dataesr/scientific-paragraphs-categorization huggingface dataset:
Data set collection and mapping All data sets were mapped to the ENSEMBL human gene set version 71, release April 2013  [53]. Resources using other identifiers (i. e. Entrez, Uniprot) were mapped to ENSEMBL gene IDs using ENSEMBL BioMART (version 71). Orthologs from non-human data sets were mapped using the ENSEMBL Compara ortholog catalogue from ENSEMBL 71, with exception of Sanger sequences from Y2H screens based on the Bovine cDNA library that were mapped to Bovine genome sequences by the BLAT tool  [54]  in the UCSC genome browser  [55]  and subsequently mapped to human orthologs using the 'non-cow RefSeq genes track' from this browser.
The normalized collision energy for collision-induced dissociation was set to a value of 35, and the resulting fragments were detected with normal resolution in the linear ion trap. The lock mass option was activated and set to a background signal with a mass of 445. 12002  [60]. Every ion selected for fragmentation was excluded for 20 seconds by dynamic exclusion. For quantitative analysis, MS raw data were processed using the MaxQuant software [61] (version 1. 5. 0. 3). Trypsin/P was set as cleaving enzyme. Cysteine carbamidomethylation was selected as fixed modification and both methionine oxidation and protein acetylation were allowed as variable modifications. Two missed cleavages per peptide were allowed. The peptide and protein false discovery rates were set to 1%. The initial mass tolerance for precursor ions was set to 6 ppm and the first search option was enabled with 10 ppm precursor mass tolerance.
The fragment ion mass tolerance was set to 0. 5 Da. The human subset of the human proteome reference set provided by SwissProt (Release 2012_01 534,242 entries) was used for peptide and protein identification. Contaminants like keratins were automatically detected by enabling the MaxQuant contaminant database search. A minimum number of 2 unique peptides with a minimum length of 7 amino acids needed to be detected to perform protein quantification. Only unique peptides were selected for quantification.
Protein-protein interaction data processing Based on three ciliary protein-protein interaction (PPI) data sets, we inferred proteins to "interact with ciliary components" as a proxy for being part of the cilium. First, we obtained protein complex purification data from a large-scale study on the identification of ciliary protein complexes by tandem-affinity purification coupled to mass spectrometry (TAP-MS) for 181 proteins known or predicted to be involved in ciliary functions  [19]. Since integration of this data set into the CiliaCarta many pull-downs were repeated and reverse experiments included. As a result, our data set includes 539 found interactors that are not part of the now published final data set (4702 proteins, S2 Table  from  Boldt et al. [19] ), and does not include 679 new interactors that have been identified since June 2013.
The complete and current data is available at http://landscape. syscilia. org/ and IntAct [IM-25054]. Second, we obtained affinity purification data for 16 bait proteins from a more sensitive and quantitative approach using affinity purification combined with stable isotope labeling of amino acids in cell culture (SILAC). In total 1301 interactors were identified by SILAC in 57 experiments (S4 Table ). Third, we also obtained direct protein-protein interaction data from several independent yeast two-hybrid (Y2H) screens against cDNA libraries derived from hTERT-RPE1 (retinal pigment epithelial) cells, as well as brain, kidney, retina and testis tissue. In total 69 Y2H screens were performed using 27 baits, identifying a total of 343 interacting proteins (S5 Table ). The SILAC and Y2H studies were focused on finding new interactors for selected ciliary proteins of interest and were not part of a systematic analysis.
Parts of the resulting PPIs were published in previous studies (four out of 16 baits for SILAC  [57, 62, 63]  and nine out of 27 baits for Y2H  [63] [64] [65] [66] [67] [68] [69] [70] [71] ), however here we consider the entire PPI data sets. The complete data sets are publicly available in S4 and S5 Tables. Because the TAP-MS and SILAC data sets are based on similar methodology and have a large bait overlap (14 out of 16 SILAC baits were used in the TAP-MS data set), we merged them into a single data set (Mass-spec based PPI) with 4799 unique proteins identified to interact with 184 bait proteins. The Y2H, SILAC and TAP-MS data were transformed to genome wide data sets by defining genes as 1 ("found") when the gene product was found to interact with the baits, and as 0 ("not found") when the gene product was not found to interact.
