dataesr/scientific-paragraphs-categorization huggingface dataset:
The exponent n was only calculated for hydrogen so far and is in all other cases issue to experimental determination. Since the experimental setup is subject to a finite energy resolution, we convoluted function f(E) with a Gaussian function to account for the experimental conditions. Thus, the function fitted to the data was σ E ð Þ ¼ b þ c ffiffiffiffiffi 2π p ρ n ⋅Γ n þ 1 ð Þ⋅exp - 1 4ρ 2 AE-E ð Þ 2 ⋅D nþ1 1 ρ AE-E ð Þ ð2Þ with Γ the gamma function, D n + 1 a parabolic cylinder function, and ρ the standard deviation of the Gaussian and energy resolution of the HEM. Some ionization efficiency curves featured two onsets. In this case, the fitting function was extended by adding a second   1 ) and convoluting this formula as for single onsets. The analysis software, based on a previous version  [45] , was written in Python using the SciPy, NumPy, and Matplotlib libraries.
Mass Spectra Electron ionization (EI) is rather a hard ionization method in which an energetic electron interacts with a molecule removing an electron from the neutral compound. Depending on the excess energy deposited, either the parent cation stabilizes or it dissociates into one positively charged fragment and one or more neutral fragments. The ionization method may influence the observed mass spectrum. The possibility of different fragment ions' formation was mentioned by Main-Bobo et al. [27]. The transmission of the mass analyzer and ion collection efficiency can further cause deviations in relative peak intensities, but the number of observed fragments should not be altered. Hence, recording the ionization mass spectra of IMI and 2NI allows the assignment of the cationic fragments and a comparison with photoionization studies  [27, 28, 39, 40] , together with EI spectra from the NIST database  [50]  and the Spectral Database for Organic Compounds (SDBS)  [51].
Imidazole In the case of the ionization mass spectrum of imidazole (see Figure  1 ), the most intense ion is assigned to the parent cation at m/z 68. This agrees well with the NIST database  [50]  but represents the major difference to the photoionization mass spectrum recorded at 21 eV by Schwell et al. [28] , where the parent cation is very weakly abundant. In contrast, the two most intense ions were reported at m/z 41 and m/z 40, assigned to C 2 H 3 N + and C 2 H 2 N + , respectively. In the present mass spectrum, these ions form the most dominant fragments, where C 2 H 3 N + yields one third the intensity of the parent cation. This cation is formed by HCN loss which was also reported by Klebe at al. [26]. Additionally, two other cationic fragments are observed with considerable yields, m/z 28 assigned to HCNH + and at m/z 67 assigned to [IMI -H] + , which corresponds to the dehydrogenated parent cation.
Ionization and Appearance Energies The ionization efficiency curves near threshold were measured for the most abundant cations in the mass spectra. The experimental data were fitted by the method described above to determine the experimental ionization and appearance energies. Subsequently, the results are presented, discussed, and compared with both calculations and earlier studies. Imidazole For IMI, the experimental data and fits are shown in Figure  3  and summarized in Table  1. The energy scale was calibrated by measuring the ion yield curve of helium and determining its onset of formation (see Figure  3a ). The mass spectrum indicates that the parent cation forms the most intense channel upon EI of imidazole. We report an experimental ionization energy of 8. 76 ± 0. 03 eV (see Figure  3f ) which is slightly lower than the calculated adiabatic ionization energy (AIE) of 8.
