dataesr/scientific-paragraphs-categorization huggingface dataset:
An important step forward that will address these challenges is to start developing an open, shared, global database of reference data (WMO, 2016). In many cases, existing reference data were collected with government support but without a corresponding requirement by funding agencies that the data be made publicly availablea policy issue that funding agencies need to address and that would further development of an open reference database. Additionally, the Group on Earth Observations (GEO) has developed a set of Data Sharing Principles that all of its 200+ member countries and participating organizations have endorsed, providing another entry point to facilitate the development of open systems. While an open reference database will not emerge overnight, its incremental growth will gradually expand the geographic coverage available for automated LC products; governments and international organizations need to support its development.
The (5a) product generation then requires (5b) reference data for training and validation and is supplied either by the user or a shared reference database. (5c) A variety of algorithms can be made available from an open source "algorithm warehouse". (6) Validation: The product is assessed for accuracy by comparing with independent validation reference data. (  7 ) Products: the approach can support a wide variety of other products by using different algorithms. between computing environments are key challenges in this context. Both of these can be a critical obstacle for developing countries wishing to generate their own products.
Availability of satellite images Although more images are now being acquired than ever before, there are gaps in the openly accessible observation record, particularly for the humid tropics where cloud cover often obscures the view of optical instruments like Landsat and Sentinel-2, currently the source of most of the optical image data. These gaps will limit the geographic scope that a system like that in Fig. 1  can support. However, radar data, for example from the German Terra-SAR-X mission, would be valuable if opened up more broadly to the EO community because it is unaffected by clouds. There are also many historical gaps, particularly in the early years of satellite Earth Observation. Gaps do not mean that data were never acquiredin fact, more than 30 sovereign states have financed satellites with global LC observing capabilities, and around 200 have been successfully launched since 1972  (Belward and Skøien, 2015).
Additional products and services through a land cover mapping system Beyond LC, governments need a broad range of other products and services such as progress indicators relevant to the SDGs and other MEAs, decision support products to assist countries in meeting their UNFCCC, CBD or UNCCD obligations, and information to bolster the bio-economy or help guarantee food security. In response, flexible systems that can generate a wide variety of products and services are beginning to be developed; DIAS, mentioned earlier, is a good example. This is important because systems like DIAS represent a cost-effective way to provide operational homes for LC processing. Another important activity is the CEOS Open Data Cube mentioned earlier. The Open Data Cube is a scalable system with an architecture somewhat similar to that shown in Fig.
1  but with the specific goal of minimizing the technical skills needed to install and operate it. Under development now, the intent is to democratize access to both the technology and the product generation algorithms, thus simplifying access to important production capabilities for countries and other users that may otherwise lack sufficient capacity. In addition to LC, the Open Data Cube is incorporating algorithms related to water detection, water quality, landslide risk, greenness, and other areas. ESA is engaging in several efforts to stimulate new cloud-based EO services that provide a high degree of interoperability ('federation of platforms') and to provide the user community with a stimulating environment that fosters co-creation and collaboration in addition to offering scalable and efficient processing solutions.
These entities could be national governments, but also could be NGOs, sub-national governments, or collaborative efforts of any size or mix of organization types. The Data Cube approach could prove very useful here. On a more technical level, important developments include those on interoperability and federation of computing environments, standardization of data and metadata as well as initial efforts working towards reference data standardization and curation platforms. Examples for these crucial elements include the web interface standards from the Open Geospatial Consortium (OGC), the ESA Exploitation platform Common Architecture (https://eoepca. github. io/), the STAC metadata specification (https://stacspec. org/) and the radiant. earth training data repository (https://www. mlhub. earth/).
Conclusions Although many MEAs and societal applications depend on up-todate, high quality LC information, systems that can generate on-demand LC products that are customized for this wide range of users are not yet operationally available. This constrains society's ability to monitor and respond in a more targeted and focused manner to the important changes occurring in land areas across the globe. Advances in both science and technology now make the development of automated systems possible that can address this shortcoming. Solutions for critical technical challenges towards the goal of truly dynamic LC mapping are now gradually emerging or maturing. While some challenges remain for developing countries or cloudy areas in terms of EO data availability, access and processing these can be gradually addressed and thus allow LC product generation anywhere on Earth.
