dataesr/scientific-paragraphs-categorization huggingface dataset:
Western blot was performed using the same antibodies as for IP. IP without antibodies was used as a Mock control. Microarray hybridization -was done with a use of Agilent Yeast Whole Genome ChIP-chip Microarray 4644 K 014810 G4493A. Three biological replicates of Bye1-Myc ChIP-chip and four biological replicates of Pol II and TFIIS ChIP-chip have been used for all quantifications. The computational data analysis was performed in R using limma package from the Bioconductor project  [33]  as described  [12]. Raw data have been deposited to Array Express under accession number E-MTAB-2161. Yeast two-hybrid assaywas performed in the Y190 strain transformed with pVV212 and pVV213 empty or recombinant vectors listed in Table  S6 , and the activation of the lacZ or HIS3 reporters was tested using X-Gal overlay plate assay or 3AT plate assay as described  [34, 35].
Sequencing of these libraries was performed on a HiSeq 2000 (Illumina) in a multiplexed single-ended setting for 50 cycles using TruSeq SR Cluster kit v3 cBot HS and TruSeq SBS kit v3 HS (Illumina). After sequence files generation using CASAVA 1. 7 (Illumina), 39 adapter sequence was trimmed using Cutadapt  [22]. These processed short reads data have been deposited in the European Nucleotide Archive (http://www. ebi. ac. uk/ena/data/ view/ PRJEB3953).
Microarray analysis of E. histolytica upon treatment with metronidazole Trophozoites of E. histolytica (4610 6 ) grown in axenic culture and treated or not with metronidazole (at 50 mM for one hour), were lysed with Trizol reagent and total RNA isolated according to the manufacturer's protocol. RNA was analyzed for integrity and the concentration determined by capillary electrophoresis using the Agilent Bioanalyzer 2100 RNA nanochip Assay (Agilent Technologies). Agilent microarrays EH-IP2008, scanning the entire amoebic genome, were used as previously described  [26]. Dye swap hybridizations were performed for the three biological replicates leading to a total of 6 competitive hybridizations. The whole data set was submitted to the ArrayExpress database (Accession number: E-MTAB-1763). After pooling data from technical and biological replicates, differential analysis was carried out as published  [26]  and includes paired Student's t-test.
Changes in gene expression determined by RNASeq upon SF9B treatment of trophozoites. Accession number from Amoeba DB data base, nc: non-significant changes. **Reads are the cDNA fragments sequenced and then mapped to the genome. Fold Change refers to the number of reads from cells in the tested condition divided by the number of reads from the non-treated control cells. doi:10. 1371/journal. pntd. 0002607. t005.
This work is supported by grants to NG from the  French National Agency for Research  ( ANR-MIE08  and  ANR 2010 GENOM-BTV: GENAMIBE ) and a grant to MFGK from  International Foundation for Science (IFS Nu 4085/P-1 University of Yaounde I Research Commission). We grateful acknowledge the  International Division of Institut Pasteur  for financial support to EMN through the  Calmette program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These communities share genetic heritage, but require different approaches to engagement and informed consent. Thus, H3Africa includes a grant program that supports empirical research on innovative approaches to informed consent; community engagement; and the ethical, legal, social, and cultural factors unique to the African research environment. The H3Africa Consortium has developed an approach that attempts to balance (i) protection of the ability of African scientists to be the first to analyze and publish findings about their main research questions, given their limited resources and capacity to deal with data as quickly as scientists in developed countries with (ii) the benefit of global access to H3Africa data and biospecimens. To reach these not completely compatible ends, the H3Africa Consortium has agreed that data will be made initially available to the consortium members via H3ABioNet until submission to the European Genome-phenome Archive, from which they will be publicly accessible (through an independent Data and Biospecimen Access Committee).
Resources generated by H3Africa are expected to be useful in future genomic research not only in Africa but also globally. H3ABioNet has also embarked on a program of training and accreditation of its bioinformatics nodes to carry out specific data analysis techniques, i. e. , of GWAS or nextgeneration sequencing data. Part of the training involves a series of workshops, often held at the nodes, to prepare for an accreditation exercise. The accreditation involves giving the nodes raw data sets to analyze, with their results being assessed by an international accreditation committee. One of the major challenges in holding training courses or even just joining working-group Skype calls, is poor Internet connectivity. H3ABioNet is using creative approaches to overcome these issues by seeking low latency alternatives and using portable devices that host data and tools and run independently of the network.
