dataesr/scientific-paragraphs-categorization huggingface dataset:
Human subjects ASPIRE study cohort Patients presenting for emergency evaluation of stroke or cerebrovascular disease were recruited and provided blood samples and neuroimaging data as approved by the UCLA Institutional Review Board (IRB # 14-001798) as previously reported. 42 Serum levels of IL-17B and CXCL5 were measured in technical duplicate using a custom Luminex assay (R&D Systems). Manufacturer protocol was followed and antigen binding within the assay was measured on a Luminex 200 System and analyzed using Milliplex Analyst 5. 1. Modified Fazekas scores were determined by blinded analysis of T 2 -weighted FLAIR images by two independent reviewers. ASPIRE study data are available at https://osf. io/92erq/. Post-mortem cohort Subjects were selected from a subset of 950 UCDavis ADC Neuropathology Core samples based on a priori selection criteria.
PIK3R5 1. 1 3333 0. 00095 PIP-Akt signaling pathway ROCK1 À1. 2784 0. 03343 regulation of actin cytoskeleton WASL À1. 8148 0. 00012 regulation of actin cytoskeleton RAC2 1. 6040 0. 00049 regulation of actin cytoskeleton PARD3 0. 4322 0. 03699 regulation of actin cytoskeleton ELMO1 À1. 4219 0. 00125 regulation of actin cytoskeleton PTK2 À0. 9568 0. 04945 regulation of actin cytoskeleton/ diacylglycerol pathway CRK 0. 4503 0. 03329 regulation of actin cytoskeleton/ diacylglycerol pathway Differentially expressed genes (FDR < 0. 05) from HFD OPCs were filtered for those found in the KEGG Chemokine Signaling Pathway (KEGG Map #04062). Functional enrichment for chemokine signaling was established using a STRING database search (FDR for pathway enrichment = 1. 62 3 10 À55 ). Gene name, log fold change (logFC), adjusted p value (adj.
Results Marko et al. first mentioned the title compound 2 in 1975, and it was obtained by treating [Co4(CO)9(arene)] with triphenylphosphite at 20 °C under CO atmosphere, along with [Co4(CO)10{P(OPh)3}2] and [Co4(CO)9{P(OPh)3}3]  [5]. There was no additional characterization data presented aside from a detailed IR analysis in solution. To prepare the compound [Co2(CO)7{P(OPh)3}], we first treated a solution of [Co2(CO)8] in toluene at ambient temperature in a 1:1 ratio (Scheme 2). The formation of 1 (2086, 2034, 1999, and 1978 cm -1 ) was revealed by IR monitoring as well as traces of [Co2(CO)6{P(OPh)3}2] (1978 cm -1 , very strong). Formation of this dinuclear bisphosphite complex is also corroborated by an NMR 31 P{ 1 H} analysis performed on a sample of the reaction mixture, which shows a singlet at 167.
10 g. mol -1 , black crystals, crystal size 0. 377 × 0. 194 × 0. 15 mm 3 , monoclinic, space group P2 1 /c; a = 12. 5987(10) Å, b = 8. 9773(9) Å, c = 27. 953(2) Å, α = 90 • , β = 96. 829(3) • , γ = 90 • ; V = 3139. 2(5) Å 3 , Z = 4, D calc = 1. 807 g/cm 3 , T = 100 K, GOF = 1. 034; R 1 = 0. 0236, wR2 = 0. 0546 for 11,958 reflections with I > = 2σ (I) and 11,958 independent reflections. Largest difference in peak/hole = Å -3 0. 58/-0. 41. Data were collected using graphite monochromated MoK α radiation with l = 0. 71073 Å and deposited at the Cambridge Crystallographic Data Centre as CCDC 2174841. (Supplementary Materials). The data can be obtained free of charge from the Cambridge Crystallographic Data Centre via http://www. ccdc. cam. ac. uk/getstructures. The structure was solved by direct methods and refined by full-matrix least-squares against F 2 (SHELXL, 2015  [22] [23] [24] ).
