dataesr/scientific-paragraphs-categorization huggingface dataset:
Diffusion MRI processing • Data pre-processing and tracts segmentation Preprocessing of diffusion-weighted images included denoising of data  (Veraart et al. , 2016) ; eddy current correction and motion Overall, patients included in this study present a similar profile to that of the larger TLE sample. LTLE/RTLE patients are matched on the clinical variables, also for the volume asymmetry between hippocampi (ASY; considering the absolute value). We observed significant differences at p <. 05 on some cognitive variables (marked with a star, namely: AMI, VMI memory scores and SFL and PFL verbal fluency scores). However, these differences did not persist at a corrected statistical threshold. See main text for description of variables and associated statistics. correction  (Andersson et al. , 2003)  as implemented in FSL (topup command;  Smith et al.
, 2004) ; bias field and Gibbs artifacts' corrections  (Tustison et al. , 2010) , and up-sampling DWI spatial resolution by a factor in all three dimensions using cubic b-spline interpolation, to a voxel size of 1 mm 3  (Raffelt et al. , 2012a). From the preprocessed data, we have estimated fiber orientation distributions using the Constrained Spherical Deconvolution model (CSD;  Tournier et al. , 2007)  with the individual response function (RF) and by using multi-tissue (3-tissue) CSD variants  (Dhollander et al. , 2019). We derived the SH peaks from the fiber orientation distribution function (FOD) maps. All preprocessing steps were conducted using MRtrix3 commands (https://www. mrtrix. org) or using MRtrix3 scripts that interfaced with external software packages. For an efficient, automatic, and fast tracts segmentation, we used TractSeg  (Wasserthal et al.
, 2018 ; openly available at: https://github. com/MIC-DKFZ/TractSeg). TractSeg algorithms propose to create bundle-specific tractograms by tracking directly on the tract orientation maps (probabilistic tracking) to perform tractography from original DWI data. Briefly, the authors have used convolutional neural networks for the tract segmentation, start/ end region segmentation, and tract orientation mapping, all based on U-Net  (Ronneberger et al. , 2015)  that receives FOD peaks as input.
Statistical learning to assess variability We applied a previously published manifold learning pipeline (TractLearn:  Attyé et al. , 2021 ; https://github. com/GeodAIsics/Tract Learn-WholeBrain). TractLearn used geodesic manifold learning as a data-driven learning task and allows a unified framework for brain fascicles quantitative analyses. As TractLearn is integrated into a Riemannian atlas framework, f(x) represents the local mean value (i. e. , the mean value of the closest HC) in the manifold space  (Lawrence, 2003; Titsias & Lawrence, 2004). This way, the TractLearn algorithm has strong advantages in the detection of both global and local variability  (Attyé et al. , 2021 , for a demonstration and a comparison with classical GLM to detect subtle differences). As a first step of the pipeline, we reduced the dimensionality of all voxels contained in each tract to obtain one point per tract and per individual in the manifold space.
The resulting projection provides insight into whether and how this variable describes the data in the reduced space. Finally, we used NiiStat scripts (https://github. com/neurolabusc/ NiiStat) to map the lesion-behavior correlations at a local scale (i. e. , at the voxel level; VLBM). Bivariate testing was based on 5000 permutations. We set the minimum of overlap at 6 -meaning that we considered only damaged voxels present in at least one-third of patientsensuring that correlation analyses are performed in regions typically impaired in TLE and avoiding contamination by low-power voxels  (Sperber & Karnath, 2017). All statistics of the multimodal investigations were systematically adjusted for multiple comparisons.
A major interest in using such a statistical learning model is to explore inter-/intra-individual heterogeneity (Fig. 3  Panel C; see also Figures S5-S6 in Appendix S2 for an example of interindividual variability in our sample regarding the distribution of abnormalities). Indeed, rather than assuming that all patients are reliably represented by measures of central tendency and a priori-defined groups, these tools can be used to directly capture and analyze patient heterogeneity  (Cearns et al. , 2019; Marquand et al. , 2016). TractLearn, such as other software and method based on artificial intelligence  (Chamberland et al. , 2019 , Chamberland et al. , 2021) , is a promising tool for moving toward precision medicine (i. e. , more personalized and individual-oriented medicine). It has a strong potential of application and has also been shown to be robust in other populations, such as patients with traumatic brain injury  (Attyé et al.
