dataesr/scientific-paragraphs-categorization huggingface dataset:
With the purpose of understanding the impact of different scanner operators, the same acquisitions and reconstructions were done on each site using the QUICK phantom. This step was followed with a series of acquisitions and reconstructions using local setup preferences as well as the site's recommendations for clinical reconstruction. The final statistical analysis of all data provided by the sites was expected to point out the impact of sites' clinical practice on absolute quantitation in terms of contrast recovery, background variability, and lung error. Results obtained using optimized protocols should be compared within the different participating sites to evaluate the global achievable accuracy of the multi-center clinical trials. In summary, the aim of this study was to estimate the global statistical multi-center quantitation accuracy taking into account the discrepancies between participating PET facilities, using a solid phantom with fixed and accurately known activities and volumes.
Consequently, it is relevant to this study to keep track of the site's preference in β value selection. This penalty term provides level of regularization based on the difference between neighboring voxels as well as their sum. A solid 68 Ge phantom (QUICK phantom) based on IEC61675/NEMA Body IQ phantom geometry and activity concentrations was manufactured for this study allowing a better identification of the eventual causes of quantitative discrepancies among the participating sites. In order to avoid discrepancies related to data analysis in each participating site, it was decided to use the built-in software provided by GE Healthcare and available on operating console to perform NEMA NU-2 2012 image quality test.
The five centers involved in the QUICK phantom acquisition are identified in this paper as QUICK sites 1-5. The data acquisition involved 5 measurements per day for a span of 5 days to account for the effect of the Poisson statistics and variability in phantom positioning, respectively. Thus, a dataset of 25 images have been used for this analysis for each site. Table  2  summarizes the acquisition and reconstruction parameters for the different tests. In test 1, the QUICK phantom was scanned with the acquisition and reconstruction parameters recommended by the manufacturer for the standard NEMA NU2-2012 procedure (same as in Table  1  for the 18 F-FDG phantom). The NEMA-IQ protocol recommended by the manufacturer does not include PSF because not all users have access to PSF, whereas a user with access to BSREM automatically includes access to PSF.
In test 2, the RAW data was re-processed using the BSREM algorithm and a β value of 25 optimized to match the background noise level (BV) observed in the corresponding OSEM reconstruction (test 1). Increasing beta value results in smoother background (preferred by physicians) at the expense of reduced contrast recovery  [16]. This avenue was not explored as the intent of the paper is to compare site to site variations, not the impact of different beta values in clinical images. In test 3 and test 4, each QUICK site re-processed the same RAW data using their own clinical reconstruction parameters optimized for OSEM and BSREM algorithms, respectively. For OSEM, the number of iterations for clinical reconstruction was the site's choice as preferred by the site's reading physicians and is a compromise between signal recovery and background noise level.
This conclusion is further supported by the small variability observed when same imaging protocol is used with the QUICK phantom. In this study, we have tried to assess and demonstrate the possibility of performing quantitative multisite clinical trials in PET using QUICK phantom. In addition, we also made an effort to understand the impact and potential causes of discrepancies among different sites if any. In order to avoid additional possible causes of discrepancies, we decided to extract quantitative data using the automatic processing available on the scanner's console. As mentioned above, two centers of the 17 sites (QUICK site 4 and QUICK site 5) showed unexpected data from the 18 FDG phantom, despite having detailed directions for filling and imaging of the phantom. Their results can be explained by placing a larger than prescribed ratio of activity concentration between spheres and background (QUICK site 5) and too high activity in the background above the NEMA prescribed value (QUICK site 4).
ACKNOWLEDGEMENTS NEC has been supported by a  Royal Astronomical Society Research Fellowship. CL is supported by a  Beecroft fellowship. RSB  acknowledges funding from the  Centre National de la Recherche Scientifique (CNRS)  on grant  ANR-16-CE31-0011. This work has made use of the  HPC resources of CINES (Jade and Occigen supercomputer)  under the time allocations 2013047012, 2014047012 and 2015047012 made by  GENCI. This work is partially supported by the  Spin(e)  grants  ANR-13-BS05-0005  (http://cosmicorigin. org) of the  French Agence Nationale de la Recherche  and by the  ILP LABEX  (under reference  ANR-10-LABX-63  and  ANR-11-IDEX-0004-02 ). We thank  S. Rouberol  for running smoothly the Horizon cluster for us. Part of the analysis of the simulation was performed on the DiRAC facility jointly funded by  STFC ,  BIS  and the  University of Oxford.
