dataesr/scientific-paragraphs-categorization huggingface dataset:
This research has made use of the SIMBAD database and VizieR catalogue access tool, operated at  CDS, Strasbourg, France. This work has made use of data from the  European Space Agency (ESA)  mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work is supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes. This work is also supported by the  PRIN-INAF   2019  " Planetary systems at young ages (PLATEA )".
Compound 2-mediated anti-hyperalgesic effects were compared to ERα17p by using von Frey test in Complete Freund's Adjuvant (CFA) model. Morphine (1 mg/kg) and vehicle (saline solution) were used as positive control and as reference, respectively. Mechanical paw withdrawal threshold (PWT) was used to evaluate pain. For each condition, eight mice were used. For all mice (Fig. 7a , n= 40), a decrease of the PWT from 0. 79 ± 0. 029 to 0. 058 ± 0. 0053 g (p < 0. 001, t-test) was recorded, seven days after CFA injection. These data are relevant to the hyperalgesia symptoms encountered during chronic inflammation. In the case of ERα17p, an increase of the PWT was observed at 45 min (0. 051 ± 0. 011 g for the vehicle versus 0. 141 ± 0. 025 g for 1. 25 mg/kg of ERα17p and 0. 293 ± 0. 055 g for 2. 5 mg/kg of ERα17p).
4 and elevated concentrations was observed. Accordingly, TEM images revealed different types of aggregates, depending on the pH. At pH 7. 4, a dense network resembling to prefibrillar oligomers was shown, whereas mature amyloid fibrils were preferred in basic conditions. These observations were strengthened by NMR data recorded over a longer period (65 h versus 40 h). No plateau was reached at pH 7. 4, even after 65 h, suggesting a slow and complex process with different types of aggregates. In this regard, we have shown in previous studies that ERα17p, which is random coil in solution, is able to fold into a β-strand regular structure to form a variety of regular spheres with a diameter ranging from 30 to 700 nm or to form a dense and rigid hydrogel, depending on experimental conditions  10, 36, 37, 46, 47.
Peptide concentrations varied from 10 to 100 μM, at pH 3. 4 (0. 2 M glycine HCl in water), pH 7. 4 (0. 2 M KH 2 PO 4 in water, 0. 2 M K 2 HPO 4 , in water) and pH 9. 1 (0. 2 M glycine NaOH in water). The microtiter plate was shaken prior to measurements. Fluorescence intensity (arbitrary units, u. a. ) was recorded every 10 min, during 40 h. Resulting curves were analyzed by using GraphPad Prism. Data are the means of experiments performed in triplicate. Nuclear magnetic resonance (NMR). ERα17p fibrilization was followed at 298 K by using a NMR spectrometer (Bruker AVANCE III 500 MHz) equipped with a triple resonance ( 1 H,  15  N, 13 C) cryoprobe. Each sample (100 μM) was prepared extemporaneously in appropriate buffer at pH 3. 4, 7. 4 and 9. 1. A 10:90 D 2 O:H 2 O mixture was added to the buffer to reach a final D 2 O percentage of 7.
Circular dichroism (CD). CD spectra were recorded on a Jasco J-815 apparatus (Lisses, France) using quartz cells (0. 1 cm path length, Hellma GmbH, Müllheim, Germany). Each peptide was dissolved (final concentration: 100 μM) in appropriate buffer solution, at pH 9. 1. After an equilibration period of 5 min at 25 °C, CD spectra were recorded over the 190 to 260 nm wavelength range. Measurements were carried out at 1 nm intervals. Data points were acquired every 0. 5 nm in continuous scanning mode (speed = 20 nm. min -1 , bandwidth = 1 nm). CD spectra were recorded after 0, 5, 22 and 28 h (unit: mean residue ellipticity [θ] MRE , in deg. cm  2. dmol -  ). Acquisitions were carried out using the software Spectra Manager. Recorded data (mean of five scans) were treated with GraphPad Prism. Buffer background was subtracted to avoid buffer and cell contributions.
Paw withdrawal and licking were considered as positive responses. 50% paw withdrawal threshold (PWT) was determined using an adaptation of the Dixon up-down method, as described previously  61. Statistical analysis. All data were analyzed using the Prism 8 software (GraphPad™ Software Inc. , San Diego, CA). Data were tested for normality (Shapiro-Wilk test) and equal variance (Fisher test). For kinetic data, multiple measurements were compared with repeated measures (two-way ANOVA). Post hoc comparisons were performed by the Tukey's test. The area under the curve (AUC, 0-180 min) of 50% mechanical threshold (individual values) was calculated by the trapezoidal rule (reference: PWT baseline after CFA injection (threshold at time T 0 )). The AUC of individual values is the sum of each area between experimental times from 0 to 180 min (equation: (time T -time before time T) × [(threshold at time T -threshold at time T 0 ) + (thresholds obtained at time T 0 or at time before time T -threshold at time T 0 )/2]).