Losses were very high in Belgium, France, Germany, Italy and the Netherlands. Surfaces remained almost stable in Luxembourg and the United Kingdom and Ireland. Surface losses calculated from the FAOSTAT database are estimated at about 15% and 10 million ha for the  between 1961 and 2007. These losses are clearly underestimated notably because of changes in survey methods over time in some countries (e. g. , Greece, Italy, Portugal). The variation of the temporary grassland area can only be calculated for short periods due to a lack of data. Between 1990 and 2007 (Eurostat) , the temporary grassland surface increased in 11 EU countries. It seems that this surface stabilised between 2001 and 2007. It is likely that temporary grassland areas used through cutting decreased over the last twenty years while grazed temporary grassland areas rose in some countries (Belgium, the Netherlands).
In the Eurostat database, 'permanent grasslands and meadows' include rough grazing. Rough grazing is defined as 'low-yielding permanent grassland, usually on low-quality soil (for example on hilly land and at high altitudes), usually unimproved by fertiliser, cultivation, reseeding or drainage, which can normally be used only for extensive grazing and are normally not mown or are mown in an extensive manner and which cannot support a large density of animals'. The majority of them can be considered as rangelands and grazed common lands. It is not always clear if for each country 'grazed common land' or all 'rough grazing' are included in the Eurostat database 'permanent grassland and meadow'. Permanent grassland and meadow not used for production, eligible for subsidies Source: http://epp.
The following structure was suggested for the classification of grassland terms into statistical databases (Table  2 ). Table  2. Classification of fodder crops and permanent grasslands into the Utilised Agricultural Area (UAA)  (Peeters et al. , 2013). These definitions recognise the existence of silvopastoral systems, like the dehesa/ montado and other Mediterranean grazed ecosystems, as permanent grasslands and make them identifiable in statistics and eligible for subsidies. It introduces semi-natural grasslands into the typology that allows data to be recorded about their evolution and to define policies for the conservation of this species-rich and threatened ecosystem. The category 'Forage crops/Leguminous plants' has been clustered with the category 'Forage crops/Temporary grass' to creating the new category 'Forage crops/Temporary grasslands'.
Acreage of temporary grasslands Data on permanent grasslands do not include temporary grasslands that are cropped on the arable land area. Temporary grasslands are combined with green maize, fodder beet, other annual forage crops and forage legumes in the 'forage crops' statistical category (Tables  8  and 9 ). Forage legumes (most likely a majority of lucerne in pure stand or in mixtures) are separated from the temporary grassland area in the Eurostat database (it has only a significant importance in Austria and Hungary). Temporary grasslands area varies considerably among countries: they represent 6%  of the UAA in the EU-27 and 3% in the EU-12-NMS; 15% of the total (permanent and temporary) grassland area in the EU-27 and 9% in the EU-12-NMS. They cover a significant area in northern Europe (35% in Sweden, 28% in Finland, 24% in Estonia and Norway, 21% in Latvia, 10% in Denmark), Ireland (16%), Switzerland (11%), the Netherlands (10%) and France (10%) (Table  9 ).
Land cover Figure  1  (Plate 1) is based on data from the CORINE Land Cover (CLC) database developed in 2002 and describes land cover (as well as some land use). Figure  2  (Plate 2) presents the area of permanent grassland in these European countries in 1995  (EC, 2008a). Arable land (including temporary grasslands), permanent crops, pastures and heterogeneous agricultural areas are in the agricultural class and represent about 47% of the total EU-27 area. The forest and artificial classes represent 30% and 4% of the total EU-27 area, respectively. Shrub and/or herbaceous vegetation associations, inland wetlands and open spaces with little or no vegetation are classified under the 'nature' category (15% of the total area). Grasslands as defined in the broadest sense are classified under the agricultural group, as well as the nature group to a smaller degree.