Due to the large overlap in baits and the largely similar methods used in the TAP-MS and SILAC data sets we decided to combine the data sets in order to avoid counting the interacting proteins multiple times and thereby artificially overestimating their CiliaCarta Scores. The mass-spectrometry data sets and Y2H data sets were found to be sufficiently different to include them as separate data sets (positive set correlation is 0. 13, S10 Fig).
Expression screen data set In expression screening  [72]  separate gene-expression data sets are weighted for their potential to predict new genes for a system by measuring, per data set, the level of co-expression of the known genes. We have already successfully applied this method to predict TMEM107 as part of the ciliary transition zone  [73]  and now extend the approach to the complete cilium. An integrated cilium co-expression data set was constructed by applying the weighted co-expression method WeGet  [74]  to ciliary genes in 465 human expression data sets available in the NCBI Gene Expression Omnibus  [75]. For individual genes, correlations of their expression profiles were determined with expression profiles of the set of ciliary components. The contribution of each data set to a final co-expression score per gene was weighed by how consistently the set of cilia components were expressed together, i.
Published data sets There are a number of high-throughput cilia data sets available from the CilDB  [17]  that can complement the data sets mentioned above. We only included data sets from mammalian species to avoid significant issues with orthology (i. e. avoid mapping to paralogous genes) and which had a broad coverage of the entire genome/proteome (S1 Fig). We excluded data sets that focused specifically on the centriole/basal body, since this structure is also affected by the cell cycle  [86, 87]  and therefore could potentially skew the Bayesian classifier towards this process. We avoided redundancy in the data sets by selecting only one data set per experiment type (e. g. proteomics, expression). We only considered proteomics datasets specifically generated for the cilium as opposed to whole cell proteomics data sets to minimize false positives.
Training sets We used the SYSCILIA Gold Standard (SCGS)  [7]  as our positive training set. We did not use GO annotations as at the time we regarded the SCGS, which has been annotated by experts in the cilium field, of higher quality. Furthermore, having an independent cilium genes dataset allowed us to prevent circularity in the expression screening analysis (see below). The SCGS, or positive set, contains a total of 302 manually curated human ciliary genes. For details about the negative set and about measures undertaken to avoid overtraining see S1 File. We constructed a negative set by selecting genes annotated in GO to function in processes and cellular compartments we deemed least likely to be (in)directly involved in ciliary processes. We selected genes annotated with at least one of the following GO Cellular Component terms: extracellular, lysosome, endosome, peroxisome, ribosome, and nucleolus.
We ensured that the positive and negative training sets do not overlap by removing genes found in both from the negative set. Since the majority of human genes are expected to be non-ciliary the similarity between the score distribution of the negative set and the remaining "other genes" indicates that the negative set overall gives an excellent representation of what we reasonably can expect to be non-ciliary genes. The final negative set contains 1275 genes. We adapted the positive training set for the PPI data sets as well as the expression screen data set to avoid overtraining. Since many of the baits used in the PPI data are known ciliary components and therefore part of the positive training set, this could lead to a potential overestimation of the predictive value of the data sets. Therefore, we excluded the bait proteins from the positive set for evaluating the Y2H, TAP-MS and SILAC data.
Optical sections were generated through structured illumination by the insertion of an ApoTome slider into the illumination path and subsequent processing with AxioVision (Zeiss) and Photoshop CS6 (Adobe Systems) software. Immunofluorescence microscopy of cells. IMCD3 and ATDC5 cells were growth to 80% confluence in DMEM-Glutamax medium with 10% Foetal Bovine Serum. Then cells were serum-starved for 24 hours and fixed in cold methanol for 5 minutes, PBS washed and blocked with 1% Bovine Serum Albumin for 1 hour before incubating with primary antibodies overnight at room temperature. Antibodies and concentrations were anti-acetylated tubulin (Sigma 6-11B-1, T7451) 1/200, anti-gamma-tubulin (Sigma GTU-88, T6557) 1/200, anti-OSCP1 Proteintech 12598-1-AP 1/100, anti-OSCP1 ATLAS HPA028436 1/100 and anti-OSCP1 Biorbyt 185681 1/100.