Scientific interest for Latin American mountain ecology and biogeography, has quickly increased over the last decades, mostly because they i) contribute to more than half of the 85,000 species pool that conform the Latin American vascular flora  (Ulloa et al. 2017) ; and ii) coincide with several noteworthy biodiversity hotspots, such as the Tropical Andes, Mesoamerica, Tumbes-Chocó-Magda-lena, and the Chilean Winter Rainfall-Valdivian Forests. Yet, broad-scale research on mountains, and highlands specifically, remains limited due to data availability  (Sabatini et al. 2021). This said, floristic data contributions are steadily increasing and being made available in national repositories, such as the SiB Colombia (https://biodiversidad. co), as well as on global platforms, for instance (www.
gbif. org) and TRY (https://www. try-db. org). In contrast, vegetation data remains scarce and difficult to access (but see  Peyre et al. 2015). This type of data is very advantageous as it provides information on the distribution of both species and plant communities, and as a result, it should be a primary research focus for Latin American mountains  (Bottin et al. 2020). In this study, we present VegAndes, the vegetation database for the Latin American highlands, a novel dataset to address the dramatic data-gap in vegetation surveys in Latin American mountains and provide strong bases for future studies in plant ecology and biogeography. The VegAndes initiative emerges from consortium discussions involving expert botanists and vegetation scientists dedicated to highland research in Latin America.
Methods The database structure was programmed in SQL, following VegPáramo  (Peyre et al. 2015). It revolves around a primary table containing the basic vegetation plot data, and five secondary tables that are connected to the primary table via the VegAndes plot ID (except for the taxon and syntaxon lists, detailed below) (Figure  2 ). Among the extensive array of data-sources consulted for Latin America, only those containing plot vegetation data expertly identified as highland or ecotonal vegetation by the data's authors were included into VegAndes. Plots were not discriminated between zonal and azonal vegetation as long as they fitted the at-or above-the-timberline rule, or equivalent in desertic areas. All plots were primary data except those proceeding from the multi-source VegPáramo dataset (www.
vegparamo. com). In this case, plots were referenced as their secondary source and inquiring users should consult the VegPáramo webpage for further details on primary data-sources. The Primary table contains the plot data, with unique values to represent species occurrence and/or cover within plots. The species list uses the initial taxon names provided by the plot authors as well as a taxon ID assigned to each name. Non-vascular plants were excluded from the datasets. All non-identified taxa above the genus level were also discarded, and those at or below the genus level were included and assigned a codified taxon ID including the source name and unique number. Useful particles to assess the certainty of taxonomic determination such as cf. , aff. and gr. , were recorded. All names will subsequently be updated pending future taxonomic determinations.
VegAndes allows for presence-absence data as well as several cover-scales: the Braun-Blanquet (1964) phytosociological scale, transformed versions of the Braun Blanquet scale, for instance  Van der Maarel's (1979) , and percentage cover (in %). The primary table can be converted into a work table containing accepted names only. To perform the transfer, all recorded names need to be cross-checked with the Taxon list (see details below), so to remove unresolved names and merge rows with identical names. Whenever accepted names are duplicated in the same plot (for example after checking two sub-species of the same species in the same plot), their cover values are summed. However, if those values come as cover coefficients, then a simple procedure based on probability rules is used to either retain or increase the resulting coefficient.
Last, it shows the plant community informal name, combining structural qualifiers and the name of up to two dominant and/or diagnostic plant species, for example "Bunchgrassland with Espeletia grandiflora and Eryngium humboldtii". The Data-source table contains the information related to the primary or secondary (when applicable) source of the vegetation plot data. Both published and unpublished sources are considered, but unpublished ones, such as private datasets and academic theses, were carefully and expertly revised beforehand. The table includes the source's full author list, publication date, sampling date, title, and journal/ book complete reference. Currently, 53 data-sources are included in VegAndes (86 when counting VegPáramo's multisource input), of which 11 are unpublished datasets (see Suppl.
