dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). These are defined as areas where biodiversity is at its richest; they essentially act as habitats for the development and reproduction of species. Sources of environmental burdens. When it comes to collecting and reusing data on anthropogenic activities that could cause environmental pressures, such as land use or the location of human infrastructures (roads, industrial activities, or polluted sites and soils), it is easier to rely on available sources. However, the main difficulty lies in the temporal representativeness of this data, since their production and sharing modes are very diverse. Although most databases are recent and do not allow for a long-term evaluation of environmental quality, some are updated annually (e. g. BDTOPO; IGN, 2023), while others are updated on longer time scales (e.
g. CORINE LAND COVER; Ministère de la Transition écologique et de la Cohésion des territoires, 2018). In some cases, databases are updated continuously, and in this scenario, no historical data is available (e. g BASIAS, BASOL, ICPE; Ministère de la Transition écologique et de la  Cohésion des territoires, 2023a Cohésion des territoires, , 2023c Cohésion des territoires, , 2023b)). As demonstrated by various examples, environmental data is highly diverse in terms of nature, production mode (spatial and temporal resolution, measurement objective, and unit) and sharing mode (tables, rasters, GIS files). This heterogeneity makes it crucial to have a comprehensive understanding of the limitations related to the intrinsic quality of these data, as well as expert knowledge of environmental processes, including emission sources, the behavior and transfer of pollutants in environmental media, and the ecosystem services provided by nature, in order to avoid misinterpretation when reusing environmental data for environmental-health research.
Exploring territories from a multi-dimensional perspective The statistical methods employed in this article are increasingly used in the field of spatial data analysis. Among the existing clustering methods, the hierarchical ascending classification and the K-means partition are the two main alternatives. Both methods have been combined in this paper. In addition, a PCA was applied upstream to reduce the number of dimensions and thus optimize the result of the clustering methods. In this study, the principal components retained for the HCA explained 62% of the total variance. By comparison,  Pecher et al. (2013)  were able to summarize 71% of the variance for the definition of landscape profiles in the Alps. Despite the challenges associated with collecting and utilizing open data, the dataset used in this study provides a fairly comprehensive description of the physical environment.
The dataset provides information on environmental contaminations, pollutant emissions, emission source locations, land use, agricultural practices, and naturalness. Due to this completeness of input a classification that goes far beyond the urban/rural dichotomy was observed in this case-study, with five different environmental profiles of rural municipalities, which is a significant finding. The seven environmental clusters identified revealed an urban-rural gradient, which is also suggested by the National Institute of  Statistics and Economic Studies (INSEE) , based on population density  (D'Alessandro et al. , 2021). The proposed framework identifies environmental features that have little correlation to population density but are associated with the presence of specific sources of emissions, agricultural practices or environmental amenities.
After linking them to the level of social disadvantage, significant differences were found. These findings demonstrated that there is unequal access to natural capital based on the level of social disadvantage of the population. In the United States,  Tran et al. (2010)  developed a typology at the watershed scale to evaluate the vulnerability profile of rivers. The dataset used is particularly rich, with 50 spatial indicators describing land use, pesticides application, surface water contamination, and distance to certain emission sources, among others. The objective was to gain a better understanding of the potential levers to improve water quality at the watershed level. In future studies, these methods could therefore be deployed using environmental pollution data to distinguish specific exposure profiles to environmental pollutants.
Acknowledgments This work was supported by the  I-SITE Foundation  from the  Lille Nord-Europe University  ( I-SITE ULNE , project  R-PILOTE -01   SANTE et ENVIRONNEMENT ), the  Lille European Metropolis , the  French National Research Agency  (Project  CROPS / ANR-20-CE34-0015 ), and the  Ministère de l'Enseignement Supérieur et de la Recherche (CPER CLIMIBIO).
The RNA extracted were reverse-transcribed in cDNA using iScript ® cDNA Synthesis kit (Bio-Rad Laboratories, USA). QPCR was used to quantify the relative levels of 16 transcripts involved in different targeted metabolic pathways; selecting genes for cDNA sequences were available in the National Center for Biotechnology Information (NCBI) database (Table  1 ). The specific primers for each gene were designed with Primer 3 plus software (https://www. bioinformatics. nl/cgi-bin/primer3plus/ primer3plus. cgi) from sequences in the NCBI database. Each biological pooled sample was analysed in technical triplicates using iQ SYBR ® Green Supermix (Bio-Rad Laboratories, USA). The relative quantities of transcripts in larvae were normalized with the Ct method using the CFX-manager software associated to the CFX96 Touch Real-Time PCR Detection system (Bio-Rad Laboratories Inc.
Acknowledgments The authors thank the  IFREMER  members for their essential help ( Josselin Caboche ,  Clémence Couvreur ,  Pierre Cresson ,  Margaux Denamiel ,  Antoine Dussuel ,  Thibaut Kersaudy ,  Valérie Lefebvre ,  Marie-Madeleine Le Gall ,  Clara Ortu ,  Jean-Baptiste Quéméneur ). Logistical aid was also provided by  Nausicaà Centre National de la Mer  and in particular by  Stéphane Hénard. The authors warmly thank the local fishermen ( CME, From Nord) in Boulogne-sur-Mer  for their help.
