dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Fundação para a Ciência e  Tecnologia (FCT)  supported the FCT Investigator contracts of  Joana Abrantes  ( CEECIND/00078/2017 ),  Ana M. Lopes  ( CEECIND/01388/2017 ) and  Pedro J. Esteves  ( IF/00376/2015 ). Clément Droillard's doctoral fellowship was co-funded by the  Conseil Général des Côte-d' Armor and by Saint-Brieuc Armor Agglomération. This work is a result of the project LAGMED supported by  FCT  ( PRIMA/0003/2018 ) and  PRIMA programme , an Art. 185 initiative supported and funded under  Horizon 2020 , the  European Union 's  Framework Programme for Research and Innovation. This work was also supported by a grant from the  French National Research Agency (ANR, Agence Nationale de la Recherche)  (contracts  ANR-14-ANWA-0004-01  and  ANR-14-ANWA-0004-02 ) within the framework of the  ECALEP  project selected during the 2nd joint call of the  ERA-Net Anihwa (Animal Health and Welfare ), a  Coordination Action  funded under the  European Commission 's  ERA-Net scheme within the Seven Framework Programme  (contract No.
The adoption of the same structure and style for the seven sign language grammars produced within the SIGN-HUB project has the welcome outcome of allowing typological comparative studies of sign language grammars and encouraging fruitful contaminations. However, not all grammars contain the same amount of grammatical description. This is due to different reasons: (i) the numerosity of the team working on the task, (ii) the absence/presence of previous studies investigating grammatical phenomena, (iii) the impossibility to collect data for a set of properties or the lack of sufficient information to write a description of a section, (iv) some sections or subsections that had been thought to hold for some sign languages might not be relevant for all of them. A Grammar of LIS, as all sign language grammars produced within the SIGN-HUB project, is written in English.
How to use the grammar Each Part of the grammar contains an introduction explaining the function of the linguistic component under investigation (e. g. Phonology) and the organisation of the Part. Each Part is composed of chapters organised in sections and subsections. Information on authorship, data and consultants is reported at the end of each chapter. At the end of the grammar, the reader can find: (i) an appendix containing the complete list of LIS handshapes and the labels we used to refer to them, (ii) a complete list of references to previous works in the literature on which the grammar is based, and (iii) a glossary of grammatical terms explaining basic concepts that are taken for granted in the text. Typically, if there is a concept/term that is mentioned but not described in a section, an indication connects it to the section where it is explained.
When relevant, information about the data gathered in order to produce the description is found at the end of the chapter. This is important because it might provide information about the particular variety represented in the description. Variation within the LIS community is well-known, but hardly studied, so this piece of information might help identify on which variation certain generalisations have been drawn. We follow the decision taken in the SignGram Blueprint to devote an independent part to Pragmatics on an equal footing with other grammar components to promote the description and analysis of so far understudied domains of LIS grammar addressing, among other issues, discourse structure, figurative meaning, and communicative interaction. The reader may be surprised not to find a part on Semantics.
Acknowledgments This publication is the direct outcome of The  SIGN-HUB  project: preserving, researching and fostering the linguistic, historical and cultural heritage of European Deaf signing communities with an integral resource (Grant Agreement 693349) which took place between 2016 and 2020 and was funded by the  European Commission  within the  Horizon 2020 framework program. We would like to thank: the  LIS consultants (Gabriele Caia ,  Filippo Calcagno ,  Nino D'Urso ,  Anna Folchi ,  Mauro Mottinelli ,  Rosella Ottolini ,  Mirko Pasquotto ) for sharing their language and answering many questions;  Ca' Foscari University of Venice  for an additional Ph. D. position working on A Grammar of LIS; the  SIGN-HUB  project coordinator prof. Josep Quer; the task 2. 1 (Grammars) leaders,  Meltem Kelepir  and  Josep Quer  for constant feedback and guidance; the task and WP leaders of the  SIGN-HUB  project; the project managers  Jordina Sánchez Amat  and  Giorgia Zorzi ; the project o icer  Jarkko Siren  for his support; the project advisory board members  Diane Brentari ,  Diane Lillo-Martin ,  Karen Emmorey ,  Manfred Krifka  and  Tobias Haug ; the project reviewers  Elisabeth Engberg-Pedersen ,  Gladys Tang ,  Myriam Vermeerbergen  and  Peter Max Wittenburg  for their invaluable feedback; the  SignGram Cost Action IS1006  for making the SignGram Blueprint possible (Quer, J.
