dataesr/scientific-paragraphs-categorization huggingface dataset:
General comments on global proteomics datasets Several observations we documented in this manuscript stood out and merit commentary: 1. Cluster analyses undertaken in this work tended to group proteins together, which either collaborate as subunits in the assembly of a given protein complex or represent paralogs of each other. While, for the purpose of this work, these observations were primarily used to validate the reproducibility of the methodology, their appearance is interesting and warrants a short explanation: The co-clustering of proteins, which contribute to the assembly of known protein complexes, validated the expectation that proteins known to contribute to the assembly of a given protein complex are frequently co-regulated and stabilized at similar steady-state levels in a given cell; there is ample precedent in the literature to recognize the widespread occurrence of this phenomenon, which constitutes a most economical solution from the perspective of the cellular energy household.
-Wollongong (Australia): the station is located in the Southern Hemisphere at Wollongong University (34 • 24 22 S, 150 • 52 44 E, 30 m a. s. l. ). The instrument, which is also a commercial high-resolution Fourier transform spectrometer (Bruker IFS 125HR), is operated by the University of Wollongong and has provided data since 1996. All three ground-based FTIR spectrometers are part of NDACC (Network for the Detection of Atmospheric Composition Change) and/or TCCON (Total Carbon Column Observing Network) networks and have monitored the concentration of CO for several years. Paris is the first ground-based FTIR station located in a European megacity which provides rare hotspot measurements of atmospheric species related to anthropogenic activities. The remote high-altitude Jungfraujoch station provides one of the longest observational time series for a variety of atmospheric gases.
Wollongong is a station exposed to moderate levels of pollution and is located on the east coast of Australia, about 80 km from the south of Sydney. Here, we analyse NDACC data for the seasonal variations of CO and compare the results from the different sites. The ground-based remote sensing measurements are compared with results from the satellite instruments IASI-MetOp (infrared atmospheric sounding interferometer;  Tournier et al. , 2002)  and  MOPITT (Measurements Of Pollution In The Troposphere; Drummond and Mand, 1996). With respect to satellite measurements, ground-based FTIR instruments are more sensitive to the boundary layer and can therefore provide complementary information which we compare with in situ measurements at the surface. Due to specific conditions at the ground, the surface and the total column seasonalities might differ from each other.
Using GEOS-Chem modelling (Goddard Earth Observing System chemical transport model, CTM;  Bey et al. , 2001)  simulations, we investigate the impact of local sources on the lower partial column and its variability compared to the total column. The paper is structured as follows. In Sect. 2, the remote sensing instruments and measurements are described. Section 3 presents the in situ analyser measurements and the GEOS-Chem model simulation data. Section 4 shows the CO total column variability obtained from both the remote sensing data and the surface in situ measurements. Both results are compared to the GEOS-Chem model simulations, which are also used to identify emission sources at each site. The FTS-Paris is a Michelson interferometer from Bruker Optics. Table  1  lists technical details for the ground-based FTIR instruments as well as the configuration used for the measurements.
Patients and tumors Families were previously selected from 1999 to 2004 from three health centers in Santiago, using standard criteria for hereditary breast cancer: 1) three women with breast cancer in at least two consecutive generations, 2) two women with breast cancer, one of them diagnosed before age of 41 and 3) at least one woman with breast and one with ovarian cancer  [23]. All patients signed a written informed consent for the publication of clinical data and BRCA1 and BRCA2 mutational screening results. This protocol was approved by the Ethics Committee at the Faculty of Medicine, Pontificia Universidad Catolica de Chile. All patients were screened for BRCA1 and BRCA2 germline mutations as described by Gallardo et al  [23]. A total of 47 formalinfixed paraffin embedded (FFPE) tumor biopsies from surgically resected breast cancer tissue were collected from these patients.
