dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Stephen Jones  for assistance in evaluating the most challenging integrals with pySecDec, and  Martijn Hidding  for his help in using diffexp for numerical evaluations. We acknowledge useful discussions with  Jean-Philippe Lansberg ,  Kirill Melnikov  and  Hua-Sheng Shao. M. B. acknowledges the financial support from the  European Union   Horizon 2020 research and innovation programme: High precision multi-jet dynamics at the LHC  (grant agreement no. 772009 ). The research of C. D. and M. A. O. was supported by the  ERC Starting  Grant  637019  " MathAm ". M. A. O. thanks the  TH Department at CERN  for hospitality while part of this work was carried out. M. A. O. also acknowledges the financial support from the following sources while at IJCLab,  Université Paris-Saclay : the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement STRONG-2020 No 824093 in order to contribute to the  EU Virtual Access NLOAccess  ( VA1-WG10 ), the funding from the  Agence Nationale de la Recherche (ANR)  via the grant  ANR-20-CE31-0015  (" PrecisOnium ") and via the  IDEX Paris-Saclay "Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) through the  GLUODYNAMICS  project funded by the " P2IO LabEx  ( ANR-10-LABX-0038 )", and partially via the  IN2P3  project  GLUE@NLO  funded by the  French CNRS.
Instrumentation Two intensive sampling campaigns were carried out in July 2019  (1. 7. -31. 7. )  and in  January-February 2020 (16. 1. -10. 2. )  at NAOK. During the campaigns, several physical and chemical atmospheric aerosol properties were measured together with complete meteorological data collected from a professional meteorological station (WMO station 11628). The size-resolved NR-PM1 chemical composition (the sum of organic, sulphate, nitrate, ammonium and chloride) was measured by a compact time-of-flight aerosol mass spectrometer (C-ToF-AMS, Aerodyne, USA,  Drewnick et al. , 2005)  with a 5-min temporal resolution. The instrument was connected to an inlet consisting of a PM2. 5 sampling head (16. 7 l min -) and a Nafion dryer (Perma Pure MD-110-24P-4). Isokinetic sub-sampling was used to split the flow into AMS (0.
Data analysis The standard data processing procedure of AMS data (i. e. , m/z calibration, baseline subtraction, and air beam correction) was carried out by running the Squirrel v1. 62 program in Igor Pro data analysis software (WaveMetrics, Inc. ). The statistical data treatment was performed using R version 3. 6. 1 (R Core Team, 2019) with the ggplot2  (Wickham, 2016)  and Openair  (Carslaw and Ropkins, 2012)  packages.
Cluster analysis For both campaigns, 96-hour backwards trajectories were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model  (Rolph et al. , 2017)  with a 500-m AGL starting position and Global Data Assimilation System (GDAS) Archive Information at a resolution of 1° × 1° as input data. The calculations were initialized every 6 hours. The trajectories were further clustered using Hysplit4 software based on the total spatial variance. From HYSPLIT, the planetary boundary layer height data were extracted using the vmixing program (https://www. ready. noaa. gov/HYSPLIT_vmixing. php). For the planetary boundary layer height calculations, the 0. 25° × 0. 25° Global Forecast System (GFS) dataset was used as input data to obtain a 3-hour temporal resolution.
PMF on PNSD PMF (US EPA, version PMF 5. 0) was applied to the seasonal 5-min PNSDs in the range from 10 nm to 800 nm to estimate the number and profile of the PNSD factors and their contributions to the receptor. Episodes in which the factor contributions to the total particle number concentrations were higher than 80 % were chosen for the subsequent particle effective density calculations. The input data were prepared by merging three consecutive bins to reduce the noise in the raw data, decrease the number of variables, and reduce the number of zeroes in the raw data  (Leoni et al. , 2018). The uncertainties were calculated according to  Vu et al. (2015). The total variables were calculated by summing all the bins (N10 -800). PMF was conducted using different uncertainty input matrices and different C3  (Vu et al.