2019 ). To our knowledge, no DNA-based molecular assays exist for the genotyping of the other inversions in An. gambiae or An. coluzzii. We recently described a strategy that exploited the An. gambiae and An. coluzzii database of natural variation across sub-Saharan Africa (Ag1000G; www. malariagen. net/projects/ag1000g) (Miles et al. 2017)  to identify tag SNPs predictive of inversion orientation for all six common inversion polymorphisms in these species. Using these tags, we developed an algorithm capable of in silico inversion genotyping based on SNPs called from whole genome resequencing data  (Love et al. 2019). This is a rapid and powerful approach assuming that whole genome sequence data are already available or will be produced for other reasons. However, it does not satisfy experimental designs in which genomic sequence data are not otherwise required, and where its procurement would be cost-prohibitive.
OA and GT-seq assay design and genotyping Because the methods we used to develop and validate the set of tag SNPs for in silico inversion genotyping  (Love et al. 2019)  were still being refined at the time the present study was initiated, the initial list of candidate molecular tags differed slightly from the in silico set, though overlap was extensive. One minor methodological difference in ascertainment was that, for the molecular tag SNPs, genotypic concordance was not based on ten bootstrap replications of a training set, but instead was based on the simple percentage of mosquitoes with a matching inversion-and SNP-genotype out of the total mosquito sample analyzed for that inversion in the Ag1000G variation database. This was calculated separately for each of three inversion genotypes [homozygous standard (i.
Assays designed for this platform require forward and reverse PCR primers which produce 100 base amplicons containing the tag SNP, and additionally require two allele-specific fluorescently labeled 30-bp probes ('reporters') that discriminate between the reference allele at the tag (VIC dye) and the alternate allele (FAM dye). Candidate SNPs were filtered out if they were surrounded by runs of nucleotides and low complexity regions that interfered with acceptable primer design parameters. Further filtering was performed if candidate SNPs were surrounded by high frequency variants in the 25 bases immediately upstream or downstream of the tag SNP. High frequency was defined as. 5% in at least one inversion genotype (i. e. , homozygous standard, heterozygous, homozygous inverted) or at least two population samples analyzed for a given inversion in the Ag1000G database.
However, the highly multiplexed nature of GT-seq, allowing simultaneous amplification of up to 500 SNP loci per individual for thousands of individuals, requires that the primer pool be optimized not only for individual amplicons (e. g. , by avoiding nucleotide runs, low complexity regions, and primer-dimer), but also to minimize primer interactions across loci and mis-priming with other amplicons. The initial list of candidate tag SNPs ranked by concordance was filtered based on the output of custom perl scripts to evaluate primer pools  (Campbell et al. 2015 , https://github. com/GTseq). Minimum concordance values varied by inversion (. 0. 8 for 2Rc;. 0. 85 for 2Rd;. 0. 9 for 2Rb, 2Rj, and 2Ru;. 0. 995 for 2La). Candidate tag SNPs for 2La, which were overly abundant, were pruned by selecting every third candidate from a list ordered by chromosome position.
Base calling was done by Illumina Real Time Analysis (RTA) v2 software. Using scripts described in the bioinformatics pipeline of  Campbell et al. (2015)  and available on Github (https://github. com/GTseq), sequencing data were demultiplexed into single fastq files for each individual sample. Individuals were genotyped at each locus with a perl script (GTseq_Genotyper_v3. pl) that counts the occurrence of each allele at a locus within individual fastq files. The ratio of allele 1 to allele 2 counts was used to generate a genotype for each locus with total read counts. 10, following the methods and cut-offs of  Campbell et al. (2015).
Data availability Figure  S1  shows the tag SNP and specimen call rates for OpenArray and amplicon sequencing (GT-seq) approaches. Table  S1  contains OA tag SNP ID numbers, locations, reference and alternate alleles, forward and reverse primer sequences, and probe sequences. Table  S2  contains GT-seq tag SNP ID numbers, locations, reference and alternate alleles, and forward and reverse primer sequences. Table  S3  contains specimen ID numbers and inversion genotypes (cytogenetic, OA, and GT-seq) for each individual mosquito. Data can be found at https://figshare. com/projects/Anopheles_coluzzii_and_An_ gambiae_molecular_inversion_karyotyping/85157 and https://figshare. com/projects/Anopheles_gambiae_An_coluzzii_and_An_funestus_ molecular_inversion_karyotyping_raw_amplicon_sequencing_data/ 81128.
