dataesr/scientific-paragraphs-categorization huggingface dataset:
The genome sequence of human papillomavirus type 20 (HPV-20; family Papillomaviridae, genus Betapapillomavirus, species Betapapillomavirus 1, type 20) was assembled by deep sequencing from nasopharyngeal swabs. The assembled genome is 0. 37% divergent over its full length from the single complete genome of HPV-20 in GenBank (U31778). We named the strain HPV-20/Lancaster/2015.
T he family Papillomaviridae consists of more than 120 viral types, divided into 49 genera  (1). Within the genus Betapapillomavirus, type 20 of the species Betapapillomavirus 1 (HPV-20) is represented in GenBank by a single complete genome (2), a circular double-stranded DNA of 7. 8 kb (U31778), and three shorter fragments  (3) (4) (5). HPV-20 is a clinically significant papillomavirus, having been implicated in epidermodysplasia verruciformis (2), skin cancer (6), ocular syringoma  (7) , nongenital seborrhoeic keratosis  (8) , conjunctival papillomas  (9) , verrucous carcinoma of the lip  (10) , condylomatous lesions of the mammillae  (11) , squamous cell carcinoma of the esophagus  (12) , and toenail onycholysis  (13). Volunteers were recruited from a general practice surgery and a general hospital in Lancaster, UK (54.
9%). Of 9 deep-sequenced nonoverlapping subsets of individuals from our 51 swabs, we detected HPV-20 in all but 2. Our frequency is therefore at least 7/51 (Ͼ13%) and possibly much higher. The significance of this clinically important papillomavirus at such prevalence in our sample remains a matter for speculation. BAM files are available from https://doi. org/10. 17635/lancaster/researchdata/145. Accession number(s). The genome sequence of HPV-20/Lancaster/2015 has been deposited in GenBank under the accession number KY969593.
7 ). Last, but not least, we found that the simulated sensitivities of evapotranspiration and surface albedo to increasing LAI are within the range of sensitivities from satellite-derived observations (Fig. 3a ; see Methods). However, there are large uncertainties in the observed sensitivities. To investigate the influence of different sensitivities, we calibrated the changes in evapotranspiration and surface albedo in equation (  1 ) combined with the observed sensitivities and the climatological values of f , S and τ from the CTRL ensemble simulations (see Methods). We found that the T a trend ranges from -0. 041 to -0. 012 • C per decade, resulting from uncertainties in different observational datasets, with an average of -0. 029 ± 0. 012 • C per decade which is close to the simulated trend (Fig.
Model and experiments. To reduce the uncertainties in the GCM simulations associated with systematic SST biases in fully coupled Earth System Models  31, 32  , we used the AMIP-type simulation strategy and thus ignored the ocean coupling by prescribing SSTs. We used the IPSLCM (version 4) coupled land-atmosphere model  19  from the Institute Pierre Simon Laplace (IPSL) modelling community to simulate the climate effects of the increasing LAI during the past three decades. The atmospheric component of the model is the Laboratoire de Météorologie Dynamique atmospheric general circulation model with zooming capability (LMDZ; refs 33,34), which is coupled with the land-surface model Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE; ref. 35). ORCHIDEE has performances comparable to other land-surface models and diagnostic datasets in simulating land evapotranspiration  36  and soil moisture  37.
-based estimate of the sensitivities of evapotranspiration and surface albedo to increasing LAI. We use the three long-term global evapotranspiration products, namely GRACE-MTE (1982-2009, based on the water balance of different catchments 43  ), FLUXNET-MTE (1982-2008, based on surface flux measurements 44  ) and an NDVI-based data set(1983-2006, based on satellite-derived Normalized Difference Vegetation Index 45  ). The long-term global land-surface albedo product is from the GLASS dataset 46  , which is generated from satellite observations and available from 1982 to 2011. The GLASS albedo product is derived from AVHRR data from 1982 to 1999, and then from MODIS data from 2000 to 2011. This discontinuity results in an irrational positive sensitivity of surface albedo to LAI during the whole study period(1982-2011).