However, freshwater species live in specific microhabitats that are not necessarily affected by local conditions (e. g. the river regime can be affected by precipitation and snowmelt occurring hundreds of kilometres upstream), and in which temperature can strongly differ from nearby terrestrial environments. In turn, the use of bioclimatic layers that do not match the microhabitat experienced by animals can affect niche estimates  (Ficetola et al. 2018 (Ficetola et al. , 2020)). In addition to climate, other factors such as water chemistry shape the distribution of freshwater species  (Jeschke and Strayer 2008). Nevertheless, there is evidence that bioclimatic variables are indeed appropriate to model aquatic species distributions  (Frederico et al. 2014 , McGarvey et al. 2018). Testing the effects of water chemistry variables on FIS niche shifts at a global level is challenging due to the lack of high-resolution global datasets  (Loo et al.
Species records and ranges We downloaded occurrence records for 40 FIS from the iNaturalist data portal (www. inaturalist. org/observations/export), filtering for 'Research Grade' data to reduce misidentification risk. We selected species with a large number of observations (range: 95-45 351; median: 1108. 5; and with > 20 records for both the native and the invasive range) and included freshwater molluscs, crustaceans, fishes and tetrapod species (amphibians and reptiles; Supporting information) with occurrence records from the period 1962 to 2021. When available, we downloaded native ranges from the IUCN Red List website  (IUCN 2022). We used the Freshwater Ecoregions of the World  (Abell et al. 2008)  to select background areas for each species in both its native and introduced ranges. This allows an appropriate selection of background areas since they were delineated by considering physical and ecological dispersal limitations of freshwater species (particularly freshwater fish;  Abell et al.
Environmental variables To test the hypothesis that niche shifts detected between native and non-native ranges can derive from the variables used for analyses, we evaluated several datasets of environmental variables that could be used to model FIS niches. We aimed to understand whether and how niche metrics are affected by 1) using general (bioclimatic) versus more specific proxy variables, 2) selecting different numbers of variables and 3) limiting the background areas to waterbodies. To achieve this goal, we downloaded different raster datasets from two sources: WorldClim  (Fick and Hijmans 2017) and EarthEnv (www. earthenv. org; Domisch et al. 2015). WorldClim provides global data for 19 different macroclimatic ('bioclimatic') variables at different resolutions, which well describe the features of terrestrial environments  (Fick and Hijmans 2017).
EarthEnv provides different near-global, standardized, 1-km resolution layers that can be used for niche modelling and other scientific purposes, and includes several river-specific variables  (Domisch et al. 2015). We downloaded data from the near-global freshwater environmental variables dataset provided by EarthEnv through the 'sdmpredictors' R package (Bosch 2020). EarthEnv includes 19 bioclimatic variables derived from WorldClim; data on river drainage networks derived from HydroSHEDS  (Lehner et al. 2008) ; and proxies for water chemistry derived from the soil properties maps of SoilGrids1km  (Hengl et al. 2014). Both bioclimatic and water chemistry variables are provided as the average or sum of the upstream values and as inverse-distance weighted average or the inverse-distance weighted sum of the upstream values.
Data preparation and analyses For each species, we kept one presence point per grid cell. Presence points located more than 1 km away from waterbodies were discarded; points located < 1 km away from waterbodies were moved to the nearest cell inside a waterbody. This was done to avoid loss of information due to limited accuracy of coordinates or raster maps, or changes in stream and river shapes, and to include occurrences of semi-aquatic animals moving nearby main waterbodies. Presence points falling on cells associated with no value in at least one of the raster maps used were discarded. Following  Hill et al. (2017) , we selected 10 000 random cells as the background for each species in both the native and exotic range. For WorldClim variables, background points were selected across the entire background areas, whereas for the EarthEnv variables the background points were selected within waterbodies.
Subsequently, we extracted the variable values for both presence and background points. Each resulting dataset was used to describe the native and exotic niches using the centroid shift, overlap, unfilling, expansion (COUE) framework, an ordination approach to quantify niche changes  (Guisan et al. 2014). Following  Broennimann et al. (2012) , we performed a principal component analysis (PCA) on the environmental space of the native and exotic ranges. Occurrence densities of the species in the native and invasive ranges were then calculated with kernel smoothing methods to limit sampling biases and projected onto the gridded PCA environmental space. We used the 75th percentile of environmental densities as a threshold to eliminate rare environments from our analyses  (Petitpierre et al. 2012 , Guisan et al.