Acknowledgements We would like to thank  Daniel Vondrák  for the graphical editing. Thanks also goes to American Journal Experts for the English proof reading of the manuscript. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www. ready. noaa. gov)used in this publication. We greatly thank the two anonymous reviewers for their effort to critically review the manuscript and for providing constructive comments. Financial support This work was supported by the  GACR  under grant  P209/19/06110Y  and by the  MEYS of the Czech Republic  under grant  ACTRIS-CZ LM2018122  and  ACTRIS-CZ RI (CZ. 02. 1. 01/0. 0/0. 0/16_013/0001315 ) as well as by  COST Action   CA16109  COLOSSAL within STSM.
MALDI-ToF Mass Spectrometry Comparing the highly specific protein spectra generated by MALDI-ToF MS with formerly created databases ('libraries') makes it possible to identify cultured microorganisms up to the species level, within a few min. The accuracy of this identification relies, both on the quality of the generated spectra (itself dependent on culture and extraction methods), and on the number of reference spectra included in the library. The use of MALDI-TOF MS in the context of CBM diagnosis requires the growth of a pure culture from a skin sample, which in the case of CBM, can be easily contaminated by environmental molds. Once the culture has grown, different protein extraction methods can be used according to the manufacturer's instructions. Indeed, the quality of the spectra and the rate of correct identification are usually improved by using long extraction protocols, which were also used for the creation of the databases.
For identification with the MALDI Biotyper ® (Bruker Daltonics GmbH, Bremen, Germany), Bruker recommends direct extraction from the solid medium in the first instance, but if this rapid procedure fails, an off-plate extraction protocol (also called ethanol-acid formic extraction), requiring an additional ten min, is proposed from the same solid medium. As a last resort, culture in liquid medium can be carried out, but this protocol extends the identification time by 24 h. When using the National Institutes of Health database, bead beating of the culture followed by the ethanol-acid formic extraction is recommended  [63]. In our experience, both rapid acid-formic-ethanol extraction and direct deposit with formic acid have yielded accurate identification results with our homemade library  [64].
Multiple deposits (minimum 3 or 4) on the ground-steel targets is also very helpful. The scarcity, or the absence of, reference spectra in commercial libraries complicates identification of CBM agents, even in the presence of high quality spectra. Regular updates of these databases by manufacturers should fill this gap over time, but in the meantime, creating homemade libraries, even if time-consuming, remains the best solution for implementing rare fungal species. We used this strategy to rapidly identify CBM causative agents in Madagascar  [2]  and generated 11 reference spectra with a Microflex Mass Spectrometer (Bruker Daltonics GmbH, Bremen, Germany) from reference or previously sequenced strains of C. carrionii, F. nubica, F. pedrosoi, and F. monophora  [2]. These homemade spectra allow us to, now, precisely and quickly identify these agents on cultures, without the need for DNA sequencing.
MALDI-ToF MS As detailed for CBM pathogen identification with MALDI-ToF MS (cf supra), the use of this powerful technology can theoretically identify any Sporothrix sp. responsible for sporotrichosis. The same prerequisites as for CBM agents are needed to correctly identify the Sporothrix species (i. e. , correct and regular maintenance of the instrument, adapted protein extraction, use of multiple deposits (3 or 4), optimized spectra acquisition methods, presence of reference spectra in the commercial or homemade library). In 2015, Oliveira et al. developed a new MALDI-TOF protocol for identifying clinical and environmental isolates of the six species belonging to the Sporothrix complex on Axima LNR equipment (Kratos Analytical, Shimadzu), with the SARAMIS ® software package (Spectral Archiving and Microbial Identification System; AnagnosTec, Postdam-Golm, Germany)  [76].
This enriched library allowed accurate identification of 64/64 (100%) clinical and environmental isolates. Subsequently, Matos et al. successfully used the same enriched database for diagnosing ocular sporotrichosis  [75]. In the multicenter study from Lau et al. , comparing the efficacy of fungal identification between eight centers, among 80 clinical isolates, two strains of Sporothrix schenckii were sent to the different centers and tested with both Bruker (V3. 0) and NIH databases  [66]. As the NIH database failed to identify these strains, the Bruker database misidentified S. schenckii as Candida chodatii, highlighting the need to enrich both databases with Sporothrix species reference spectra. In 2013, we began a large epidemiological study on sporotrichosis in Madagascar  [11]. At that time, reference spectra were scarce in the available databases (2 MSPs in the Bruker database, and 1 profile in the NIH database for Sporothrix sp.
