dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment We thank  Romain Feron  and  Camille Marchet  for their support and implication in this project. Antoine Limasset 's postdoctoral position is funded by the  Fédération Wallonie-Bruxelles  via a " Action de Recherche Concertée" (ARC)  grant to  Jean-François Flot. Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11 ,as well as by GenOuest platform (genouest. org).
Statistical Analysis All the data were presented as means with their standard errors. The number of fish measured for each parameter is specified in tables and figures. Data were tested for normality and homogeneity of the variances with Levene's test. Two-way ANOVA was used to determine the main effect of the diet factor (D), the environment factor (E), and their interaction (DxE). One-way ANOVA was used for growth performance parameters, color evaluation and tissue AX content, followed by a Student-Newman-Keuls test. Significant differences were accepted at p ≤ 0. 05. The Pearson correlation coefficient was calculated to analyze the significance of linear relationships between skin and muscle redness (a*) and the studied variables. Correlations with p ≤ 0. 05 were considered significant.
Acknowledgments: The authors wish to thank  P. Borges  for his valuable input in the initial project and  F. Terrier ,  A. Lanuque  and  F. Sandres  for the preparation of diets, care of fish, and hyperoxia device installation. They are also very grateful to  A. Surget ,  M. Cluzeaud ,  L. Attard , and  L. Bouakaz  for technical assistance and  C. Héraud ,  K. Dias , and  M. Parailloux  for sampling.
In continuously pumped production boreholes, this latter procedure was not necessary. Other in situ measurements such as temperature (T°) and alkalinity were also made at the wellhead. Samples were filtered (0. 45-μm membrane) and analysed for δ 18 O, δ 2 H and 3 H. Groundwater-level monitoring data were compiled from both low-frequency observations, collected using a dipper at two piezometers,  P2-5 (1987-1991).
7 TU. Dating of groundwater by decay of tritium is based on the assumption that the tritium input to the recharging water is known and that the residual 3 H measured in groundwater is the result of radioactive decay alone. 1 ). These data regress (R 2 = 0. 99) along a local meteoric waterline (LMWL), δ 2 H = 7. 4 • δ 18 O + 5. 6, that reflects the impacts of evaporative enrichment (e. g. lower slope) relative to a previously computed LMWL by  Travi et al. (1987) , δ 2 H = 7. 9 • δ 18 O + 10 (R 2 = 0. 97), based on a set of seven monthly samples collected from rainfall stations across and -15‰, respectively, relative to rainfalls sampled in July and August; such differences during the monsoon are not evident in the national-scale dataset of  Travi et al. (1987). In the data from 2008, a relationship between rainfall amount and depletion in the heavy isotope of O or H, known as the Bamount effect^, is not evident.
Isotopic composition of groundwaters δ 18 O and δ 2 H values in sampled groundwater from the Thiaroye aquifer exhibit wide range of values that span >4 and >19‰, respectively, in datasets during two sampling periods in Table  2  (δ 18 O: -1. 5 to -5. 7‰; δ 2 H: -19 to -38‰; n = 39) and Table  3  (δ 18 O: -0. 4 to -5. 2‰; δ 2 H: -10 to -37‰; n = 43). Linear regression of both datasets generates slopes (3. 9 ± 0. 4, 5. 2 ± 0. 2) that are consistent with enrichment in the heavy isotope of O and H through evaporation (δ 2 H = 3. 9 • δ 18 O -16, R 2 = 0. 77; δ 2 H = 5. 2 • δ 18 O -8. 3, R 2 = 0. 95). Both curves regress to the same isotopic composition on the LMWL (δ 18 O: -6. 3; δ 2 H: -47‰), which is depleted relative to the weighted mean average composition of rainfall estimated from the limited dataset summarised in Table  1  (δ 18 O: -5.