Z. Szantoi, et al. Environmental Science and Policy 112 (2020) 28-35 holding over 16 million geospatial measurements (https://www. pangaea. de) including other open reference datasets. The online tool called LACO-Wiki (See et al. , 2017) links reference datasets to LC, land use and change monitoring (LandSense Citizen Observatory), while novel approaches are being developed for collecting multi-purpose reference data as part of the Copernicus Global Land Monitoring Service (CGLS) (Tsendbazar et al. , 2018). All these data, once assessed and validated, will become freely available. GEO, perhaps through its emerging Knowledge Hub concept, may be an appropriate organization to facilitate linking shared reference databases together. As regional level examples, the European Soil Bureau Network (Montanarella et al.
, 2005) and Eurostat's Land Use/Cover Area frame Survey (LUCAS, https://ec. europa. eu/eurostat/web/lucas/data/database, Gallego and Delincé, 2010) provide high-quality reference datasets. They are sampled at a regular predefined 2 km grid, have been surveyed at three-year intervals since 2006 and provide a wealth of information on LC, land use and topsoil properties. They have been proved to be extremely valuable in EO-based LC mapping exercises after some metadata-related filtering. As such, they have been employed for continental LC mapping in Europe (Pflugmacher et al. , 2019) and for validating LC products (Karydas et al. , 2015). More recently, the BigEarthNet initiative emerged, which contains almost 600 K image patches from Sentinel-2 labelled using CORINE LC 2018 for 10 European countries (http:// bigearth.
To achieve quadruplicate samples for sorting, each of the two STN structures was dissected from four male mCherryTRAP Pitx2-Cre mice at age P28 and nuclei were extracted. To maintain independent samples, extracted nuclei were not pooled but single-sorted into two 384-well plates. Each plate contained 191 single nuclei from two mice and processed using Smart-seq2 protocol  30  for full-length single snRNAseq analysis (Fig. 1b  and Supplementary Fig. 1 ). 138 nuclei did not fully meet the criteria set for quality control and were excluded from further analysis (Supplementary Fig. 2a-d ). To integrate data for grouping and identification of sources of heterogeneity, the Seurat R package was next employed  31, 32. This enabled the grouping of the remaining 630 samples into six clusters (Supplementary Fig.
3a ). The number of detected genes was similar across the clusters, with the exception of the smaller yellow cluster, which contained nuclei with a higher number of detected genes (Supplementary Fig. 3b ). Since signature markers for the yellow cluster were associated with the oligodendrocyte lineage (Tmem125, Tmem63a, Cd9, Car14, Lpar1), this cluster (n = 11) was excluded from further analysis (Supplementary Fig. 3c-i ). The filtered dataset was subjected to a second round of unsupervised clustering. In this final Seurat analysis, 1946 variable genes were used and the remaining 619 nuclei again grouped into six clusters (clusters 0-5) with similar number of detected genes among the clusters (Fig. 1c-e ). The median number of detected genes was 5379 and the median number of counts was 354,443 and was similar between the two plates and the four animals (Fig.
1f , g, Supplementary Data 3). Nuclei from both experimental plates and from all four biological replicates were represented in all clusters (Fig. 1h ). Cluster analysis identifies a glutamatergic phenotype and allows characterization of gene expression profiles. Next, Pitx2 was visualized in a t-SNE plot to verify that the dataset represented the expected population (Fig. 2a ). Indeed, all clusters were Pitx2-positive and also shown to be of glutamatergic lineage marked by the strong expression of Slc17a6 (Vglut2) and the sparse expression of GABA markers, such as the Slc32a1, encoding the Vesicular inhibitory amino acid transporter and Gad1, encoding Glutamic acid decarboxylase (Fig. 2a ). In addition, other genes previously associated with the STN  24, 33, 34  such as Lmx1a, Foxa1, Foxp1, Foxp2, Barhl1, Epha3 were expressed in most clusters, while Lmx1b was barely detected (Supplementary Fig.
2, 70 mM KCl, and 2 mM MgCl 2 ) containing an additional proteinase inhibitor (Complete, Roche) and an RNAse inhibitor 40 U/μL (RNAseOUT, Invitrogen). Resuspended nuclei were filtered through a 30-μm nylon cup filcon (BD Biosciences, 340625) into BSA-coated FACs tubes for sorting. Fluorescence-Activated Cell Sorting (FACS) was performed using a BD Influx System and the BD FACS TM software (BD Biosciences) to isolate mCherry-tagged nuclei and single-sort into two 384 well plating containing 2. 3 μl of ice-cold lysis buffer with external RNA controls consortium (ERCC) spike ins for subsequent quality control analysis. The nuclei were identified by forward-and side-scatter gating, using a 633-nm laser with a 610/20 filter. A 140-μm nozzle, a sheath pressure of 4. 30 psi, an acquisition rate of up to 1200 events per second and a drop frequency of 6.