The Taxon list also includes family names and higher clades to allow for easy data-searches in: (i) Angiosperms, including Magnoliids, Monocots and Eudicots; (ii) Gymnosperms; (iii) Lycophytes; (iv) Monilophytes. It is expected that the Taxon list will undergo annual synonymy revisions and grow as new data is added to VegAndes. The Syntaxon list is in its initial stages and compiles all existing information on syntaxonomy as displayed in the VegAndes data-sources. As a result, this table contains hierarchical syntaxon terms for classes, orders, alliances and associations. However, there has been no revision of the complete classification to date, so it is advised to handle this information as highly provisional. At present, 5,804 taxon names, corresponding to 3,858 accepted names, as well as 136 syntaxon names are recorded in the VegAndes lists.
Distribution of the VegAndes plot data The VegAndes plots are spread over the Latin American highlands from Lat. 20°N to 39°S in a fairly uniform manner, except for the Patagonian Andes region in the Southern Andes (Table  1 , Figure  3A ). Most VegAndes data comes from the Northern Andes (almost 60% of the plot total) and especially from Colombia and Ecuador (together summing up to 52%). The Central Andes come second with almost 23% of the plot total, essentially from Peru (16%). Certain areas contribute disproportionately to the dataset, for instance the Nevados sector in Colombia (Lat. 4. 5°N -Long. 75. 5°W) with 472 plots in 10,000 km 2 , and to lesser extent the Moquegua mountains in Peru (Lat. 16. 5°S -Long. 70. 5°W) with 385 plots in 10,000 km 2. Differences in sampling efforts are usually due to expert location and ease of access.
For example, the guerrilla conflict in Colombia substantially limited fieldwork in certain mountain areas over the last decades, many of which have only become accessible after the peace treaty of 2016  (Negret et al. 2017). Conversely, other areas are now becoming difficult of access due to civil tensions or local conflicts, for instance related to illegal extractive practices. The remaining spatial data gaps in VegAndes should be addressed and filled whenever possible, especially in the Patagonian Andes, central Peru and Central American mountains. From an altitudinal standpoint, the VegAndes plot data can serve as simple proxy and give approximations about the current timberline position and highland elevation range throughout Latin America (Figure  3B ). The Veg-Andes plots show a hump-shaped pattern for the Andes, with high elevations concentrated at mid-latitudes and around the equator, and lower elevations towards the edges of the main cordillera around Lat.
10°N and 40°S. This pattern reflects alpine ecological theory, which states that altitudinal belts typically occur at higher elevation and expand towards the equator  (Körner et al. 2011; Körner and Spehn 2019). Moreover, the plot data manifests the biogeographical transition at Lat. 5-7°S, with relatively low elevation plots in comparison to the high-elevation Central and Northern Andes  (Weigend 2002). Similarly, a clear depression in plot elevation is observed at the biogeographical transition between the Northern Andes and Mesoamerica. According to the VegAndes data, the approximate timberline by proxy appears to be highest in the Altiplano region of the Central Andes (Lat. 20-30°S) and in the northernmost Mesoamerican mountains (Lat.
The ranking found in family representation resembles other tropical mountain floras, such as the Papua New Guinea and Afromontane highlands  (Sklenář et al. 2014). The VegAndes data also finds good representation of Fabaceae and Lamiaceae that are very important families in the subtropical and temperate highlands. At this stage, these results should be interpreted with caution since the data analysed might not represent the full spectrum of plant communities in the Latin American highlands and should be cross-checked with complementary floristic data types (e. g. Bottin et al. 2020). A comparison of species richness patterns of the Latin American highlands based on VegAndes data shows complex patterns (Figure  4B ). On average, the plot data summed up to about 20 families per 10,000 km 2 , while certain areas doubled that number, principally in Ecuador (Lat.
Future perspectives With the emergence of global vegetation initiatives covering wide spatio-temporal frames, such as sPlot, the global vegetation database  (Bruelheide et al. 2019) , Latin America has more than ever been put on the map for its floristic richness and uniqueness (e. g. Testolin et al. 2021). Research boundaries should be pushed to improve our understanding of the fascinating Latin American highlands and their vegetation, and to do so, data limitations need to be overcome. VegAndes is a pioneer dataset that not only fills in data-gaps, but also offers an array of research and collaboration opportunities for regional vegetation scientists. At this early stage, VegAndes can already provide input for transcontinental studies and complement global initiatives with a better representation of Latin American mountains.