This publication has been possible thanks to the  SIGN-HUB project , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 693349. URL https://edizionicafoscari. unive. it/it/edizioni/libri/978-88-6969-474-5/DOI http://doi. org/10. Branchini,  Lara Mantovan  (edited by) -1. ed. -Venezia:  Edizioni Ca' Foscari -Digital.
Photographs of the external morphology as well as male and female genitalia were taken using a Leica DMC 2900 Digital Camera attached to Leica M205C stereomicroscope using Leica Application Suite. Furthermore, specimens were examined with a JEOL JSM-6360LV scanning electron microscope in the Electronic Microscopy Center of Universidade Federal do Paraná. The length and width measurements of the species represent the average of the examined specimens. The terminology used in the descriptions follows  Ślipiński (2007). Labels of the type specimens are arranged in sequence from top to bottom, where the data for each label are within double quotes (""), a slash (/) separates the rows, and information between brackets ([]) provides additional details written on the labels. Examined material is deposited in the following collections: Coleção Entomológica Pe.
J. S. Moure, Universidade Federal do Paraná, Curitiba, Paraná, Brazil (DZUP) and in Muséum National d'Histoire Naturelle, Paris, France (MNHN). We used the mitochondrial genome of Nephus species previously sequenced by  Magro et al. (2020): N. reunioni, N. includens, N. voeltzkowi and N. apolonia sp. nov. (voucher number: NeSpa1) , together with the mitogenome of N. oblongosignatus sequenced in the present study (accession numbers: see Table  1 ) following the same protocol (see details in  Magro et al. 2020). Molecular characterization and distance analyses were conducted on the cytochrome c oxidase I (COI) gene using MEGA v. 7  (Kumar et al. 2016). Pairwise distances were estimated between specimens using the Kimura-2-parameters model  (Kimura 1980). We reconstructed the phylogenetic relationships between the Nephus species based on the mitogenome sequences (all protein coding and tRNA genes, but we deleted the control region because of the high divergence between species and the presence of repeated sequences, leading to low quality alignments in this region), using as outgroup the available sequence of Cryptolaemus montrouzieri which belongs to the same tribe as Nephus (i.
e. , Coccidulini sensu  Seago et al. 2011)  together with other Coccinellidae species (accession numbers: see Table  1 ). Sequences were aligned using MAFFT default parameters  (Katoh and Standley 2013). We inferred maximum likelihood trees and bootstrapping with RAxML 8. 2. 10 (Stamatakis 2014) under the best-fitting model of sequence evolution for the dataset (GTR+G model), selected using the Akaike information criterion (AIC) using jModelTest 2  (Darriba et al. 2012).
Acknowledgements We are extremely grateful to our late colleague and friend,  Serge Quilici  ( CIRAD, St Pierre ) for his logistic support during AM visits to Reunion and more than everything for his kindness. We also would like to thank  J. Rochat  for access to samples from the  Insectarium de La Réunion , and the  Electronic Microscopy Center (UFPR)  for the pictures. This study was supported by the  CAPES Program "Sciences without borders " ( 401366/2014-6 ), PhD fellowship to JMCS ( 1578651/2016 ) and  CNPq  for the research fellowship to LMA ( 308992/2017-2 ) Brazil. Furthermore, AM, EL, and JLH were supported by the  "Laboratoires d'Excellence" LabEx TULIP  ( ANR-10-LA-BX-41 ) and  CEBA  ( ANR-10-LABX-25-01 ).
This suggests that CSF t-tau levels reflect a trait rather than a stage. We then studied whether the CSF proteome differed for t-tau subgroups in 559 individuals with proteomic data (sTable 1b). This included targeted proteomics of 248 proteins in ADNI and targeted and untargeted proteomic measures of 1458 proteins in EMIF-AD (sTable 2c). Compared to controls, AD individuals with abnormal CSF ttau had increased levels of 33% (ADNI) to 52% (EMIF-AD) of the proteins measured, and decreased levels of 1 to 3% proteins (figure  1 , sTable 2a,c). AD individuals with normal CSF t-tau, showed an opposite pattern with increased levels of 3 to 4% of the proteins and decreases in 28% (ADNI) to 43% (EMIF-AD) of the proteins. Around 50% of the proteins increased in individuals with abnormal t-tau were decreased in individuals with normal t-tau in both cohorts.