The reads were mapped and aligned to mouse genome (mm10) with the STAR aligner 49 using 2-pass alignment to have improved performance of de novo splice junction reads, filtered for only uniquely mapping reads. number of reads per nuclei was 682000. About 80% of the reads showed unique alignments to the mouse genome with 25% of the mapped reads aligning within exons. Only exons were included in the analysis. Parameters for quality control included uniquely mapping reads (<80%), ERCC detection (< mean -2 standard deviations), ERCC ratio (>0. 1) fraction of reads mapping to exons (<10%), detected with RPKM > 1 (>3000 and < mean +2 standard deviations) and a maximum correlation to another nuclei (< 0. 3). Bioinformatics analysis. The obtained RPKM data was analyzed using the Seurat package 2. 3.
A minimum of 3 nuclei and minimum of 200 genes was a preliminary criterion for the Seurat object. For the analysis the Seurat package was run twice following the removal of one cluster that did not meet the neuronal criteria. For the two runs variable gene selection was set for the x-low, x-high and y cut-off at 0. 8, 10 and 1 for the first run and 0. 6, 10 and 1 for the second run, respectively. Clustering was run with the default SNN clustering at resolution of 0. 8. The number of Principal components (PCs) used for the analysis was based on a Jackstraw analysis for the first 12 PCs with a cut-off of 0. 001. For the first run PCs 1-7 and 12 were used, while for the second Seurat only the first 6 PCs were included. Fluorescent in situ hybridization analysis using single or double probes (FISH).
Brains were dissected and post-fixed overnight. Brains were cryo-protected with 30% sucrose and cut using a cryostat at 30 µm slice thickness. mCherry was detected by the endogenous fluorescence without any additional use of an antibody. Images were captured using a NanoZoomer 20. 2-HT. 0 with a 20× objective and processed using the Ndp2. view software (Hamamatsu). Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements The authors thank  Professors James Martin ,  Baylor College of Medicine (Houston, Texas, USA ) for sharing the Pitx2-Cre mice and  Jan Stenman  ( Ludwig Institute for Cancer Research, Stockholm Branch, Sweden ) for the mCherryTRAP reporter mice. Konstantinos Toskas  and  Dr Erik Södersten  are thanked for advice on nuclear isolation, and  Dr Johan Holmberg  is thanked for providing the lab facility for nuclear isolation ( Ludwig Institute for Cancer Research, Stockholm Branch  and  CMB ,  Karolinska COMMUNICATIONS BIOLOGY  | (2020) 3:338 | https://doi. org/10. 1038/s42003-020-1028-8|  www. nature. com/commsbioInstitutet, Sweden). The authors thank  Jaromir Mikes  for technical assistance at the  FACS at Science for Life Laboratory (SciLifeLab, Sweden ) and  Marie-Laure Niepon  at the  Image platform at Institute de la Vision (Paris, France ) for slide scanning.
P ( Stiftelse Olle Engkvist Byggmästare ) and  N. K (Hjärnfonden). Å. K. B is financially supported by the  Knut and Alice Wallenberg Foundation  as part of the  NBIS facility at SciLifeLab, Sweden. A previous version of this manuscript, containing the snRNASeq results, was published in BioRxiv (https://doi. org/10. 1101/677724 ). Open access funding provided by  Uppsala University.