We used active galactic nuclei (AGNs) with X-ray luminosities, L X,2-10 keV ∼ 10 42. 5-44 erg s -1 , from the COSMOS-Legacy survey that lie within the UltraVISTA region and cross-matched them with the LEGA-C catalogue. The latter provides measurements of the calcium break, D n 4000, and H δ Balmer line that allow us to study the stellar populations of AGNs and compare them with a galaxy reference catalogue. Our samples consist of 69 AGNs and 2176 non-AGN systems, within 0. 6 < z < 1. 3, that satisfy the same photometric selection criteria. We constructed the spectral energy distributions (SEDs) of both population and used the CIGALE code to investigate the effect of the two indices in the SED fitting process. Our analysis shows that the inclusion of D n 4000 and H δ allows CIGALE to better constrain the ages of the stellar populations.
(2018)  used measurements provided in the LEGA-C catalogue for two age-sensitive absorption line indices, the equivalent width (EW) of the H δ Balmer line  (Worthey & Ottaviani 1997) , and the calcium break D n 4000  (Balogh et al. 1999)  to study the stellar populations of the galaxies included in the LEGA-C dataset. D n 4000 is small for young stellar populations and large for old, metalrich galaxies. On the other hand, the EW of H δ rises rapidly in the first few hundred million years after a burst of star formation, when O-and B-type stars dominate the spectrum, and then decreases when A-type stars fade (e. g. ,  Kauffmann et al. 2003a; Wu et al. 2018). Based on the analysis of  Wu et al. (2018) , galaxies at z ∼ 0. 8 present a bimodal D n 4000-H δ distribution, implying a bimodal light-weighted age distribution.
This enriches our sample with the D n 4000 and H δ measurements included in the LEGA-C dataset. We also compiled a galaxy reference catalogue (control sample) that lies within the same area and redshift range as the AGNs (0. 6 < z < 1. 3) for which we also have the available D n 4000 and H δ measurements from the LEGA-C catalogue (Sect. 2). First, we constructed the spectral energy distributions (SEDs) of both AGNs and sources in the reference galaxy catalogue using photometry from optical-to-far-infrared and fitted their SEDs using the CIGALE code  (Boquien et al. 2019; Yang et al. 2020 Yang et al. , 2022)). The purpose of this exercise is to examine the effect of the inclusion of D n 4000 and H δ in the SED fitting measurements (Sect. 3). Specifically, we want to check whether the two spectral indices help CIGALE break the degeneracies of the SFH parameters, leading to more robust measurements of the galaxy properties.
X-ray sample The X-ray sample was extracted from the X-ray dataset described in  Marchesi et al. (2016)  and includes observations from the COSMOS-Legacy survey  (Civano et al. 2016 ). The A145, page 2 of 14 0:6 0:7 0:8 0:9 1:0 1:1 1:2 1:3 redshift latter is a 4. 6 Ms Chandra programme that covers 2. 2 deg 2 of the COSMOS field  (Scoville 2007). The X-ray catalogue includes 4016 sources. Marchesi et al. (2016)  matched the X-ray sources with optical and infrared counterparts using the likelihood ratio technique  (Sutherland & Saunders 1992). Of the sources, 97% have an optical and IR counterpart and a photometric redshift, and ≈54% have a spectroscopic redshift. Only X-ray sources within both the COSMOS and UltraVISTA  (McCracken et al. 2012 ) regions were used in our analysis. The reason for restricting the X-ray dataset in the UltraVISTA region is that the LEGA-C catalogue with which we cross-matched the X-ray sample consists of galaxies in this region.
Galaxy reference catalogue In our analysis, we compare the Hδ and D n 4000 of X-ray AGNs with that of non-AGN galaxies. The galaxy reference catalogue is provided by the HELP collaboration. About 500 000 sources are in the UltraVISTA region and approximately 230 000 meet the photometric criteria applied to the X-ray dataset. About 50% of the sources in the galaxy reference catalogue also have available measurements in the far-IR (Herschel).
Final LEGA-C samples We cross-matched the X-ray and galaxy reference catalogue described above with the LEGA-C dataset using a 1 radius and the optical coordinates provided in each catalogue. 134 X-ray AGNs and 3105 sources in the reference catalogue have counterparts in the LEGA-C sample. We exclude sources with FLAG_SPEC = 2, which indicates that the photometrybased flux calibration showed significant imperfections, compromising the measurement of absorption and emission indices (Sect. 4. 2 in van der  Wel et al. 2021). We further excluded sources with FLAG_MORPH = 1 or 2. This flag is used to indicate cases where the light coming from the slit is not from a single galaxy with a regular morphology. Finally, we only used line indices for galaxies that have a measured stellar velocity dispersion.