Acknowledgements This work was supported by  FWF, Vienna ,  P30332. R. M. and P. L. -V. received support from the  Portuguese National Funding Agency FCT-MCTES  through  PD/BD/114452/2016  and  PTDC/FIS-AQM/31281/2017. This work was also supported by  Radiation Biology and Biophysics Doctoral Training Programme (RaBBiT ,  PD/00193/2012 );  UID/FIS/00068/2019  (CEFITEC),  UID/Multi/04378/2013  ( UCIBIO ). L. F. received financial support from the  University of Innsbruck  via grant  P7440-035-011 , the  Institut de Physique Nucléaire de Lyon (Institut de Physique des 2 Infinis de Lyon) , and the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Université de Lyon  within the  program BInvestissements d'Avenir^ ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
Acknowledgments We are thankful to  Shmuel Fishman  for deep discussions of quantum chaos problems and related scientific topics during many years. NOTE: This work represents the contribution to the Special Issue in memory of  Shmuel Fishman  to appear at MDPI Condenced Matter journal. This work was supported in part by the  Programme Investissements d'Avenir   ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT  (project  THETRACOM ). This work was granted access to the  HPC GPU resources of CALMIP (Toulouse)  under the allocation  2018-P0110.
Invasive alien species are a great threat to biodiversity and human livelihoods worldwide. The most effective way to limit their impacts and costs is to prevent their introduction into new areas. Identifying invaders and invasions before their occurrence would arguably be the most efficient strategy. Here, we provide a profiling method to predict which species-with which particular ecological characteristics-will invade, and where they could invade. We illustrate our approach with ants, which are among the most detrimental invasive species, as they are responsible for declines of numerous taxa, are involved in local extinctions, disturb ecosystem functioning, and impact multiple human activities. Based on statistical profiling of 992 ant species from an extensive trait database, we identify 18 native ant species with an ecological profile that matches that of known invasive ants.
Until now, species distribution models have forecasted the most suitable areas, and hence the areas most at risk from known IAS  (7, 19, 20). Other types of models have tried to identify what characterizes IAS based on the drivers of their invasion success, their life-history traits, or their occurrences worldwide (e. g. , ref. 18). Such models have been built for various taxa, including plants, ants, fish, amphibians, reptiles, birds, and mammals  (21) (22) (23) (24) (25) (26) (27) (28) (29) (30). For ants, studies based on various databases characterize certain ecological traits often associated with exotic or invasive species  (28, 31, 32). All these pioneer studies provide a valuable starting point, but they still have fundamental limitations. First, they remain limited in terms of coverage; notably, all studies based on interception lists rely on only a few countries.
Second, the studies exclusively consider the few species that have complete data for all of the studied traits, thus removing the majority of species. Removing species with missing data drastically reduces the sample size and statistical power and also introduces biases and thus improper predictions  (33) (34) (35) (36). Finally, the existing models are still mostly limited to species of already known invasive status (i. e. , invasive or exotic) or to providing traits associated with invasiveness; thus, existing models do not use these traits to predict new invaders yet to move to another area. Nevertheless, this corpus of pioneer studies shows that, by using modern statistical inference tools, improved ecological knowledge on current IAS, and large databases on species ecological traits, it is now possible to reach a higher level of prediction in invasion biology and identify in advance which species could become invasive.
Based on the example of ants, we provide a profiling method to predict future invaders and future invasions, and in this manner, we identify 18 ant species likely to become new invaders and regions at risk for these invasions. Conservation of Nature (IUCN) list of the "100 of the world's worst invasive alien species," a feat equaled by no other taxonomic family  (48). In this study, we will follow the definition of invasive species adopted by the Invasive Species Specialist Group of the IUCN, excluding species that are native but expanding, as well as exotic species with no impact. Here, we have built a profiling tool that identifies which species have the ecological profile that makes them highly likely of being the next invading species. Our approach is to first define an ecological profile of the most invasive ant species and then compare it with the ecological profile of other species based on an extensive dataset of ant species traits, imputed through finely tuned and up-to-date machine-learning algorithms.
Results Future Invaders. Our pool of ant species retained for the ecological profiling of invasive species was kept high on account of the setup imputation procedure, which enabled us to estimate missing values in the traits and thus have all trait values, either observed or estimated, for 992 species. This was done while keeping the imputation error very low (whole dataset imputation error: 5. 14%), thus ensuring robust model estimates and predictions. We used this imputed dataset of four ecological traits to calculate the probability of species being invasive and detect ant species with a profile highly similar to known ant invaders. Our models enabled us to efficiently discriminate between likely and unlikely invasive species [Area Under the receiver operating characteristic Curve (AUC) = 0.
