dataesr/scientific-paragraphs-categorization huggingface dataset:
Only 11 islands (1. 8%) exhibited a contraction of human occupancy, including 9 islands that were completely abandoned following the 2004 Indian Ocean tsunami -e. g. Kandholhudoo (No16 in the database, Raa Atoll) population was first hosted on Ungoofaaru island (No71, Raa Atoll) and then relocated on the newly inhabited Dhuvaafaru island (No67, Raa Atoll)-or in the frame of the 2009 Population Consolidation Programme  34  partly aiming at decentralising the population -e. g. Faridhoo, Maavaidhoo and Kuburudhoo populations (islands No2, No132 and No137, respectively; Haa Alifu-Noonu Atoll) were relocated on Nolhivaranfaru island (No136, Haa Alifu-Noonu Atoll). As a result, 32. 7% of the sample islands (199) exhibited a very high human footprint (i. e. >2/3 of island land area; see Materials and Methods for categorisation) in 2014-16, against 23.
Discussion the major role of anthropogenic control. Based on an extended dataset including 608 islands (52. 9% of the Maldives Islands) from 23 atolls and oceanic reef platforms, this study constitutes the first detailed, nationwide assessment of human-driven undermining of the coastal protection services provided by the reef ecosystem to island communities. It confirms that over the last decade, human activities have been a major driver of reef-island morphological change  35. Increasing human pressure led, in just one decade, to the exploitation of 56 natural islands (9. 2% of the sample islands) and to the expansion of human occupancy on 47 already inhabited and exploited islands (7. 7%). As a result, the proportion of islands exhibiting a very high human footprint (>2/3 of island land area) increased by 9.
2% (from 143 to 199 islands). Additionally, most of the islands that were already inhabited or exploited in 2004-06 underwent substantial changes, especially through artificial expansion. Around 64. 6% of these islands had reclaimed areas in 2014-16, and the proportion of islands that experienced humandriven areal expansion between 2004-06 and 2014-16 increased by 51. 2% (from 123 to 186 islands). More than half of these islands (33 islands) experienced high (>10%) island growth rates, with respectively 17 and 16 islands Figure  4. Illustration of the 5 island types highlighting the degree of human disturbance affecting the reefisland system. Panel A Maafinolhu Island (No7 in the database), Ihavandhippolhu Atoll, illustrates the situation of an "undisturbed" (here, "natural", i. e. unexploited) island exhibiting no human disturbance (Type 1, T1).
Materials and Methods presentation of the island sample. The total number of islands in the Maldives is constantly changing, due to the highly unstable character (i. e. disappearance and reformation) of the smallest islands. Here, we used the statistics generated by Naseer and Hatcher  43  , which constitute the most precise dataset documenting the physical characteristics of the archipelago. We therefore consider the total number of Maldivian islands to be 1,149. The 608 sample islands considered in this study therefore represent 52. 9% of the Maldives Islands and are distributed among 23 atolls and oceanic reef platforms out of the 25 inventoried (Alifushi and Vattaru, which have one single island, are not documented in this study). These 608 islands are the islands for which the 2004-06 and 2014-16 satellite images provided by Google Earth and used in this study were available and treatable.
• 'Sediment dredging from reef flat' excludes dredging works related to the establishment of boat channels and harbours, which are covered by dedicated variables. Inventory and classification of human disturbances affecting the 'reef-island system'. The 'reef-island system' considered in this study (Fig. 1 ) includes two interdependent geomorphic features, that is, the island and the reef ecosystem that supports it, which supplies the island with sediment and attenuates wave energy, especially generated by storms. Human disturbances affecting the reef-island system were assessed based on satellite image analysis, using high resolution (0. 50 m) images from two time periods, 2004-06 and 2014-16, and freely provided by Google Earth. Such free remote-sensing data were already used to conduct nation-wide assessments, e.
e. 2 or more harbour basins), and may also exhibit other disturbances, such as boat channels dredged across and/or sediment dredging from the island's reef flat and/or marine protection structures. Because they have several harbours dredged in the reef flat, generally on both their lagoon and ocean sides, these islands exhibit highly to very highly disturbed sediment production and transport patterns. Data generation and analysis. In some cases, some data are available (e. g. for island land area), but because these data are provided without their metadata (i. e. no information on how and when they were generated), we decided to not use them. Moreover, for those variables for which official data exist, one single dataset was generally available, which did not allow the analysis of change. For other variables (e.