Acknowledgements This study was supported by the  National Natural Science Foundation of China  ( 41530528  and  41561134016 ),  National Youth Top-notch Talent Support Program in China , and the 111 Project ( B14001 ). We thank the  National Supercomputer Center in Tianjin, China (NSCC-TJ) , the  National Computer Center IDRIS of CNRS in France , the  Commonwealth Scientific and Industrial Research Organisation in Australia , and the  Oak Ridge National Laboratory in the United States  for providing computing resources. J. M. is supported by the  Biogeochemistry-Climate Feedbacks Scientific Focus Area  project and the project under contract of DE-SC0012534 funded through the  Regional and Global Climate Modeling Program , and the  Terrestrial Ecosystem Science Scientific Focus Area  project funded through the  Terrestrial Ecosystem Science Program in the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the US Department of Energy Office of Science.
Code availability. IPSLCM GCM model code is available at http://forge. ipsl. jussieu. fr/igcmg/svn/modipsl/trunk. The program used to generate all the results is MATLAB. Analysis scripts are available by request to S. Piao. Data availability. The satellite-observed LAI3g data sets are available from the NASA Earth Exchange (NEX) web site (https://nex. nasa. gov/nex). The AMIP SST and sea ice boundary conditions are available at http://www-pcmdi. llnl. gov/ projects/amip. Model outputs are available on request from S. Piao.
Data analysis A complete case analysis was conducted where the difference in weighed intake (consumption minus wastage) between the two eating sessions was considered as a proxy for how well the Nudge worked. A mixed general linear model analysis was performed to explore changes within the three nudges for rice, chilli con carne and total vegetable intake. The intake was set as the dependent variable, time as the fixed effect, subjects as the random effect and measures for appetite were included as co-variates. Q-Q plots and histograms were used to check for normality. Oneway ANOVA with post hoc test was performed to explore any changes between the three nudges for: total intake, total chilli con carne intake, total rice intake and total vegetable intake. The intake difference in each food group was set as the dependent variable and the intervention was set as the fixed factor.
Measurements of ovalbumin-specific and total immunoglobulin type G and haptoglobin The total concentration of the immunoglobulin type G (IgG) was measured by ELISA as already described  (Accensi et al. , 2006). Briefly, goat anti-rabbit IgG (Fc fragment specific) was used to capture antibodies (Bethyl; Interchim, Montluçon, France). Horseradish peroxidase (HRP)-labeled goat antirabbit IgG was used to detect antibodies in conjunction with TMB substrate (3,3',5,5'-tetramethyl benzidine; Thermo Fisher Scientific, Courtaboeuf, France). The enzyme-substrate reaction was stopped by the addition of H 2 SO 4. Absorbance was read at 450 nm using an ELISA plate reader (Spectra Thermo, Tecan France, Lyon, France) and Biolise 2. 0 data management software. Plasma samples were quantified by reference to standard curves constructed with known amounts of rabbit IgG.
Statistical analyses All statistical analyses were carried out using R software, version 3. 0. 2 (R Development Core  Team, 2008). Categorical data (morbidity, mortality and health risk index) were analyzed using a χ 2 -test. Non-repeated numerical data (growth performance, fermentative parameters and bacterial relative abundances) were subjected to ANOVA using a two-factor modelhousing hygiene and intake leveland their interaction. To stabilize variance, a square root transformation was applied to bacterial community taxonomic data. Repeated numerical data (specific and total IgG and haptoglobin) were analyzed by a linear mixed model including age, and a two-factor modelhousing hygiene and intake levelwith their interactions as fixed effects and the rabbits as a random effect. Differences were considered significant at P < 0.
05. The Tukey HSD test was used to perform mean comparisons. A sparse Partial Least Square Discriminant Analysis was performed in order to identify the most discriminative OTUs in relation to treatments using the mixOmics package of R software  (Lê Cao et al. , 2011). Three components were retained to discriminate the four groups. The number of OTU to be considered on each of the three components was assessed through the calculation of the lowest average error rate classification using a cross-validation procedure.
Acknowledgements This study received financial support from the  Animal Physiology and Livestock System Division of INRA. The authors thank,  Patrick Aymard ,  Jean-Marie Bonnemère ,  Annette Debrusse ,  David Labatut ,  Michel Moulis  and  François Richard  from  INRA PECTOUL  for animal care, and  Béatrice Gabinaud ,  Muriel Segura  and  Carole Bannelier  for their laboratory expertise.