). We, thus, generated 20 main spectra profiles (MSP) from reference or previously sequenced strains of Sporothrix schenkii  [11]. This homemade library was successfully used for identifying S. schenkii in our study  [11]. In 2018, as an external control, all our identifications and reference spectra were compared with the freely available and external user-developed online platform Mass Spectrometry Identification (MSI, available online at https://msi. happy-dev. fr/ (accessed on 1 April 2022)), which confirmed 100% of our S. schenkii identifications  [11]. Of note, the open-access MSI database is, as far as we know, the only database which freely provides a significant number of reference spectra for the identification of Sporothrix schenkii and its related cryptic species (12 reference spectra for S.
Conclusions and Perspectives Molecular methods for diagnosing CBM and SPT may increase the sensitivity of the detection of the fungal agents from clinical specimens and culture. In addition, they improve the accuracy of fungal identification at the species level, resulting in the discrimination of sibling species of the Fonsecaea or the Sporothrix genera that cannot be distinguished by morphological features. Indeed, correct identification has both epidemiological and clinical impacts. As an example, DNA sequencing led to a revision of F. nubica, previously described as F. pedrosoi, as the main agent responsible for CBM in Madagascar  [2]. This led to the Maldi-ToF databases being expanded, in order to further facilitate and improve diagnosis; and, thus, patient management. For SPT, the rapid identification of the emerging zoonotic and highly pathogenic species is required for optimal treatment.
Progresses in diagnosing CBM and SPT using these molecular techniques will help in the evaluation of their actual burden, and may have a major impact on public health policies, to improve patient management. Now, these techniques must make the leap from the research or reference laboratories, to the peripheral facilities in regional and district structures, in order to reach the rural and remote populations who are targeted. The main limitation of the molecular diagnosis of CBM and SPT is its cost, which includes the need for specific equipment and the presence of trained laboratory employees to perform these techniques efficiently. This cost is still prohibitive for the health systems in endemic countries. Although simple and accurate, MALDI-TOF MS is not well adapted to these settings: it still requires culture, reference spectra are lacking in the common databases, and the equipment is rather expensive.
, 1994; Rabosky, 2010). Recent studies have raised concerns on difficulties in identifying parameter values when working with birth-death models under rate variation scenarios  (Stadler, 2013; Burin et al. , 2019) , showing that speciation (birth, λ) and extinction (death, μ) rates sometimes cannot be inferred from molecular phylogenies  (Louca and Pennell, 2020). This calls for (i) analysing 'congruent' models with potentially markedly different diversification dynamics but equal likelihood for any empirical tree  (Louca and Pennell, 2020) , or (ii) implementing a solid hypothesis-driven approach, in which a small number of alternative hypotheses about the underlying mechanism are compared against data  (Morlon et al. , 2022). Based on an unparalleled comparative phylogenetic dataset containing 150 well-sampled specieslevel molecular phylogenies and 12,512 extant species, we evaluate the prevalence of macroevolutionary scenarios 1-4 (Figure  1 ) as general explanations for Neotropical diversification at a continental scale (Q1), their drivers (Q2), and their variation across biogeographic units (Q3) and taxonomies (Q4).
Neotropical phylogenetic dataset We constructed a dataset of 150 time-calibrated clades of Neotropical tetrapods and plants derived from densely sampled molecular phylogenies (Figure  2 ; Figure  2 -source data 1;  Meseguer, 2021). The dataset includes a total of 12,512 species, consisting of 6222 species of plants, including gymnosperms and angiosperms (66 clades, representing 5-7% of the described Neotropical seed plants); 922 mammal species (12 clades, 51-77% of the Neotropical mammals); 2216 bird species (32 clades, 47-59% of the Neotropical birds); 1148 squamate species (24 clades, 30-33% of the Neotropical squamates); and 2004 amphibian species (16 clades, 58-69% of the Neotropical amphibian diversity). Each clade in our dataset includes 7-789 species (mean = 83. 4), with 53% of the phylogenies including more than 50% of the described taxonomic diversity (sampling fraction mean = 57%).
