dataesr/scientific-paragraphs-categorization huggingface dataset:
Chlorophyta contribution to photosynthetic phytoplankton in coastal waters. The global OSD dataset provided 1,103,675 reads of the 18S rRNA V4 regions that could be assigned to photosynthetic organisms. Dinoflagellates were excluded from photosynthetic organisms because about 50% of the species are not photosynthetic  28  and it is difficult to precisely assign dinoflagellate OTUs to photosynthetic vs. non-photosynthetic species as no reference database with this functional information is currently available. Moreover, dinoflagellates have a large number of rRNA gene copies  29  which causes an inflation of their representation in metabarcode studies. Chlorophyta represented 29 ± 25% of photosynthetic reads (Fig. 2A ) and constituted the second most represented photosynthetic division in terms of percent of reads and number of OTUs after Ochrophyta (mostly diatoms, Fig.
Ulvophyceae and Chlorophyceae had more OTUs (respectively 95 and 94 OTUs) than expected from their relative contribution (respectively 3. 5 and 3. 2%). Several classes with low overall contributions had a quite large number of OTUs. For example, the Palmophyllophyceae and Pedinophyceae represented about 2 and 0. 3%, respectively but had 3. 4 and 1. 5% of the OTUs respectively (Fig. 1 ). In order to estimate the level of novel diversity in each class, we computed the fraction of the OTUs with less than 98% BLAST similarity to any sequence from GenBank originating from cultures (Fig. S2 ). Without surprise, 100% of the OTUs from classes which have not been brought in cultures met this criterion, such as prasinophytes clade IX or VIII. In contrast, 100% of the Chloropicophyceae, despite the fact that it is a recently created class  7  , appear to match sequences from cultures suggesting that the cultivation effort has been very exhaustive for this group in coastal waters as it had been shown previously to be in oceanic waters  10.
They were recorded in temperate coastal waters, especially off the USA and European Atlantic coasts (Fig. 4 ). Trebouxiophyceae were not recorded at high latitudes nor at oligotrophic stations such as Hawaii, French Polynesia or Azores. The 3 major OTUs (7,703, 1,423 and 1,317 reads, Supplementary Data S2) were assigned to the highly diversified marine coccoid genus Picochlorum  30. Ulvophyceae maximal contribution was recorded at OSD169 (North Sea off UK, 70%). Ulvophyceae were mostly present along the North Atlantic European coast, at some stations of the Mediterranean Sea (OSD78 in the Adriatic Sea and OSD 123 off Israel, for example), in warm waterd (OSD28, 124 and 147) and in Antarctica (OSD187, Fig. 4 ). The most abundant Ulvophyceae OTU (Supplementary Data S2) was assigned to the macroalgal genus Ulva, in particular matching with 100% similarity sequences from U.
They contributed to more than 1% at 27 stations (Fig. 5 ), mostly in the Mediterranean Sea and along North Europe coasts (Fig. S3 ). Maxima were recorded off Cyprus (OSD19, 77%) and in the Skagerrak (OSD157, 37%). Interestingly they were present (but accounting for less than 1% of the reads, Fig. 5 ) at 62 other stations suggesting that they are probably an ubiquitous but minor component of the Chlorophyta in many environments. Chloropicophyceae represented more than 1% at 20 stations (Fig. 5 ) mostly located in tropical oceanic waters. They reached their highest contribution at the Azores station OSD97 (45%) and off Bermuda (OSD8, 29%, Fig. S3 ). The major OTUs (Supplementary Data S2) corresponded to the species Chloroparvula pacifica and sp. (clades B2) and Chloropicon roscoffensis. Nephroselmidophyceae represented more than 1% at 12 stations (Fig.