Discussion Using a large database on ant species traits and an up-to-date phylogeny of ant genera, we developed a robust methodology to quantify the relative importance of ecological traits in shaping their invasiveness probability and used it to both characterize the ecological profile of current invasive ants and identify 18 noninvasive species that share this profile. We distinguished between invasive and highly invasive species based on their probability of invasiveness. We complemented this prediction by identifying regions most at risk for invasion from these species: tropical regions, both mainland and islands. Our predictive approach directly points to species that have not yet invaded and areas that are not yet invaded, but are likely to do so in the future. Pioneer risk-assessment models are all based on datasets that only comprise information on exotic ant species, i.
e. , species that have already been moved and detected outside of their native zone  (24, 29) , or pest species, i. e. , species that are already known to have concerning impacts  (49, 50). As a result, these models cannot point to novel species of potential risk. In contrast, our screening tool is based on a large and collaborative dataset that contains information for 25 ecological traits for the 19 ant species recognized as invasive by the IUCN (47) as well as over 2,000 native species, of which 992 had all information (observed or inferred) for the traits selected in our model. The fact that this dataset comprises species from 47% of the ant family originating from all of the world's ecozones (SI Appendix, Fig. S1 ) makes our predictive model genuinely compatible with the anticipation of future invasions worldwide.
Unlike most current trait-based predictive models, our methodology includes a robust and accurate way of dealing with missing values, which are commonplace in life-history trait datasets in general. Indeed, the removal of cases with missing values is known to result in reduced statistical power and biased parameter estimates  (31, 33, 51). We overcame this issue by bringing together the most recent and accurate imputation algorithms  (52, 53)  and finely calibrating them to fit our data. We comprehensively examined the performance of tuning parameters for multiple imputation and included the most relevant number of phylogenetic eigenvectors to impute each trait. The crucial issue of how many phylogenetic eigenvectors should be included for trait imputation, which we carefully consider in the present paper, has been rarely discussed in the literature  (54).
Our analysis, in accordance with previous work, also revealed the existence of some patterns of phylogenetic signal above the species level (SI Appendix, Table  S4 ), supporting the fact that our genus phylogeny was a useful predictor to include when imputing the ant species dataset  (20). After imputing the dataset multiple times, the multimodel inference enabled us to establish strong relationships between species traits and invasion potential and to efficiently discriminate between likely and unlikely invasive species. Our results highlighted several traits of importance that were previously highlighted as commonly found in invasive ant species: supercoloniality, generalist nesting type, affinity for disturbed environments, and expansion through independent colony founding  (37, 55). Moreover, our study goes beyond trait analysis by using it to calculate species invasiveness probability and applying it to a large set of species not yet known to be invasive.
For example, one could argue that using the trait "supercolonial" in our model may hinder its ability to detect new potential invaders, since this behavior has mostly been observed in ant colonies after they have become invasive. However, supercoloniality has already been outlined as an important characteristic of invasive species  (37, 65). It might be worth, in this context, completing the Antprofiler database (that currently contains about one-sixth of the known ant species), as having more data is the first step to obtaining more (and more accurate) identifications. A complementary and relevant study would be to analyze which traits condition each step of the invasion process (introduction, establishment, spread, and impact), thus providing some information on which invasion stage is the most critical in order to design a targeted control strategy for each species according to its specific combination of traits  (30).
Materials and Methods To simultaneously identify the most relevant traits and keep the highest number of species, we proceeded in two steps. First, we identified and selected traits that performed well at predicting invasiveness. Second, we optimized our usable dataset by removing species with too many missing values and by estimating the values of the selected traits for the species with few missing values (using all available traits). We then used the dataset with the selected traits, either observed or estimated, to predict invasiveness probability with Generalized Linear Models. Finally, we predicted areas at risk for invasion of species identified as potential invaders, using Species Distribution Models. Our step-by-step process is illustrated Fig. 3  and is presented below, with further details in SI Appendix.