0610040B10Rik 1110001J03Rik 1110008F13Rik 1110038B12Rik 1110046J04Rik 1110058L19Rik 1110059E24Rik 1300002E11Rik 1500009L16Rik 1600020E01Rik 1600023N17Rik 1700001J03Rik 1700007K13Rik 1700010K23Rik 1700012B09Rik 1700018G05Rik 1700019B03Rik 1700019D03Rik 1700019G24Rik 1700028E10Rik 1700029I15Rik 1700040L02Rik 1700048O20Rik 1700065J18Rik 1700086L19Rik 1700093K21Rik 1700101I11Rik 1700120K04Rik 1810009A15Rik 1810010H24Rik 1810012K16Rik 1810020O05Rik 1810055G02Rik 1810062O18Rik 2010107E04Rik 2010204K13Rik 2010308F09Rik 2010315B03Rik 2310002D06Rik 2310030G06Rik 2410004B18Rik 2410021H03Rik 2500004C02Rik 2610316D01Rik 2700038G22Rik 2700062C07Rik 2700069I18Rik 2700097O09Rik 2810001G20Rik 2810002D19Rik 2810006K23Rik 2810029C07Rik 2810032G03Rik 2810468N07Rik 2900005J15Rik 2900041M22Rik 2900055J20Rik 3110056K07Rik 4732416N19Rik 4833417C18Rik 4921511H03Rik 4930404I05Rik 4930426D05Rik 4930429B21Rik 4930453L07Rik 4930474N09Rik 4930479D17Rik 4930483O08Rik 4930500J02Rik 4930502E09Rik 4930517E11Rik 4930520O04Rik 4930548H24Rik 4930563F08Rik 4930596I21Rik 4933416I08Rik 5031434O11Rik 5830454E08Rik 6330403K07Rik 6330418K02Rik 6720468P15Rik 6720483E21Rik 6720489N17Rik 6820408C15Rik 8430429K09Rik 9030204H09Rik 9030612E09Rik 9330151L19Rik 9330179D12Rik 9430016H08Rik 9430021M05Rik 9430091E24Rik 9530052E02Rik 9930012K11Rik 9330162012Rik A230070E04Rik A230103J11Rik A330009N23Rik A330035P11Rik A330050F15Rik A330102I10Rik A530088E08Rik Ghr Haus7 Haus8 Hcrt Hcrtr1 Hddc2 Hddc3 Hdhd3 Hebp1 Hes1 Hes5 Hilpda Hinfp Hist1h2bc Hmox2 Hnmt Hnrnph3 Hprt Hrh2 Hrsp12 Hsd11b1 Hsd17b2 Hsd17b10 Hsd17b12 Hspb8 Hspb11 Htatip2 Htr2c Htr7 Htra1 Oraov1 Orc5 Ormdl1.
Data availability All relevant data are available from the authors upon request (Å. W. M). RNA-sequencing data have been submitted to the Gene Expression Omnibus (GEO) database repository under the accession code GSE133953. Data are currently stored in resources provided by Uppsala University Multidisciplinary Center for Advanced Computational Science (UPPMAX/SNIC). All in situ hybridization data are available in the figures of the manuscript. Source data underlying plots shown in figures are provided in Supplementary Data 3.
Effects were consistent, and ORs slightly larger, when the summary statistics for smoking initiation were obtained from a GWAS meta-analysis in GSCAN which excluded 23andMe and UK Biobank (IVW OR 3. 3, 95% CI = 1. 8, 6. 1) and a GWAS conducted in the UK Biobank only (IVW OR 5. 2, 95% CI = 1. 8, 14. 9) (Table  1 ). Findings of a causal effect of smoking initiation on oral and oropharyngeal cancer risk were further supported by MR analysis using 108 SNPs associated with a lifetime measure of smoking behaviour, the comprehensive smoking index (CSI) (Supplementary Data 1). Here, a 1 standard deviation (SD) increase in the CSI (equivalent to an individual smoking 20 cigarettes a day for 15 years and stopping 17 years ago, or an individual smoking 60 cigarettes a day for 13 years and stopping 22 years ago) was found to increase oral and oropharyngeal cancer risk combined, with an IVW OR of 3.
MR estimates may reflect the effects of lifelong alcohol exposure, in comparison to the short-term effects captured in observational studies. Furthermore, while multivariable MR can estimate the independent (and unconfounded) effect of alcohol on oral/oropharyngeal cancer risk, it cannot determine the extent to which this effect is modified by smoking status, i. e. , is it observed among smokers only or the whole population. Further individual level data analysis is required to determine this. Several mechanisms have been suggested to explain the observed associations between alcohol and risk of head and neck cancer, including contaminants present in alcoholic drinks (e. g. , Nnitrosodiethylamine in beer)  21  , free radical damage and impairment of DNA repair capacity  22, 23. Ethanol is oxidised to acetaldehyde, which has a direct carcinogenic effect and moreover alcohol may act as a 'solvent' for tobacco carcinogens and the induction of carcinogen-metabolising enzymes  24, 25.
For further detail on the terms used in this study please read the Mendelian randomisation dictionary 36. Summary-level data from GSCAN and UK Biobank. Summary-level genomewide association studies were obtained for alcohol consumption (drinks per week, n = 941,280) and smoking initiation (a binary phenotype indicating whether an individual had ever smoked regularly) (n = 1,232,091) from the GSCAN study  9. The comprehensive smoking index (CSI) was derived by Wootton et al in the UK Biobank (n = 462,690)  12. This included information on smoking duration, heaviness and cessation, which were combined into a lifetime smoking index along with a simulated half-life (τ) constant. Summary-level GWAS data were obtained for this phenotype in the UK Biobank  12. Summary-level data from GAME-ON. GWAS was performed on 6,034 HNSCC cases and 6,585 controls from 12 studies which were part of the GAME-ON network  30.
