dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary of Recommendations for the Use of SAR to Characterize Wetlands Table  3  summarizes the guidelines regarding the use of SAR properties and techniques for the detection of free water, herbaceous-vegetated wetlands and wetlands under forest canopy. On the basis of these recommendations, Figure  5  proposes a framework based on SAR earth observation data for the production of wetland classification maps. In this framework, the layer containing free water information is extracted from the combination of optical imagery and X-band or C-band SAR. Thresholding over SAR amplitude can be used to discriminate and map wetland types by combining C-and L-band SARs. The polarization of SAR signals provides information regarding the vegetation type over the wetlands and helps refine the classification based on amplitude thresholding.
Int. J. Environ. Res. Public Health 2018, 15, x 13 of 26 Summary of Recommendations for the Use of SAR to Characterize Wetlands Table  3  summarizes the guidelines regarding the use of SAR properties and techniques for the detection of free water, herbaceous-vegetated wetlands and wetlands under forest canopy. On the basis of these recommendations, Figure  5  proposes a framework based on SAR earth observation data for the production of wetland classification maps. In this framework, the layer containing free water information is extracted from the combination of optical imagery and X-band or C-band SAR. Thresholding over SAR amplitude can be used to discriminate and map wetland types by combining C-and L-band SARs. The polarization of SAR signals provides information regarding the vegetation type over the wetlands and helps refine the classification based on amplitude thresholding.
SAR for Wetlzand Classification in the Amazon Region From 1973 to 1985, the Brazilian government funded the project RADAMBRASIL (Radar da Amazônia do Brasil) dedicated to the mapping of natural resources based on airborne SAR data. Later, other studies used SAR data to produce wetland maps in the Amazon region using various sensors, such as SIR-C (NASA/JPL, Pasadena, CA, USA), JERS-1 (JAXA, Tokyo, Japan) ALOS-PALSAR (JAXA, Tokyo, Japan), RADARSAT-2 (CSA, Mac Donald Dettwiller Geospatial Services Inc. , MDA GSI, Quebec, QC, Canada) or RADAR altimetry data from ENVISAT (ESA) or TOPEX-POSEIDON (NASA/CNES). Table  4  summarizes these studies, indicating the type of SAR technology used, the main results and the resulting products. Extent of flooded areas in the Amazon basin during low and high water seasons  [81]  Table  4  shows that the diversity of SAR sensors (C-band, L-band, SAR properties (e.
Efficient prevention and vector control depends on the availability of up-to-date data and land cover maps combined with malaria case databases (such as the SIVEP database in Brazil  [88] ) at the local and regional scales in order to support targeted interventions. In many countries, national malaria control programs lack detailed disease-risk maps to guide intervention and reinforce vector control. Such maps could: (i) optimize the use of larvicide in targeted areas where the presence of suitable candidate wetlands and population has been acknowledged; (ii) optimize the timing of insecticide distribution for impregnating bed nets; (iii) restrict the distribution of antimalarial drugs to periods of known disease risk; (iv) help defining local environmental engineering actions and (v) help defining more specific prevention messages.
Improving the Characterization of Wetlands to Improve Vector Control Despite discrepancies between published articles, it is evident throughout the recent literature that multi-dimensional SAR data sets are achieving an accepted role in operational situations that require information regarding wetland presence, spatial extent and characteristics in time. We strongly believe that with the development of increasingly numerous SAR sensors and the growing possibilities offered in terms of free access to the data (such as Sentinel 1), image processing freeware and user community growth and expertise, this technology has a strong future regarding the monitoring of vector-borne diseases and, more generally, public health. From the perspective of remote-sensing, improving our knowledge of wetland dynamics in space and time will improve our capacity to build efficient vector control strategies.
By combining SAR Altimetry and InSAR techniques, this new space mission is designed to make the first global survey of the Earth's surface water, observe the fine details of the ocean's surface topography, and measure how water bodies change over time. By measuring water storage changes in all wetlands, lakes, and reservoirs and making it possible to estimate discharge in rivers more accurately, SWOT will contribute to a fundamental understanding of the terrestrial branch of the global water cycle. SWOT will also map wetlands and non-channelized flow, and it appears to be a highly promising sensor for applications in public health related to vector-borne diseases. Figure  10. The contribution of remote-sensing and land cover mapping using optical and SAR data in the definitions of hazard and vulnerability for the production of human exposure to vector risk maps.