Analyses The hybridized microarrays were scanned with a GenePix 4100A scanner (Molecular Devices) and signal processing was done with either Feature Extraction software (Agilent Technologies) or GenePix Pro (Molecular Devices). Raw data was normalized using R package CGHnormaliter from Bioconductor (http://www. bioconductor. org/packages/2. 6/bioc/html/CGHnormaliter. html). Deletions and gains were identified with DNA Analytics (Genomic Workbench, Agilent Technologies) using the ADM-1 (Aberration Detection Method-1) algorithm with a log2 ratio filter of 0. 2, and a threshold of 4. 0.
Acknowledgments We thank  FONDECYT  grants  1040779  and  1120200 ,  CONICYT and Fulbright Foundation  for  C. Alvarez  fellowships. This project has been funded in part with federal funds from the  National Cancer Institute, National Institutes of Health , under contract  N01-CO-12400. The content of this publication does not necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  U. S. Government.
Group size From 52 aerial surveys the mean group size of eiders was estimated at four sites (Central Wadden Sea, Offshore area, Lister Deep and Ho Deep) in autumn (1 October -31 December) and winter (1 January -28 February). To analyse group size of eiders in relation to hunting density we made a stepwise test scheme, first analysing all data and based on these results we analysed the significant variables to determine if there were effects of sites and season. First, for all data we tested if eider group size (as response variable) could be explained by site, season and the density of hunting motorboats and the interaction effects of site and hunting motorboats, season and hunting motorboats, site and season together with site, season and hunting motorboats as predictor variables. Year and total number of eiders in the Danish Wadden Sea were random effects in this and in the following tests of group size.
To these ends, various institutions including the UN, Eurostat, and the OECD, maintain international databases of a wide range of city-level indicators of well-being. The scope of such well-being indicators varies from accessibility to hospitals to local crime rates. In the scientific literature there is also thoroughgoing discussion on how to measure and assess the live-ability, well-being or quality of life of urbanites who rely on different amenities (e. g. Hagerty et al. 2001; Liao 2009). This paper contributes to the assessment of urban well-being and natural amenities. Natural amenities may add to urban inhabitants' well-being significantly, as empirical studies show: natural amenities lead to higher prices of nearby houses, they attract interregional migration flows, and they increase the happiness and life satisfaction of individuals  (Bertram and Rehdanz 2015; MacKerron and Mourato 2013; Waltert and Schla ¨pfer 2010).
Natural Land Use Our data on natural land use has been acquired from the European Environmental Agency in the form of the 2006 CORINE land cover dataset  (version 17, 2013). 1  This dataset measures land cover, including land cover by natural land use, across European countries on a 100 9 100 m grid. As such, its overall accuracy is assessed at 87 %  (Fuller and Gaston 2009). While higher accuracy through using national data is possible, those data are not as comparable across countries as the CORINE data are. Nature is defined by any land use present in the CORINE data broadly classified as 'natural' (Fig. 1a ). These include water bodies, wetlands, forest and semi-natural areas, agricultural areas, and artificial nonagricultural vegetated areas (e. g. parks). Such natural land cover amounts to 92, 94 and 87 % of total areas of, respectively, Germany (357,791 km 2 ), Denmark (43,075 km 2 ), and the Netherlands (34,976 km 2 ).
Natural Amenity-Level High-amenity nature is operationalized using data from the Hotspotmonitor (HSM) database, which currently has international coverage for the Netherlands, Denmark and Germany (see http://hotspotmonitor. eu). The HSM is a Google Maps-based survey tool that can be described as a Participatory Geographical Information System (PPGIS) (see Brown and Kytta ¨2014). The HSM asks respondents to use a so-called 'marker' to pinpoint a natural place on an online map that they perceive as attractive-for their own subjective reasons. As such, HSM data measure of Cultural Ecosystem Services (c. f. MEA 2005 in Sect. 2. 1) by aesthetic and recreational amenities, essentially  (MEA 2005). The HSM survey specifies to respondents that attractive natural places may be on land or water, inside or outside urban areas, and should satisfy the condition that 'nature' be featured in a broad sense.