5 ) and their maximal contribution between 5 and 6% of the Chlorophyta reads were recorded in the coastal North Atlantic Ocean (OSD106 off Iceland, 152 off Canada and 157 off Norway, Fig. S3 ). The Nephroselmidophyceae also reached 2% at several stations in the Eastern Basin of the Mediterranean Sea (such as OSD123 off Israel, Fig. S3 ). The two major OTUs belonged to the genus Nephroselmis (Supplementary Data S2). Pedinophyceae represented more than 1% of the Chlorophyta reads only at 9 stations (Fig. 5 ) and were mostly present at stations located off the USA Atlantic coast (OSD35, 46, 143, 186) and in the Mediterranean and Black Seas (OSD64 and 78, Fig. S3 ). The highest contribution (7. 1%) was recorded in Chesapeake Bay (OSD35). The two major OTUs belonged to the genus Marsupiomonas (Supplementary Data S2).
The order Pseudoscourfieldiales had more than 1% of the Chlorophyta reads at only two stations (Figs  5  and S3 ) from the Adriatic Sea (OSD 48 and 99, 1. 8% and 1%, respectively). Prasinophytes clade VIII was the least represented group in this dataset with more than 1% at a single station (Fig. 5 ) off the Iberic Atlantic coast (Fig. S3 ). Finally, at 13 stations (Fig. 5 ), more than 1% of the Chlorophyta reads could not be classified in any Chlorophyta class (Fig. S3 ). The maximum fraction of unclassified sequences was found in the Mediterranean Sea off Cyprus (OSD18,16%), in the Atlantic Ocean off Belize (OSD 28,8. 1%) and off the East Coast of the US (OSD58, 7. 5). Other unclassified reads were recovered from the Mediterranean Sea and off Iceland (OSD128). Chlorophyta community structure in coastal waters.
Discussion Green algae (Chlorophyta) are clearly significant photosynthetic contributors in coastal waters as demonstrated by the OSD dataset where they constituted the second major photosynthetic group (dinoflagellates excluded) after Ochrophyta (mostly diatoms) both in terms of read contribution and number of OTUs (Fig. 1 ). The importance of Chlorophyta had already been highlighted previously in some specific environments. In European coastal waters, the contribution of Chlorophyta to photosynthetic 18S rRNA clones was found to be 42%  35. In the English Channel and North Sea, from 85% to 47% of the picoeukaryote cells hybridized by TSA-FISH were Chlorophyta, more precisely Mamiellophyceae  [36] [37] [38]. Similar contributions were also observed in another OSD dataset  39  focusing on a smaller number of stations and using both the V4 and the V9 18S rRNA regions (26% and 20%, respectively).
In comparison, Chlorophyta have a lower overall contribution (13% in average) in the Tara Ocean V9 dataset from oceanic waters  10. The number of Chlorophyta OTUs (745 at 99% similarity) was of the same order than found in other studies: in European coastal waters  40  , 314 V4 OTUs were found at 97% similarity or in the Tara Ocean dataset  12  , 1420 V9 OTUs were found with the SWARM algorithm which uses natural clustering rather a fixed similarity level  41. In the OSD dataset, the percentage of Chlorophyta was maximum in tropical waters with oceanic characteristics (94% OSD7 off Moorea, Fig. 3 ). Such high Chlorophyta contribution in oceanic waters have been also been observed in clone library studies  34  as well as in the Tara Ocean dataset  10. In contrast, low Chlorophyta contribution (less than 1% Chlorophyta reads) was observed at very few stations in the North Atlantic Ocean (e.
g. off Norway OSD155 and 157) and in the Arctic (OSD128). Such low contribution of Chlorophyta does not mean that their abundance is always low in these waters since sampling was restricted to a single day and Chlorophyta have been previously isolated in these environments, e. g. in Norwegian coastal waters  42. In the OSD dataset, Mamiellophyceae (especially Micromonas, Ostreococcus and Bathycoccus) was the major Chlorophyta class in coastal waters under a wide range of environmental conditions, as previously reported by many studies in coastal and nutrient-rich environments from the Arctic Ocean to the Mediterranean Sea through the Pacific and Indian Oceans  36, 37, [43] [44] [45] [46] [47]. Not et al. 48 found Micromonas to be the most prevalent genus in the world ocean coastal waters and at a more local scale, Micromonas dominates coastal picoplankton in the Western English Channel  37.