Public Health Actors' Needs vs. Space Agencies' Requirements The fact that many sensors are being developed by space agencies for future research applications is a good point for the scientific community. It is even better news when such programs as Copernicus offer free access to high quality optical and SAR data, and when increasingly abundant archived data become free of charge (SPOT World Heritage program, for example). However, it is of prime importance that the technical capacities of future sensors match the needs of users (e. g. , scientific community, local authorities, and governmental agencies) as closely as possible, especially in such domains as public health (i. e. , the development of "thematic" sensors).
Acknowledgments: This study was carried out in the frame of several projects:  Lept-OI (Léptospirose dans l'Océan Indien ,  2012-2015 ) funded by the  FEDER Réunion, TELEPAL (Télédétection pour le PALudisme)  project ( TOSCA 2015-2017 ) funded by the  French Space Agency (CNES) , and  GAPAM-Sentinela  ( 2015-2018 ) funded by the  GUYAMAZON Program. Results were produced with the support of  Young Team  associated to IRD GITES (JEAI GITES) and  International Mixed Laboratory Sentinela (LMI Sentinela ) funded by the  IRD.
Larger seeds had enhanced seedling emergence To examine the effect of AGPase suppression and starch reduction on seed vigor, we conducted seed germination experiments. When incubated on wet filter papers at room temperature, almost all seeds from Suneson and RNAi lines had germinated the following day (data not shown). Similarly, we did not observe differences between the RNAi and Suneson seeds in the soil in the greenhouse, where all lines showed seedling appearance from the soil surface in 2 days and nearly 100% had completed germination by 3 days after sowing (Fig. 9a ). These results indicated that the AGPase suppression did not have negative effects on seed viability. We then tested the seedling emergence from soil by sowing seeds at different depths in the greenhouse (Additional file 1: Fig.
Measurement of seed size and weight and microscopic analysis The size of dried seeds was determined by measuring their length and width using the Smartgrain software described previously  [38]. The cell size of cotyledons was determined by measuring the area of embryonic cotyledon cells using the NIH ImageJ software (https ://image j. nih. gov/). The weight of 100 seeds was determined using a microbalance with four replications, and the average was calculated. To determine curves of fresh and dry weight accumulation, developing seeds of wild type and three independent AGPase RNAi lines were weighted and then seeds were dried to constant weight. The water content was calculated from the difference between fresh and dry weight. For microscopy of sections, developing seeds of wild type and RNAi lines were fixed in 3.
Ethics statement All procedures were completed in accordance with the ethical standards of the responsible committee on human experimentation and with the guidelines laid down in the Helsinki Declaration. Ethical approval for the study was received from the regional committee for human experimentation (approval number: 392/13, Vaud, Switzerland) and written informed consent obtained from all participants. This trial was retrospectively registered at clinicaltrial. gov on July 21, 2014 (registration number: NCT02230345).
Samples were centrifuged and the pellet washed with 70% ethanol before resuspension in 10 μl RNAse-free water. Libraries were prepared using the TruSeq 1 Stranded Total RNA Library Prep Kit with Ribo-Zero Globin Set A and B (Illumina 1 , USA) with multiplexing of six samples per lane. Next generation sequencing was performed on the samples using the Illumina 1 HiSeq™ 2500. Base calling was completed using Real Time Analysis software (v1. 18. 6) (Illumina 1 ). Library preparation and sequencing were performed at the Lausanne Genomic Technology Facility (UNIL, Lausanne). The raw and processed RNA data generated from this study have been deposited in NCBI's Gene Expression Omnibus  [29]  (GEO Series accession number GSE98645): https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE98645.
9. 6a), applying cutadapt (v1. 9. 1)  [34] , aligning reads with STAR (v2. 5. 0c)  [35] , and counting sequences per gene with featureCount (v1. 4. 4)  [36]. Non protein-coding genes and genes with no assigned counts were removed prior to normalisation. The edgeR package (v3. 12. 1)  [37]  was used to calculate normalisation factors for the scaling of raw library sizes applying the 'TMM' method that uses the weighted trimmed means of logratios (with respect to the reference)  [38]. These factors were then used to normalise the data by the voom function  [39]  (Limma package, v3. 26. 9  [40] ). This process of normalisation was completed in two steps: firstly, for the purpose of identifying genes with low counts (all genes with a mean expression of less than zero after normalisation were removed), secondly, the raw counts were normalised for the dataset with these low count genes excluded.
