light beam within a 400 cm 3 air volume. The volumetric sampling of the VPF730 presents an advantage over 2-D sampling instruments, which is that it does not miss particles with a much larger horizontal (due to strong wind) than vertical (falling) speed. The downside is that the instrument does not straightforwardly distinguish between falling and blowing snow  (Bellot et al. , 2011). A Biral proprietary algorithm directly provides precipitation rates from the size-speed matrix. Because this is based on various assumptions, including on the phase, shape and density of the particles, particularly unwarranted in the atypical Antarctic environment, the database described here presents the matrices rather than the estimated precipitation.
Data, samples and conclusions Table  1  summarizes the data streams from the APRES3 measurement campaign. Grazioli et al. (2017a)  extensively process and discuss the data from the different instruments. Further analyses and presentation are beyond the scope of this data paper, and only a few snapshots are provided to illustrate the content of the database. Figure  2  shows the cumulative precipitation during the intensive summer campaign, as yielded by the Pluvio2 snow gauge and the processed MRR at the lowest useful level and at 741 m above sea level. Only 28 out of 31 MRR levels are provided in the database. This is because several simplifications necessary for a tractable quantitative interpretation of radar signal power do not apply in the two lowest levels. Data processing is based on assumptions that are not valid as it may lead to overestimation of reflectivity  (Peters et al.
, 2005). In the uppermost level, the data become noisy, as according to  Kneifel et al. (2011) , the detectability is highest close to the ground, at -2 dBz (35 GHz equivalent) at 500 m, but decreases with height to 3 dBz at 3000 m. Precipitation rates were retrieved from MRR data following  Grazioli et al. (2017a) : the reflectivity was converted into liquid water equivalent rate by fitting the prefactor and exponent of a Z-S relationship using carefully filtered nearby Pluvio2 data. Censoring the Pluvio2 data for windinduced biases such as vibrations and turbulence effects by cross-referencing with the MRR data removes up to 30 % of the quantities  (Grazioli et al. , 2017a) , as visible by the accumulation of the Pluvio2 in the time periods between snowfall events. As the Pluvio2 is a standard instrument but there is no standard correction method for wind effects, others might want to test other approaches, and the primary rather than the censored data are shown here and distributed in the database.
No less than 426 229 photographs of falling snow particles were collected during the season. Each picture is processed as described in  Praz et al. (2017). The database offers the processed results in the form of a classification, rather than the photographs themselves. Figure  4  cumulates all single estimates of the degree of riming in the database. The degree of riming is defined in this context as a continuous index between 0 (no riming on the particle detected) and 1 (fully rimed, graupel-like particle). Almost half of the particles are close to fully rimed, in-  dicating that cloud liquid water is very frequent in summer. Finally, Fig. 5  shows precipitation from the MRR dataset over the full record in the database, for more than a year from 21 November 2015 to 11 December 2016. Again, reports from two elevations, 341 and 741 m a.
Conclusions In conclusion, observations at DDU carried out as part of the APRS3 project provide an unprecedented dataset of precipitation at the coast of Antarctica, complementing existing documentation efforts  (Gorodetskaya et al. , 2015)  in a region which otherwise suffers from a severe shortage of such data. Our analysis of the data yields new insights into the characteristics and particularities of Antarctic snowfall, in particular that a large fraction of the precipitation formed in the atmosphere sublimates before reaching the surface. This information could only be obtained with instruments that can profile through the atmospheric layers, like radars here. However, the dataset goes beyond radar data and provides extensive complementary characterization of snow particle geometry and cumulative quantities of snowfall at the surface.
Except for the dataset from the MXPol dual-polarization scanning radar during the summer campaign, the size of which (about 4 TB) is too large to be shared online but can be obtained by direct request to the authors, all data are now distributed  (Berne et al. , 2017)  and can be freely accessed from the PANGAEA repository (https://doi. org/10. 1594/PANGAEA. 883562). Table  1  provides a summary of the the variables and periods covered and distributed online for each instrument. At the time of writing this paper, the project carries on with continuous collection of precipitation profiles with the MRR, and a planned contribution to the Year Of Polar Prediction (YOPP, http://www. polarprediction. net/, last access: 1 August 2018) international austral special coordinated observation period from November 2018 to February 2019, the data from which will also be made available to the community.
One of the genes upregulated almost 40-fold in trNKs is Slc40a1, encoding ferroportin (FPN1), which is the main exporter of iron from cells, suggesting that iron metabolism may be of importance in uterine trNK cells. Our dataset shows high expression of Nfe2l2 (NRF2) and significantly upregulated Nfe2l3 (NRF3) in trNK cells  44. Both can bind to antioxidant stress response elements and Nfe2l3 locus has been shown to be associated with endometriosis through genome-wide association studies  45  (Supplementary Fig. 3F ). The fifth comparison between uterine ILC1s and trNK cells highlights transcriptome profiles specific to uterine resident cells (Fig. 3f ). The most highly upregulated gene in uterine ILC1s compared to trNK is Aplnr, encoding for the adipokine apelin receptor, which is highly expressed in mouse endometrium  46.