Proteomic analysis ADNI: Details on protein measurements can be found at http://adni. loni. usc. edu/data-samples/biospecimen-data/. YKL-40, neurogranin, soluble APP beta, neurogranin, neurofilament light, alpha synuclein, phosphorylated alpha synuclein, HBB, CFH, sTREM2, VILIP-1, and BACE1 activity were measured by ELISA or related assays. 190 analytes were measured using the multiplex Human DiscoveryMAP panel from Rules Based Medicine (MAP-RBM)  (43). This panel assesses proteins related to cytokines, chemokines, metabolic markers, hormones, growth factors, tissue remodeling proteins, angiogenesis markers, acute phase reactants, cancer markers, kidney damage markers, and CNS biomarkers. In addition, mass-spectroscopy measures of isoprostanes, Aβ1-38, and Aβ1-40  (44) , and 567 peptides representing 222 proteins were performed (the "Biomarkers Consortium CSF Proteomics MRM data set")  (43, 45).
Proteins and peptides were selected based upon their previous detection in CSF, relevance to AD, and results from the MAP-RBM. We used the quality checked and finalised 'Normalized Intensity' data as described in the "Data Primer" document at https://adni. loni. usc. edu/wpcontent/uploads/2012/01/2011Dec28-Biomarkers-Consortium-Data-Primer- Protein selection: Proteins and protein fragments (ADNI) values were normalised according the mean and standard deviation values of the control group. For ADNI, we averaged peptides that mapped to the same protein into a composite protein score when they correlated with r >. 5, and included as a single peptide otherwise. When the same protein was measured by different platforms in ADNI, values were averaged if they correlated with r>. 5 and otherwise we included them as separate proteins.
Proteomic annotation Biological process: We performed pathway enrichment analysis for Gene Ontology biological processes for proteins that differed between groups using the online Panther application (48) using FDR corrected Fisher exact tests. Predominant cell-type of protein production: Based on the BRAIN RNASeq database (http://www. brainrnaseq. org) (49) , we labelled proteins as being predominantly produced by a certain cell type when levels were higher than 40% of old individuals produced across cell types. Transcription factor enrichment: We performed transcription factor enrichment analysis using ChIP-X ChEA through Enrichr and report on adjusted p-values  (50). SynGO: We mapped proteins that differed between a+t+ and a+t-to location at the synapse using SynGO 1. 0  (51).
Neuroimaging analysis We studied neuroimaging data only from ADNI, because this study collected longitudinal scans. As a measure of brain atrophy we took cortical thickness data All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 3, 2020. ; https://doi. org/10. 1101/2020. 10. 29. 20211920 doi: medRxiv preprint 18 from 34 cortical areas from the longitudinal processing pipeline in Freesurfer version 4. 3 for 1. 5T T1-weighted MRI scans, and v5. 1 for 3T T1-weighted MRI scans (http://adni. loni. usc. edu). As a measure of amyloid accumulation, we used the regionspecific SUVR values for florbetapir binding assessed by PET imaging in 34 cortical areas as parcellated by Freesurfer v4.
19 (UKSH, Campus-Kiel). A total of 936 EMIF-AD MBD samples passed postexperiment QC. Genotype imputation and quality control: Data processing and quality control was performed using GenomeStudio Software (v2. 0. 04, Illumina, Inc. ), PLINK software (v1. 9) and bcftools (v1. 9), as described previously  (66). Briefly, SNPs were excluded if they had a minor allele frequency (MAF) less than 1%, deviated significantly from Hardy-Weinberg equilibrium (HWE) (p<5x10-6) in the total sample of founder individuals, or had a call rate of less than 95%. We only used SNPs with no more than 2% genotype missingness and removed samples with excess heterozygosity rate (>5 SD). Individuals with sex mismatches were excluded from analysis. To identify ethnic outliers, a principal component analysis of ancestry (PCA) was performed based on 1000 Genomes clustering, phase 3 using PLINK)  (67).