Collado-Fabri et al. 49  and Rii et al. 11  found that Mamiellophyceae (Micromonas, Ostreococcus and Bathycoccus mostly) were dominant in the upwelling-influenced coastal waters off Chile. Using quantitative PCR, Marie et al. 44  found Bathycoccus to be dominant in a transect through the Mediterranean Sea. In contrast, the contribution of Mamiellophyceae was low at oceanic OSD stations, which confirms data from the oceanic Tara Ocean dataset, where only 17% of the Chlorophyta reads belonged to Mamiellophyceae  10. Nutrient depleted environments have been previously reported to host Chloropicophyceae 10 and clade IX  11, 50, 51. These two groups however appear to be differentially distributed in the OSD dataset (Fig. 6 ) with prasinophytes clade IX in more oligotrophic areas than Chloropicophyceae, as observed previously in the South China Sea  51  or the Pacific gyre  34.
Picocystophyceae (formerly prasinophytes clade VIIC 7 ) were completely absent from the OSD dataset, confirming that this class is absent from marine waters  10. Pyramimonadales were recovered everywhere in OSD and were the second most abundant Chlorophyta class as found in the Tara Oceans dataset  12  and often co-occurred with Mamiellophyceae (Fig. 6 ). They were particularly prevalent in the Mediterranean Sea and the North Atlantic Ocean, where microplankton microscopy inventories previously recorded the presence of the genera Halosphaera and Pterosperma  [52] [53] [54]. In the OSD dataset, Pyramimonadales did not show any environmental preferendum supporting the observation made by Viprey et al. 5  that Pyramimonadales were found in almost all metadata ranges they sampled in the Mediterranean Sea.
Pyramimonadales strains have been isolated from a large range of environments including polar  55, 56  , Mediterranean  57  and various coastal waters  58. Surprisingly, Pyramimonadales were not recovered (Fig. 4 ) from coastal waters of Japan (OSD124), while numerous strain or natural samples sequences from GenBank originate from this area  23, 59  , and South Africa, where a wide diversity of Pyramimonas have been isolated  60. ). Circles are proportional to the average contribution of a given class to total Chlorophyta. For salinity, OSD10 was not taken into account since it is located in a freshwater lake. In the OSD dataset, Chlorodendrophyceae replaced Mamiellophyceae at some stations in particular in the Mediterranean Sea and contributed to Chlorophyta off the US coast and in the Indian Ocean.
7 ). At some other OSD stations, classes from the UTC clade dominated the Chlorophyta communities. Chlorophyceae showed clear environmental preferences for low salinity and low temperature waters in this dataset. Some Chlorophyceae such as Dunaliella have been shown to be tolerant to a large salinity range from freshwater to marine water  68, 69  and have been recorded in coastal Arctic, Southern Ocean and Northern Europe samples  23, 64, 65, 70. In contrast, Trebouxiophyceae and Ulvophycae did not show environmental preferences in the OSD dataset. Some Ulvophyceae OTUs corresponded to macroalgae from the wide-spread genus Ulva. These OTUs could have originated from unicellular stages (gametes or zoospores) since these stages can range from 5 to 15 µm 71 and can survive for almost one day in the water  72  or from fragmented macroalgal thalli, since the sampling was done without prefiltration.