Cancer cases comprised the following ICD-10 codes: oral cavity (C02. 0-C02. 9, C03. 0-C03. 9, C04. 0-C04. 9 and C05. 0-C06. 9) and oropharynx (C01. 9, C02. 4 and C09. 0-C10. 9). The study population included participants from Europe (45. 3%), North America (43. 9%) and South America (10. 8%). Details of the studies included, as well as the genotyping and imputation performed are published  30, 37. Univariable Mendelian randomisation. To assess causal effects of smoking and alcohol consumption, we identified 60 single nucleotide polymorphisms (SNPs) for alcohol consumption and 176 SNPs for smoking initiation reaching genome-wide significance (p < 5 × 10 -8 ) in the GSCAN GWAS which were present in the GAME-ON GWAS (Supplementary Data 1, Supplementary Data 2). We also identified 108 independent SNPs associated with the CSI at p < 5 × 10 -8 in the UK Biobank which were present in the GAME-ON GWAS (Supplementary Data 1, Supplementary Data 2).
8). Two-sample MR analyses were conducted using the TwoSampleMR package (version 0. 5. 5) in R (version 3. 5. 3), to extract the SNPs instrumenting the risk factor from the oral/oropharyngeal cancer GWAS  38. We next performed harmonisation of the direction of effects between exposure and outcome associations, where palindromic SNPs were aligned when minor allele frequencies (MAFs) were less than 0. 3 or were otherwise excluded. SNP-specific Wald estimates were calculated (SNP-outcome estimate divided by SNP-exposure estimate) and an inverse variance weighted (IVW) random effects method applied to meta-analyse these to obtain an estimate for the causal effect of the risk factor on oral/oropharyngeal cancer risk. To further assess the robustness of findings, we inspected the Cochran's Q statistic which assesses heterogeneity between individual genetic variants, and which may indicate the presence of invalid instruments (e.
We also conducted further sensitivity analyses to estimate causal effects using summary-level data obtained from the GWAS of smoking initiation and drinks per week in GSCAN excluding UK Biobank and 23andMe, and summary-level data obtained from a GWAS of smoking initiation  38  and drinks per week  13  in the UK Biobank only. Multivariable Mendelian randomisation. We next conducted two-sample multivariable MR analysis which included SNPs which were genome-wide significant in either the GSCAN GWAS of alcohol consumption or the UK Biobank GWAS of comprehensive smoking index. After excluding SNPs with a pairwise r 2 greater than 0. 001, 168 independent SNPs were used in the analysis. To remove sample overlap between the GWAS for alcohol consumption and CSI in the analysis, we used summary-level data obtained from the GWAS of drinks per week in GSCAN, excluding UK Biobank and 23andMe (n = 226,223) and summary-level data obtained from the GWAS of CSI in the UK Biobank (n = 462,690).
Both the IVW and MR-Egger framework have been extended to estimate causal effects in multivariable MR analysis  46, 47  , which was conducted using both the MVMR (version 0. 2. 0) and Mendelian Randomization 48 (version 0. 5. 0) packages in R. We used generalised versions of Cochran's Q statistical tests to assess both instrument strength and validity in the two-sample summary data setting, where the covariance between the effects of the genetic variants on each exposure was fixed at zero by using non-overlapping samples for each exposure. This was performed using the MVMR package (version 0. 2. 0) in R. Investigating other risky behaviours. We used 124 SNPs associated with risk tolerance and 118 SNPs associated with lifetime number of sexual partners from a large GWAS meta-analysis  13. We performed MR using the same univariable methods described in the main analysis and also evaluated the effects of these exposures stratified by cancer subsite.
Acknowledgements M. G. was a  National Institute for Health Research (NIHR ) academic clinical fellow and is currently supported by a  Wellcome Trust   GW4-Clinical Academic Training PhD Fellowship. This research was funded in part, by the  Wellcome Trust  [Grant number  220530/Z/20/Z ]. For the purpose of Open Access , the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. M. G. also received a bursary from the  Association of British Academic Oral and Maxillofacial Surgeons (ABAOMS)  to help support this work. R. C. R. is a de Pass VC research fellow at the  University of Bristol. J. T. is supported by an  Academy of Medical Sciences (AMS)   Springboard award , which is supported by the  AMS , the  Wellcome Trust ,  Global Challenges Research Fund (GCRF) , the  Government Department of Business , Energy and Industrial strategy, the  British Heart Foundation and Diabetes UK  ( SBF004\1079 ).