The implication is that if those assumptions are violated in natural populations, both approaches may agree on the wrong genotype. The tags were ascertained in the Ag1000G variation database, whose content was heavily biased toward An. gambiae at the time of their discovery  (Love et al. 2019). Available samples of An. coluzzii were more limited in numbers and geographic representation, although Burkina Faso was one of two countries represented for this species. In addition to the issue of sampling limitations is the issue of unsuspected (cryptic) population structure that could affect the performance of these tags. Population structure could arise from several non-mutually exclusive scenarios: (i) a lack or reduction of connectivity between natural populations; (ii) local heterogeneity in selection pressures acting on targets inside the inversion; and/or (iii) violation of the assumption that the focal inversion arose uniquely (i.
Here we demonstrate that tag SNPs highly correlated with inversion status across the species range represented in the Ag1000G database can be used for joint molecular genotyping of common inversions in An. gambiae and An. coluzzii across the genome (i. e. , for karyotyping). Molecular genotyping methods, both OA and GT-seq, can be performed at scale and the results are comparable or superior to traditional cytogenetic karyotyping. These tools invite a renewal of investigations into the role of chromosomal inversions in anopheline behavior and environmental adaptation. n■ Table  3  Concordance between CYT and both molecular methods by inversion genotype (0, 1, 2) for specimens for which both molecular methods agree. Shown are the numbers of specimens scored for each pairwise comparison.
ACKNOWLEDGMENTS We thank the technicians of  IRD/IRSS/Centre Muraz of Burkina Faso  for their fundamental contribution to the field work, as well as the inhabitants of the sampled villages for their kind collaboration. We thank  M. Kern  and  R. Montañez-Gonzalez  ( University of Notre Dame ) for assistance with DNA extraction. The  Notre Dame Center for Research Computing  provided technical support. We thank  Dana-Farber / Harvard Cancer Center in Boston, MA , for the use of the  Genotyping and Genetics for Population Sciences Core , which provided SNP analysis using the Taqman OpenArray Genotyping System. We acknowledge the  Notre Dame Genomics and Bioinformatics Core Facility  for assistance with GT-seq project development, library preparation, and sequencing. This work was supported by the  National Institutes of Health  ( R01 AI125360  awarded to NJB).
Patient cohort Pituitary tumor specimens were obtained from a multicenter cohort of 48 patients (15 female, 33 male), with a median age 45 (range 16-73 years) at diagnosis. Inclusion criteria were at least 1 pituitary surgery and tumor progression despite radiotherapy, and/or while on treatment with dopamine agonists or somatostatin analogues, or metastatic disease. Thirty patients had APTs and 18 had PCs with cerebrospinal and/or systemic metastases. The median time from diagnosis of the pituitary tumor to metastases was 8. 5 (range 1. 2-36) years (Table  1 ). The patients were treated at specialized centers in 11 European countries (Belgium, Denmark, Finland, France, Hungary, Italy, Norway, Poland, Serbia, Sweden, and UK). Patients' data and tumor characteristics at the first presentation, treatments given, and outcome were collected in anonymized standardized questionnaires filled in by all participating centers.
In the majority of cases, IHC was performed at Uppsala University Hospital in a DAKO-Autostainer Link 48 with heat-induced epitope retrieval at high pH. Purified polyclonal anti-ATRX antibody (HPA001906, Atlas Antibodies; dilution 1:100; incubation time 20 minutes) was used. Specimens from 2 adult astrocytomas, 1 with ATRX mutation and 1 without ATRX mutation, both confirmed by using molecular genetic analysis, were used as negative and positive controls. In addition, immunolabelled endothelial cells served as an internal positive control. Four cases from Foch Hospital (Suresnes, France) and a case from University Hospital in Copenhagen, Denmark, were stained in Ventana Benchmark by using the same antibody and according to the locally optimized protocols.