2014 , Li et al. 2014 , Liu et al. 2017). Niches were then defined by using 95% of the species occurrences to avoid the inclusion of the most marginal populations. Subsequently, we calculated the adjusted Schoener's D, a measure of niche overlap  (Broennimann et al. 2012 ). Low Schoener's D values are a first indication of limited niche overlap; nonetheless, large Schoener's D values do not necessarily imply the absence of niche shifts  (Guisan et al. 2014). After that, we performed niche equivalency and similarity tests. Niche equivalency tests consist in pooling all occurrences and splitting them into two datasets, calculating Schoener's D and repeating the process N-times; niche similarity tests were performed by randomly shifting the entire observed density of occurrences in the exotic range and calculating the overlap of the simulated niche with the observed niche in the native range  (Broennimann et al.
2012 ). Both tests were run with 1000 random replicates using the 'ecospat' (ver. 3. 2) R package  (Di Cola et al. 2017). When testing for niche conservatism, a significant (p < 0. 05) equivalency test implies that overlap between native and exotic niches is higher than expected under randomness, while a significant similarity test implies that the focal species occupies environments in the native and exotic range that are more similar than would be expected by chance. Finally, we calculated niche expansion, i. e. the proportion of the exotic niche non-overlapping with the native niche, and niche unfilling, i. e. the proportion of conditions inside the native niche but outside the exotic niche  (Guisan et al. 2014 ). Niche expansion is often considered to be the only measure that truly describes shifts in the realized niche  (Petitpierre et al.
Results Overall, we obtained data from seven molluscs, five crustaceans, 19 fishes and nine tetrapod species (six amphibians and three reptiles; Supporting information). After spatial filtering, the mean number of retained records per species ± SD was 1413 ± 2337 (range: 61-10 321). The mean number of raster cells occupied in the native range was  853 ± 1969 (15-10 199) , while the mean number of occupied cells in the invaded range was 561 ± 798 (6-3494). The average residence time was 153 years ± 89 (42-525). Residence time was not correlated to invaded range extent (Pearson's r 38 = 0. 219, p = 0. 175). For each species, each niche dynamic measure was calculated multiple times, using different sets of ecological variables. We obtained a total of 560 values for each niche dynamic measure (40 species × 14 sets of variables).
2021). Datasets aiming to represent instream conditions at a global level are broad-scale estimates, mostly based on soil chemistry and geological features of nearby environments, and hence in some cases they may not accurately represent the actual conditions experienced by organisms. Moreover, several water chemistry parameters relevant for FIS distribution (e. g. calcium concentrations for molluscs) are not always available at high resolution at the global scale  (Jeschke and Strayer 2008). Distance-weighted variables led to contrasting results. While their use increased niche overlap, equivalency and similarity between native and invaded ranges, it also increased niche expansion and unfilling. Validation exercises suggested that distance-weighted variables may represent some instream conditions less accurately than the unweighted ones  (Domisch et al.
Additional factors influencing observed shifts Regardless of the robustness of modelling designs, niche modelling has some inherent weaknesses that are difficult to overcome. Global analyses are based on large databases of species distribution. These databases do not necessarily represent the actual distribution of the species, owing to geographical biases in data collection, uneven search efforts and reporting biases  (van Strien et al. 2013 , Uyeda et al. 2020). Approaches like the COUE scheme are thought to limit the effects of sampling bias by calculating smooth densities of species occurrences in a gridded environmental space  (Broennimann et al. 2012 , Guisan et al. 2014 ), but there are other biases that can undermine the reliability of models. Occurrences of dispersing individuals or non-viable populations, such as continuously restocked fish populations that occupy sub-optimal environments, should be excluded from analyses to avoid niche overestimation.
To limit this issue, we only selected observations from countries where the considered FIS are known to be established, and excluded marginal observations from niche analyses. However, niche overestimation caused by non-breeding populations might have occurred in large countries and for species with wide distributions. Finally, even the best global datasets of ecological variables currently available have some limitations, and they can provide an imperfect representation of conditions experienced by organisms  (Domisch et al. 2015). The development of high-quality datasets that well represent microhabitat conditions is one of the major issues for modelling studies, and recent broad-scale efforts promise improvements that will be extremely useful for future studies  (Potter et al. 2013 , Bennie et al.
Acknowledgements -We wish to thank the Subject Editor  Dr Carsten Dormann  and two anonymous reviewers who helped us to greatly improve the previous draft of the manuscript. Funding -This work was carried out in University of Milan facilities. Part of this research was funded by the  European Union -NextGenerationEU, Italian National Recovery and Resilience Plan (Mission 4 ,  Component 2, Investment 1. 5 'Innovation Ecosystems '), project  MUSA.