g. land reclamation), existing datasets are incomplete, that is, not updated, and not always covering all of the Maldives Islands. Due to these constraints, we generated all of the data that are used in this study, using high resolution (0. 5 m) and good quality (i. e. excluding images exhibiting cloud cover) satellite images from the 2004-06 and 2014-16 periods that are freely provided by Google Earth. Due to image availability constraint, we used a set of images (and therefore different images from one island to another) to document the situation of each island over the two periods considered in this study. The precise date of the images used for each island is indicated in the database delivered with this paper. Data were generated based on satellite image interpretation, in line with previous studies on atoll island and shoreline change  19, [23] [24] [25] 27, 28.
Acknowledgements This work was supported by the  French National Research Agency  under the  STORISK  project (No. ANR-15-CE03-0003 ). VKED was also funded by the  INSeaPTION  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  BMBF (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO  ( ES ),  ANR (FR)  with co-funding by the  European Union  (Grant  690462 ). AKM also thanks the  ANR programme "Investissements d'avenir " (No. ANR-10-LABX-14-01 ).
As a result, the bootstrap analysis results in a great number of solutions whose environmental representativeness has to be assessed, which requires a systematic approach in relating the separated factors to specific sources. This factor classification has been typically based on the contributions of certain markers (e. g. , C 2 H 4 O + 2 to identify biomass burning OA or CO + 2 to identify secondary OA;  Daellenbach et al. , 2017 Daellenbach et al. , , 2018;; Bozzetti et al. , 2017a; Vlachou et al. , 2018)  in the case of the application of PMF to AMS data. However, in datasets with similar factor profiles, for example two oxygenated OA factors or more, this type of sorting can become challenging, especially when a bootstrap iteration does not provide the expected factors. To assess the quality of the different bootstrap solutions, users typically compare the factor time series to external marker measurements, when available, and discard suboptimal solutions using a set of acceptance criteria  (Ulbrich et al.
, 2009; Q. Zhang et al. , 2011; Norris et al. , 2014; Bozzetti et al. , 2017a, b; Y. L. Zhang et al. , 2017). In this study, we propose a novel technique for evaluating the selection of the PMF solutions generated through a large number of bootstrap iterations. The method is based on the examination of the correlation matrix between the base case and bootstrap iterations for both time series and profiles, without setting a priori a defined threshold in the correlation coefficient. We assess the performance of the technique (accuracy and probability of false positive and false negative results) by comparing the factors' time series to the available specific markers. We applied the technique to an unprecedented dataset of 150 PM 10 filter samples from three sites in Estonia covering a full year (September 2013-September 2014), where anthropogenic and natural emissions of primary and secondary organic aerosols could be extracted.
A thorough apportionment of EC and the calculation of HOC will be discussed in Sect. 4. 1. The variability in the AMS input dataset was best described by a seven-factor solution, which will be thoroughly described in Sect. 3. 1 below. To assess the stability of the PMF solution and the sensitivity of the model for the WSHOA subtraction, we performed 250 bootstrap runs by perturbing the HOA : EC and R HOA parameters within their errors (1 standard deviation, σ ), assuming a normal distribution. Note that this number of runs was restricted by computational limitations. The sorting of the factors and the concomitant selection of the retainable solutions out of the 250 runs was based on the correlation (R Spearman -R s ) of the time series between the base case (which is the PMF result before bootstrapping) and each bootstrap iteration n as well as the correlation (R s ) of each factor profile between the base case and each iteration n.
4. 3. To define the recoveries, we used a non-negative multilinear fit. The starting points of the fitting for each R k with the exception of R oil were obtained from the literature  (Bozzetti et al. , 2016; Daellenbach et al. 2016; Vlachou et al. , 2018)  and were randomly varied within their literature range with an increment of 10 -4. The final distributions of the recoveries are shown in the Supplement (Fig. S5 ). The recoveries in this study were all shifted to the lower end of the recoveries reported in the literature. While the reason remains unclear, the water solubility of OA is specific to the dataset; therefore we can expect differences to other datasets. Moreover, we re-measured, in a different laboratory, the OC concentrations from a subset of 21 samples covering all sites and all seasons.