8; δ 2 H: -38‰). Neither curve connects with seawater (δ 18 O = 0‰, δ 2 H = 0‰) as a potential end member (Fig. 2 ). The spatial distribution of stable isotope ratios of O in groundwater (Fig. 4 ) shows that signatures more enriched in the heavy isotope ( 18 O) occur proximate to ephemeral (seasonal) and perennial surface waters and wetlands (niayes) that represent groundwater discharge zones along the northern coast of the peninsula. Contours of hydraulic head from datasets amassed in 2007 and 2017 (Fig. 5 ) generate flowlines that are consistent with the general flow of groundwater from south to north towards the seasonal lakes and wetlands as well as the perennial saline Retba Lake. Tritium activity in groundwater (Fig. 6 ) shows a much less uniform distribution with pockets of predominantly modern groundwaters with higher tritium activities (≥3.
Funding information The authors gratefully acknowledge financial support from the  AfriWatSan  project (ref. AQ140023) funded with UK aid from the  UK government  through the  Royal Society-DFID Africa Capacity Building Initiative  as well as the  International Center for Research Development (ICRD)  and the  German Academic Exchange Service  (DAAD). RGT acknowledges support from  The Royal Society (UK)  and  Leverhulme Trust Senior Fellowship (Ref. LT170004 ).
Measurements of concentrations and fluxes of methane showed that all studied water bodies of the BZT are oversaturated with CH4, and depressions, subsidence, and small water bodies (<100 m²) are characterized by the highest concentrations of DOCs. These small water bodies are not represented on existing maps and databases of world lakes, and contain 3 to 70 times higher methane than large lakes. The contribution of these small reservoirs to the total coverage of the surface of the BZT area is significant, and their consideration can greatly change the assessment of methane fluxes from the arctic tundra. The research was supported by the Russian foundation for basic research, projects no. 18-55-80015, 19-05-00290.
DISCUSSION In summary, we presented here ADDomer, our plug-and-play multiepitope display platform for antigenic epitopes from different origins, including emerging human viral pathogens or veterinary infectious diseases. We determined ADDomer architecture at near-atomic resolution by cryo-EM, enabled by a heterogeneous computation cluster and created and executed automatically with public cloud infrastructure. Our approach presents a novel, fast, and cost-effective general mechanism to quickly analyze and process cryo-EM data and sets the stage for comprehensive (re)design of the ADDomer particle, also to fine-tune self-adjuvanting potential by modulating sequence plasticity  (25). ADDomer is easy to modify and produce in high yields using our recombinant system. It is remarkably thermostable, which could render deployment of ADDomer-based vaccines independent of a cold chain, representing a crucial asset.
ADDomer design We used the penton base protein from human Ad3 (GenBank Z29487) as a starting point and implemented a BioBrick format  (22)  to functionalize one locus within the VL and two loci in the RGD loop for facile and rapid custom DNA insertion (fig. S1 ). The insertion loci were flanked by unique restriction sites that were eliminated from elsewhere in the Ad3 penton base encoding gene. To preserve structural integrity of the penton base protomer, the restriction sites were designed in silico to be located outside of and adjacent to known secondary structure elements in the penton base protein. The resulting gene encoding for ADDomer was synthesized, codon-optimized for recombinant expression, and pasted into plasmid pACEBac1 (Geneva Biotech SARL, Switzerland) (26) using Bam HI and Hind III restriction sites, giving rise to pACEBac-ADDomer.
Cryo-EM and data collection Five microliters of purified ADDomer (0. 1 mg/ml) was applied to glow-discharged holey carbon grids (Quantifoil, R 2/2 300 mesh). Samples were incubated on the grid for 2 s and blotted for 0. 4 s at 90% relative humidity and 18°C inside a Leica EM GP, before plungefreezing in liquid ethane. Cryo-EM data were collected at 200 kV with a FEI Talos Arctica microscope equipped with a Gatan K2 direct electron detector and an energy filter using automated acquisition software (EPU). A total of 1060 dose-fractionated movies each containing 32 frames (0. 25 s per frame) with an accumulated total dose of 42 e -/Å 2 were recorded at a nominal magnification of ×130,000 corresponding to a pixel size of 1. 06 Å. Images were recorded with a defocus range of -0. 8 to -3. 2 m (table  S1 ).