Trait Data Selection and Imputation. We searched for trait combinations that are associated with invasive species. We extracted ant species traits from our AntProfiler online database (https://antprofiler. ese. u-psud. fr). This database includes information on 2,176 ant species (134 genera) and ecological traits related to their occurrence, morphology, behavior, and invasive status  (66). From this database, we selected traits noncorrelated to each other but correlated to the invasive status. The full description of the trait selection process is given in SI Appendix, Supplementary Material and Methods and illustrated in SI Appendix, Fig. S3. Despite our data collection efforts, the database contained many missing values. We therefore used an imputation technique (missForest) to estimate the missing values  (53).
Before imputation, we removed the species with too many missing values to keep a dataset with a maximum of ∼60% of missing information (following ref. 52 ). We then explored how missing data could be imputed into our database to minimize bias and maximize prediction accuracy. To improve the estimation of missing values, we used phylogenetic information and all traits initially downloaded from Antprofiler. We used out-of-bag error estimations to calibrate the models as these were shown to accurately estimate imputation error, and we repeated the imputation 100 times (Fig. 3  and SI Appendix, Supplementary Material and Methods). We selected a final subset of four traits: colony foundation, colony structure, nesting type, and association with disturbed areas and 992 species (of 2,176). Predictive Model Building.
Potential future invasive species. We modeled the invasive profile using generalized linear models with our four selected traits as predictors and a binomial distribution. We ran 100 models for each of the previously imputed datasets and used model predictions to determine future invasive species. For each model, we identified potentially new invasive ant species as those with a predicted invasiveness probability above the lower fifth centile of the 19 known invasive species probability distribution. Each identified species was considered to be potentially invasive if it was selected in at least 90 of the 100 models. Areas at risk. SDMs are based on correlations between environmental variables and geolocalized species records and can be used to delineate potential species distributions  (67).
Finally, we also combined these individual predictions by summing each species potential distribution as binary maps to obtain a cumulative invasion risk map from these future invaders. Binary transformation was based on the threshold that maximized the TSS for each species. Data Availability. All data and scripts used in this study are available at https://github. com/caterinap/Antprofiler. ACKNOWLEDGMENTS. We thank Cleo Bertelsmeier and Sebastien Ollier with whom we worked on different statistical approaches for a preliminary study and who provided useful comments on an earlier version of the manuscript; five anonymous reviewers who provided useful comments that led to improved analyses; Guillaume Guénard and Daniel J. Stekhoven for their advice, which helped improve our imputation methodology; Benoît Guénard and James Wetterer for sharing their ant occurrence data; and Corrie Moreau and Philip Ward who provided information and data on ant phylogeny.
Despite these findings, no large molecular study has directly investigated ectomycorrhizal fungal communities in rainforests in this biodiversity hotspot. One preliminary study tried to investigate specifically the ectomycorrhizal fungal diversity in a New Caledonian rainforest, but with a very limited number of samples (8 ectomycorrhizal root tips and 27 fruit bodies successfully genotyped)  (Perrier, 2005). Furthermore, in New Caledonia, as more generally in the tropics, molecular data on fungi are very scarce. High quality and reference DNA sequences are thus needed to complement next generation sequencing approaches  (Gourmelon et al. , 2016). In the present work, ectomycorrhizal fungal diversity of three main types of rainforests on ultramafic soils were investigated. Two of the rainforests were dominated by a single phylogenetically distant tree species, either Nothofagus aequilateralis (Nothofagaceae) or Arillastrum gummiferum (Myrtaceae).