Individuals of non-European descent were excluded from analysis. Relatedness was assessed through identity by descent, and family relations up to second degree were excluded. After filtering, the ADNI genotype data included 747 individuals and EMIF-AD 931 individuals. SNPs were locally imputed using Minimac 3  (68)  to the Haplotype Reference Consortium (HRC) reference panel  (69) , leaving 4,583,773 SNPs (ADNI) and 7,778,465 SNPs (EMIF-AD) for final analyses. To account for population structure, principal components (PC1-PC20) were computed on a subset of relatively uncorrelated (r2<0. 2) SNPs. Classification of APOE genotypes: For ADNI subjects, APOE ε4 genotype was assessed with two SNPs; rs429358 the 'ε4-allele' and rs7412 the 'ε2 allele'), using DNA extracted by Cogenics from a 3mL aliquot of EDTA blood.
We used imputed DNA microarray genotype data available from ADNI-1, ADNI-GO, ADNI-2 processed with GenomeStudio v2009. 1. APOE ε4 genotypes in EMIF-AD were All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 3, 2020. ; https://doi. org/10. 1101/2020. 10. 29. 20211920 doi: medRxiv preprint 20 generated as described elsewhere  (70). Briefly, APOE ε4genotypes were determined either directly (rs7412) or by imputation (rs429358). For 80 samples for which no local APOE genotype was available, and for 45 mismatches between locally derived and GSA-derived genotypes (4. 8%), APOE ε4 genotype was determined using TaqMan assays (ThermoFisher Scientific, Foster City, CA) on a QuantStudio-12K-Flex system in a 384-well format.
GWAS and post-GWAS analyses Genome-wide association study (GWAS) analyses on CSF t-tau abnormality status in subjects with abnormal amyloid were performed separately in ADNI (a+t+=246, a+t-=238) and EMIF-AD (a+t+=294, a+t-=155) using PLINK software (V1. 90). We used a logistic regression model including the PC1-PC3. Genome-wide significance was defined as p≤5e-08. Meta-analysis on ADNI and EMIF-AD GWAS summary statistics was performed using METAL  (71). MAGMA software package was used to compute gene scores and geneset scores for biological pathways based on p-values of the meta-analysis summary statistics. We considered for further analysis gene scores based on at least 6 SNPs and geneset scores with information of at least 6 genes.
Cross-sectional analysis The group comparisons between AD subgroups and controls were performed using ANOVA correcting for age and gender. Longitudinal analysis: Change in cognition and imaging markers were assessed using ADNI data with linear mixed models including as main terms group, time and the interaction group*time, and correcting for age and gender, and additionally for level of education for cognitive markers. Protein change with disease severity: We investigated within the AD normal t-tau and AD abnormal t-tau groups whether proteins differed between stages by performing All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 3, 2020.
22 MANOVA tests. We calculated p for trend over disease severity categories. A protein was considered to decrease or increase with disease severity if the linear trend was statically significant or if there was a difference between controls and MCI, controls and dementia, or MCI and dementia and the trend analysis supported an increase or decrease with severity. Genetic analysis: Analysis were performed in all individuals with GWAS data and information on abeta42 and t-tau status. All (P)GRS were compared between AD subgroups using linear models. Associations between GRS scores for risk genes and CSF protein levels were tested with linear models adjusted for age and sex. In order to reduce number of tests GRS scores were selected for SNP inclusion threshold that best differentiated within AD between abnormal and normal t-tau.
FINAL1. pdfProteomic analysis EMIF-AD: Neurogranin, neurofilament-light, abd YKL-40, abeta38 and abeta40 were measured by ELISA and Aβ38 and Aβ40, using the V-PLEX Plus Ab Peptide Panel 1 (6E10) Kit from Meso Scale Discovery (MSD, Rockville, MD) (3). Mass spectrometry was performed using tandem mass tag (TMT) technique with 10+1 plexing as previously described, using high-pH reverse phase HPLC for peptide prefractionation (46)  (Tijms et  al in press). The EMIF-AD massAll rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted November 3, 2020. ; https://doi. org/10. 1101/2020. 10. 29. 20211920 doi: medRxiv preprint spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier 10.
6019/PXD019910 (47). Normalised abundances with associated clinical data can be requested from the EMIF-AD consortium (3). Peptides were mapped to 2535 proteins using the software ProteomeDiscoverer v. 2. 2. (Thermo Scientific), using Mascot (MatrixScience) for protein identification (precursor Dm tolerance 15 ppm, fragment tolerance 0. 05 Da, max missed cleavage sites 2), searching the human subset of the UniProtKB SwissProt database (www. uniprot. org). Percolator (MatrixScience) was used for scoring peptide specific matches, and a strict 1% false discovery rate (FDR) was set as threshold for identification. For reporter ion quantification the following settings were used: Integration tolerance 20 ppm; Integration Method Most Confident Centroid; normalize on the reference protein average.