Acknowledgements We are grateful to  Lucie Gonzalez  ( Sorbonne Université ) and  Evin Cetinkaya  ( Sorbonne Université ) for their technical contribution. We thank  Ecole Doctorale 406  ( Sorbonne Université ). We would like to thank  Dr. Thierry Dubois  ( Institut Curie ) for his generous gift of HS578T breast cancer cells. We are also grateful to  Dr. W. T. Khaled  ( University of Cambridge, UK ) for providing the pSpCas9 (BB)-2A-Puro (PX459) plasmid.
scabiosae sample HS03 were excluded from the fungal analysis as no fungal ITS2 reads were detected in these samples. The most abundant ASV in the 16S rRNA dataset was assigned to Brevibacillus borstelensis which was detected in all samples. ASVs of the genera Brevibacillus, Apilactobacillus, Acinetobacter, Weissella and Rosenbergiella were the most abundant, but their abundance varied between bee species. Four of the 20 most abundant ASVs could not be classified at the genus level; at family level three could be assigned to Enterobacteriaceae and one was assigned at order level to Corynebacteriales. The majority of ITS2 reads were attributed to an ASV that could not be classified beyond the order Saccharomycetales. Other abundant genera in the ITS2 dataset included Metschnikowia, Starmerella and Candida.
Megachile sculpturalis gut microbial community dissimilarity in native and invasive bees The M. sculpturalis microbiota composition was determined for bees sampled from their native region (Japan) and from two invaded regions (USA and France). PERMANOVA analysis indicated that bacterial and fungal communities of M. sculpturalis differed significantly between sampled locations (R 2 = 0. 62, p < 0. 001 and R 2 = 0. 24, p < 0. 05, respectively). Analysis of group dispersions showed that the bacterial data were heterogeneously dispersed (p < 0. 001), reducing the explanatory power of the PERMANOVA test, whereas the fungal data were homogeneously dispersed ( p = 0. 158). Pairwise PERMANOVA tests demonstrated that the bacterial communities of M. sculpturalis at the two locations in Japan were not significantly different ( p adj = 0.
sculpturalis gut microbiota compositions in both invaded regions are so similar. New York and Marseille were recently identified as highly suitable areas for M. sculpturalis  (Lanner et al. , 2022). However, comparison of bioclimatic and landscape variables for the sampling sites retrieved from CHELSA version 2. 1  (Karger et al. , 2017)  and Land Use Harmonisation dataset version 2  (Chen et al. , 2020) , respectively, showed distinct temperature, precipitation and land use conditions for both locations (data not shown). Remarkably, at both locations M. sculpturalis was repeatedly documented to forage preferentially on pollen from exotic species such as the ornamental Japanese pagoda tree (Styphnolobium japonicum)  (Le Féon et al. , 2018; Matteson et al. , 2008; Parys et al. , 2015; Quaranta et al.
Merged paired reads with a length between 401 bp and 428 bp were retained for further analyses. ITS2 primers were removed using cutadapt version 3. 4  (Martin, 2011)  and reads with a minimum length of 50 bp were retained through the rest of the pipe- ASVs attributed to chloroplasts, mitochondria, Archaea, Eukarya and unclassified phyla were removed from the 16S rRNA dataset, whereas only ASVs assigned to fungi were retained in the ITS2 dataset for further analysis.
line. Taxonomy was assigned to the resulting 16S rRNA and ITS2 amplicon sequence variants (ASVs) using the DADA2 formatted training FASTA files of the SILVA SSU database version 138. 1 (McLaren & Callahan, 2021) and the General FASTA release files from the UNITE ITS database version 8. 3 (All eukaryotes) (Abarenkov et al. , 2021).
ACKNOWLEDGEMENTS We thank our collaborators  Sarah Kornbluth  and  Jay Holmes  for providing samples from the  USA , and  Tomoyuki Yokoi  for the samples from Japan. BG thanks the city of  Marseille  for granting access to the parks, and  Camille Ruel ,  Floriane Flacher-Geslin ,  Lucie Schurr ,  Marion Delobeau  and  Jean-Yves Meunier  for their occasional assistance with field work.