2013). As a result of the stratified sampling of respondents from regional capitals, our sample includes HSM markers from respondents a from urban areas across each observed country. The (single) markers that respondents have placed pinpoint nature that is attractive specifically at the national scale (N = 1264 for the Netherlands; N = 1018 for Denmark; N = 4887 for Germany). An advantage of observing national scale markers is that these allow for consistent within-country comparisons of densities of markers. The validity of Hotspotmonitor data is demonstrated in several studies, including  Sijtsma et al. (2012a , b, 2014 ), De Vries et al. (2013) ,  Daams and Sijtsma (2013) , and  Daams et al. (2016). The strength of the Hotspotmonitor data is in combining the measurement of holistic preferences with spatial precision.
Urban Areas OECD functional urban areas (FUAs) are used to delineate the borders of distinct urban areas in the Netherlands, Denmark and Germany (Fig. 1c ). FUAs support robust statistical comparisons of urban areas, as they are defined based on the economic coherence of cities and their hinterland rather than on the administrative boundaries of cities (OECD 2012). The OECD database on FUAs covers 29 countries and includes more than 1100 urban areas. FUAs comprise high-density urban cores and their respective commuting zones. An urban core is identified as a cluster of high density areas, where the core is composed of all contiguous 1-square kilometer cells of at least 1500 inhabitants and having a total population of at least 50,000 inhabitants. Commuting zones are instead identified by selecting all contiguous (urban or rural) municipalities that send a minimum of 15 % of their labor force to the urban core (details are given in OECD 2012).
Population Distributions Population distributions across FUAs in the Netherlands, Denmark, and Germany are derived from the GEOSTAT 2011 population grid (version 1. 0). This grid, the result of a common project of Eurostat and the European Forum for GeoStatistics, measures total population per 1 9 1 km grid cell. These geo-referenced data include nearly 100 % of the Dutch (16. 7 m), Danish (5. 6 m), and German (80. 2 m) populations included in the 2011 European census, and does so with high regional consistency. The spatial distribution of the measured population is represented using the ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) coordinate system-as are all datasets used in this study. This coordinate system is compliant with the EU's 2007 INSPIRE Directive principle that ''spatial information should be comparable across member states,'' and thereby supports consistent measurement of spatial indicators across our study area.
Identifying High-Amenity Nature To identify high-amenity nature, CORINE data on natural land use are combined with clustered Hotspotmonitor (HSM) data on the locations of attractive nature in a systematic way. This approach identifies nature perceived as attractive at national level by multiple HSM respondents, thus reflecting consensus among respondents: that the nature within each cluster is of high-amenity. The procedure used to construct the measure of high-amenity nature is set out below. It closely follows the detailed procedure introduced in  Daams et al. (2016). Broadly summarized, spatial cluster analysis is performed on HSM markers, which pinpoints natural places that people have perceived as attractive at national level. This procedure is carried out for each observed country, separately, in the following five steps: (1) A 250 9 250 m grid that covers the observed country is generated.
Sensitivity Analyses We next perform two sensitivity analyses on the population-weighted distance to highamenity natural spaces at FUA-level. In so doing, we restrict our FUA sample to the 35 FUAs located in the Netherlands, since only for this country are the data available that both sensitivity analyses require. First, we examine how sensitive the main results for Dutch FUAs are to an increase in Hotspotmonitor (HSM) sample size-as this may identify more places with high-amenity nature than the HSM sample in the main analysis does. In this examination we establish two distinct rankings of FUAs based on the proximity of their populations to high-amenity nature. For one ranking, high-amenity nature is measured using the HSM sample (N = 1264) applied in our main analysis; for the benchmark ranking we use a sample of 8613 HSM markers that pinpoint high-amenity nature.
In doing so, we apply a 0. 1 9 0. 1 km population grid covering the entire land mass of the Netherlands. This grid, which is acquired from Statistics Netherlands, measures the distribution of the Dutch population in 2011, as do the Eurostat data. Based on each of both population grids, we calculate a separate measure of FUAs' population-weighted distance to high-amenity nature. When comparing the results, the distance measure based on 0. 1 9 0. 1 km grid-data is taken as a benchmark for the distances generated using the 1 9 1 km grid-data. This assessment shows that in our main analysis the measurement error in FUAs' population Fig. 6  A scatterplot of two sets of rankings for FUAs: one based on the rank for population-weighted distance to high-amenity nature from an HSM sample with N = 1236 (y axis), the other ranking based on the similar measure but from an HSM sample with N = 8613 (x axis).