The Land Use/Cover Area frame Statistical (LUCAS) survey, a large scale and fully harmonised land survey, was conducted in 2009 in 23 EU Member States and resulted in an unique dataset (Table  10 )  (Eurostat, 2010a). These results correspond well with the CLC 2000 database when divided into three groups: 1) agriculture (cropland + grassland), 2) forest + nature (forest and other woodland, shrubs, water and wetland) and 3) artificial area (constructed and other artificial areas). On average, 20% of the EU-23 area is covered by natural or agricultural grasslands, but major differences exist among countries. Grassland rich countries are Ireland (64%), the United Kingdom (42%), the Netherlands (38%) and Belgium (33%), while Finland (3%), Sweden (4%), Greece (13%), Portugal (14%) and Spain (14%) have much lower proportions.
Legume forage crops are of variable importance in most of the European countries. The Eurostat database is incomplete for these crops. For example, there is data on grass/clover for only about half of the countries. The lack of data is most likely because grass/clover mixtures are not distinguished from the pure grass mixtures used in temporary grasslands. Species like lucerne and other legumes cultivated in pure stands (sainfoin and red and sweet clovers) probably had little or no importance in the countries where no data were delivered. The area of grass/clover mixtures and pure stands of red clover is very high in Latvia (7. 7% UAA), Estonia (5. 3% UAA) and Lithuania (4. 4% UAA). In terms of number of hectares, Germany (213 000 ha), Latvia (141 000 ha), Romania (118 000 ha) and Lithuania (117 000 ha) are the only countries with more than 100 000 ha of this crop.
Eurostat reports a sharp drop in grassland areas (> 10%) in Belgium, France, Ireland, Italy and the  Netherlands between 1990 and 2007 (Eurostat). This decline was moderate (≤ 10%) in Denmark and Luxembourg. In Spain and the United Kingdom, there was a moderate increase (≤ 10%), and a strong rise in Greece and Portugal (> 10%). The average variation of the grassland area in these countries is an overall decrease of about 5% of the grassland area during this period (Table  13 ). During the same period, the UAA varied much less (a few percent of 1990 levels), except in Greece (+11%), Italy (-15%) and Portugal (-13%) (Table  11 ). Based on data from the FAOSTAT database, the conclusions are slightly different between the early 1990s and 2007 (Table  13  and 16 ). The decline in grassland area was considerable (> 10%) in Cyprus, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Poland, Slovakia, Sweden and Switzerland; it was moderate (≤ 10%) in Austria, Bulgaria, the Netherlands, Romania and Slovenia.
There was a moderate increase (≤ 10%) in Spain and the United Kingdom, and a significant increase (> 10%) in the Czech Republic, Denmark, Lithuania and Portugal. The main difference between the two databases concerns Denmark (moderate decline according to Eurostat; large increase according to FAOSTAT) and Greece (significant increase or sizeable decrease, respectively). The decline in permanent grassland area is due to urbanisation, conversion to arable land and afforestation. Marginal grasslands and rangelands tended to be abandoned, especially in mountainous and Mediterranean areas. According to Eurostat, the drop in permanent grassland area seems to have slowed or even stopped after 2003 (Table  16 ). This trend is not so clear in the FAOSTAT database (Table  18 ). After 1989 and the fall of communist regimes, many agricultural areas and especially grassland areas were abandoned in countries in transition.
Rate of variation In the Eurostat database, the rate of decline was high in the  1971-75, 1985-87 and 1990-93  periods. The rate of variation was positive (increase) between 1987 and 1990 because of the German reunification. Since 2005, there has been a positive trend in the variation rate of surfaces in the  12, 15 and 27. This means that the grassland area has tended to increase in these groups of countries, at least slowly. In the EU-6, the variation rate is still negative but is lower than in former periods. The FAOSTAT database shows that the annual rate of decline was particularly high during the  1975-80, 1980-85 and 1990 -95 periods (Figure  7 ). One year after protection of the permanent grassland area was added to the cross-compliance principle in 2003, the decline was significant but partly made up in 2005.