Acknowledgements This work has been supported by  ZonMW Memorabel  grant programme # 73305056  (BMT) and # 733050824  (BMT and PJV), the  Swedish Research Council  (# 2018-02532 , HZ), the  European Research Council  (# 681712 , HZ) and  Swedish State Support for Clinical Research  (# ALFGBG-720931 , HZ), the  Alzheimerfonden  (Grant no. AF-930934 ) and  Dtiftelsen Gamla  tjänarinnor (JG), and the  Innovative Medicines Initiative Joint Undertaking  under EMIF grant agreement # 115372  (PJV, HZ, SV, IB). Statistical analyses were performed at the VUmc Alzheimer Center that is part of the neurodegeneration research program of the  Neuroscience Campus Amsterdam. EMIF-AD MBD proteomic analyses were performed at the  Department of Psychiatry and Neurochemistry , the  Sahlgrenska Academy at the University of Gothenburg, Sweden.
The VUmc Alzheimer Center is supported by  Stichting Alzheimer Nederland  and  Stichting VUmc  fonds. HZ is a  Wallenberg Academy Scholar. The Leuven cohort was funded by  Stichting Alzheimer Onderzoek  (# 11020 , # 15005 , # 13007 ) and the  Vlaamse Impulsfinanciering voor Netwerken voor Dementieonderzoek  (IWT # 135043 ). The Lausanne cohort was supported by the  Swiss National Research Foundation SNF  (# 320030_141179 ). We thank  Drs. Andre Franke  and  Michael Wittig  as well as  Mrs. Tanja Wesse  and  Sanaz Sedghpour Sabet  for their help with the genome-wide genotyping experiments. We thank  Dr. Fabian Kilpert  for his help with the processing and management of the genome-wide genotyping data. Data was used for this project of which collection and sharing was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health.
Acknowledgments This study is a contribution to the  VILLUM  project " Past and Future Dynamics of the Greenland Ice Sheet : what is the ocean hiding?" (10100). A. A. B. was funded by the  Carlsberg Foundation  (Grant  CF17-0529 ). We thank the  CNRS  for salary support of M. A. S. and V. K. , and the  NAIV  project funded by  LEFE/INSU. We thank  James Lea  and an anonymous reviewer for their insightful comments.
Otsu thresholding leads to the lowest Root Mean Squared Error (RMSE) and high overall accuracies on selected Sentinel-2 and Landsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. On MODIS imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km 2 ) water bodies despite the 500 m spatial resolution. Over 1999-2019, MODIS observations reduce average daily RMSE by 48% compared to the full Landsat 7 and 8 archive and by 51% compared to the published Global Surface Water datasets. Results reveal the need to integrate coarser MODIS observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full Landsat time series before 2013.
In recent years, several works have harnessed the rising number of free, high spatial and temporal resolution imagery from passive and active sensors, mapping and monitoring small water bodies  [4] [5] [6] [7] , lakes, floodplains and wetlands  [8] [9] [10] [11] [12] [13] [14]. In parallel, numerous surface water databases have been developed at the global and regional scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) with up to twice daily observations from the Aqua and Terra constellation of sensors has notably been used in several land cover applications. Carroll et al. [15]  produced a global inventory of surface water using MODIS and Shuttle Radar Topography Missions (SRTM) elevation data for the year 2000, updated by the 250 m Global Water Pack produced by Klein et al.
[16]. Khandelwal et al. [17]  developed an approach for global eight day monitoring of surface water extent using 500 m MODIS imagery, while D'Andrimont and Defourny  [18]  used twice daily observations to characterise surface water at a 10 day time step over 2004-2010 across Africa. The multiplication of higher resolution sensors notably Landsat and Sentinel missions has led global-scale analyses to move towards moderate resolution  [19]. Feng et al. [20] , Verpoorter et al. [21]  for instance produced a 30 m inland water body dataset for the year 2000 based on Landsat imagery. Yamazaki et al. [22]  developed the Global 3 arc-second (90 m) Water Body Map (G3WBM) based on Landsat multi-temporal imagery and the Global Land Survey (GLS) database. Using the full catalogue of Landsat imagery, Pekel et al.