Conclusion The OSD dataset has some clear limitations. It corresponds to a snapshot in time and is mostly limited to surface waters near the coast. It is strongly biased towards the Northern hemisphere and very few environmental metadata are available. Still it offers an opportunity to gain insights into the contribution and distribution of Chlorophyta classes in marine coastal waters. It highlights that Chlorophyta can be the main photosynthetic group in some ecosystems. In most cases (Fig. 6 ), a single Chlorophyta class dominates at any given site. This work has confirmed that the Mamiellophyceae are the dominant group in coastal waters, being present at nearly all the stations (Fig. 5 ). One unexpected finding is that Chlorodendrophyceae can replace Mamiellophyceae as the dominant group in particular in the Mediterranean Sea.
Acknowledgements Financial support for this work was provided by the  European Union projects MicroB3  ( UE-contract-287589 ),  ANR PhytoPol  ( ANR-15-CE02-0007 ) and  TaxMArc (Research Council of Norway ,  268286/E40 ). MT was supported by a PhD fellowship from the  Université Pierre et Marie Curie  and the  Région Bretagne (ARED GreenPhy ). We would like to thank the  Ocean Sampling Day consortium  for providing sequence data and the ABIMS platform in Roscoff for access to bioinformatics resources.
Data Availability Supplementary Data S1-S3 (mothur script used to process the data, OTU fasta file, Excel file with the OTU taxonomic assignation, OTU read abundance at each station along with OSD metadata), R script to produce some of the figures, as well as Supplementary information have been deposited to Figshare at https://doi. org/10. 6084/ m9. figshare. 6794585.
The discrepancy in Hg 0 gas measurements observed between the two Tekran instruments along the two seasons was on average 7 %; concentration data showed here were adjusted using the differences between the two atmospheric inlets. Air sampling was alternated between different snowpack heights every 5 min so that a full sequence of air extraction from the snowpack (six inlet heights) was achieved every 30 min. Interstitial snow, soil pore, and atmospheric measurements continued through the entire winter with only small time periods of interruptions www. the-cryosphere. net/12/1939/2018/ The  Cryosphere, 12, 1939 Cryosphere, 12, -1956 Cryosphere, 12, , 2018  due to power failures or other technical problems. Additional trace gases were measured along with Hg 0 gas , including concentrations of CO 2 using a LI-840A (LI-COR Inc.
Data processing and statistical analyses We performed all data processing and statistical analyses with RStudio 1. 1. 383 (RStudio Inc. , Boston, Massachusetts, USA) using R 3. 4. 2 (R Foundation for Statistical Computing, Vienna, Austria). Averaged data and variance in figures and tables are shown as mean ± standard deviation. Significant differences were determined with the Kruskal-Wallis test (α = 0. 05). We performed plots with ggplot2, ggtern, and lattice R packages and used normality (eq. L -1 ) for the ternary diagram. Geographical maps were prepared us- ing Quantum GIS 2. 18 (Quantum GIS Development Team, 2017).
Acknowledgements. We thank  Toolik Field Station  staff for their support in this project over 2 years, especially  Jeb Timm ,  Joe Franish , and  Faye Ethridge  for helping with snow collection. We also thank  Martin Jiskra  ( Geosciences Environnement Toulouse ) and  Christine Olson  ( DRI ) for their field support,  Christopher Pearson ,  Olivia Dillon , and  Jacob Hoberg  ( DRI ) for their support with laboratory analyses, and  Dominique Colegrove  and  Tim Molnar  ( University of Colorado ) for helping with field work and data processing. We finally thank  Alexandra Steffen  for providing mercury snow data from Alert. Funding was provided by the  US National Science Foundation (NSF)  under award no. PLR 1304305  and cooperative agreement from  National Aeronautics and Space Administration (NASA EPSCoR   NNX14AN24A ).
In exploratory post-hoc analyses, tuberculosis diagnoses were also compared between study groups using the same subgroups as the primary outcome (study site, baseline CD4 cell count, severe anaemia, and clinically suspected tuberculosis) to investigate whether the absence of mortality benefit was accompanied by a lack of difference in tuberculosis diagnosis. Diagnostic yields of urine-based tuberculosis tests were calculated post hoc as a proportion of all microbiologically confirmed tuberculosis to better understand the respective contributions of TB-LAM and urine Xpert. We did all the analyses using SAS (version 9. 4). This study is registered with the ISRCTN registry, number ISRCTN71603869.