Reliability of Hδ and D n 4000 calculations from CIGALE In this section, we examine how accurately CIGALE can recover the Hδ and D n 4000 indices. For that purpose, we used the 69 AGNs and the 2,176 sources from the galaxy reference catalogue that have measurements available for both indices in the LEGA-C catalogue and ran CIGALE without including the two indices in the fitting process. We then compared CIGALE's calculations for Hδ and D n 4000 with those from the LEGA-C catalogue. Figure  10  compares the D n 4000 measurements of CIGALE to the data values, for the AGNs (top panel) and the galaxies in the reference catalogue (bottom panel). The index is not included as input in the fitting process. For both populations, the algorithm successfully recovers the value of D n 4000. As discussed earlier, this could be due to the extended wavelength coverage of the dataset.
Comparison of Hδ and D n 4000 between AGN and non-AGN systems In this section, we compare the values of the Hδ and D n 4000 indices between the AGNs and sources in the reference catalogue. First, we use the full datasets for both populations, as described in Sects. 2 and 3. 2. Then, we restrict the comparison to star forming systems by excluding quiescent (Q) sources. Finally, we split AGNs and galaxies based on their morphology and compactness.
Using this criterion, we find 42 (∼60%) AGNs hosted by Q systems in our sample. A similar fraction of Q is found among galaxies in the reference catalogue (∼50%). SB systems are identified as those galaxies that have sSFRs 0. 6 dex higher than the mean value of the  Mountrichas et al. (2022b)  sample at the redshift range of our dataset (e. g. ,  Rodighiero et al. 2015). ∼8% and ∼10% of the AGN and galaxies in the reference catalogue, respectively, are SBs. The remainder of the sources that are not Q or SBs are considered SF. The fraction of Q systems appears high compared to the ∼25% found in  Mountrichas et al. (2022b)  at a similar redshift range (see their Table  2 ). Figure  12  presents the M * distribution of the X-ray AGNs used in  Mountrichas et al. (2022b). We notice that our X-ray sources, which are a subset of the sources used in  Mountrichas et al.
Exclusion of quiescent systems In  Mountrichas et al. (2022a,b) , the lower SFR of AGN compared to non-AGN systems was found after excluding quiescent systems from their datasets. To examine whether our results from the comparison of the two indices (H δ , D n 4000) for the two A145, page 9 of 14 populations corroborate their findings, we excluded sources that are identified as quiescent from our samples (see previous section). ¡2 ¡1 0 1 2 3 4 5 6 7 EW(H ± ) º A ¡ data ¡2 0 2 4 6 EW(H ± ) º A ¡ CIGALE 1 : 1 ¡2 ¡1 0 1 2 3 4 5 6 7 EW(H ± ) º A ¡ data Figure  15  presents H δ versus D n 4000 for AGNs and sources in the reference catalogue, after excluding quiescent systems from both datasets. We notice that for both populations the vast majority of sources (100% of AGNs and 98% of sources in the reference galaxy catalogue) with high D n 4000 values (D n 4000 > 1.
2. 2 in  Ni et al. 2021). We note that type 1 sources have been removed from the  Ni et al. (2021)  catalogue, since they can potentially affect host galaxy morphological measurements (see their Sect. 2. 4). From the 69 AGNs and 2176 sources in the reference catalogue used in our analysis above, 57 and 1782, respectively, are included in the  Ni et al. (2021)  dataset. Out of the 57 AGNs, 16 (28%) are classified as BD, and a similar fraction is found among galaxies in the reference catalogue (459 sources -25%). A145, page 11 of 14 (mass-to-size ratio), for AGN (red circles) and galaxies (blue contours). AGN do not have Σ 1 values lower than 9. 0, whereas sources in the reference catalogue go down to 7. 5. This implies that AGN reside in more compact systems compared to non-AGN. Both populations present an increase of D n 4000 and a decrease of H δ with Σ 1 , at Σ 1 9.