dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA was extracted from maternal, paternal, and proband samples. Trio WES was performed on a NextSeq 500 Sequencing System (Illumina, San Diego, CA, USA), with a 2 × 150 bp highoutput sequencing kit after a 12-plex enrichment with SeqCap EZ MedExome kit (Roche, Basel, Switzerland), according to the manufacturer's specifications. Sequence quality was assessed with FastQC 0. 11. 5, reads were then mapped using BWA-MEM (v. 0. 7. 13), sorted, and indexed in a BAM file (SAMtools 1. 4. 1), duplicates were flagged (sambamba 0. 6. 6), and coverage was calculated (Picardtools 2. 10. 10). Variant calling was done with GATK 3. 7 Haplotype Caller  61. Variants were then annotated with SnpEff 4. 3, dbNSFP 2. 9. 3, gnomAD, ClinVar, HGMD, The Human Variome Project Great Middle East, and an internal database.
Sequencing was performed on an Illumina NovaSeq6000 (2 × 100 bp, Illumina, San Diego, CA, USA) by a commercial provider (CeGaT, Tübingen, Germany) on our request. Bioinformatics processing and filtering was performed using the software Varfeed and Varvis (Limbus, Rostock, Germany). This study was approved by the ethics committees of the University of Leipzig (402/16-ek). The LGD mutation in patient MA. p1 was detected by WES at University of Washington Center for Mendelian Genomics (UW-CMG, Seattle, WA, USA). Library construction and WES were performed for the proband-parents trio. The exome was captured using the NimbleGen SeqCap EZ Exome 2 (Roche NimbleGen, Madison, WI, USA). Exome libraries were sequenced on an Illumina HiSeq platform (Illumina, Inc. , San Diego, CA, USA) with 75 bp paired-end reads at the Northwest Genomics Center at the University of Washington (Seattle, WA, USA).
Sequence reads were aligned to the hg19 reference genome using Burrows-Wheeler Aligner (BWA) version 0. 7. 8. Variants were called by the GATK unified genotyper, version 3. 1. 1, and annotated with SeattleSeq, version 138. Variants not predicted to impact protein-coding sequence or present (AC > 3) in the Genome Aggregation Database, gnomAD version 2. 0, were excluded. All putative de novo variants were validated using Sanger sequencing. The LGD mutations in patients CC1. p1, TI. p1, AA. p1, and GU. mo were detected using smMIPs. The smMIP sequencing and analysis was performed as previously described  19, 20  and summarized above. For patient CC1. p1, the splicesite mutation was detected using smMIP-based targeted sequencing of 2514 probands with a primary diagnosis of ASD. For patient TI.
aCGH was performed according to standard protocols. Further analysis with CGH 244K kit and real-time PCR was performed on the proband's sample to refine the breakpoints of the detected alteration. Array results were analyzed with DNA Analytic 4. 0 Software (Agilent, Santa Clara, CA, USA) and genomic positions were based on Human Genome Assembly NCBI Build 37/hg19. The CNV in patients CF. p1 and CF. p2 was detected using an aCGH 8 × 60k kit (Agilent, Santa Clara, CA, USA) at CHU Lille, Clinique de Génétique Guy Fontaine, Lille, France. aCGH was performed according to standard protocols. Further analysis with a FISH study with BAC RP11-191F21 was performed to detect the mosaic status  64  of the father CF. fa (9 of the 150 mitoses). Analysis of TANC2 expression in the developing human brain.
To interrogate the expression pattern of TANC2 and TANC1 in the developing human cerebral cortex, we analyzed a single-cell RNA sequencing dataset generated from across 48 individuals provided in a recent publication  26. In short, processed normalized expression values, and the associated metadata, were downloaded from the UCSC cell browser (https://cells. ucsc. edu/dev/?ds=cortex-dev). Cluster assignments for each cell were directly used from the source study, and biological interpretation of each broad cell type was made based on the published analysis and is further summarized in Supplementary Table  3. For each cell type, we calculated enrichment of TANC2 or TANC1 using an odds ratio, and statistical significance of enrichment or depletion was calculated using Wilcoxon rank sum test (using wilcox.