Data availability statement The presence-only data, the areas used as backgrounds for the native and the invasive ranges of the considered species, as well as data and scripts used for niche comparisons and GLMMs are available at: https://figshare. com/articles/ online_resource/ Datasets_and_script_for_niche_compari-sons_and_GLMMs/19388282 (Lo Parrino et al. 2022).
Trichophyton indotineae is an emerging pathogen which recently spread from India to Europe and that is more prone than other species of the Trichophyton mentagrophytes complex to show resistance to terbinafine, resulting in the necessity of rapid identification. Here, we improved the online MSI-2 MALDI-TOF identification tool in order to identify T. indotineae. By multiplying the culture conditions (2 culture media and 6 stages of growth) prior to protein extractions for both test isolates and reference strains, we added 142 references corresponding to 12 strains inside the T. mentagrophytes complex in the online MSI-2 database, of which 3 are T. indotineae strains. The resulting database was tested with 1566 spectra of 67 isolates from the T. mentagrophytes complex, including 16 T. indotineae isolates.
Using the newly improved MSI-2 database, we increased the identification rate of T. indotineae from 5% to 96%, with a sensitivity of 99. 6%. We also identified specific peaks (6834/6845 daltons and 10,634/10,680 daltons) allowing for the distinction of T. indotineae from the other species of the complex. Our improved version of the MSI-2 application allows for the identification of T. indotineae. This will improve the epidemiological knowledge of the spread of this species throughout the world and will help to improve patient care.
MSI-2 is an application developed by Sorbonne University (Paris, France) and the Belgian collection of microorganisms (BCCM-IHEM, Brussels, Belgium) devoted to MALDI-TOF spectra identification, and using original identification algorithms and original reference databases (Fungi, Leishmania, Dipters, Amanita, Phlebotomus, and Enterobacter cloacae). The fungal MSI-2 database, of interest here, contains 16,442 references corresponding to 1581 fungal species. Even if MSI-2 has recently been improved for the identification of frequently encountered dermatophyte species  [23] , the identifica-tion of T. indotineae requires supplementary developments. After the database update by Jabet et al. , it has been reported by MSI-2 users that the application was unable to identify T. indotineae, despite the presence of references from two collection strains in the database.
Database In this study, an improved version of the MSI-2 database was built by adding new spectra to the online MSI-2 library (called MSI-2-improved throughout the manuscript). These new reference spectra were obtained at various stages of growth (days 3, 5, 7, 10, 12, and 14) and on two culture media-IDFP (Conidia, Lyon, France) and SDA-CG (Oxoid, France).
Panels of Strains and Isolates The capacity of the improved MSI-2 database to identify Trichophyton indotineae was tested with two different panels to assess both sensitivity and specificity. Panel 1 is composed of 2879 spectra corresponding to 67 isolates belonging to the T. mentagrophytes species complex that have been obtained from cultures of human samples in several French mycology laboratories, from the Sciensano collection and from animal samples. The list and origin of the spectra constituting Panel 1 can be found in Supplemental Table  S2. Among the 67 isolates, 1313 spectra from 34 strains were obtained by Jabet et al. in 2022  [23] , under the same culture conditions as the ones described above. Regarding those 34 strains that are present in both the MSI-2-improved database and Panel 1, selfrecognition of spectra belonging to the same strain were discarded in order to obtain only the best identification with a different reference strain.
Trichophyton mentagrophytes Genotyping All T. mentagrophytes isolates were typed by sequencing the ITS region (ITS1: 5 -TCC GTA GGT GAA CCT GCG G-3 ; ITS4: 5 -TCC TCC GCT TAT TGA TAT GC-3 )  [26] , with an annealing temperature of 55 • C. Distinctions between the genotypes were made by comparison with the reference sequences published by Taghipour et al. [4]  and by Klinger et al. [5]  (accession numbers of the reference sequences can be found in Supplemental Table  S3 ). Sequences were aligned using MEGA X software, and genotypes were distinguished by comparison of the nucleotides one by one in order to distinguish genotypes that differ by one deletion or insertion (as is the case between genotype I and genotype II). Typing results were verified with the Python script (https://github. com/Ivan-Pchelin/genotyping-bysequencing, accessed on 7 October 2022) developed by Ivan-Pchelin in 2019  [4].