Human respiratory cells were analyzed by immunofluorescence microscopy as previously described  [89]. The following rabbit polyclonal antibodies were purchased from Atlas antibodies: anti-CCDC113 (HPA040869), anti-RIBC2 (HPA003210), anti-ARMC3 (HPA037824), anti-C6orf165 (HPA044891) and anti-OSCP1 (HPA028436). Mouse handling and experiments. C57Bl/6J wild-type mice were kept on a 12 h lightdark cycle with unlimited access to food and water. All procedures were in accordance with the guidelines set by the ARVO statement for the use of animals in Ophthalmic and Vision Research and the local laws on animal protection. The following antibodies were used for immunofluorescence/immuno-EM analysis of murine retina sections: rabbit anti-CCDC113 (1:500/1:500), anti-RIBC2 (1:250/1:250), anti-EFHC1 (1:500/-), anti-WDR69 (1:250/-), C6orf165 (1:50/1:200), anti-ARMC3 (1:250/1:500), anti-OSCP1 (for IF 1:100, biorbyt, Cambridge, UK; 1:100, proteintech, Manchester, UK; for EM: 1:100, Atlas, Stockholm, Sweden), mouse anti-centrin-3 (1:100;  [90] ), rabbit anti-rootletin (1:100,  [91] ).
Acknowledgments We kindly acknowledge  Prof. Dr. N. Katsanis  and  J. R. Willer  from  Duke University USA  for providing several of the clone constructs used as baits in the SILAC and Y2H data sets. We also thank the  Caenorhabditis elegans Genetics Center (Minnesota, USA) , the  National Bioresource project (Tokyo, Japan) , the International C. elegans gene knockout consortium, and the C. elegans Million Mutation Project for nematode reagents. We thank  Emine Bolat ,  Ideke Lamers ,  Moniek Riemersma , and  Karlien Coene  for performing yeast two-hybrid and SILAC experiments.
This work was supported by the  European Community 's  Seventh Framework Programme  [ 241955 ,  278568  to MU and KB, 602273 to RS]; the  Virgo consortium , funded by the  Dutch government  [ FES0908  to TvD, RvdL and MAH]; the  Netherlands Genomics Initiative  [ 050-060-452  to TvD, RvdL and MAH]; the  Canadian  john/syscilia/ciliacarta/. Institutes of  Health Research  [ MOP-142243 ,  MOP-82870  and  PJT-156042  to MRL];  Michael Smith Foundation for Health Research  to MRL and VLJ;  Kidney Research Scientist Core Education  and  National Training  fellowship to VLJ; The  Foundation Fighting Blindness  [ PPA-0717-0719-RAD  to UW, RR, and MU]; the  Dutch Kidney Foundation  "KOUNCIL" consortium [CP11. 18 to RHG, PLB and RR]; The  Deutsche Forschungsgemeinschaft  [ Excellence grant CellNetworks  to RBR and QL,  CRC1140  " Kidney Disease -From Genes  to Mechanisms" to MS, collaborative research center grant  SFB-1411  KIDGEM to MS];  Metakids Foundation  to RS; the  National Institute for Health Research  to PLB and VH-H.
In the case of this study, major metaphorical uses may be missed. They conclude that a combination of both is a perfect means to avoid the problems mentioned above. Semino et al. (2017: 61 ) also explain that "as there does not exist yet any fully automated corpus-based method for identifying or analysing metaphor, analyses of large datasets often begin with a qualitative analysis of a sample of the data. " However, I would also argue that a small corpus alone may be used depending on the objective of the research. This is one of the reasons why the corpus under scrutiny is a rather small one, along with other reasons, which are explained below.
For each group, we compiled data on procurement mechanisms used; volume and value of procurement for 2016 and 2017; procurement shares by source (multinational or developing country manufacturers); share of UNICEF Supply Division (SD) in vaccine procurement in the region; and vaccine prices paid and year of new vaccine introductions, focusing on pneumococcal conjugate vaccines (PCV) and rotavirus vaccines (RV). We used data from UNICEF SD and data shared by the 14 countries out of 20 reporting prices through the World Health Organization (WHO)-UNICEF Joint Reporting Form (JRF). A literature review was conducted using the following search terms: ''vaccine", ''procurement", ''supply", ''middle-income countries", ''Middle East and North Africa", and ''developing countries. " First, the authors searched for published articles.