The subwatersheds included in the GRW served as the objects of our GeOBIA and were classified in function of their hydrological similarities, which are defined as the resemblances in the hydrogeomorphic structure of the subwatersheds. A total of seven classifications were run to span the period 1985-2019. The most recent classification results provided the current hydrological configuration of the GRW, and the seven-classification time series provided understanding of the landscape changes that occurred in the last 35 years. To better grasp the nature of these changes, a Tasseled Cap (TC) trend analysis based on Landsat images dataset was performed to assess land cover changes at a resolution of 30 m. Using this methodology, we define the landscape properties that are likely to influence the hydrological processes at work in a large subarctic watershed to infer how properties and processes are changing and affecting the watershed hydrological regimes over time.
Watershed and subwatersheds delineations The delineation of the George River watershed (GRW) was computed from the Canadian Digital Elevation Model (CDEM) dataset using the hydrology tools of the Spatial Analyst Toolbox in ArcMap 10. 6. 1. The GRW subwatersheds' delineations were computed from the resulting Digital Elevation Model (DEM) clipped with the GRW delineation, using the ArcHydro Toolbox extension in ArcMap 10. 6. 1. The DEM was first leveled and reconditioned by burning the CanVec lakes and streams shapefiles over it. The outlet of each subwatershed was then set at every confluence where the affluents were draining an area of at least 100 km 2 , resulting in the delineation of 218 subwatersheds contained in the GRW, with sizes ranging from 6 to 773 km 2 with an average of 193 km 2.
Computation and extraction of hydro-geomorphic metrics A total of 10 hydro-geomorphic metrics were computed and extracted from our subwatersheds delineations. These metrics were categorized into three types: topographic, land cover and geological metrics, respectively (Table  C1 ). Our topographic metrics contain the mean elevation, the mean slope, the drainage density, and the form factor of our 218 subwatersheds. The computation and extraction were completed using the CDEM dataset, the Spatial Analyst Tools and the ArcHydro Toolbox extension in ArcGIS 10. 7. 1, and a Python script. For land cover metrics, a total of three spectral indices were computed : (i) the normalized difference vegetation index (NDVI), correlated to vegetation density  (Tucker, 1979) ; (ii) the normalized difference moisture index (NDMI), correlated to soil and vegetation moisture content  (Gao, 1996) ; and (iii) the normalized difference water index (NDWI), correlated to the presence of surface water, including lakes, streams, but also snow  (McFeeters, 1996).
-Only the Mean Annual Temperature (MAT) and the Total Annual Precipitation (TAP) variables were acquired. -Each dataset is divided into four subwatersheds covering the entire George River Watershed, one for the Lower George region (north) and three for the Upper George region (south). as subglacial till, glaciolacustrine, marine, and organic deposits, which allow for the formation of segregation ice. For each subwatershed, the fractional coverages of thaw-stable and thaw-sensitive deposits were computed using a Python script.
Fuzzy c-means clustering The unsupervised GeOBIA is based on the methodology of  Choubin et al. (2017). The ten hydro-geomorphic metrics were used as the input variables of the GeOBIA, and the subwatersheds were used as the objects to classify. After computation and extraction, the input variables were normalized to assure an equivalently weighted classification. The clustering algorithm used was the Fuzzy C-Means (FCM) algorithm  (Dunn, 1973; Bezdek, 1981) , which provides, for each object, a set of membership coefficients corresponding respectively to each cluster. Each classification returned a Fuzzy Partition Coefficient (FPC), which described how well-partitioned our dataset was. More details on the Fuzzy C-Means algorithm are presented in Appendix E. The complete clustering methodology is summarized in Fig.
Current hydrological configuration of the GRW With the clustering results of our GeOBIA for the period 2015-2019 (Fig. 4b , 4c and 4d), it is now possible to assess the typical hydrological configurations of both clusters, and of the overall GRW. The hydro-geomorphic metrics statistical distributions previously presented (Fig. 5 ) focus on metrics with high partitioning effects on the dataset which give insights about the general regime, water balance and hydrological response characterizing both Cluster 1 (C1) and Cluster 2 (C2) and allow an efficient comparison of these clusters. Snowmelt is the major hydrological event affecting the runoff regime in the GRW (FitzGibbon and  Dunne, 1981). Thus, the GRW's annual hydrograph presents high discharge in early summer, followed by subdued hydrological responses to rainfall events that interrupt the summer baseflow; and a runoff decrease in winter induced by an increase in snow storage.