Acknowledgments This study was funded by the  Joint Global Health Trials Scheme of the Medical Research Council , the  UK Department for International Development , and the  Wellcome Trust  ( MR/M007375/1 ). AG-W has received a  Royal College of Physicians London JMGP Fellowship , ELC has received a  Wellcome Trust Fellowship  ( WT200901/Z/16/Z ). We thank all participants and staff, the trial steering committee ( Anthony Harries ,  Andrew Ramsay , and  Frank Cobelens ), and the data safety and monitoring board ( Andrew Nunn ,  Anton Pozniak , and  Tom Harrison ).
We use an LSM specifically developed for high-latitudes, that correctly simulates Eurasian C-stocks and fluxes from observational records [3], in order to evaluate the sensitivity of the Eurasian sink to the strong high-latitude warming occurring between 1930 and 1950. We show that the LSM with improved high-latitude phenology, hydrology and soil processes, contrary to the group of LSMs in  [2] , is able to represent enhanced vegetation growth linked to boreal spring warming, consistent with tree-ring time-series  [4]. By compiling a dataset of annual agricultural area in the Former Soviet Union that better reflects changes in cropland area linked with socio-economic fluctuations during the early 20th century, we show that land-abadonment during periods of crisis and war may result in reduced CO 2 emissions from land-use change (44%-78% lower) detectable at decadal time-scales.
The reasons for such budget gaps may differ depending on the period considered, but are most likely due to (i) the systematic underestimation by landsurface models (LSMs) of inter-annual variability in the natural terrestrial sink and (ii) the high uncertainty in land-use change (LUC) estimates  [2]. During the 1940s, atmospheric CO 2 concentrations stabilized, without a clear process having been so far identified  [2] : the sink calculated from current land and ocean models is 0. 9-2 Pg C. yr -1 lower than the sink needed to balance CO 2 sources at the time. Decadal changes in the ocean sink alone likely do not explain more than 0. 5 Pg C. yr -1 , implying a sink gap in the land biosphere of 0. 4-1. 5 Pg C. yr -1. Bastos et al  [2]  proposed two main processes influencing uncertainty in reconstructions in this period: (i) enhanced vegetation growth in response to high-latitude warming not fully captured by LSMs and (ii) natural vegetation expansion following cropland abandonment during the second World War (WWII) period, not accounted for in current LUC datasets.
Such early-season warming could have promoted vegetation growth by advancing the growing season and enhancing photosynthesis, explaining the increase in tree-ring width reported in Eurasian sites by D'Arrigo et al  [4]. Reconstructions of LUC emissions (E LUC ), including those from TRENDYv4 models  [2, 6, 18] , commonly apply LUC datasets from  [19]  (HYDE 3. 1) and  [26]  (LUH1, itself based on HYDE 3. 1). These are based on UN Food and Agricultural Organisation statistics (FAOSTAT) of country-level agricultural area data after 1961. Before 1961, cropland statistics from national literature exist only for some countries and are thus hard to compile and harmonize in a global product. In order to ensure consistency between countries, HYDE 3. 1 extrapolate country-level agricultural area backwards using country-level total population and cropland area per-capita ratios.
On a second step, we compare cropland area statistics from LUH1 with annual values of agricultural area in the FSU from official Russian Empire (during the pre-Soviet period,  [1913] [1914] [1915] [1916]  and Soviet statistics between 1917 and 1961. We evaluate whether the datasets capture the socio-economic changes in FSU prior to 1961 by comparing them with demographic and economic statistics. Finally, we derive land-cover maps based on the new cropland data to calculate the resulting E LUC over Eurasia in the early 20 C. We compare our new estimates of C-sequestration by natural ecosystems and E LUC with the ones from TRENDYv4 LSMs  [2, 6, 18]  in order to understand if an improved description of soil-C and phenology in high-latitudes by LSMs and the use of countrylevel land-use statistics prior to 1961 might contribute to explain the gap between observations and GCB reconstructions in  [2].