A fast response open-path CO 2 / H 2 O infrared gas analyser (IRGA LI-7500A, LI-COR, USA) installed at a lateral distance of around 0. 2 m to the sonic path measured CO 2 and H 2 O fluctuations. O 3 mixing ratios were measured by a highfrequency, dry chemiluminescence O 3 detector (NOAA, USA) and its Teflon PFA inlet tube (length = 2. 8 m, internal diameter = 0. 32 mm) was positioned in-between the sonic path and the IRGA at a lower height. The high-frequency signals were recorded at 20 Hz by a Labview program developed in the laboratory. In accordance with  Lee et al. (2015) , highfrequency (10 Hz) time series of NO and NO 2 were determined by two fast-response and closed-path chemiluminescence NO analysers (CLD 780TR, EcoPhysics, Switzerland), one being coupled to a photolytic converter (blue light converter, BLC, Droplet Measurement Technologies Inc, USA) for the detection of NO 2 (see Fig.
Acknowledgements. This work was funded by the  FP7 projects ECLAIRE  (grant number  282910 ) and  INGOS  (grant agreement  284274 ), the  French ANR  project  ANAEE , and  ICOS France. The authors acknowledge the director of the  AgroParsiTech Farm Dominique Tristan  for allowing access to the field. We are grateful to the  Max Planck Institute for Chemistry (Mainz, Germany)  for the loan of a CLD 780TR analyser for the duration of the experiment. We also thank  Gerardo Fratini  for the precious support on issues concerning EddyPro, and  Christof Ammann  and  Veronika Wolff  for fruitful discussion about NO x -O 3 chemical reactions. We also thank  Polina Voylokov  for her thorough correction of the paper and  Anaïs Durand  for helping on NO emission inventory evaluations. Edited by: M. Williams Reviewed by:  N.
Synergistic Study Experimental and theoretical combinations between usnic and pinastric acids on one hand and usnic and vulpinic acids on the other hand were considered. Five percent of each compound were combined in solution to reach a maximum of 10% in the well. The absorbances were recorded to lead to experimental indexes after mathematical treatment. Theoretical absorbances of the 10% compounds mixtures were calculated by summing experimental values obtained from each molecule tested alone at 5%. These calculated data were then transferred into the Excel spreadsheet to get the expected indexes of the mixture. The synergism was evaluated by comparing these results with the experimental ones. The evaluation was conducted before and after UVA and UVB irradiation. Each determination was done in duplicate and averaged.
Acknowledgments: The authors acknowledge  N. Brandhonneur  and  L. Coiffard  for providing the emulsion and  E. Hitti  for informatics support and wish to thank  Sébastien Le Meur ,  Anne Malargé , and  Mylène Marie  for carrying out the in vitro measurements. We are also grateful to  J. Akrman  for spectral irradiation of terrestrial sunlight and erythemal action spectrum values. We thank also the  Research team Expression  des gènes et oncogenèse of the  UMR 6290-IGDR (Rennes)  for the access to the Stratalinker apparatus and IRSET ( UMR_S 1085 ) for the access to the microplate reader. The authors are indebted to  D. Rondeau  for the DART-HRMS analyses that used the mass spectrometry facilities of the  DREAM platform of IETR (Rennes).
For a species to be able to respond to environmental change, it must either succeed in following its optimal environmental conditions or in persisting under suboptimal conditions, but we know very little about what controls these capacities. We parameterized species distribution models (SDMs) for 135 plant species from the Algerian steppes. We interpreted low false-positive rates as reflecting a high capacity to follow optimal environmental conditions and high false-negative rates as a high capacity to persist under suboptimal environmental conditions. We also measured functional traits in the field and built a unique plant trait database for the North-African steppe. For both perennial and annual species, we explored how these two capacities can be explained by species traits and whether relevant trait values reflect species strategies or biases in SDMs.