Clade ages range from 0. 5 to 88. 5 million years (Myrs) (mean = 29. 9; Figure  2 -figure supplement 1). In this dataset, amphibian phylogenies are significantly larger than those of other clades (p<0. 05) (Figure  2 -figure supplement 1). Amphibian and squamate phylogenies are also significantly older (p<0). Groups also differ in sampling fraction: plant (p<0. 01) and squamate (p<0) phylogenies are significantly less sampled than phylogenies of other groups. Our dataset triples the data presented in previous meta-analyses of the Neotropics in terms of number of species, for example, 214 clades and 4450 species in  Antonelli et al. , 2018c , and quadruples it in terms of sampling, with 20. 8 species per tree in  Antonelli et al. , 2018c.
Variation of diversification dynamics across taxa, environmental drivers, and biogeographic units We evaluated the prevalence of macroevolutionary scenarios 1-4 (Figure  1 ) across environmental drivers (Q2), biogeographic units (Q3) and taxonomies (Q4) (see Methods). uplift-and time-dependent models tended to best fit clades with exponentially increasing diversity (p=0. 03) (Figure  5c ). In contrast, there is no evidence to suggest that species richness dynamics are related to a given geographic location when considering the whole dataset (Figure  6c -f, Figure  7 ; Q3). Results of Table  1. Alternative species richness dynamics (Sc. 1-4) and the corresponding diversification processes (a-c) able to explain Neotropical diversity. Species richness dynamics represent scenarios of expanding (Sc.
Rates from PDR models are significantly different between plants and animals (p≈0. 00), in agreement with results based on traditional models (Table  2 ). Diversification trajectories derived from these rates are not different, with plants and animals exhibiting an equivalent fraction of phylogenies showing a decrease of speciation (Figure  3b ). Since extinction dynamics cannot be derived from PDR models, we do not know if speciation slowdowns detected in plants were accompanied by larger extinction declines. Thus, we cannot rule out the scenario of expanding dynamics (Sc. 1, 2) for plants found based on traditional birth-death models. Differences in the phylogenetic composition of the plant and tetrapod datasets do not explain this contrasted pattern. On average, plant phylogenies are not significantly younger or species-poorer than tetrapod phylogenies (Figure  2 -figure  supplement 2 ).
1 and 0. 1 for the temperature models. These 10 trees represent 6% of our dataset, suggesting that there is a low proportion of trees susceptible to suffer from this bias. In our study, the relative support for time-, temperature-and uplift-dependent models remained stable to AIC variations (Figure  5 -figure supplement 1). Model support also remained stable regardless of the paleotemperature curve considered for the analyses (Figure  5 -figure supplement 2). Furthermore, the use of an hypothesis-driven framework has been suggested as a potential solution to alleviate the problem of non-identifiability of diversification parameters, by setting up explicit prior assumptions and delimiting the potential parameter space  (Louca and Pennell, 2020; Morlon et al. , 2022; Magee et al. , 2020). Here, we do not evaluate every possible factor that could potentially explain Neotropical biodiversity, but only confront scenarios capturing well-established hypotheses on Neotropical diversification.
We focus on the role of the Cenozoic change in Andean elevation and climatic oscillations because they have previously been pinpointed as essential for explaining Neotropical biodiversity  (Hoorn et al. , 2010; Rangel et al. , 2018; Hagen et al. , 2021). Thus, our main interest is to explore which of these factors likely explains the data compiled, although other factors could have played a role. We have compiled as many Neotropical clades (and as many species per clade) as possible, resulting in a phylogenetic dataset representing, to our knowledge, one of the largest assembled to date. Yet, we are keenly aware that we still come up short, especially with the plant database. Our plant dataset (>6000 species, 66 clades) includes just a small fraction (~7% of the total species) of the vast diversity described in the region.
As such, our results, which show contrasting diversification dynamics between plants and tetrapods, should be taken with caution. Future investigations would be necessary to confirm the generality of the expanding trend for plants. Basic knowledge of the real Neotropical diversity (and phylogenetic relationships) also remains incomplete, for example,  Kier et al. , 2005, and  we anticipate the discovery of additional patterns by expanding the database. Similarly, we did not manage to sample evenly across all regions. Our conclusions on the spatial patterns of diversification are derived from the study of a fraction of the Neotropical diversity, where tropical rainforest lineages from the broad 'pan-Amazonian' region are most abundant. Although sample size in our biogeographic comparisons is large (150 observations), some categories of these variables are poorly represented, which might limit the performance of some statistical tests.