The secondary for HRP used was anti-rabbit Alexa-405 number: 711-475-152 (Jackson ImmunoResearch). All secondaries were diluted 1:250 in 0. 5% Triton X-100 in PBS and incubated at 4 °C overnight, washed 4× and mounted in RapiClear® (#RC147001-SunJin Lab Co. ), and imaged with confocal microscopy (Zeiss LSM880). Images were processed using Imaris. For NMJ analysis, primary and secondary antibodies were used: rabbit anti-HRP (1:1000; code number: 323-005-021; Jackson ImmunoResearch, West Grove, PA, USA); mouse anti-DLG (4F3; 1:50; DSHB); mouse anti-GluRIIA (8B4D2; 1:50; DSHB); Alexa 488-or cy3-conjugated anti-mouse and anti-rabbit secondary antibodies (1:500; Jackson ImmunoResearch, West Grove, PA, USA). All images were collected using a ZEISS LSM880 confocal microscope and analyzed with image J software (National Institutes of Health).
Statistical analysis was performed using GraphPad Prism 6. 0 software (GraphPad Software, Inc. ). Statistical significance was performed using one-way ANOVA for comparisons of all group means. Data expressed as the means ± SEM p values <0. 05 were considered to be statistically significant. The analysis was performed double blind. Behavior testing. Housing and handling-All flies were grown in a temperature and humidity controlled incubator at 25 °C and 50% humidity on a 12-h light/dark cycle. Flies were reared on standard fly food (water, yeast, soy flour, cornmeal, agar, corn syrup, and propionic acid). Collection of socially naïve adults was performed by isolating pupae in 16 × 100 polysterine vials containing approximately 1 mL of fly food. After eclosion, flies were anesthetized briefly with CO 2 to ensure they were healthy and lacked wing damage.
Anesthetized flies were returned to their vials and allowed a full 24 h to recover before testing. Courtship paradigm-Courtship assays were performed in a custom-made sixwell plate with 40 mm circular wells, with a depth of 3 mm and 11°sloped walls (milled  66  ). One Canton-S virgin female (aged 6-10 days) and experimental male (aged 3-5 days) were simultaneously introduced into the chamber via aspiration. Recordings were taken using a Basler 1920UM, 1. 9MP, 165FPS, USB3 Monochromatic camera using the BASLER Pylon module, with an adjusted capturer rate of 33FPS. Conversion of captured images into a movie file was performed via a custom MatLab script, and tracking of flies in the movie was performed using the Caltech Flytracker  37. Machine learning assessment of courtship was performed using JAABA  36  and 20% videos were manually verified for accuracy post screening.
Software and statistics-Data analysis was performed using Microsoft Excel and GraphPad Prism. Determination of significance in behavior tests was performed using the Kruskal-Wallis one-way analysis of variance and the Dunn's multiple comparison test. P values of 0. 05 or less were considered significant. Outliers were identified and eliminated using a ROUT test (Q = 1%). Evaluation of significance when examining the number of successful copulations was determined via Chi-Square test. P values of 0. 05 or less were considered significant. Climbing and bang-sensitivity assays. Flies were anesthetized with CO 2 and housed in individual vials 24 h prior to testing  67. For climbing, flies were given 1 min to habituate to an empty vial and tapped to induce a climbing response and timed to reach the 7 cm mark.
Acknowledgements We thank  Tonia Brown  for assistance in editing this manuscript. We thank all of the families participating in this study. We are grateful to all of the families at the participating SSC sites, as well as the principal investigators ( A. Beaudet ,  R. Bernier ,  J. Constantino ,  E. Cook ,  E. Fombonne ,  D. Geschwind ,  R. Goin-Kochel ,  E. Hanson ,  D. Grice ,  A. Klin ,  D. Ledbetter ,  C. Lord ,  C. Martin ,  D. Martin ,  R. Maxim ,  J. Miles ,  O. Ousley ,  K. Pelphrey ,  B. Peterson ,  J. Piggot ,  C. Saulnier ,  M. State ,  W. Stone ,  J. Sutcliffe ,  C. Walsh ,  Z. Warren ,  E. Wijsman ). We appreciate obtaining access to phenotypic data on SFARI Base. Approved researchers can obtain the SSC population dataset described in this study (https://www. sfari. org/resource/resources/simons-simplex-collection/)by applying at https://base.