, 2008). PCR reactions were performed as described in  Carriconde et al. (2008b). Sequencing was conducted with the primers ITS1  (White et al. , 1990)  or ITS1f and the reverse primer ITS4  (White et al. , 1990)  at the 'Plate-Forme du Vivant de la Nouvelle-Cal edonie' (PFV-NC) (Noum ea, New Caledonia) using the Big Dye Sequencing Kit v. 3. 1 (Applied Biosystems, Foster City, CA, USA). Generated sequences were edited with Sequencher v. 4. 10. 1 (Gene Codes Inc. , Ann Arbor, MI, USA). Taxonomic assignment was carried out by comparing all ITS sequences to UNITE and INSD databases, using the MassBLASTER module implemented in PlutoF 2. 5  (Abarenkov et al. , 2010). Some root tips were identified as endophytic (e. g. Oidiodendron) or saprotrophic species (e. g. Mycena) and consequently excluded from the dataset.
Thereafter, sequences were partitioned into operational taxonomic units (OTUs), using a cut off of 97% sequence identity (e. g. Nilsson et al. , 2008). For Cortinarius a 98% identity was applied due to the fact that the ITS region is more conserved in this genus  (Stefani et al. , 2014). After removing the flanking 18S and 28S rDNA partial sequences with the ITSx module from PlutoF v. 2. 5, OTUs were delineated using the software cd-hit-est (http://weizhonglab. ucsd. edu/cdhit_suite/) (Huang et al. , 2010). OTUs were checked by aligning the sequences belonging to a same lineage (see below) using the software MEGA v. 6  (Tamura et al. , 2013)  and manually adjusted. Final taxonomic assignment was performed by combining BLAST and OTU clustering. Taxa were assigned to ectomycorrhizal phylogenetic lineages defined by  Tedersoo et al.
(2010a)  and  Tedersoo and Smith (2013). These lineages correspond to monophyletic groups that have independently evolved the ability to form ectomycorrhizal symbiosis. Several of our sequences belonged to "groups" constituted by both ectomycorrhizal and non-ectomycorrhizal lineages: Catathelasma, Sistotrema, Xenasmatella, Atheliales, Helotiales, Agaricomycetes and Sordariomycetes. In order to determine whether these sequences fit into known lineages within these "groups", phylogenetic trees were constructed for all these fungal groups independently. Reference sequences (e. g. according to  Moncalvo et al. , 2006; Tedersoo et al. , 2009; Tedersoo and Smith, 2013)  were downloaded from GenBank or UNITE databases and aligned with sequences from this study. To improve the quality of the analyses, due to the occurrence of multiple insertions and/or deletions in the ITS alignment, a gap cut off of 0.
Combining both communities, within N. aequilateralis, A. gummiferum and mixed rainforests, 167, 129 and 100 ectomycorrhizal fungal OTUs were detected, respectively. Out of the 311 OTUs detected, only 20 showed a species level match to sequences available in the UNITE database (98% for the Cortinarius and 97% for the others genera). The matched specimens originated from the Australasian region (New Caledonia, Australia, New Zealand and Papua New Guinea) and other regions of the world (China, Madagascar, Estonia and Finland). Five samples matched described species, namely Cantharellus decolorans, Cortinarius picoides, Lactarius clarkeae, Sebacina dimitica and Sistotrema muscicola (Table  S1 ). Of these 20 OTUs, 15 showed a correspondence with samples collected outside New Caledonia. Thus, 296 OTUs (ca.
4B ). The A. gummiferum-dominated and mixed stands overlapped. The PERMANOVA test indicated that the variance was not significantly partitioned by plant formation or by site (Table  3 ), although plant formation was marginally significant (27. 8%; pvalue ¼ 0. 076). Significant correlations were obtained between host density and fungal below-ground richness (r ¼ 0. 72; pvalue ¼ 0. 03) and above-ground fungal richness (r ¼ 0. 9; pvalue < 0. 01). The targeted plant ITS was successfully sequenced from 26, 20 and 25 root tips collected under N. aequilateralis, A. gummiferum and mixed rainforests. Thus, in total 66. 4% (71/107) of the samples were successfully sequenced. When comparing to GenBank database and sequences obtained from leaves, all ectomycorrhizas from N. aequilateralis and A. gummiferum monodominant vegetation types correspond to the dominant plant species, respectively, as Nothofagus and A.