Acknowledgments The views expressed in this paper are those of the authors and do not reflect those of the OECD or its member countries. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The Pearson correlation between the high-amenity nature distance indicator and the 'nature in general' distance indicator based solely on natural land cover, as measured in the CORINE data is -0. 24, and thus is weak. The correlated variables are log transformed to approximate normal distributions; normality is checked using Shapiro-Wilk tests. Further assessment indicates the difference in how FUAs rank on both indicators: this difference is on average 55 rank-positions, with a standard deviation of 38 rank-positions. This underlines that FUAs may rank quite differently on a nature based proximity indicator depending on whether the amenity-level of nature is accounted for.
bbsrc. ac. uk/projects/chipmonk), using windowed T-tests to identify differentially methylated probes (Figure  S1 ). log 2 gene methylation levels were derived from the mean probe ratios within 700 bp of the transcriptional start site (Table  S3 ). The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [39]  and are accessible through GEO Series accession number GSE71958 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc = GSE71958).
Protein Extraction and Western Blotting Cultured cells were washed with ice-cold PBS and lyzed in cell lysis buffer (Cell Signaling, New England Biolabs, Hitchin, Hertfordshire, UK), with complete mini inhibitors (Roche, Burgess Hill, Sussex, UK) for 10 min on ice, and then sonicated for 5 min at high intermittent pulses (30/30) (Diagenode, Bioruptor, Oxford, UK). Neuroblastoma tumor samples were homogenized in cell lysis buffer, then processed as for cultured cells. Samples were centrifuged for 10 min at 10 000g at 48C to remove any cell debris and typically 25 mg proteins were separated on a SDS-polyacrylamide gel and analyzed by Western blotting. Fetal adrenal protein was from Biochain. Primary antibodies were against MEGF10 (rabbit, Sigma HPA026876) and b-ACTIN (rabbit, Abcam AB8227), followed by secondary HRP-labelled anti-rabbit (Sigma A6154).
Identification of Differentially Methylated Genes We used windowed T-tests to identify genes that showed significant hypermethylation or hypomethylation in neuroblastoma cell lines compared to hNCC (Figure  S1 , Table  S6 ). About 93 genes were significantly differently methylated between all four neuroblastoma cell lines and hNCC, of which 26 (28%) were hypermethylated and 67 (72%) were hypomethylated (Table  S6 ). The hypermethylated genes were enriched in high-CpG and intermediate-CpG promoters (Table  S6 ; Figure  1D ), whereas the hypomethylated genes were enriched in low-CpG promoters (LCP; Table  S6 )  [42]. Using publicly available microarray data (GSE19274) we found no direct correlation between gene expression and DNA methylation; however, across the four cell lines, the most highly expressed genes were mostly hypomethylated; 96% of the genes in the top 90% rank of expression were hypomethylated (Figure  S3 ; P ¼ 0.
Full results shown in Table  S5. (D) Genes identified by MCIP as hypermethylated in four neuroblastoma cell lines compared to hNCC. The first five columns ("Gene methylation") are a heatmap of gene methylation values (blue ¼ low, red ¼ high). CGI properties: PRC shows genes that are polycomb marked in ES cells, HCP, ICP, and LCP define which promoters have high, intermediate, or low CpG content. For quantitative DNA methylation results and further explanation of PRC, HCP, ICP, and LCP, see Table  S6. NB data: "Survival" shows genes whose decreased expression is significantly associated with reduced relapse-free survival in neuroblastoma (P < 0. 05, log rank test); data generated in R2 using GSE16476; and "Expression" shows genes whose RNA expression is decreased in both neuroblastoma cell lines (GSE28019) and neuroblastoma tumors (GSE16476) compared to neural crest cells (GSE14340); comparison was made using the "Megasampler" function in R2 Genomics Analysis and Visualization Platform (http:// r2.