Second, grey literature on the subject was collected from experts and immunization managers and staff working in vaccine procurement and immunization policy. We conducted face-to-face interviews with experts and government officials during the UNICEF MENA Regional Office Consultative Review held in November 2017 in Amman, Jordan to understand the existing purchasing mechanisms and practices; current issues in access to quality and affordable vaccines; strengths and weaknesses of country and UNICEF procurement mechanisms; and reported barriers to new vaccine introductions in the public sector. Most of the data are self-reported by countries in their annual JRF submissions. We compared the reported prices of RV, PCV, and pentavalent (DTwP-HepB-Hib) vaccines among the MENA countries, as well as to the UNICEF SD and PAHO Revolving Fund prices.
Acknowledgement This work was partly funded by  Gavi , the  Vaccine Alliance. The authors are grateful to  Heather Deehan ,  UNICEF Supply Division  for providing data and updates, and to  Daniel Ngemera ,  UNICEF Regional Office  for MENA reviewing the manuscript. Senior government health officials and other experts and individuals from international organizations took time out of their busy schedules to be interviewed. The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of UNICEF.
Acknowledgment This research was supported by grants from  ANR ( ANR-10-MALZ-0004 ), the  Region Centre Val de Loire / FEDER  (ImAD,  2008 00036081 ;  BIAlz ,  2014 00091547 ), the  Cosmi FEDER program , the  Labex IRON  ( ANR-11-LABX-18-01 ),  Labex Synorg  ( ANR-11-LABX-00-29 ),  Fondation de l'Avenir  ( ETO-536 ),  Association France Alzheimer  (AAP SM 2011, # 268 ). The authors thank  Sylvie Bodard  for technical assistance.
Acknowledgements. We acknowledge  Michael Rosticher  for valuable assistance with cleanroom equipment and  Pascal Morfin  for his assistance with the growth setup. We also acknowledge discussions with H. Graef. BAA, JD and TW acknowledge funding from  ANR-LabEx  grant  ENS-ICFP ANR-10-LABX-0010/ANR-10-IDEX-0001-02PSL. JD was partially supported by the  Northeastern University Coop  fellowship. K. W. and T. T. acknowledge support from the  Elemental Strategy Initiative  conducted by the  MEXT, Japan ,  A3  Foresight by  JSPS  and the  CREST  ( JPMJCR15F3 ),  JST.
Background: In order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO 2 ), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO 2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG).
Open Access Carbon Balance and Management *Correspondence: Ignacio. Pisso@nilu. no 4 Present Address: NILU, 2027 Kjeller, Norway Full list of author information is available at the end of the article databases  [4]. Their accuracy depends on the reliability of information about the consumption of fossil fuels and industrial activities within the studied areas. Therefore, complementary independent assessment is desirable. International agreements to limit greenhouse gas emissions require verification to ensure that they are effective and fair  [5]. A concerted effort is needed to transform emerging scientific methods and technologies into an operational monitoring system to support urban carbon management decisions  [6]. In situ measurements of atmospheric CO 2 concentration contain information about upwind CO 2 surface sources.
Results We assessed CO 2 anthropogenic fluxes from the Tokyo Bay area with observation-based constraints. The topdown estimates are based on a composite data set of CO 2 observations and a Bayesian inversion methodology. The measurements correspond to two ground sites (Dodaira and Kisai), a tall tower (Tsukuba) and a commercial flight measurement project (CONTRAIL). Night time observations are not used except in sensitivity estimates in order to prevent model biases. The transport operator (source-receptor relationship, or SRR) is calculated using backward Lagrangian calculations based on ECMWF (European Center for Medium-Range Weather Forecasts) winds. The background CO 2 can be obtained from the measurements themselves (although alternative representations have been tested, see "Methods" section).