Climate data The CRU/NCEP v5. 4 dataset, used to force models in TRENDYv4  [2, 6, 18] , provides 6 hourly data of air-surface temperature, rain and snowfall, surface radiation and air specific humidity over 1901-1999 at 0. 5 • spatial resolution. For comparability with TRENDYv4, we used CRU/NCEP v5. 4 in our analysis and as forcing of ORCHIDEE-MICT simulations.
Reference LUC dataset The LUH1  [26]  is a state-of-the-art dataset used to force LSMs in several GCB studies  [2, 18, 6]. LUH1 provides annual values of fractional cover and transitions of cropland, pasture, and primary and secondary vegetation, at 0. 5 × 0. 5 degree lat/lon from 1500 until 2100. Cropland, pasture, urban, and ice/water fractions between 1500 and 2005 are based on the HYDE 3. 1 database  [19]  that provides gridded time series of historical population and land-use for the Holocene. HYDE3. 1 values are based on national statistics compiled by FAOSTAT  [27]  from 1961 onwards, and calculated from cropland per capita ratios (varying little) combined with national population statistics prior to 1961. Forest, crop and grassland fractional cover and transitions data from LUH1 were used to produce 2 × 2 degree lat/lon maps of the 13 plant functional types (PFTs) from ORCHIDEE-MICT, following the method in  [28].
These PFTs consist of bare soil, eight forest PFTs, two crop PFTs (C3 and C4 crops) and two grass PFTs (C3 and C4 grass). Since ORCHIDEE-MICT does not explicitly simulate grazing and pasture management, pastures are treated as natural grassland. Henceforth, we refer to this dataset simply as LU REF. It should be noted that small differences between the original LUH1 and LU REF are expected due to the conversion to PFTs and to a coarser grid. Since we perform a sensitivity study by comparing pairs of simulations, these differences should not significantly affect the results. The geographical distributions of forest, grassland and cropland over the 20 C in FSU are shown in figure S1.
Official statistics of the FSU For certain countries, such as the FSU, national landuse statistics are available before 1961. We collected annual cropland area reported in official books of statistics from the State Statistics Bureau (GOSKOM-STAT), responsible for compiling economic and demographic data (incuding the ones used by FAO-STAT for subsequent years). We collected and harmonized cropland area statistics prior to 1961 for the Russian Empire during the pre-Soviet period  (1913) (1914) (1915) (1916) (1917)  and for the FSU between 1917 and 1961 by GOSKOMSTAT. Official data for 1941-1945 was compiled in 1959, but only became declassified in 1990 by the end of the Perestroika  [29] , allowing to evaluate the impact of WWII in E LUC in northern Eurasia. The data was likely originally collected at oblast level (administrative units roughly comparable to states or provinces), but official records provide aggregated values by country (former Soviet Republics) or the FSU total.
We produced new PFT maps by combining spatial patterns from LU REF with the changes in total crop area in FSU from GOSKOMSTAT, analogous to other modelling studies  [38]. A step-by-step description of the conversion from national totals to spatially-explicit maps is provided in supplementary methods available at stacks. iop. org/ERL/13/065014/mmedia. This dataset is hereafter referred to as LU NEW. Population and economic statistics provide useful proxies to evaluate the variability of crop area in LU REF and LU NEW. We collected population information reported by the GOSKOMSTAT for the same period of LU NEW (supplementary data). National statistics provide FSU population totals and discriminate between urban and rural population based on the designation of permanent residence. This does not necessarily mean population working in agriculture, and data further have a gap during 1940-1945, when LU NEW reports a big cropland drop.