Ensembl IDs were converted to gene symbols using BioMart (v2. 26. 1, Ensembl Genes 86, Dataset version GRCh37. p13) [41, 42]. The composition of the three main white blood cell types was estimated using CellMix package (v1. 6) [43]  that applies a deconvolution method using gene expression profiles of blood cell types  [44]. Gene expression analyses. All gene expression data analysis was completed in R (v3. 2. 5)  [32]. Data exploration was carried out using principal components analysis and hierarchical cluster analysis. Inter-individual variation was detected in this analysis and the methods used for the gene expression analyses were specifically chosen to account for this effect. The postprandial response was thus assessed by calculating, for each subject and each dairy test, the difference in the gene expression profile of the three postprandial measures (2, 4, and 6 h) and the gene expression profile for the corresponding fasting sample, prior to completing correlation or differential analyses  [45].
On the basis of this cluster analysis and the functional grouping defined for the Hallmark collection  [47] , six groups were defined to resume the genes contributing to the enriched genesets (S2 Table ). In addition, GSEA analysis was completed specifically for the KEGG 'Insulin Signaling Pathway' geneset to target insulin-related genes (C2: curated geneset, mSigDB, Broad Institute, v5  [47]  derived from KEGG database  [50] [51] [52] ). Gene expression analyses: Targeted AhR analysis. Metabolomics analysis of the postprandial samples for this study identified four indoles derivatives that were differentially regulated after the intake of acidified milk or yoghurt (indole-3-lactic acid (ILA), indole-3-acetic acid (IAA), indole-3-acetaldehyde (IAAld) and 3-indolepropionic acid (IPA)) (Pimentel et al.
05) and at 6 h after yoghurt intake (p adj = 0. 04). It should be noted that this geneset comprised genes of both glycolysis and gluconeogenesis metabolic pathways (as described by the KEGG pathway database  [50] [51] [52] ) but the two significant negative enrichments observed after the dairy intake concerned both pathways (S3 Fig) The other major group of genesets that was regulated after both acidified milk and yoghurt intake comprised the immune or inflammatory-related pathways. The significant postprandial changes associated with the immune or inflammatory pathways were broadly characterised by positive enrichments during the early postprandial response (2 to 4 h), however the 'inflammatory response' geneset was dynamically regulated as illustrated in Fig 3. The postprandial response of the geneset was characterised by an increase in gene expression at 2 h that was for the most part common to both acidified milk and yoghurt (respectively p adj = 0.
suis genomes, respectively  60  , while the two genomes had 94. 5% of genes matching each other. The higher gene count in the published genomes may be the result of gene fragmentation resulting from having more than 10-fold as many contigs in the genome assembly (Supplementary Table  S1 ). Here, we identify biological functions enriched among early larvae (kinases, peptidases, T. suis-specific gene functions), 28-day larvae (cell adhesion, motor activity), and adult stage T. suis (toxin responses, carbohydrate metabolism, T. suis-specific function) using DESeq2 to identify differentially-expressed genes (Supplementary Table  S4 ). The previous genome paper 60 also had RNA-Seq datasets from L1/L2, L3, L4 and adult stages (in addition to tissue-specific datasets from the male and female posterior body, and the stichosome) but did not perform a similar statistical differential analysis for enrichment for comparison to our results.
repeatmasker. org/RepeatModeler. html), rRNA genes were identified using RNAmmer (http://www. cbs. dtu. dk/cgi-bin/nph-sw_request?rnammer), and tRNAs were identified with tRNAscan-SE  91. Non-coding RNAs were identified by sequence homology search of the Rfam database (http://selab. janelia. org/software. html). Repeats and predicted RNAs were then masked using RepeatMasker  92. Repeat count data are available in Supplementary Table  S6. A total of 9,832 protein coding genes as predicted using a combination of the ab initio programs Snap  93  , Fgenesh  94  , and Augustus  95  , and the MAKER annotation pipeline  96. A consensus gene set based on these predictions was generated using a hierarchical approach developed at The Genome Institute  97  and gene product naming was determined by BER (http://ber.