For instance, there are 97 phylogenies assigned to the biogeographic cluster 1, while only 10 in cluster 2. Note that there are other clades (39) containing species on poorly represented regions that fall in the 'mixed' category, as they share species with different areas. Our sampling, however, includes representatives from all the main regions in the Neotropics. Yet, we did not identify a common diversification trajectory or diversification rates, among the fewer clades distributed on poorly represented regions (e. g. , southern South America clades experienced all gradual, exponential, and declining dynamics, as did the clades from other regions; Figure  6 ). It is also reasonable to assume that our sampling reflects a fair proportion of species per region, considering the extension of these regions in the Neotropics and the representativeness of our dataset; at least for tetrapods, it includes ~60% of all described species.
gbif. org/), the PanTHERIA database (https://omictools. com/pantheria-tool), BirdLife (http://www. birdlife. org), and eBird (http://ebird. org/content/ebird), all accessed in 2018, in a procedure similar to  Meseguer et al. , 2020. Next, we pruned the trees to extract the most inclusive clades that contained at least 80% Neotropical species, as previously defined. This procedure ensures that the diversification signal pertains to the Neotropics. In addition, phylogenies of particular lineages not represented in the global trees (or with improved taxon sampling) were obtained from published studies or reconstructed de novo in this study (for caviomorph rodents, including 199 species; Supplementary file 2). In the case of plants and mammals, most phylogenies were obtained from individual studies, given the low taxon sampling of the plant and mammal large-scale trees.
Neotropical bioregionalization We used a quantitative approach to identify geographic units of long-term Neotropical evolution. We divided the Neotropical region into 13 operational areas based on the WWF biome classification  (Olson et al. , 2001)  and similar to other studies, for example,  (Antonelli et al. , 2018c; Hutter et al. , 2017)  -Amazonia, Atlantic Forest, Bahama-Antilles, Caatinga, Central Andes, Cerrado, Chaco, Chocó, Guiana Shield, Mesoamerica, the Northern Andes, temperate South America, and an 'elsewhere' region -and assessed the distribution in these areas of the 12,512 species included in our 150 phylogenies. Georeferenced records were downloaded for each species through GBIF using the R package rgbif 0. 9. 9  (Chamberlain et al. , 2017). We removed points with precision below 100 km, entries with mismatched georeference and country, duplicates, points representing country capitals or centroids, using the R package CoordinateCleaner 1.
0-7  (Zizka et al. , 2019). Then, we created 13 georeferenced polygons delimiting each operational area using the WWF terrestrial ecoregions annotated shapefile in QGIS, and species were assigned to each polygon according to coordinate observations using the R package speciesgeocodeR 1. 0-4. GBIF records can result in an overestimation of widespread ranges  (Maldonado et al. , 2015) , so species distributions were manually inspected for completeness and accuracy with reference to databases (AmphibiaWeb 2018, Uetz et al. 2018, GBIF. residual error controlling for the main effects in the model and the tree were normally distributed. We applied phylogenetic corrections in this case because phylogenetic signal was detected for sampling fraction (K sampling = 0. 12, p=0. 001) and crown age (K age = 0.
Positive μp(0)>0 values are possible under both increasing and decreasing speciation rates, in which case speciation trends are designed as 'unknown'. Figure3-source data 1 provides the data to construct a and Figure4a. Figure3-source data 2 provides the data to construct Figure3b. Figure3-figure supplement 1 shows the proportion of phylogenies fitting different pulled diversification models for a reduced dataset including only trees with more than 20 species (N=99), or with a sampling fraction over 20% (N=137).
Positive μp(0)>0 values are possible under both increasing and decreasing speciation rates, in which case speciation trends are designed as 'unknown'. Figure 3-source data 1 provides the data to construct a and Figure 4a. Figure 3-source data 2 provides the data to construct Figure 3b. Figure 3-figure supplement 1 shows the proportion of phylogenies fitting different pulled diversification models for a reduced dataset including only trees with more than 20 species (N=99), or with a sampling fraction over 20% (N=137). The online version of this article includes the following source data and figure supplement(s) for figure 3: Source data 1. Provides the data to construct Figure 3a and Figure 4a. Source data 2. Provides the data to construct Figure 3b.