Image processing Image processing was performed with the RELION 2. 1 software package  (29). The micrographs were motion-corrected using MotionCorr2  (30) , and contrast transfer function (CTF) information was determined using ctffind4. 1  (31). Together, 262 micrographs with CTF rings extending beyond 4. 6 Å were selected for further processing. A total of 3600 particles were boxed using RELION autopicking software. After two-dimensional (2D) classification (fig. S3C ) and 3D classification with imposed icosahedral symmetry, initial 3D autorefinement led to a reconstruction of ~4. 2-Å resolution. Further rounds of 3D classification/refinement were carried out on polished particles before using postprocessing for masking and automatic B factor sharpening. The resolution of the final map was determined to be 3.
5 Å based on the Fourier shell correlation (FSC) 0. 143 cutoff criterion (fig. S3D )  (32). Local resolution was calculated using local resolution estimation program in RELION (fig. S3E ). Processing and 3D classification of ADDomer were performed using public cloud resources provided by the Oracle Cloud Infrastructure. Using this rather than an institutional cluster allowed a faster turnaround time of analysis. Because of the particular nature of the computational work carried out by the steps within RELION, a heterogeneous cluster was prepared with both central processing unit (CPU)-and graphical processing unit (GPU)-focused resources. A pipeline of workloads was created, allowing the work best suited to each compute type to use just the resources needed and to automatically release compute resources when no longer needed.
Model building and refinement Homology modeling was performed using I-TASSER (33) using the human adenovirus Ad3 structure as template  (17). The model was adjusted to fit into the map manually using COOT (34) before further iterative positional and B factor refinement in real space using Phenix Real-Space refinement software  (35). Final adjustments were carried out in COOT before evaluating the model using MolProbity  (36). Refinement statistics are summarized in table  S1.
Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields.
We compiled an extensive database comprising 89 studies that measured richness and abundance of pollinators, pest natural enemies, and associated ecosystem services at 1475 sampling locations around the world (Fig. 1A ). We focused on the ecosystem services of pollination and biological pest control because these services are essential to crop production and have been the focus of much research in recent decades  (26). We quantified pollinator and pest natural enemy richness as the number of unique taxa sampled from each location (field), abundance as the number of observed individuals, and evenness (or the complementary term, dominance) as the E var index  (27)  that reflects the relative abundance of those taxa. We derived a standardized index of pollination services using measures of pollination success (fruit set or seed set) and plant reproduction (pollen limitation) and of pest control services using measures of natural enemy activity (predation or parasitism rates) and crop damage (pest activity) (see Materials and Methods).
Database compilation We compiled data from crop studies where measures of richness and abundance of service-providing organisms (pollinators or natural enemies) and associated ecosystem services (pollination and biological pest control) were available for the same sites. If available, we also included information on yield. Studies were identified by first searching the reference lists of recent meta-analyses  (7, 21, 39, 42, 43)  and then directly contacting researchers. For pest control, data were mostly provided from a recent pest control database  (39). Of 191 researchers initially contacted, 86 provided data that met our criteria. As similar studies were frequently performed in the same area, occasionally in the same year, and studies with multiple years usually used different sites each year, we did not nest year within study.
Likewise, some studies collected data in different crops. Thus, we considered each year of multiyear studies (that is, 10 studies) and each crop of multicrop studies (that is, 5 studies) to be an independent dataset and used study-crop-year combinations as the highest hierarchical unit. Overall, we analyzed data from 89 studies and 1475 fields in 27 countries around the world (table  S1 ). Twenty-nine crops were considered, including a wide array of annual and perennial fruit, seed, nut, stimulant, pulse, cereal, and oilseed crops. Studies represented the spectrum of management practices, including conventional, low-input conventional, integrated pest management, and organic farming. Few studies compared different management practices in similar landscapes, crop types, or sampled year. In 76% of fields, pest control experiments were performed in insecticide-free areas.
A taxon was defined as a single biological type (that is, species, morphospecies, genus, and family) determined at the finest taxonomic resolution to which each organism was identified. In almost 70% of cases, taxonomic resolution was to species level (averaged proportion among all studies), but sometimes, it was based on morphospecies (15%), genus (8%), or family levels (7%). Taxon richness per field varied between 1 and 49 for pollinators and between 1 and 40 for natural enemies. Abundance reflected the sum of individuals sampled per study, method, and field. Pollinator richness was calculated either including or excluding honey bees (A. mellifera). A. mellifera was considered as the only species within the honey bee group for consistency across all datasets  (43). Other Apis bees (that is, A.