5, based on the complete deduced proteomes from the T. suis genome annotation, and of T. muris, T. trichiura  61  , and host species Homo sapiens, Mus musculus, and Sus scrofa (retrieved from Genbank, January 2014)  108. Transcriptome sequencing. Paired-end cDNA libraries were generated as previously described using standard protocols  109  from mRNA preserved from freshly isolated T. suis parasites from different life stages (2 replicates for each larval stage and 3 for adult worms). Adapter trimming, sequence quality trimming, length filtering, complexity filtering, and contaminant filtering were performed as previously described  110. The remaining high-quality RNA-Seq reads were aligned to the genome assembly using Tophat2 111 (version 2. 0. 8, default parameters). The mean number of reads associated with each feature for the replicates was determined using HTSeq-Count 112 (Supplementary Tables  S3, S4 ).
6) (AB Sciex, Framingham, MA), and the seven most intense ions were submitted to fragmentation. Spectra were searched against a database of predicted tryptic peptides derived from the predicted protein sequences translated from the T. suis genome annotation using MASCOT version 2. 3. 02 (Matrix Science, London, UK). MS data was analyzed with Scaffold (version 4. 0. 1, Proteome Software Inc. , Portland, OR) with a peptide confidence of 95. 0% with a minimum of 1 peptide in each sample for a given life-stage. This protocol respects the procedures on good animal practice provided by the Canadian Council for animal care. BMDC were obtained by differentiating precursor cells as previously described  22. Bone marrow cells (2. 5 × 10 5 cells/mL) were differentiated for 7 days in complete medium (RPMI 1640 supplemented with 5% heat-inactivated FBS, 10 mM HEPES, 2 mM L-glutamine 20 µg/mL gentamycin, and 50 µM β-ME) (Wisent, St-Bruno, QC, Canada) enriched with 20% Ag8.
ELISA. Secreted cytokines in culture supernatants from treated BMDC or BMDM cultures were measured by sandwich ELISA. Plates were coated with the following capture antibodies: anti-IL-10 (clone JES5-16E3), anti-IL-12p35 (clone C18. 2), anti-TNFα (clone 1F3F3D4) (eBioscience), anti-IFNγ (clone R4-6A2), and anti-IL-4 (clone 11B11) (BioLegend). Wells were blocked with PBS 0. 05% Tween 20 (PBS-T) with 1% BSA (Sigma-Aldrich) for 1 h at room temperature (RT), then culture supernatants were added to wells and incubated O/N at 4 °C. On the following day, the following biotinylated antibodies were used: anti-IL-10 (clone JES5-2A5), anti-IL-12/IL-23p40 (clone C17. 8), anti-TNFα (clone XT3/XT22) (eBioscience), anti-IFNγ (clone XMG1. 2), and anti-IL-4 (clone BVD6-24G2) (BioLegend). Concentrations were calculated from standard curves generated using linear regression analysis of data obtained from serial dilutions using recombinant murine IL-10, IL-12p70, TNFα (eBioscience), IL-4, and IFNγ (BioLegend) standards.
2), APC-anti-mouse-TNFα (clone MP6-XT22), APC-anti-mouse-IL-10 (clone JES5. 16E3) (eBioscience). UltraComp eBeads (eBioscience) were stained with each fluorochrome in the panel of markers as compensation controls. Samples were acquired using either a BD FACSCalibur or Fortessa (BD Biosciences), and data were analyzed using FlowJo software (Tree Star, Ashland, OR). In vitro suppression assay. Purified CD11c + BMDC (5 × 10 4 /100 µL) were pretreated with T. suis ESP (50 µg/mL) for 2 h or medium alone. Splenic CD4 + cells from OT-II mice were negatively selected using a mouse CD4 + T cell isolation kit (StemCell Technologies, Vancouver, BC) and labeled with CFSE, as previously described  116. CFSE-labeled CD4 + T cells (2. 5 × 10 5 /100 µL) were added to CD11c + BMDC cultures (i. e. T cell-to-BMDC ratio of 5:1) and stimulated with 1 nM OVA 323-339 peptide (AnaSpec, Fremont, CA).