Consistently with this notion, we detected a number of upregulated genes involved in pathways of microbial molecular pattern recognition. Genes associated with B-cells, or for protein turnover, antigen processing and presentation, as well as the macrophage-associated F4/80 marker are upregulated in ILC1s. The role of these B-cell and myeloid markers on uterine ILC1s is unknown, and a recently described subset of NKB cells is controversial  64, 65  , but might be induced in SIV and HIV infection  66. The deconvolution model we have applied here confirms that uterine g1 ILCs have expression profiles aligned with those of cell types other than NK cells and including myeloid cells. A clear limitation of the model is that there are no available ILCs defined in the dataset. Therefore, our results provide new data specific to uterine g1 ILCs at midgestation as a new resource for building predictive phenotype models.
RNA-sequencing and analysis. Group 1 ILCs from uterus and liver were sorted from Eomes-GFP reporter mice into the TRI Reagent (Sigma). RNA extraction was carried out by following TRI Reagent manufacturer's instructions, followed by RNeasy Micro kit (Qiagen). cDNA was amplified using Ovation RNA-seq system V2 (NuGEN) and DNA libraries were produced using Ovation Ultralow System V2 (NuGEN) and following manufacturer's instructions. All mRNA and cDNA quality controls and quantifications were performed using RNA 6000 Pico and High Sensitivity DNA kits (Agilent) on the 2100 Bioanalyzer instrument (Agilent). RNA sequencing was performed on Illumina Hiseq4000 at the Cancer Research UK Cambridge Institute Genomics Core. Data were aligned to GRCm38 mouse genome (Ensembl Release 84) with TopHat2 (v2.
1. 1, using bowtie2 v2. 2. 9) with a double map strategy. Alignments and QC were processed using custom Cluster-Flow (v0. 5dev) pipelines and assessed using MultiQC (0. 9. dev0). Gene quantification was determined with HTSeq-Counts (v0. 6. 1p1). Additional quality control was performed with feature counts (v 1. 5. 0-p2), qualimap (v2. 2) and preseq (v2. 0. 0). Differential gene expression was performed with DESeq2 package (v1. 16. 1, R v3. 4. 2) and with the same package read counts were normalised on the estimated size factors. Proportions of specific immune cell types from bulk RNA-Seq can be estimated using reference data generated from known proportions of the cell types of interest  71. DeconRNASeq 72 was applied to take these tables of known cell proportions, defined by gene expression profiles, and used to deconvolute bulk dataset generated in this study to estimate cell proportions within each of the sequence's samples.
Gene Ontology (biological process) analysis was performed using PANTHER (Protein ANalysis THrough Evolutionary Relationships database http://www. pantherdb. org). Lists of differentially expressed genes (adjusted p < 0. 05 and log2 fold change two or above) were submitted to PANTHER Overrepresentation Test (released 2017-12-05), with Fisher's exact testing utilising a false discovery rate multiple test correction and a Mus Musculus reference list. Further details on annotation versions and release dates can be found in the Supplementary Data 1. RT-qPCR. Whole uterus was isolated at time-points of interest, collected in RNAlater (Invitrogen) to stabilise the RNA and kept at 4 °C overnight. Tissue was homogenised in the Lysing Matrix S homogenisation tubes (MP Biomedicals) on a FastPrep-24 5G Homogenizer (MP Biomedicals) using RNeasy Plus Universal Kit (Qiagen) according to manufacturer's instruction.
Isolated RNA quality control and quantification was performed using RNA 6000 Nano kit (Agilent) on the 2100 Bioanalyzer instrument (Agilent). cDNA was amplified using SuperScript VILO cDNA Synthesis kit (Invitrogen) and qPCR was performed using PowerUP SYBR Green Master Mix (Applied Biosystems), on the Quant Studio 6 Flex Real-Time PCR System (Thermo Fisher -Applied Biosystems). Gapdh was used as a reference gene. Data were analysed using the ΔΔCt method. Primers were as follows: Spp1, 5′-TTCACTCCAATCGTCCCTAC-3′ (forward) and 5′-TTAGACTCACCGCTCT TCAT-3′ (reverse); Ogn, 5′-TGCTTTGTGGTCACATGGAT-3′ (forward) and 5′-GAAGCTGCACACAGCACAAT-3′ (reverse); Ptn, 5′-TGGAGAATGGCAGTGG AGTGT-3′ (forward) and 5′-GGCGGTATTGAGGTCACATTC-3′ (reverse) and Gapdh, 5′-TGCACCACCAACTGCTTAG-3′ (forward) and 5′-GGATGCAGGGA TGATGTTC-3′ (reverse).