Landscape simplification Landscapes were characterized by calculating the percentage of cropland (annual and perennial) within a radius of 1 km around the center of each crop field. This landscape metric has been used as a relevant proxy for characterizing landscape simplification  (28, 29)  and is often correlated with other indicators of landscape complexity  (30, 31). Moreover, we used this metric because cropland data are readily accessible from publicly available land cover data and are more accurate than other land use types such as forests and grasslands  (47) , especially when detailed maps are not available. The 1-km spatial extent was chosen to reflect the typical flight and foraging distances of many insects including pollinators  (48, 49)  and natural enemies  (50, 51). For studies where this information was not supplied by the authors, land uses were digitized using GlobeLand30 (  52 ), a high-resolution map of Earth's land cover.
We used weakly informative priors: normal (0, 10) for the population-level parameters (a, b) and half-Student t (3, 0, 5) for the group-level SD and residual SD. In building the model, we ensured that no residual correlation between ES i and RIC i , between ES i and EVE i , or between ES i and ABU i was estimated [see the "set_rescor" function in the package brms;  (58) ].
The mediation analysis was implemented using the R package sjstats [v. 0. S1 ), the data allowed us to use a multilevel path analysis to examine cascading effects of landscape simplification on final crop produc-tion via changes in richness, abundance, evenness, and ecosystem services. We expected that (i) landscape simplification would have a direct effect on richness, abundance, and dominance of serviceproviding organisms and (ii) richness and abundance dominance would relate positively to intermediate services, which, in turn, (iii) would increase final crop production (Fig. 4  and fig. S4 ). The indirect effects of richness and abundance (Eq. 10, 11, 13, 15; Fig. 4 ) or richness and evenness (Eq. 10, 12, 14, 15; fig. S4 ) were tested separately RIC i e Nða j½i þ b j½i LAND i ; s j Þ ð 10Þ ABU i e Nða j½i þ b j½i LAND i ; s j Þ ð 11Þ EVE i e Nða j½i þ b j½i LAND i ; s j Þ ð 12Þ ES i e Nða j½i þ b j½i RIC i þ b j½i ABU i ; s j Þ ð 13Þ ES i e Nða j½i þ b j½i RIC i þ b j½i EVE i ; s j Þ ð 14Þ PROD i e Nða j½i þ b j½i ES i ; s j Þ ð 15Þ where RIC i is richness of service-providing organisms, LAND i is landscape simplification measured as the percentage of arable land surrounding each study site, ABU i is abundance, EVE i is evenness, ES i is the ecosystem service index, PROD i is crop production, and the index j [i] represents observation i of study j.
Parameter estimation All analyses were conducted in the programming language Stan through R (v. 3. 4. 3) using the package brms [v. 2. 2. 0;  (58) ]. Stan implements Hamiltonian Monte Carlo and its extension, the No-U-Turn Sampler. These algorithms converge much more quickly than other Markov chain Monte Carlo algorithms especially for high-dimensional models  (61). Each model was run with four independent Markov chains of 5000 iterations, discarding the first 2500 iterations per chain as warm-up and resulting in 10,000 posterior samples overall. Convergence of the four chains and sufficient sampling of posterior distributions were confirmed by (i) the visual inspection of parameter traces, (ii) ensuring a scale reduction factorð RÞ below 1. 01, and (iii) an effective size (n eff ) of at least 10% of the number of iterations.
Sensitivity analyses Given that different methods were used in different studies to quantify richness, ecosystem services, and final crop production, we measured the sensitivity of our results to methodological differences. (1) We verified whether treating each annual dataset from multiyear studies separately could incorrectly account for the dependence of the data. We refitted the model testing the relationship between richness and ecosystem services including year nested within study (that is, study defined as study-crop combination). Then, we compared models (yearindependent model versus year-nested model) using leave-one-out (LOO) cross-validation, a fully Bayesian model selection procedure for estimating pointwise out-of-sample prediction accuracy  (64). We calculated the expected log pointwise predictive density ðelpd loo Þ ^, using the log-likelihood evaluated at the posterior simulations of the parameter values.