This work is also supported by  NIH  ( U54NS093793 ) to H. J. B. , S. Y. and M. J. W. ; H. J. B. is supported by  NIH  ( R01GM067858 ,  R24OD022005 ); S. Y. and M. F. W. by a  Simons Foundation   Functional Screen Award  ( 368479 ). M. A. G. was supported by the  U. S. National Institutes of Health  ( T32HG000035 ). We would like to thank  Hongling Pan ,  Danqing Bei  and  Kai Yuan 's lab for technical assistance. Confocal microscopy at  Baylor College of Medicine  is supported in part by  NIH  grant  U54HD083092  to the  Intellectual and Developmental Disabilities Research Center (IDDRC) Neurovisualization Core. P. C. M. is funded by  CIHR  and the  Stand by Eli Foundation. J. C. A. is supported by  NIH   F32  ( NS110174-01 ). WES for proband MA. p1 was provided by the  University of Washington Center for Mendelian Genomics (UW-CMG)  and funded by the  National Human Genome Research Institute  and the  National Heart, Lung and Blood Institute  grant  HG006493  to  Drs.
Data availability The MIP sequencing data for this study have been deposited in the NIMH data repository National Database for Autism Research (NDAR) (https://doi. org/10. 15154/ 1252218) and is available to all qualified researchers after data use certification. A reporting summary for this article is available as a Supplementary Information file. Underlying data in Figs. 1a;  4; 6b, c; 7d-f; Supplementary Figs. 5, 6a, b are provided as Source Data file.
This latter point is of particular interest in view of operational monitoring and forecasting applications. In this study, GPP estimates by the Sim model are validated using GPP derived at eddy covariance flux sites in Europe and compared to the standard MODIS GPP product (MOD17) used as benchmark  [18]. The research questions addressed in this study are: (1) can a simple growth model use modelled meteorological variables and assimilate EO data to adequately simulate crop and grassland GPP? and,  (2)  what is the estimation accuracy and how does it compare with that of the standard MODIS GPP product? 2. DataError! Reference source not found. Table  1  provides an overview the data used by the study and their specific use. Detailed description of the datasets is given in Sections 2. 1 to 2.
MODIS NDVI, Reflectances and GPP As source of EO data we selected MODIS data as it provides a relatively long archive of observations (i. e. , from year 2000) and a good compromise between spatial resolution (i. e. , 250 m) and temporal resolution of acquisition (i. e. , daily revisit). We retrieved NDVI from the MODIS MOD13Q1 and MYD13Q1 version 6 products using the Google Earth Engine. These are 250 m spatial resolution 16-day NDVI composite from Terra and Aqua satellites produced with an 8-day phase shift. Besides NDVI, the following information was retrieved for each observation: reflectances in the blue, red, NIR, and MIR bands, day of acquisition, sun-sensor-target geometry (SZA, Solar Zenith Angle; VZA, View Zenith Angle, RAA, Relative Azimuth Angle), and Vegetation Index (VI) quality and usefulness.
Only reliable observations were retained from further processing, i. e. , observations with VI reliability greater than two and VI usefulness greater than seven where excluded. MOD09A1 and MYD09A1 (surface reflectance 8-day composite at 500 m) were also acquired to determine the background contribution to canopy NDVI as explained in Section 3. 2. In addition, we retrieved the MODIS GPP 8-day version 6 product (MOD17A2H) at 500 m spatial resolution. MODIS GPP 8-day was scaled to mean daily value (gC m -2 d -1 ) over the 8-day period. We used the flux tower locations to extract the time series of MODIS data by selecting the pixel where the flux tower coordinates falls within.
Eddy Covariance Flux Tower GPP Eddy covariance (EC) is an established technique to quantify turbulent exchanges of CO2 between the surface and the atmosphere (for a descrption see Baldocchi et al. [21] ; Franz et al. [22] ). European flux data have been collected from the European Flux Database Cluster (EFDC, a repository of flux observations for various European projects), and the FLUXNET2015 database (a coordinated Comment citer ce document : Meroni, M. (Auteur de correspondance), Fasbender, D. , Lopez-Lozano, R. ,  Migliavacca  effort to harmonize flux observations for as many sites as possible globally  [23] ). For the purposes of this study, only flux measurements over cropland and grassland were acquired for a total of 58 sites. Among the different GPP products available in the two datasets we selected half-hourly GPP estimated from net ecosystem exchange (NEE) measurements quality-checked, filtered for low friction velocity conditions, gap-filled and partitioned as consolidated in the FLUXNET methodology  [24].