Expression and Biological Function of MEGF10 QPCR analysis of neuroblastoma tumors and cell lines showed significantly lower MEGF10 RNA expression than in normal tissues (Figures  2A  and S6 ). Our RNA expression results were replicated in publicly available expression microarray datasets (Figure  2B ) and importantly, these microarray data demonstrated a significant association between low-MEGF10 expression and reduced relapse-free survival in neuroblastoma patients, especially in MYCN non-amplified tumors (Figure  2C ). Western blotting analysis of MEGF10 protein expression (Figure  2D ) showed that 87% (20 out of 23) of neuroblastomas had lower MEGF10 protein levels than that found in fetal adrenal (Figure  2E ), in agreement with the RNA expression data (Figure  2A ). This suggests that MEGF10 expression is primarily controlled at the transcriptional level.
MEGF10 expression was consistently down-regulated in neuroblastoma tumor tissue compared to normal tissue, as shown by our own results and by those in publicly available datasets (Figure  2A  and B ). Thus our genome-wide epigenetic analysis, based on studying cultured cells, has correctly identified MEGF10 as a repressed gene with direct relevance to neuroblastoma in vivo. Genome-wide mutation analyses of neuroblastomas have not identified genetic abnormalities in MEGF10 [4-6] and there was only infrequent (4/46; 7%) hypermethylation of MEGF10 in tumor tissue (Figure  3A  and E ). It therefore seems likely that MEGF10 expression is silenced in most neuroblastomas by epigenetic mechanisms other than DNA methylation, such as repressive histone modifications H3K27me3 and/or H3K9me2, as we have shown in the SHIN cell line (Figure  4 ), and as has been reported for other neuroblastoma tumor suppressor genes  [23, 24, 55].
Interestingly, analysis of publicly available DNA methylation data (GSE39626;  [32] ) gave similar results to those reported in this paper, with 2/25 (9%) of neuroblastomas having MEGF10 DNA hypermethylation. Both of those MEGF10-hypermethylated tumors were high-risk  [32]  and in our data all the hypermethylated tumors were stages 3 or 4, and showed decreased overall survival (Figure  3F ), suggesting that MEGF10 DNA hypermethylation occurs predominantly in an aggressive subset of neuroblastomas. Most neuroblastoma cell lines are derived from high-risk tumors  [59]  and we found the majority of cell lines to be DNA hypermethylated at MEGF10 (Fgure 3A-C). It therefore possible that many neuroblastoma cell lines derive from a subpopulation of cells within high-risk tumors that is hypermethylated at MEGF10.
005, NS; not significant, Mann-Whitney test). (F) Kaplan-Meier overall survival curve taken from dataset of NB patients in (A) for whom survival data were available. Me À, tumors with no DNA methylation compared to fetal adrenal and neural crest; Me þ, tumors with increased DNA methylation compared to fetal adrenal and neural crest (using the average of assays 01_PM and 02_PM). Me þ tumors showed decreased overall survival compared to MeÀ, tumors (P ¼ 0. 0009; log rank test).
ACKNOWLEDGMENTS We thank  Charlene Crosby  at the  Bristol Genetics Laboratory, Southmead Hospital , for expert help with pyrosequencing,  Prof. M. Schwab  and  Prof. A Sala  for the kind gift of cell lines. JC was funded by a PhD studentship from the  Fonds National de la Recherche Luxembourg (FNR). This work was funded by  CLIC Sargent UK, Cancer Research UK  grant number  C20791/A12743 , the  John James Bristol Foundation  and the  University of Bristol Cancer Research Fund. HCE was funded by  Institut National de la Sant e et de la Recherche M edicale (INSERM). BA was funded by a scholarship from  the government of Saudi Arabia.
Data scoring Data were recorded manually using GeneMapper 5 (Applied Biosystems), and only clear polymorphic bands were scored for presence (1) or absence (0). The results of AFLP were confirmed by repeating the analyses of 23 randomly selected plants of each of the six species. The replicated profiles were compared, and markers with more than 5% errors were removed from the data sets. Also single profiles with significantly higher or lower number of bands compared to the average were removed as we assumed that to be the result of imperfect PCRs.