Cloudy pixels were masked using the quality assessment (QA) band. -Acquired using the Google Earth Engine (GEE) Data Catalog. Surface deposit Geological metrics computation Every surface deposit unit larger -Produced by the Ministère des Forêts, de la Faune et des Parcs map of northern than 100 ha du Québec Quebec (https://www. donneesquebec. ca/recherche/dataset/carte-des- depots-de-surface-du-nord-quebecois/resource/db364178- 0d70-47db-83a8-d0e912d7ec65). -Based on geomorphologic interpretations of RapidEye images (6. 5 m resolution) taken between 2010 and 2013. Reanalysis Climate data comparison with land Watershed scale -Produced by Environment and Climate Change Canada climate data cover metrics time series (https://climatedata. ca/).
Assessing reliability of photographic identification We measured traits from photos posted and identified to research grade on iNaturalist. org platform, retrieved by searching for both species' names. To build a valid reference database of morphological traits (ratio values, see further), we first limited our search to the spatial extent where the distinction between A. puissanti and A. thalassinus has already been conducted for several years (France, Spain, and Portugal; pers. obs. ); moreover, we only used records with a 'Research Grade' quality rate on iNaturalist, that is, for which consensus among experts was reached on the given identifications, that were also confirmed by some of the authors (MP and BN), and thus considered as reliable. Each observation was considered as an independent record.
Clarifying species' ranges Values of correct classification rates >0. 7 are indicative of a good model for classification for DFA  (Eiras et al. , 2021). As such, we selected DF models reaching high efficiency (>0. 7) to classify iNaturalist photographic records from outside of the known range of co-occurrence of our target species, selecting geographical areas where records of A. thalassinus are present and A. puissanti is likely to occur. Namely, we screened for suitability, and subsequently tested, records from Italy, Croatia, Albania, Greece, Crete, Turkey, Cyprus and Israel. Data selection and measurements from photographic records followed the very same procedure specified for method calibration. Records reaching excellent assignment probabilities (>90%) were considered as reliable, and identified as the species assigned by the qDFA.
ACKNOWLEDGMENTS This work takes its roots from the citizen science platform iNaturalist, and its aims are partly directed to that and its participants. So, we deeply thank all the naturalists/observers who transmitted their photos on iNaturalist and thus made this work possible. We also thank  Joaqu ın Ortego  and an anonymous reviewer, whose comments significantly improved a first version of this manuscript. LA was supported by the  National Recovery and Resilience Plan (NRRP) ,  Mission 4 Component 2 Investment 1. 4   -Call  for tender No. 3138 of 16 December 2021, rectified by Decree n. 3175 of 18 December 2021 of  Italian Ministry of University  and Research funded by the  European Union -NextGenerationEU ; Project code  CN_00000033 , Concession Decree No. 1034  of  17 June 2022  adopted by the  Italian Ministry of University and Research ,  CUP B83C22002930006 , Project title ' National Biodiversity Future Center -NBFC '.
Fat is physiologically embedded within the interosseous ligaments in the posterior sacroiliac (PSIJ). This composite of fat and ligaments is hypothesised to serve a shock-absorbing, stabilising function for the sacroiliac joint and the lumbopelvic transition region. Using a novel Python-based software (VolSEQ), total PSIJ volume and fat volume was computed semi-automatically. Differences within the cohort and the viability of the program for the quanti cation of fat in routine CT scans were assessed. In 39 CT scans of heathy patients the PSIJ were rst manually segmented as a region of interest (ROI) in OSIRIX. Within VolSEQ, 'fat' Houns eld units (- 150 to - 50 HU) are selected and patient DICOM les of the patient and associated region of interest le from OSIRIX were imported and the pixel sub volumes were then automatically computed.
Volume comparisons were made between sexes, sides and ages (≤ 30, 31-64   and > 65 years). PSIJ volumes in both software (VolSeq vs OSIRIX) were non-different (both 9. 7 ± 2. 8cm 3 ; p = 0. 9). Total PSIJ volume (p = 0. 3) and fat volume (p = 0. 7) between sexes was non-different. A signi cant difference in total PSIJ volume between sexes (p < 0. 01) but not in fat volume (p = 0. 3) was found only in the ≥ 65 years cohort. Fat volume within the PSIJ remains unchanged throughout life. PSIJ volume is sex-dependant after 65 years. VolSEQ is a viable and user-friendly method for sub-volume quanti cation of tissues in CT.