, 2016). The comparison of the mass spectra of fumigated and non-fumigated samples is illustrated in Fig. 7  for two samples: with high and low contribution of dust. In the example of KJ (5 June 2014), where the dust factor exhibited the highest contribution, f 44 was substantially decreased after fumigation (Fig. 7a ). In the case of Tallinn (19 January 2014), where the dust factor concentration was negligible, f 44 remained stable after fumigation (Fig. 7b ). Overall, the comparison of the f 44 modeled (= f 44 total -f 44 org ) from the initial dataset of 150 filters and the f 44 measured (= f 44 non_fum -f 44 fum ) from the subset of 19 filters showed consistent results (Fig. 8 ). Taken together these results provide strong confidence on the nature of the dust factor extracted by PMF.
Acknowledgements. This work was funded by the  Estonian-Swiss cooperation program  " Enforcement of the surveillance network of the Estonian air quality: Determination of origin of fine particles in Estonia ". María Cruz Minguillón  acknowledges the  Ramón y Cajal Fellowship  awarded by the  Spanish Ministry of Economy, Industry and Competitiveness. The  Labex OSUG@2020  ( ANR-10-LABX-56 ) provided the funding for part of the analytical equipment at  Institut des Géosciences de l'Environnement (IGE; France). We also acknowledge the contribution of the  COST Action   CA16109 COLOSSAL. Review statement. This paper was edited by  James Allan  and reviewed by two anonymous referees.
Establishing an accurate ONTbased targeted sequencing screening process As ONT has a higher error rate than other next generation sequencing technologies  (Jain et al. , 2016) , we first assessed the feasibility of unequivocally recognising known sequences and defined which quality thresholds can be used for such analysis. We analysed sequencing data from six PCR amplicons amplified from WT animal biopsies with tailed primers flanking genomic intervals ranging from 0. 9 to 2 kb in size (Experiment A, Supplemental Table  ST1 ). PCR amplicons were barcoded, assembled in sequencing libraries and sequenced with a MinION. Using the Porechop tool, reads that showed both barcoded ends were selected and demultiplexed for each barcode. We then filtered reads according to a range of read quality filters (q84 to q96) with Filtlong.
Run capacity We have used targeted sequencing of relatively short PCR amplicons (up to approximately 3 kb) permiting the generation of ultradeep (1,000X to 10,000X) coverage datasets. This uses a fraction of the sequencing data production capacity of a MinION run. We used a kit that supports twelve barcodes so that twelve animals can be sequenced in parallel analysing the same genomic segment. It would be possible to further multiplex samples by designing alternative primer pairs to amplify the same core region of interest but which include different flanking region lengths. Differentiation between individual animals for the same locus/project within a run can then be achieved using the genomic context flanking the region as an internal barcode. With the alternative format of 96 barcodes, a conservative set-up of 96 individuals (each under one barcode), if they were all mosaic, would require in the order of twenty gigabases sequenced to achieve 10,000X coverage of a 3 kb segment (96 animals for a given project containing up to eight genetic identities), which can be produced in a single MinION run.
A simple process Although the approach appears a step change at first glance, the use of long-read sequencing turns out to involve a fairly simple and accessible process. It requires only minimal investment in sequencing equipment. The generation of sufficient sequencing data utilises a small fraction of the system capacity and the datasets that are produced do not represent a challenge for existing analysis tools. We further facilitate access to the process by packaging the analysis in a simple However, the extensive characterisation of the target loci that ONT sequencing supports, in both founder and subsequent generations, does not suffice to validate newly mutated lines. Indeed, checks of off-target events  (Anderson et al. , 2018; Iyer et al. , 2018) , in particular, those physically linked to the locus of interest) and copy counting of the donor sequence by ddPCR to eliminate additional integrations remain essential and complementary steps to fully validate G1 animals  (Supplemental Figures S3 and S4 and Codner et al.
Analysis of ONT sequencing data A nextflow  (Di Tommaso et al. , 2017)  workflow for the bioinformatics processes was assembled and is available on gitlab (https://gitlab. com/nick297/cas9po int4). In brief, reads were demultiplexed with Porechop  (Wick et al. , 2017) , requiring the recognition of two barcodes (both extremities of the PCR amplicon sequenced). Reads were then filtered for a minimum quality (q score) using Filtlong (https://github. com/rrwick/Filtlong). Filtered reads were aligned against the relevant reference sequence using minimap2  (Li, 2018)  and filtered using samtools for a mapping quality score of q90  (Li et al. , 2009). Alignments were then visualised using IGV (http://software. broadinstitute. org/ software/igv/;  Thorvaldsdóttir et al. , 2013). After alignment, corresponding reads were filtered for the presence of mutant specific sequence determinants using BLASTn  (Altschul et al.