Imported feed components have high transportation costs, tremendous environmental impact and their quality and safety can be extremely variable. A greater reliance on 'home-grown' legume-based protein sources would improve the feed traceability, enhance consumer confidence in the final market product and promote ecologically sound farming systems  (De Vliegher and Carlier, 2008; Carlier et al. , 2008). In this section, we will look at the example of lucerne in France. When considering the data from the last four decades, what is most striking is the constant decline of pure legumes, which are mainly lucerne. Moreover, a quarter of lucerne acreage is used for the dehydration industry. In fact, this situation is part of a long tradition. It is important to measure the changes over a much longer period than illustrated in Figure  10.
The expansion of forage legumes was reported by Gilbert in 1788 who wrote a precise report on legume cultivation in the Paris Basin for the Royal Agricultural Academy, where he described the presence of lucerne, sainfoin and red clover, depending on the soil characteristics. Statistical data from 1887 indicated that the acreage of forage legumes cultivated for hay reached 1. 6 million ha, 2. 8 million ha and 3. 1 million ha in 1842, 1862 and 1882, respectively. Until 1946, forage legumes and lucerne acreages remained fairly constant. Lucerne reached about 1. 2 million ha, red clover 500 000 ha and sainfoin 600 000 ha, accounting for a total of 2. 3 million ha (Figure  11 ). This large area of forage legumes grown for haymaking already existed in the previous century. Forage legumes played a key role in the sustainability of the farming systems, both as a source of quality, proteinrich hay and as a source of nitrogen, either through their nitrogen residues when ploughed or through manure.
Hume and Corrall (1986)  attempted to create this type of map by using the data of a meteorological network and a grass growth model. This model was able to take into consideration water stress. Jones and Carter (1992)  also drew up a map of annual rainfed herbage yields. They used a grass growth model for grazed grasslands, based on radiation and temperature. The effect of soil moisture deficit in depressing potential dry matter yields was calculated. They assumed a change in sward status from reproductive to vegetative simultaneously across all of Europe. A synthesis of all this information has been made  (Peeters and Kopec, 1996). The results are presented in Figure  16  (Plate 8). As expected, major differences in the annual productions are recorded between sites. The highest yield for ryegrass reaches almost 20 t DM/ha in Germany (Kiel) whereas the lowest yield is only 2 t in Portugal.
Animal performance Animal performances have increased dramatically. The milk yield per dairy cow rose steadily in most countries. Performances can be analysed in two ways. The first method involves the use of national statistics and a simple ratio of the total milk production to the number of dairy cows. Data is available for a very long period of time for the Netherlands (Figure  35 ) and for the last four decades for the United Kingdom (Figure  36 ). In the case of the Netherlands, the only periods when yield increases were interrupted were during the First and Second World Wars. The second method is to analyse the most productive herds using a specific survey. Such data may be collected from the national survey system or from the ICAR database. This is illustrated in Figure  37  for three countries: France, Ireland and Poland.
Surface losses calculated from the FAOSTAT database are estimated at approximately 15%-10 million ha-for the EU-13 (EU-15-Belgium and Luxembourg) between 1961 and 2007. These losses are clearly underestimated. The variation of the temporary grassland area can only be calculated for short periods of time because of a lack of data. Between 1990 and 2007, the temporary grassland surface increased in 11 EU countries (Eurostat). This surface appeared to stabilise between 2001 and 2007. It is likely that cutting temporary grassland areas decreased during the last twenty years while grazed temporary grassland areas increased in some countries. The dairy cow population fell by 10 million head in the  EU-9 between 1975 EU-9 between and 2007 EU-9 between (40% drop from 1975 levels) levels). This decline started after the implementation of the milk quotas in 1984.
This last category is divided into 'temporary grass' (D18A; 'temporary grasslands' in this book) and 'other green fodder' (D18B). Other green fodder includes 'green maize' (D18B1; 'green maize' in this book) and 'annual legume plants' (D18B2; 'forage legumes' in this book). In this book, the total fodder area is the sum of the permanent grassland and forage crops areas. Data on temporary grasslands are only available for some countries after 1990 in Eurostat and after 2001 in FAOSTAT. Statistics on grasslands and fodder area as a whole must be interpreted with caution. Statistics for the same category, the same country and the same year may differ in the FAOSTAT and Eurostat databases or in national statistical systems. In some cases, not all types of grassland areas are reported and some areas reported as forage crops are not used by grazing livestock.