Acknowledgements We wish to acknowledge  Daniel Defoy at the Proteomics Platform of the Quebec Genomics Center part of the Centre de Recherche du Centre Hospitalier de l'Université Laval (CRCHUL)  ( Quebec City, QC, Canada ) for performing the LC-MS/MS analyses. Research was supported in part from a grant from  Natural Sciences and Engineering Research Council of Canada Discovery  Grant # 238249 , (A. J. ) and  Canadian Institutes of Health Research  grants to (A. J. , M. M. S. ). The work at Washington University was supported by  NIH  grants  AI081803  and  GM097435  to M. M. The work at the  USDA  was supported by  ARS  Research Project  1265-32000-094-00D. Funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Availability Raw reads from transcriptomic analyses were deposited in the GenBank Sequence Read Archive under BioProject ID PRJNA179528 (SRX1838974 -SRX1838986). Genomic sequencing and gene annotation data are available on the NCBI sequence read archive (Biosample ID SAMN02721381, BioProject ID PRJNA179528). All other datasets generated and analysed during the current study are available upon request.
This paper investigates the information transmission between the most important cryptocurrencies -Bitcoin, Litecoin, Ripple, Ethereum and Bitcoin Cash. We use a VAR modelling approach, upon which the Geweke's feedback measures and generalized impulse response functions are computed. This methodology allows us to fully characterize the direction, intensity and persistence of information flows between cryptocurrencies. At this data granularity, most of information transmission is contemporaneous. However it seems that there are some lagged feedback effects, mainly from other cryptocurrencies to Bitcoin. The generalized impulse-response functions confirm that there is a strong contemporaneous correlation and that there is not much evidence of lagged effects. The exception appears to be related to the overreaction of Bitcoin returns to contemporaneous shocks.
Surprisingly, what we find is that not only there is not any Bitcoin dominance in terms of information flows, but in fact most of the lagged feedback occurs in the other direction, from other cryptocurrencies to Bitcoin. The remainder of the paper is organized as follows. Section 2 describes the main features of cryptocurrencies and presents the five most important cryptocurrencies. Section 3 provides a brief literature review, with a focus on market efficiency. Section 4 presents the data and characterizes the cryptocurrencies' markets in term of several trading variables, such as volume, capitalization, price and return. Section 5 presents the VAR analysis, Geweke feedback measures and generalized impulse response functions. Section 6 concludes the paper.
If such delay exists then our results are biased against Bitcoin. This problem is potentially more serious in 2017 and in the first quarter of 2018 when there was a Bitcoin trading frenzy. In future work we intend to test the robustness of these results using other cryptocurrency databases available online. We also intend to investigate the determinants of the information flows between cryptocurrencies, such as, for instance, the relative trading volume, price trend and internet sentiment.
Surface visibility The observational data of surface visibility from the Global Surface Summary of the Day (GSOD;  Smith et al. , 2011)  are used in our study to identify the days with low visibility due to particulate pollution, i. e. , haze events. The GSOD is derived from the Integrated Surface Hourly (ISH) dataset and archived at the US National Climatic Data Center (NCDC). The daily visibility data are available from 1973 onward.
Particulate matter (PM 10 ) The surface concentrations of particulate matter with sizes smaller than 10 µm (PM 10 ; measured in µg m -3 ) in Malaysia are derived from the Air Quality Index (AQI; named Air Pollutant Index, API, in Malaysia) records obtained from the website of Ministry of Natural Resources and Environment, Department of Environment, Malaysia (http://apims. doe. gov. my/public_v2/home. html, last access: 27 April 2018). When PM 10 is reported as the primary pollutant with a maximum pollutant index, the 24 h PM 10 concentrations are calculated from AQI based on the equations in Table  S1  in the supplement  (Malaysia, 2000). Data from 51 AQI observation stations are available in Malaysia from October 2005 onward. AQI number is reported twice daily (11 00 and 17 00 local time), and the data reported at 11 00 are used in this study.
Carbon monoxide (CO) and ozone (O 3 ) Surface mole fractions of CO and O 3 are measured by the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) station in Bukit Kototabang, which is located on the island of Sumatra, Indonesia. Hourly data are archived at the World Data Center for Greenhouse Gases (WDCGG) under the GAW program (http://ds. data. jma. go. jp/gmd/wdcgg/, last access: 27 April 2018).