We developed fine-grained SDMs based on information on local occurrences and environmental conditions  (Guisan & Thuiller, 2005)  and quantified the predictive performance of these SDMs for each species. We then correlated rates of false-positive and false-negative predictions to a set of functional traits and niche traits (marginality and specialization of species environmental distributions). Given that most of the species studied had not already been recorded in global trait databases, the trait values used in this study were measured in the field or in the lab for the purposes of this study, thus resulting in what might be the first large database of functional traits for Northern African steppe species. We asked three questions: (i) How can species capacities to follow their optimal environmental conditions or to persist under suboptimal conditions be explained by trait values reflecting reproductive strategy, dispersal ability, stress tolerance, and pollination strategies? (ii) Do different trait values affect these two capacities differently? (iii) Do relevant trait values reflect species strategies or methodological biases? We conducted analyses separately for annuals and for perennials as the two groups differ fundamentally in their ability to establish the above-mentioned traits and might hence employ different strategies to follow optimal and persist in suboptimal environments.
| Study area and vegetation survey Our study area was located in Southern Algeria and encompasses the Algerian steppes that extend from the Tellian Mountains in the north to the vast Saharan areas in the south. The study area extends from 2° to 4° Eastern longitude and from 34° to 36° Northern latitude (Figure  S1 ). The steppe covers a wide range of environmental conditions, as there is a gradual transition from a sub-Mediterranean climate in the north to a comparatively more desert-like climate in the south  (Pacini & Nicolson, 2007). We used plant distribution data from database "sol-vegetation-Algeria," which contains 1210 phytosociological plots recorded between 1968 and 1975 across a grid at 1 km² resolution, covering 350 species (see Figure  S1 ). From this database, we selected 832 plots which covering most of the steppes of Southern Algeria as the study region (except forest vegetation).
We added the category of monocarpic (or "semelparous"), that is, species that reproduce once and then die, as these species often have a shorter life span than other perennials  (Pérez-Harguindeguy et al. , 2013). Leaf area (LA), specific leaf area (SLA), and leaf dry-matter content (LDMC) were measured for 84 species based on 15 mature leaves harvested from five different adult individuals per species, following the protocol established by Rodríguez-Gironés et al. (Torres & Galetto, 2002). The fresh leaves were cut from the stem, weighed, and scanned. Subsequently, we measured each leaf's surface area in mm² using the software program ImageJ (Image processing and analysis in Java). Leaves were then dried at 70°C for at least 72 hr before being weighed again. To determine SLA, we divided the one-sided area of a fresh leaf (mm²) by its oven-dry mass (mg).
| Modeling species distribution We used the database "sol-vegetation-Algeria," which contains comprehensive surveys of vegetation with real presences/absences for the 135 species that we related with environmental variables, and we parameterized species distribution models using the biomod2  (Thuiller, 2003; Thuiller, Lafourcade, Engler, & Araújo, 2009)  library in the statistical programming environment "R" (3. 2. 3). We ran all the model types: two regression methods (generalized linear models, GLM; and generalized additive models, GAM), two machine learning methods (artificial neural networks, ANN; random forest, RF), and three classification methods (factorial discriminant analysis, FDA; classification tree analysis, CTA; and generalized boosted models, GBM). Models were parameterized with a random subset containing 75% of all plots.
These species would have escaped our attention had we only looked at their preferred environmental conditions, namely sandy habitats and high drought-tolerance, conditions that per se will become more abundant due to future environmental change. It is interesting that some of these species have succeeded to be naturalized or to be weedy in other parts of the world, notably Salvia verbenaca ssp. clandestina; Onopordon arenarium; and Carthamus lanatus ((Born & Böcher 1998), e. g. , from Australian Plant Census https://biodiversity. org. au/nsl/services/apc database). A possible explanation is that the optimal, postcultural habitats of these species are spreading by the extension of crops to the detriment of steppe vegetation and that the species profit from this spread, despite the limited capacities of following preferred environments.
ACKNOWLEDGMENTS We thank  B. Kadik  and  B Sahel  for valuable field assistance. Exchange funding by Université Rennes 1 This work contributes to the  Labex OT-Med  (no  ANR-11-LABX-0061 ) funded by the  French government  through the  A*MIDEX project  (no  ANR-11-IDEX-0001-02 ).
Sites of tick collection Senart forest. The Senart forest (3156 ha) is located in the suburban area, 22 km south of Paris in the Essonne department. The main tree species are oak, chestnut, hornbeam, birch, Scots pine and other conifers. Depending on the plot, the distribution of the main tree species is different (S1 Table ). The fauna consists in wild boar, roe deer, foxes, hares, rabbits and squirrels. Among the bird species found in the forest are pigeons, woodcocks, crows, ducks, magpies, blackbirds and jays. 8 plots (SE1, SE2, SE3, SE4, SE5, SE6, SE8 and SE9) were drawn randomly using Epi Info software (Fig  2 ) (S1 Table ). In the field, the stations are found by GPS. Chipmunks were identified in plots SE2 to SE6, few were seen in plot SE1 whereas plots SE8 and SE9 are not invaded yet by these rodents.