, 2010). In the FAOSTAT database, data are notably available for cattle, sheep and goats. Categories are not distinguished among cattle. In the Eurostat database, 'livestock' (J) data include data on 'granivores' (pigs and poultry) and 'grazing livestock'. Grazing livestock (J) includes 'equidae' (J01), 'cattle' (J02-08), 'sheep' (J09) and 'goats' (J10). The cattle category is divided into 'bovine < 1-year-old - total' (J02), '1 year ≤ bovine < 2 years - males' (J03), '1 year ≤ bovine < 2 years - females' (J04), 'bovine 2 years and older - males' (J05), 'heifers, 2 years and older' (J06), 'Dairy cows' (J07), 'other cows, bovine 2 years old and over' (J08). This last category includes mainly 'suckling cows'. Data from animal performance were obtained from national databases such as 'contrôle laitier' in France.
Data for seed and seed industries were collected from the national certification offices and computed. Data for dairy and meat industries were obtained from national statistics and were crosschecked with European data. In this book, there are regular mentions of the EU-6, EU-9, EU-12, EU-15 and EU-27. The EU-6 includes Belgium, France, Germany, Italy, Luxembourg, the The European project Multisward (http://www. multisward. eu/multisward_eng/) aims at supporting developments and innovations in grassland use and management in different European farming systems (including low-input and organic farming systems), pedoclimatic and socio-economic conditions i) to enhance the role of grasslands at farm and landscape levels to produce environmental goods and to limit the erosion of biodiversity and ii) to optimise economic, agronomic and nutritional advantages for the development of innovative and sustainable ruminant production systems.
Proportion of the Natura 2000 network in the UAA (%). , at a favourable conservation status, natural habitats and species of wild fauna and flora of Community interest. Network management financing is coordinated with existing aid instruments. The support of farming activities inside Natura 2000 sites is thus part of the CAP financial support and structural interventions, since they are part of rural and regional development policies. This creates a strong relationship between AEM and Natura 2000 implementation on agricultural land. Traditional Speciality Guaranteed) (EC No 1898/2006) and OF. Healthy food, superior taste and positive effects on the environment are the main expectations of the consumers when they see these labels. Organic farming legislation started in the EU in 1991 (regulation EEC 2092/91, completed and revised several times since); OF is supported by legislation and direct payments.
EU-27 EU-15 EU-12-NMS 1 11. 3 11. 2 11. 6 2 9. 9 9. 4 11. 2 Source: (1) EEA Natura 2000 geodatabase (ETCBD) + Corine Land Cover 2000 in the European Union (2008); (2) Natura 2000 spatial dataset (Mid 2009) + Corine Land Cover 2000 in the European Union (2009). restore.
The enriched fraction is subsequently next generation sequenced. Despite the successful development and application of hybridization capture methods, a variable but substantial fraction ranging from 25-38% of the sequences generated do not match the target of interest  [6, 7]. This sequence fraction is largely discounted from further analysis when the data is processed using bioinformatics. While some studies have examined such data from exome analysis, it remains unexplored if there are consistencies among the off-target sequence reads or why they are generated  [8]. To investigate this fraction, we performed solution based hybridization capture on Illumina libraries prepared from a variety of sample types. The samples included several Southeast Asian rodent species, modern and historical koalas, (Phascolarctos cinereus) and uropathogenic bacteria extracted from human urine.
Cycle threshold (C t ) values were calculated by the LightCycler software, and relative amounts of each amplicon were determined by the differences in C t values. While this experiment does not allow for CapFlank to proceed iteratively as the homology between targets and their flanking sequences at each step is limited to the 80 bp M13 adaptor on each side of the amplicon, it would establish whether initiation of CapFlank is enhanced by homology.
A burn-in period of 25% of total generations was determined graphically and the effective sample size (ESS) of the trace of each parameter was checked using Tracer 1. 5 (http://tree. bio. ed. ac. uk/software/tracer/). The branch supports were estimated using posterior probabilities (PP). Complete mtDNA genomes of Rattus exulans and R. norvegicus available in GenBank were used as outgroups.