MALDI-TOF Mass Spectra All spectra used for Panel 1 were acquired from cultures on both SDA-CG agar plates and IDFP agar plates at various stages of growth between 1 and 21 days of culture. The cultures were sampled with a sterile scalpel blade and introduced into a sterile microtube containing 70% ethanol. A full extraction method consisting of an inactivation step in 70% ethanol, a lysis step with 70% formic acid, and a protein precipitation step with 100% acetonitrile was performed in all cases, as previously published  [21]. Protein extracts were deposited in successive quadruplicates, as proposed in previous MALDI-TOF MS studies on filamentous fungi and dermatophytes  [27, 28]. The identification obtained with the highest score for the replicate harvesting was retained for the assessment of both databases.
Method of Spectral Comparison All spectra of T. indotineae obtained for this study were compared to observe modifications of the spectra related to the stage of the culture. To visualize the differences, we used ClinProTool software (Bruker Daltonics, version 3. 0). Spectra corresponding to the   [23]. * For isolates that are in both the reference database and in the test panel, self-identification at the strain level is not allowed.
Method of Spectral Comparison All spectra of T. indotineae obtained for this study were compared to observe modifications of the spectra related to the stage of the culture. To visualize the differences, we used ClinProTool software (Bruker Daltonics, version 3. 0). Spectra corresponding to the six stages of extraction per culture medium were loaded as six independent groups of spectra. The standard preparation workflow of the software, consisting of baseline subtraction and normalization, was applied using the default parameters. Then, a recalibration, consisting of reducing the mass shifts that may occur during the measurement of the spectra, was performed. Spectra were visualized in Gel/Stack View mode.
Peak Picking and Distinction between the Two Most Represented Genotypes, i. e. , T. interdigitale and T. indotineae As the two groups represented for all stages of growth by a sufficient number of spectra are T. interdigitale genotype II (1108 spectra) and T. indotineae (905 spectra), we compared the spectra of those two species to search for specific peaks after a pretreatment step of the spectra, consisting of baseline subtraction, smoothing, alignment, and normalization of the intensities. Boxplots were then computed to compare peak intensities in relation to the species (T. interdigitale genotype II versus T. indotineae). Finally, we extracted the list of peaks present in all of the spectra obtained at days 3, 5, 7, 10, 12, and 14 using Flex Analysis software, and we calculated the percentage of spectra that contained the species-specific peaks (T.
Trichophyton mentagrophytes Genotyping Control of the genotypes present in the MSI-2 database using their ITS sequences shows the presence of 8 genotypes among the references (T. interdigitale genotypes I and II; T. mentagrophytes genotypes II*, III, III*, IV, and XXIV; T. indotineae), represented by 1 to 25 strains (Supplemental Table  S1 ). Among the 67 isolates constituting panel 1, we obtained 8 genotypes: T. interdigitale genotype I (n = 8), T. interdigitale genotype II (n = 26), T. mentagrophytes genotype II* (n = 3), T. mentagrophytes genotype III (n = 1), T. mentagrophytes genotype III* (n = 8), T. mentagrophytes genotype VI (n = 1), T. mentagrophytes genotype VII (n = 1), and T. indotineae (n = 19).
MSI-2 Improved Database A total of 142 new reference spectra were added to the database previously published by Jabet in 2022. New references represented not only T. indotineae (n = 3), but also T. interdigitale genotypes I (n = 2 strains) and II (n= 3 strains) and T. mentagrophytes genotypes II*, III*, VI, and VII (n = 1 strain each). To build the MSI-2-improved database, T. interdigitale and T. indotineae strains were randomly selected from all the strains that were available for this study. A list of the references of the T. mentagrophytes species complex can be found in Supplemental Table  S1.
Identification Performances Using the MSI-2-Improved Database Using the MSI-2-improved database, we built a contingency table for all culture conditions combined. The identification results per isolate and per culture condition (n = 722) are computed in Table  1. The number of spectra, number of true and false positive and negative identifications, sensitivity, and specificity for the three species are compiled in Table  2. Regarding the 5212 spectra constituting the outgroup, there was no misidentification as T. indotineae. The only spectra that matched against references belonging to the T. mentagrophytes species complex were 16 spectra of Trichophyton tonsurans that were misidentified as T. interdigitale (n = 11) and T. mentagrophytes (n = 5). While only 12. 12% of the T. indotineae spectra acquired before 5 days of growth and 5.