Definition of εmax Values in the range from 0. 86 to 2. 5 (gC m -2 d -1 MJ -1 ) have been reported for grassland and crop εmax in the literature  [45] [46] [47] ). Various definitions also exists for it in different communities and in different modelling setups (e. g. , depending on weather non-photosynthetic tissues are included in the definition of FAPAR)  [48]. Here we used an empirical parametrization that was derived using EC GPP data of two crop and two grass sites randomly extracted from the EC database (BE-Lon and FR-Aur for a total of 17 site-year crop samples; CZ-BK2 and IT-MBo for a total of 17 site-year grassland samples). A nested inversion of the Sim model was run on the two calibration sites. The inversion set-up was configured in standard mode (Section 3. 5) and run iteratively for the calibration sites changing the value of εmax in steps of 0.
Validation Simulated and observed daily GPP are temporally aggregated to 8-day MODIS temporal resolution. GPP data from 28 crop and 25 grassland EC sites are used in the validation. Simulation performances are assessed analyzing the simulated vs. observed GPP scatter plots in terms of: coefficient of determination (R 2 ), root mean square error (RMSE), and mean bias error (MBE), slope and offset the ordinary-least-squared (OLS) regression model. All these metrics inform about the predictive power of the model as the GPP data were not used for model calibration. Finally, results were compared to those obtained with MODIS GPP product MOD17 that was used as benchmark model. Model performances in space (across sites) and time were further assessed by analyzing  (1)  the correlation between site averages of annual cumulative GPP observed and modeled, and (2) the ability of the models to track site-level inter-annual variability of GPP.
In general, we do not observe a systematic underestimation of maize GPP (see Sections 4. Figure  4  shows the grassland site HU-Bug (Hungary, 47. 84690°N, 19. 72600°E;  [59] ), a semi-arid sandy grassland extensively grazed. The Sim model detects two growing cycles per year that are originated from a biomass reduction during the summer due to grazing and/or sever water limitations  [60]. In years when no double cycles is visible in the measured GPP, NDVI shows only a moderate reduction during the summer and coherently, the two season modelled by Sim are nearly merged together (e. g. , years 2005 and 2006). GPP estimation accuracy is high for both the Sim model and MOD17 for this site (see scatter plots of Figure  4 )   Figure  6  shows the results for a grassland site where estimation errors are among the largest in the dataset (IT-Amp, Italy, 41.
Conclusions Comment citer ce document : Meroni, M. (Auteur de correspondance), Fasbender, D. , Lopez-Lozano, R. , Migliavacca, M. (2019). Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model. Remote Sensing , 11 (7), 21 p. , DOI : 10. 3390/rs11070749 A simple crop growth model composed by few equations building on the framework of the GRAMI model was coupled with the canopy radiative transfer model PROSAIL5B and reparametrized against MODIS NDVI observations to estimate GPP of crop and grass ecosystems over Europe. The model requires four meteorological variables as input (incident radiation, air temperature, precipitation and reference potential evapotranspiration) and a preliminary identification of the growing period. In this study, we used meteorological variable estimates from a global circulation model (ECMWF ERA-INTERIM) and the results of a satellite-based land surface phenology retrieval for the initial definition of growing period.
We present the SemEval-2019 Task 12 which focuses on toponym resolution in scientific articles. Given an article from PubMed, the task consists of detecting mentions of names of places, or toponyms, and mapping the mentions to their corresponding entries in GeoNames. org, a database of geospatial locations. We proposed three subtasks. In Subtask 1, we asked participants to detect all toponyms in an article. In Subtask 2, given toponym mentions as input, we asked participants to disambiguate them by linking them to entries in GeoNames. In Subtask 3, we asked participants to perform both the detection and the disambiguation steps for all toponyms. A total of 29 teams registered, and 8 teams submitted a system run. We summarize the corpus and the tools created for the challenge. They are freely available at https://competitions.
Introduction Toponym resolution, also known as geoparsing, geo-grounding or place name resolution, aims to assign geographic coordinates to all location names mentioned in documents. Toponym resolution is usually performed in two independent steps. First, toponym detection or geotagging, where the span of place names mentioned in a document is noted. Second, toponym disambiguation or geocoding, where each name found is mapped to latitude and longitude coordinates corresponding to the centroid of its physical location. Toponym detection has been extensively studied in named entity recognition: location names were one of the first classes of named entities to be detected in text  (Piskorski and Yangarber, 2013). Disambiguation of toponyms is a more recent task  (Leidner, 2007). With the growth of the internet, the public adoption of smartphones equipped with Geographic Information Systems and the collaborative development of comprehensive maps and geographical databases, toponym resolution has seen an important gain of interest in the last two decades.