Acknowledgements This work was funded by a  Wellcome Trust   Investigator Award   200841/Z/16/Z , the  Centre for Trophoblast Research (CTR) , and the  Cambridge NIHR BRC Cell Phenotyping Hub  to F. C. , the  Associazione Italiana Ricerca per la Ricerca sul Cancro (AIRC) - Special  Project  5×1000  no. 9962 , AIRC IG 2017 Id. 19920 and AIRC 2014 Id. 15283 to L. M. , and  Ministero della Salute   RF-2013 ,  GR-2013-02356568  to P. V. I. F. was funded by a  CTR PhD  fellowship. The Authors are grateful to  Thierry Walzer ,  Gerard Eberl  and  Ionel Sandovici  for sharing resources.
Data availability All relevant data are available from the corresponding author upon reasonable request. RNA-sequencing data have been deposited in ArrayExpress database at EMBL-EBI under accession number E-MTAB-6812 (https://www. ebi. ac. uk/ arrayexpress/experiments/E-MTAB-6812). An open-source repository (https:// github. com/CTR-BFX/2018-Filipovic-Colucci) has been created with access to the code used in this study. Received: 24 May 2018 Accepted: 26 September 2018.
Study design The Outcome Measures in Rheumatology (OMERACT), Core Outcome Measures in Effectiveness Trials (COMET), and Core Outcome Set-STAndards for Reporting (COS-STAR) guidelines were used for developing and reporting this COS  [21, 29, 30]. The scope of the COS was 'Medication review among patients aged 65 years and older with polypharmacy (≥5 daily medications) and multi-morbidity (≥2 chronic morbidities)'. The project was registered on the COMET database (http://www. comet-initiative. org/studies/ details/806?result=true). Details on the study protocol have been published elsewhere  [31]. The COS-STAR checklist is detailed in Additional file 1: Table  S1. The study was performed in four countries (Belgium, Ireland, Switzerland, and the Netherlands). The medical centers included tertiary academic medical centers with a wide range of surgical and internal medicine specialties, including geriatric medicine (Belgium, Switzerland, and the Netherlands), and a secondary medical center specialized in geriatrics (Ireland).
List of potential outcomes for inclusion in Delphi survey As a first step, a systematic review identified all outcomes previously used or planned to be used for evaluating the effect of medication review among older patients. It was achieved through an update of a recent systematic review on medication review published by Lehnbom et al. [9]  combined with a systematic search in randomized clinical trials registries and on the Cochrane Database. All data extractions on outcomes were performed by two independent reviewers. Any disagreement was resolved by discussion and consensus. Details have been published elsewhere  [32]. In parallel, a qualitative study identified unknown and relevant outcomes that should be included in the Delphi survey. We held semi-structured one-to-one interviews with patients and family caregivers.
Acknowledgements This work is part of the project OPERAM: OPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  6342388 , and by the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  15. 0137. The opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views of the EC and the  Swiss government. The authors are grateful to all the patients, health professionals, researchers, and other experts who gave up their time to participate in the Delphi surveys and consensus meetings. The health professionals and experts who consented to have their name cited and participated in this study are listed here in alphabetical order:  S.
Moreover, the analysis of oEVs protein content also revealed changes across the estrous cycle. Mass spectrometry identified 336 clusters of proteins in oEVs, of which 170 were differentially abundant across the estrous cycle (p-value< 0. 05, ratio < 0. 5 or ratio > 2). Our data revealed proteins related to early embryo development and gamete-oviduct interactions as well as numerous ribosomal proteins. CONCLUSIONS Our study provides with the first molecular signature of oEVs across the bovine estrous cycle, revealing marked differences between post-and pre-ovulatory stages. Our findings contribute to a better understanding of the potential role of oEVs as modulators of gamete/embryo-maternal interactions and their implications for the reproductive success.
2a ). Based on this finding, a statistical analysis in comparison of the stages S2, S3, and S4 to S1 was performed. The total number of differential transcripts (FDR 0. 001) for these comparisons was 854 (Additional file 1: Table  S4 ). The overlap of differential transcripts between the comparisons is shown in the Venn diagram (Fig. 2b ). Hierarchical cluster analysis of the differential transcripts confirmed that the main difference in mRNA abundance was between S1 and the other stages and that transcript frequency between S2, S3, S4 was more or less similar (Fig. 2c ; Additional file 1: Tables S1-S3 and Additional file 2: Figure  S2. ). About Functional analysis of the top 500 most abundant mRNAs identified in EVs across the bovine estrous cycle was performed using DAVID functional annotation clustering (list of 500 most abundant transcripts in Additional file 1: Table  S5 ).