Rambouillet and Notre-Dame forests. Rambouillet forest is one of the main forests of I ˆle-de-France. It is a wooded area of 14 550 ha. The tree population consists mainly of oak, up to 68%, and conifers (Scots pine and Corsican pine) to 25%. The large mammal fauna is composed of deer, roe deer and wild boar and among the small mammals, we find the hare, rabbit, European badger, fox, weasel, marten, skunk, dormouse, wood mouse, bank voles, shrews, moles, squirrels and hedgehogs. Many bird species benefit from different habitats provided by the forest, including a number of birds protected at European level as the black woodpecker, the red-backed shrike species, woodpecker, little bittern, the nightjar of Europe and the honey buzzard. 8 plots (R2, R5, R6, R7, R8, R10, R17 and R20) were drawn randomly using Epi Info software (S1 Table ).
The Notre-Dame forest (2050 ha) is composed of chestnut (60%); oaks (11%); birch (11%); acacia (10%); other deciduous trees (6%); conifers (2%). It has a rich and diverse fauna and has two types of large animals involved in the life of the ecosystem, wild boars, and roe deer. Small mammals (bank voles), birds (Turdus sp. ) as well as amphibia were also described. Two plots (ND10 and ND11) were drawn randomly using Epi Info software (S1 Table ).
Ixodes ricinus' density. Ticks' density (d), expressed in number of ticks per 100 m, is estimated from the total collected ticks' number according to  [7]. Infection rates. The ticks' infection rate is calculated using the following formula: To compare infection rates, we use the χ 2 test (Chi2) Mantel-Haenszel or Fisher's exact test, calculated using Epi-Info. The correlation between the data is made using the Spearman test. p ¼ f = k f: Multivariate analysis: We performed a principal component analysis (PCA), which is a popular dimension reduction method to visualize data. PCA aims at compressing the size of the data in order to extract the most important information. This method offers a simplification of the data while analyzing the structure of both observations and variables  [24]. A log10 transformation was applied on the 4 density variables (infected/non infected, nymph/adult) as it increases the inertia of the first 2 principal components leading to a better visualization of the dataset.
Note that applying a monotonically increasing transformation such as log10 does not affect the interpretation of correlations. In this article, the widely used scree rule  [25]  that was applied to determine the number of components to retain. PCA was performed using the open source statistical software R and the R package FactoMineR that provides a tool to perform and visualize results of PCA. The few missing data were imputed using the R package missMDA that performs multiple imputation using multivariate data analysis. The principal components are computed from active variables, meaning the 4 density variables (infected/non infected, nymph/adult), the 2 infection rates (nymph, adult), the percentages of chestnuts, oaks and pines, temperature, hygrometry and hour. Therefore, the year and the qualitative variables, meaning the month, the chipmunk status and the plot number are projected as supplementary variables on the factorial map.
According to D. Richter et al. [31] , the rrf-rrl intergenic spacer is a good locus for Borrelia taxonomy because it varies widely. The DNA corresponding to the Borrelia restriction profiles that are difficult to identify were sent to be sequenced in order to perform multiple alignments of the sequences obtained with sequences of reference strains using Clustalw. Distances were calculated with Jukes and Cantor correction. From these alignments, a tree was constructed with UPGMA in MEGA 3. 1 software.
We performed a PCA analysis in order to extract the most important information. The elbow rule suggests retaining 2 dimensions, which explain 31. 22% of inertia for first axis and 19. 85% for second axis. This means that 51. 07% of the information contained in the data can be summarized on a 2 dimensions map. According to the correlation circle displayed in    each forest. The density of nymphs and adult ticks, their rate of infection and the density of infected ticks according to the month of collection in these 2 forests were compared to the same parameters in the Senart forest over the same time period (S4 Table ). The nymph and adult densities were similar in Senart, Notre-Dame and Rambouillet forests (S4 Table) while the infection rate of nymphs was significantly higher in Senart than in Rambouillet (p<0.