Related Work The Entity Linking task aims to map a name of an entity with the ID of the corresponding entity in a predefined Knowledge database  (Bada, 2014). Entity linking has been largely studied by the community  (Shen et al. , 2015). Toponym resolution is a special case of the entity linking task where strategies dedicated to toponyms can improve overall performances. Three main strategies have been proposed in the literature. The first exploits the linguistic context where a toponym is mentioned in a document. The vicinity of the toponym often contains clues that help the readers to interpret it. These clues can be other toponyms  (Tobin et al. , 2010) , other named entities  (Roberts et al. , 2010) , or even more generally, specific topics associated more often with a particular toponym than with others  (Speriosu, 2013; Adams and McKenzie, 2013; Ju et al.
These metadata are of various kinds, but they all indicate, directly or not, geographic areas to help interpret toponyms mentioned in documents. Such metadata can be geotagging of social media posts  (Zhang and Gelernter, 2014)  or external databases structuring the information detailed in a document  (Weissenbacher et al. , 2015). These three strategies are complementary and can be unified with machine learning algorithms as shown by  (Santos et al. , 2015)  or  (Kamalloo and Rafiei, 2018).
The disambiguator has to select the GeoNames IDs corresponding to the expected places among all possible candidates. GeoNames 2 is a crowdsourced database of geospatial locations and freely available. Following with our previous example, given the position of Nanjing in the sentence, a perfect disambiguator, regardless how, would have to choose among 12 populated places named Nanjing located in China in GeoNames and return the entry 7843770 in GeoNames. The disambiguator has to infer the expected place based on all information available in the article and not only based on the sentence. This subtask allows one to measure the performance of the disambiguation algorithms independently from the performances of the toponym detector used upstream.
A Case Study: Epidemiology of Viruses The automatic resolution of the names of places mentioned in textual documents has multiple applications and, therefore, has been the focus of research for both industrial and academic organizations. For this challenge, we chose a scientific domain where the resolution of the names of places is key: epidemiology. One aim in epidemiology might be to create maps of the locations of viruses and their migration paths, a tool which is used to monitor and intervene during disease epidemics. To create maps of viruses, researchers often use geospatial metadata of individual sequence records in public databases such as NIH's GenBank (Benson et al. ,   2 https://www. geonames. org/ 2017) 3. The metadata provides the location of the infected host. With more than 3 million virus sequences 4  , GenBank provides abundant information on viruses.
However, previous work has suggested that geospatial metadata, when it is not simply missing, can be too imprecise for local-scale epidemiology  (Scotch et al. , 2011). In their article  Scotch et al. , 2011  estimate that only 20% of GenBank records of zoonotic viruses contain detailed geospatial metadata such as a county or a town name (zoonotic viruses are viruses able to infect naturally hosts of different species, like rabies). Most GenBank records provide generic information, such as Japan or Australia, without mentioning the specific places within these countries. However, more specific information about the locations of the viruses may be present in articles which describe the research work. To create a complete map, researchers are then forced to read these articles to locate in the text these additional pieces of geospatial metadata for a set of viruses of interest.
Corpus Collection Our corpus is composed of 150 full text journal articles downloaded from the subset of PubMed Central (PMC) in open access 5. All articles in this subset of PMC are covered by a Creative Commons license and free to access. We built our corpus using three queries on GenBank. Subset A: For the first 60 articles, we downloaded 102,949 GenBank records that were linked to NCBI taxonomy id 197911 for influenza A. The downloaded records were associated with 1,424 distinct PubMed articles and 598 of them had links to an open access journal article in PubMed Central (PMC). We randomly sampled 60 articles from this set of 598 articles for manual annotation. Subset B: We selected 60 additional articles by expanding our search to GenBank records linked to influenza B and C, rabies, hantavirus, western equine encephalitis, eastern equine encephalitis, St.
Louis encephalitis, and West Nile virus. Our query returned a total of 544,422 GenBank records. We randomly selected a subset of records associated with 1,915 unique open access PMC articles. From these 1,915 articles, we randomly selected for toponym annotation a stratified sample of 60 articles, where strata were based on the number of GenBank records associated with the articles. Subset C: We completed our corpus with 30 biomedical research articles to decrease bias and increase the generalizability of our corpus beyond toponym mentions in virus related research articles. From the 1,341 research articles returned by the search in PMC of the journal titles with the Article Attribute of Open access, we randomly selected 30 articles from top epidemiology journals, as determined by their impact factor in September 2018.