Model comparison was implemented using R package loo [v. 2. 0. 0; (65) ]. We found that the year-nested model had a lower average predictive accuracy than the year-independent model for both pollination ð Delpd loo ¼ À1:79Þ and pest control ð Delpd loo ¼ À1:09Þ and therefore retained the year-independent model in our analysis. (2) We verified whether taxonomic resolution influenced the interpretation of results. We recalculated richness considering only organisms classified at the fine taxonomy level (species or morphospecies levels) and refitted the model testing the effect of richness on ecosystem services. We found no evidence that taxonomic resolution influenced our results. With a fine taxonomic resolution, the effects of richness on ecosystem services (b pollinators = 0. 1535, 90% HDIs = 0.
Acknowledgments: We thank  R. Carloni  for producing icons in Figs. 1 to 4. We also thank the  Department of Innovation, Research and University of the Autonomous Province of Bozen/ Bolzano  for covering the Open Access publication costs. For all further acknowledgements, see the Supplementary Materials. Funding: This work was funded by  EU-FP7   LIBERATION  ( 311781 ) and  Biodiversa-FACCE ECODEAL  ( PCIN-2014-048 ). Author contributions: M. D. , E. A. M. , and I. S. -D. conceived the study. M. D. performed statistical analyses and wrote the manuscript draft. The authors named from E. A. M. to T. T. and I. S. -D. discussed and revised earlier versions of the manuscript. The authors named from G. K. S. A. to Y. Z. are listed alphabetically, as they contributed equally in gathering field data and providing several important corrections to subsequent manuscript drafts.
Characterisation Powder X-ray diffraction (XRD) patterns were collected on a PANanalytical X'Pert PRO and a Bruker D8 Advance Vario 1 diffractometers both equipped with Johansson monochromators and using CuKα-1 radiation (λ = 0. 15406 nm). On the X'pert PRO diffractometer, data were collected in continuous mode in Bragg-Brentano geometry over the 2θ range of 3-40° using a ¼° divergence slit. A 1/32° slit was used to observe low angle reflections (2θ = 1-10°). On the D8 diffractometer, data were collected in Debye-Scherrer geometry, on capillaries of 0. 3 mm diameter over the 2θ range of 1-40° using a 0. 05mm (~0. 025°) divergence slit. Scanning electron microscopy (SEM) images were collected on a MIRA TESCAN microscope equipped with a field emission gun. The images were collected with an acceleration voltage of 30 kV.
Samples were platinum sputtered before measurement. Nitrogen sorption isotherms were measured at liquid nitrogen temperature (-196°C) using a Micromeritics Triflex instrument. Samples were outgassed under turbomolecular pump vacuum at 300°C for 6 h before measurement. The BET area was evaluated using adsorption data in a relative pressure ranging from p/p 0 = 1. 1. 10 -7 to p/p 0 = 0. 01 (corresponding to the growing part of Rouquerol BET plot). The t-plot method  [18]  was applied to determine the external surface area (S ext ). Micropore volume (V mic ) was determined from non-local DFT pore size analysis using a model for N 2 on oxide surface with cylindrical geometry (part of the Micromeritics user software). The adsorbed amount of nitrogen at p/p 0 = 0. 95 was used to determine the total pore volume (V total ).
From the earliest observations of ozone in the lower atmosphere in the 19th century, both measurement methods and the portion of the globe observed have evolved and changed. These methods have different uncertainties and biases, and the data records differ with respect to coverage (space and time), information content, and representativeness. In this study, various ozone measurement methods and ozone datasets are reviewed and selected for inclusion in the historical record of background ozone levels, based on relationship of the measurement technique to the modern UV absorption standard, absence of interfering pollutants, representativeness of the well-mixed boundary layer and expert judgement of their credibility. There are significant uncertainties with the 19th and early 20th-century measurements related to interference of other gases.