Acknowledgments We thank  Svetlana Popova ,  Uppsala University Hospital , for her help in performing immunohistochemical analyses;  Ansgar Heck ,  Kristin Astrid B. Øystese  and  Jens Bollerslev  ( University of Oslo, Norway ),  Bjarne Winther Kristensen  and  Janne Christiansen  ( Department of Pathology, University of Southern Denmark, Odense ),  Olli Tynninen  ( Department of Pathology, University of Helsinki and HUSLAB, Helsinki, Finland ),  Angeliki Papagiannopoulou  ( Department of Pathology, Linköping University Hospital, Sweden ),  Eva C. Lindberg  ( Department of Pathology, Lund University Hospital, Sweden ),  Alia Shamikh  ( Karolinska University Hospital, Sweden ),  Lilla Reiniger  and  Hajnalka Rajnai  ( Department of Pathology and Experimental Cancer Research, Medical Faculty, Semmelweis University, Budapest, Hungary ),  Gavin Baker ,  Brian Herron  ( Royal Victoria Hospital, Belfast ) and  Anastasia M.
Lapshina  ( Department of Pathology, University Hospital Moscow, Russia ) for their contribution in the selection of the patients and collection of the surgical specimens;  Christine Gaasdal Kassentoft and Britt Elmedal Laursen, Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark , for molecular analysis performed at one of the ATRX mutated cases, and  Evelina Sjöstedt ,  Uppsala University , for formatting Fig. 1. Financial Support:  Olivera Casar-Borota  was supported by the  Swedish Cancer Society  (grant number  190157  Fk) and by a grant from the  Swedish state  under the agreement between the Swedish government and the county councils (ALF-agreement). Henning B. Boldt was supported by the  Region of Southern Denmark. Camilla Schalin-Jäntti  received funding from the  Helsinki University Hospital Research Funds  ( TYH2018223 ,  TYH20191254 ).
Acknowledgements This work was supported by  National Natural Science Foundation of China  ( 61735016 ,  81430010 ,  61975172 ,  81702508 ,  91632105 ,  31627802 ),  Zhejiang Provincial Natural Science Foundation of China  ( LR17F050001 ,  LY17C090005 ) and the  Fundamental Research Funds for the Central Universities  ( 2019QNA5001 ).
Statistical Methods In order to understand how the drivers we highlighted previously impact iron assimilation efficiency (AE), we performed a statistical analysis using the R package relaimpo (https://cran. rproject. org/web/packages/relaimpo/index. html), resolving the following generalized linear model (GLM): ironAE = α(Food quantity) + β(Food quality) + γ (%detritivory) + δ(temperature) + ǫ (3) From this analysis, we can calculate the proportion of the variance in iron assimilation efficiency explained by each factor of Equation (  3 ) and by all of the factors combined, allowing us to examine how each factor impacts iron assimilation efficiency individually and in combination in our biogeochemical model results. An alternative way to perform this assessment would be to conduct a suite of sensitivity experiments with the biogeochemical model where each driving factor (food quality, quantity, etc.
Over arid and semi-arid areas of Africa, GRACE land water solutions were used to reveal the depletion in the North Western Sahara Aquifer System  [19] [20] [21]  and the Nubian Sandstone Aquifer System  [22] [23] [24] [25] , and the increase in GW storage in the Niger River Basin  [26]  and the Chad Lake Basin  [27]. The goal of this study is to determine the importance of climate and anthropogenic factors on the GW changes determined using the GRACE-based TWS in the three major transboundary aquifers located in arid North Africa (i. e. , the Tindouf, North Western Sahara and Nubian Sandstone aquifer systems) during the GRACE era (i. e. ,  [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012] [2013] [2014] [2015] [2016]. The study areas and the datasets used to derive time variations of GW anomalies and hydrological fluxes (rainfall and evapotranspiration) are first presented.
GLEAM The Global Land Evaporation Amsterdam Model (GLEAM) simulates evaporation and root-zone soil moisture using satellite data  [45, 46]. Potential evapotranspiration is estimated using observations of surface net radiation and near-surface air temperature based on the Priestley and Taylor equation. It is converted into actual evaporation using a multiplicative, evaporative stress factor. This factor is derived from the vegetation optical depth (VOD), and the root-zone soil moisture (SM) simulations. The VOD parameterizes the extinction effects due to the vegetation affecting the microwave radiations propagating through the vegetation canopy and is related to the vegetation water content  [47]. Root zone SM is obtained using a multi-layer soil profile of the rainfall infiltration. Rainfall interception loss is simulated using the Gash model  [48].