The continuous progression in methods of protein identification using high resolution strategies have provided extensive information about the human SP proteome, which can be used as a tool to identify biomarkers of reproductive function. A comparative study of the protein composition of goat buck, boar, ram, bull, stallion, alpaca and camel SP revealed considerable divergence of SP proteomes between the species  (Druart et al. 2013) , while ram sperm proteins appear to be highly conserved across species with 95% proteins reported in other species  (Pini et al. 2016). As with all 'Omics' studies, the major challenge now is to characterise the biological functions of these proteins. There have been more than 700 proteins identified in ram SP (of which 40 were identified as being of sperm origin) including a high abundance of Binder of Sperm family proteins (BSP1, BSP5, SPADH1, SPADH2), the spermadhesin family (bodhesin2), lactoferrin and newly identified proteins like UPF0762 (C6orf58 transcript;  Soleilhavoup et al.
Data Analysis We used mean values per plot for all variables and data were first evaluated for assumptions of normality and homoscedasticity prior to further statistical analyses. To meet these assumptions, the soil CH 4 flux data were log transformed with a base of 10 (gas flux + most negative gas flux in dataset + 1). A Tukey-Kramer test was used to compare the changes in nutrient availabilities in the soil after fertilization. In addition, a principal component analysis (PCA, Figure  S1 ) was performed to identify main relationships between the different soil properties and plots later assigned to treatments. To assess how the drought and fertilization treatments affected CO 2 and CH 4 fluxes in the soil before and after roof removal, linear mixed-effects models were run, where fertilization treatment (+N, +P, +NP, Control), drought treatment (Dry, Control) and sampling campaign were treated as fixed factors and plot as random factor.
ACKNOWLEDGMENTS The authors would like to thank  Fred Kockelbergh  and  Joke Van den Berge  for building the chambers. We are grateful to  Jocelyn Cazal ,  Maricar Aguillos ,  Benoit Burban ,  Jean-Yves Goret ,  Baptiste Rabineau  and the master students of the Module FTH for their help in the field. Finally, we also wish to thank the reviewers who provided incisive, constructive, and informed criticism of the manuscript.
FUNDING This work had benefited from an  Investissements d'Avenir grant  managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ) and was supported by the  European Research Council Synergy  grant  ERC-2013-SyG-610028-IMBALANCE-P  and  European Commission  ( Marie Curie Excellence Grant RainForest-GHG ,  H2020-MSCA-IF-2017-796438 ).
Juxtaposition of migration and transcriptome changes in Epi cells overexpressing full-length or truncated variants of HOTAIR strongly suggested that HOTΔL cells maintain their epithelial balance to a larger extent than HOT or HOTΔP cells, both able to interact with Lsd1. This observation nourished a hypothesis that Lsd1/HOTAIR crosstalk may affect epithelialmesenchymal balance. To define other genes involved in this regulation, we performed a differential expression analysis of Epi-CTR and Epi-HOTΔL transcriptomes against Epi-HOT and Epi-HOTΔP. Knowing that Lsd1 is a component of multiple complexes with repressor or activator activities, we assigned all PCGs that are significantly up-regulated in Epi-CTR and Epi-HOTΔL datasets relatively to Epi-HOT and Epi-HOTΔP (n=148) (DESeq, FC > 1. 5 and p-value < 0.
, 2006) , the nuclear receptor PRICKLE1/RILP implicated in the nuclear trafficking of REST/NRSF and REST4 transcription repressors  (Shimojo and Hersh, 2006). HMS genes highly expressed in Epi-HOT and Epi-HOTΔP included the cell cycle regulator CCND1, the DNER activator of the NOTCH pathway, but also the CD44 EMT marker (Figure  3D ). Input-DNA signal  (Zang et al. , 2009). The blacklisted by ENCODE genomic regions were excluded from further consideration (ENCODE Project Consortium, 2012) and only peaks showing at least 1 nucleotide overlap in two replicates were merged and retained for further analysis (Figure  4A ). The number of detected Lsd1 peaks was strikingly heterogeneous between conditions, in particularly, CTR and HOTΔL datasets showed as much as 20 times more peaks than HOT and HOTΔP regardless the identical ChIP-seq metrics (Figure  S5A , S5B).