Introduction Quanti cation of tissue volumes and subvolumes using medical imaging techniques is not a new technique  [1] [2] [3] [4] [5] [6] [7] [8]. Commonly, data sets derived from magnetic resonance imaging (MRI)  2, [9] [10] [11] [12] [13] [14] [15]  or computed tomography (CT) are used for this purpose. Available software such as OSIRIX (Pixmeo Sarl 2016, Geneva, Switzerland), Horos (The Horos Project, MD, USA; https://horosproject. org/), SliceOMatic (TomoVision, Montreal, QC, Canada), Amira (Zuse Institute Berlin, Germany) or ImageJ (National Institutes of Health, Bethesda, MA, USA) are well established tools for both the qualitative and quantitative assessment of images based on the manual segmentation of regions of interest (ROI)  1- 6,9-15. The quanti cation of certain tissues in small speci c regions is often challenged by the limited resolution of clinical CT and MRI datasets  [16] [17] [18]  and has to be performed manually with all consequences of potential subjectivity.
A previous study  7  successfully quanti ed the total fat volume in this region and determined sex and age-related differences in fat content using OSIRIX. Major drawbacks were that it was limited to an elderly population, the segmentation was based on digital photographs of cadaveric specimens, which was a time-consuming and potentially subjective approach as some smaller fat pixels may be invisible to the naked eye. Thus, a more recent study  Poilliot, et al. 8  introduced a semi-automated method using MATLAB (MathWorks, Natick, MA, USA) to quantify fat within the PSIJ using clinical CT scans. This method was found to be objective, time e cient with a higher degree of sensitivity than the previous manual segmentation method in Poilliot, et al. 7. A major drawback of the semi-automated method utilised in  Poilliot, et al.
8  was that the study was limited to a small number of CT scans and these were all elderly individuals. Furthermore, utilising MATLAB requires training and may therefore be unaccessible to certain individuals, not to mention costly. Therefore, using a similar approach, a novel cost-effective Python program (VolSEQ) was created to quantify the fat content in the PSIJ region in a cohort of healthy individuals CT scans with a larger age range in adulthood. This given research work aims to determine the total volume of the PSIJ and subsequently compute the fat volume in the region to determine differences within a generic 'healthy' SIJ population and test the viability of the VolSEQ program for the quanti cation of fat in CT scans. The following hypotheses were investigated and revisited using this new software: Fat volume decreases with age within the sacroiliac joint Females have a higher fat content as well as total PSIJ volumes than males Side has no in uence on fat content.
Ethical approval was acquired for the use of patient datasets used in research studies for diagnostic and therapeutic purposes as in other works  23, 24. These patients were used in previous studies for the completion of a student thesis by the author  23, 24. Approval was granted on the grounds of existing patient datasets. Informed consent was obtained from all living subjects of this study. All methods were carried out in accordance with relevant guidelines and regulations. The University of Otago Human Ethics Committee (Health) [ref: H17/20]) approved the present study. All the data were analysed strictly anonymously.
Fat quanti cation using VolSEQ Using a Python algorithm, a novel software (VolSEQ v1. 0. 0, Python, freely available at: https://github. com/4lom/VolSEQ. git) was developed by software developer L. Zeissloff 25 for the purpose of this study. VolSEQ is intended to help quantify pixels of selected tissue based on a known range of Houns eld units (HU) on conventional CT scans (SOMATOM as64 open, Siemens, Munich, Germany; Aquilion one, Toshiba, Tokyo, Japan). In this study, only HUs representing fat in the CT scans were targeted based on the range - 150 to - 50 HU, as de ned in previous studies  1, 4, 8. The regions of interest (ROIs) were applied to the patient series when it was processed using VolSEQ to act as a boundary to only quantify pixels in the PSIJ area of the image using pre-made ROIs.
The ROIs were rst manually outlined in OSIRIX on each slice, and the speci c ROI information was exported into a converted '. csv' le usable in Excel (Microsoft Corp. , Redmont, WA, USA). The boundaries of the PSIJ have been described previously  7, 8. Since the boundaries of the posterior sacroiliac ligament were di cult to clearly visualise in the CT scans, the posterior boundary was set as an arbitrary line running directly between the ilium and the most posterior part of the sacrum (Fig. 2A ). Results computed by the program were the pixelated volume of fat within the PSIJ converted into a volume in cubic centimetres (Fig. 2B ). Furthermore, total volume was computed in OSIRIX and in VolSEQ to compare the reliability of the calculations of the program.