• v3. 3a which is a global record of actual evaporation and its different component, as well as root zone SM derived from satellite-based SSM, VOD, and snow water equivalent (SWE), reanalysis air temperature and radiation, and a multi-source precipitation product • v3. 3b which is fully satellite-based and quasi-global (50 • N-50 • S)  [46]. These datasets are available upon request at [49] from January 1980 to December 2018 and from June 2003 to December 2018 for v3. 3a and v3. 3b respectively, at a spatial resolution of 0. 25 • , and at the daily and monthly temporal resolutions.
Water Volume of Lake Nasser from Hydroweb The Hydroweb database, developed by Collecte Localisation Satellites/Centre National d'Etudes Spatiales/Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (CLS/CNES/LEGOS)  [50] , made available several thousands of time series of water levels of rivers and lakes from multi-mission radar altimetry  [51]. For a large number of lakes, estimates of inundated surfaces are derived from MODerate resolution Imaging Spectroradiometer (MODIS) multispectral images thresholding spectral bands and indices  [52] , and combining water level and extent, estimates of water volume assuming a pyramidal shape for the lake bathymetry as in  [53] [54] [55] [56]. For Lake Nasser, the time series of water level and volume are available from September 1982 to March 2020.
Monthly Rainfall from Tropical Rainfall Measuring Mission/Multi-Satellite Precipitation Analysis (TRMM/TMPA) 3B43 The Tropical Rainfall Measuring Mission/Multi-Satellite Precipitation Analysis (TRMM/TMPA) 3B43 v7 product is a combination of monthly rainfall estimates at a spatial resolution of 0. 25 •. This dataset is the combination of satellite information from the passive TRMM Microwave Imager (TMI) and Precipitation Radar (PR) onboard the TRMM, from November 1997 to March 2015, the Visible and Infrared Scanner (VIRS) onboard the Special Sensor Microwave Imager (SSM/I) and rain gauge observations. It is currently available from January 1998 to now. The dataset results from the coupling of the TRMM 3B42-adjusted merged infrared precipitation with the monthly accumulated Climate Assessment Monitoring System or Global Precipitation Climatology Center Rain Gauge analyses  [57, 58].
This dataset is available at  [62]. These maps provide a description of the Earth's surfaces in 37 LC classes following the United Nations Land Cover Classification System (UN-LCCS)  [63]. As discrepancies on cropland areas estimates were reported between the ESA-CCI LC product and Food and Agricultural Organization of the United Nations statistical data (FAOSTAT) in non-Organization for Economic Co-operation and Development (OECD) countries  [64] , and due to the presence of mosaic pixels including croplands in the study areas, LC classes corresponding to cropland were not included in the analyzes presented below. The only LC class taken into account is the one corresponding to urban areas and their temporal evolution used as an indicator of changes in population.
Direct Comparisons of the Different Data Sources Direct comparisons of the different sources used to compute the anomaly of GW storage were performed. Bias, root mean square difference (RMSD), relative RMSD (RRMSD) and Pearson correlation coefficient (R) between the different sources of GRACE-based TWS solutions, SM and ET model outputs were estimated at grid-point and basin scales. RRMSD was computed as the ratio between the RMSD and the average of the mean annual amplitude between years m and n (A m. n ) of the two compared parameters a and b: RRMSD(a, b) = RMSD(a, b) 1 2 A m. n (a) + A m. n (b) , (1) where A m. n is the difference between the mean annual maximum and minimum. The Mascon GRACE-based TWS products from CSR were downscaled to the lower spatial resolution of the Mascon GRACE-based TWS products from JPL applying a simple average.
Direct Comparisons of the Different Datasets Due to the lack of in situ data to validate the results that are presented in this study, the first step is to determine if the different datasets used to analyze the spatio-temporal variations of the hydrological fluxes and storages exhibit a similar behavior. As explained in Section 2. 2, GRACE mascon solutions were selected for TWS as they are less affected by spurious meridian noise compared with the global ones. For SM and ET, two versions of GLEAM model outputs were chosen, as this model provides reliable and accurate estimates of these parameters over Africa and semi-arid areas  [68] [69] [70]. Owing to the use of a medium resolution (250 m) land-cover fraction, larger fractions of bare soil are taken into account in arid and semi-arid areas compared to previous versions and other land-surface models.