Firstly, intersection of the Lsd1-associated genes between conditions revealed high cell-line specificity of Lsd1 loci with only one gene (RNU2-38P, snRNA gene) common to Epi-HOT and Epi-HOTΔP and 555 genes shared by Epi-CTR and Epi-HOTΔL (Figure  4C ). We considered the common Lsd1 associated genes of Epi-CTR and Epi-HOTΔL datasets as genomic locations independent of Lsd1/HOTAIR interactions and specific to the low migration phenotype. Among them, 312 represented PCGs and 243 were noncoding genes. Gene enrichment analysis revealed Jak-STAT signaling pathway as the significantly enriched KEGG pathway and several biological processes tightly linked to EMT, such as positive regulation of cell motion and TGF-beta signaling, cytokine-mediated signaling pathways among PCGs presenting Lsd1 peaks 5kb upstream of their TSS and within the gene body (Figure  S5E ).
Fluorescent microscopy and image analysis For each cell line, three images per well were acquired as stacks composed of twenty slices acquired with an interval of 0. 5 µm, each generated from 3 ApoTome images over the height of the cells with a 40X objective on a Zeiss AxioVision microscope equipped with an ApoTome module (PiCT-BDD platform, Institut Curie). All images were captured with identical exposure time for DAPI and Alexa Fluor 594 using AxioVision software and then analyzed using the Fiji software  (Schindelin et al. , 2012). Stacks were processed with the « Z-project » tool (« Sum Slices » setting) and an identical minimum threshold-value was set for all images. Signal was then quantified using the « Measure » tool and normalized by the number of cells in the image. Integrated density per cell was again normalized on overall image background signal to reduce hybridization bias between slides and presented as a mean ± SEM for at least 11 power field units.
RNA-seq data analysis Reads were mapped allowing 3 mismatches using TopHat 2. 0. 4  (Trapnell et al. , 2009)  and the human genome version hg19. Uniquely mapped reads were assembled using the BedTools suite  (Quinlan and Hall, 2010)  and merged in segments if mapped in the same strand to the Gencode V15 annotation to extract protein-coding genes and annotated noncoding genes including lncRNA, antisense, sense_intronic, sense-overlapped and pseudogenes. Differential expression analysis was performed using the DESeq R package  (Anders and Huber, 2010). Only genes with an expression fold change (FC) above 2 and adjusted p-value below 0. 05 were retained as significantly differentially expressed. Gene ontology (GO) biological process, KEGGS pathways and cellular compartment terms enrichment analysis was performed using DAVID 6.
DNA concentration of IP-and Input-DNA was measured by Qubit with dsDNA HS Assay Kit (Thermo Fisher Scientific). ChIP-seq library preparation and sequencingChIP-seq libraries were prepared with the TruSeq ChIP-Sample Prep Kit-Set A and PCR Box (Illumina) from 5 ng of IP-and Input-DNA. Library was amplified by 10 PCR cycles following the manufacture's procedures. Its concentration and quality was assessed with Qubit with dsDNA HS Assay Kit (Thermo Fisher Scientific) and High Sensitivity DNA Analysis Chip Kit (Agilent Technologies). Libraries were sequenced on a HiSeq 1500 device in a fast single-end mode and 50 bp read length. A range of 10-25 million of reads passing Illumina quality filter was obtained for each sample. Raw ChIP-seq data is available at GEO under accession number GSE106262.
cDNA was diluted 10-50 times and quantified by real-time PCR with LightCycler® 480 SYBR Green I Master (SIGMA). The RPL11 gene was used as a reference to quantify relative abundance of each cDNA. Error bars represent standard deviations in relative quantity of cDNA prepared from at least three independent experiments. sharing, and all members of Morillon's lab for reading and commenting the manuscript. The HOTAIR cDNA containing LZRS plasmid was a kind gift of H. Y. Chang. Funding RNA-seq efforts were supported by the grant from the ICGex program of Institut Curie (to A. M. ) and benefited from the facilities and expertise of the NGS platform of Institut Curie, supported by Agence Nationale de la Recherche (ANR-10-EQPX-03, ANR10-INBS-09-08). A. M. , M. P. , and Z. S. were supported by the European Research Council (ERC-consolidator DARK-616180-ERC-2014) attributed to A.