The measurements for January 2007 were separated into six time series corresponding to the stations at Kisai (13 m. a. s. l. ) and Mt. Dodaira (840 m. a. s. l. ), the three levels of the Tsukuba tower (base at 33 m. a. s. l. , inlets at 25, 100, and 200 m above ground level) and the composite of the CONTRAIL data (variable heights from ~ 500 to 2000 m. a. s. l). Hourly averaged data is used for Kisai, Dodaira and Tsukuba. We avoid using the nighttime data due to lack of confidence in nocturnal simulations. CONTRAIL measurements are carried out continuously. The bottom altitudes of the ascents and descents, near the Narita runways, are removed from the analyses to prevent highly localized contamination. Observations from layer of high values above the Narita airport was also removed. CONTRAIL commercial flights take place daily (see Additional file 2: Figure  S2  and Additional file 3: Figure  S3  for the vertical distribution and the hourly distribution within the day).
Sensitivity to the prior flux inventory The key ingredient regularising the inversions are the prior fluxes. We carried out large perturbations to the a priori EDGAR emission inventories (2× and 3×). When 2×EDGAR and 3×EDGAR were adopted for the a priori emissions, the atmospheric inversion resulted in a difference of 6% and 9% with respect to the reference respectively. In addition we retrieved the flux with the CDIAC inventory. The prior is much lower that that of EDGAR (11 vs. 34 Mt month -1 ). However, the resulting posterior integrated flux en the urban areas is 33 Mt month -1  showing that the observations provide and effective constraint for the fluxes. We carried out sensitivity tests in order to estimate the impact of neglecting the biogenic fluxes. We used biogenic priors based on CASA and VISIT (see "Methods: description of the data and numerical models" section).
Differences in inverted fluxes for the whole region amounted to 13% on average and up to 21% in the highest flux cells adding aircraft data from the CONTRAIL dataset. • Errors in the modelled meteorological transport largely affect the flux estimates. Among the tested case studies, the impact of using different meteorologies (23% on fluxes from 10 to 15% in the residence times) is comparable to shifting in the transport patterns of between 20 and 40 km (21% and 32% respectively). • Assumed background concentrations impact the results and must be assessed. Background concentrations impacts were determined by the linearised transport operator. Replacing the background obtained directly from the observations for those calculated form a clean air station and a global Eulerian model (AGCM) amounted to differences of 54% and 15 respectively.
Methods: description of the data and numerical models Description of the urban area selected for the study The Tokyo Bay Area (TBA) is located in the Kanto region of Japan, which includes and surrounds the Greater Tokyo Area and encompasses seven prefectures: Gunma, Tochigi, Ibaraki, Saitama, Tokyo, Chiba, and Kanagawa. The region has varied topography and a complex coastline in the East. Within its boundaries, slightly more than 45% of the land area is the Kanto Plain. The rest consists of the hills and mountains that form the land borders. It is a highly developed area with a significant industrial activity and a complex transportation network. The population was about 42 million inhabitants according to an official census count on October 1, 2010 by the Japan Statistics Bureau. This corresponds to approximately one-third of the total population of Japan.
Measurements: CO 2 mixing ratios measured in commercial aircraft, tower and surface stations We have selected a set of measurements taken during the winter months for the years 2005 to 2009 for analysis. Figure  8  shows the spatial distribution of data used within the region. We have combined CO 2 data from ground stations, a tall tower and in situ aircraft measurements covering the Tokyo Bay Area (Fig. 9 ). The total number of data points used in this work is 176,414. In situ highresolution measurements being used include: (i) Tsukuba tall tower measured CO 2 mixing ratio in sampled air from inlets located at 25, 100, and 200 m. Introduced by the diaphragm pump to a nondispersive infrared sensor (NDIR) in the experimental field building  [41, 42]. The mixing ratio standard used for calibration of the instrument was MRI-87 scale, described by Inoue and Matsueda  [41].
The difference of MRI-87 standard and the World Meteorological Organisation (WMO) mole-fraction is less than 0. 2 ppm for the ambient CO 2 level, although it depends on the mixing ratios  [43]. There are therefore six simultaneous time series of measurements: Dodaira, Kisai, the three levels of the Tsukuba tower and CONTRAIL. We developed a workflow in order to combine data from different origins Data were used as provided by the data generator. In the case of the two ground stations and the Tsukuba tower, the data provided was hourly averaged. CONTRAIL data was provided averaged every 10 s (see Additional file 6). Averaging CONTRAIL data hourly would result in the receptors becoming a long transect. As the cruise speed of the aircraft is ~ 900 km h -1 , at 10 s time resolution, a typical distance between data points along the flight path is about 2.