1. Density of known exoplanets versus their equilibrium temperatures. These data come from the online DACE database maintained by Observatory of Geneva (https://dace. unige. ch/) and are taken from the catalog of  Otegi et al. (2020) , which is an exoplanet catalog based on reliable, robust, and -to the greatest possible extent -accurate mass and radius measurements of transiting planets up to 120 earth masses  (Otegi et al. 2020). Top: density vs. equilibrium temperature for all planets. Bottom: same as above but limited to planets with radii of smaller than 3 Earth radii. The dotted line shows a simple least-square fit to the data. spectra. However, the resolution of Spitzer spectra is too low to permit any clear identification of the molecular species in the partial pressures expected above such a rocky planet.
Validation against molecular dynamics calculations However, our calculations are limited to species that are included in the thermodynamic tables, such as JANAF, which may not be complete. For the sake of completeness, we compare our thermodynamic model against ab initio molecular dynamics simulations. For this, we employ the VASP package based on the planar augmented wavefunctions  (Kresse & Hafner 1993)  of the density functional theory. We consider pyrolite melt, with the bulk silicate Earth composition  (McDonough & Sun 1995) : 1/2Na 2 O. 2CaO. 3/2Al 2 O 3. 4FeO. 30MgO. 24SiO 2. This six-component composition was used previously to model the crystallization  (Caracas et al. 2019 ) and the structure of the magma ocean  (Solomatova & Caracas 2019). We performed simulations well inside the liquid-vapor dome at 3000 K and a density of 0.
Acknowledgements. S. C. , A. F. , P. T. acknowledge financial support by  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ) and by the  CNES (Centre National d'Études Spatiales ). P. T. would also like to acknowledge and thank the  ERC  for funding this work under the  Horizon 2020 program  project  ATMO  (ID:  757858 ). R. C. acknowledges support from the  European Research Council  under  EU   Horizon 2020 research and innovation program  (grant agreement  681818 -IMPACT  to RC), the  Research Council of Norway  with project number  223272  and project  HIDDEN 325567 , and access to supercomputing facilities via eDARI stl2816 grants,  PRACE   RA4947 grant ,  Uninet2 NN9697K  grant. P. S. thanks the  Swiss National Science Foundation (SNSF)  via an  Ambizione Fellowship  ( 180025 ), an  Eccellenza Professorship  ( 203668 ) and the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  MB22.
Imaging method The images were taken by an Xradia Versa 520 highresolution microCT. The X-ray energy was 90 kV and the power was 8W. The number of projections for each radio was 2001. The pixel size of all images was equal to 2. 07 µm. All tomograms were reconstructed into 3D images using Zeiss reconstruction software. Dry, KI_300, SWi_aged, Spont_Imbib. , Forced_Imbib. , Spont_Drain. , Forced_Drain. images were first stitched together to cover the whole length of the sample. After, they were registered to the dry images to have the same orientation and to be easily comparable. A nonlocal means filter was used to remove noise. The image analysis was performed with the help of software packages: Mango  [8]  for the routine actions, ImageJ for the trainable Weka segmentation  [2].
Imaging and enumeration of immunomarkers Cells were analysed with a fluorescence microscopy Zeiss Axioplan equipped with an Apotome and Axiovision software or Cell Voyager CV1000, confocal scanner box (Yokogawa). g-H2AX and 53BP1 foci (DSB markers) were enumerated in at least 30-50 cells/condition, from 3 independent experiments. For differentiation analysis, cells positive for one or more myogenic markers were enumerated per condition, and the percentage was evaluated on the total number of nuclei (Hoechst staining), by assessing 2,000-5,000 nuclei/condition and 5-10 fields/condition. The fusion index was calculated with the number of nuclei/myosin heavy chain positive myotubes, by assessing 2,000-5,000 nuclei/condition and 5-10 fields/condition. Image analyses were performed with the ImageJ 2.
Acknowledgements This work was supported by  AFM  (research grant ( 16580 ), and thesis grant ( 18425 )). We thank the lab of  Shahragim Tajbakhsh  for the material provided to perform in vivo muscle injuries and antibodies targeting myogenic factors,  Hiroshi Sakai  for guidance to initate the in vivo injury experiments, and  Shahragim Tajbakhsh  for helpful discussion,  Sebastien Mella  for his advice on statistical analysis. We thank the  Virus and Immunity Laboratory at Institut Pasteur  (director  Dr. Schwartz ) for quantification of p24 viral particles, and the center for  Translational Science (CRT)-Cytometry and Biomarkers Unit and Phtonic BioImaging Unit of Technology and Service (CBUTechS and PBI UTechS) at Institut Pasteur  for technical support in this study.
Dental microwear analysis The occlusal surface of all the available teeth (58 extant goats and 16 archaeological samples) was carefully cleaned using acetone followed by 96% ethanol to remove any remaining sediment and dust. This was done using cotton to prevent the surface becoming damaged. The occlusal surface was then moulded using high-precision polyvinylsiloxane (President Light Body, Coltene Waledent). A first impression was made to remove any remaining dust embedded in the occlusal surface, and the second impression was analysed. The moulds were analysed using a Sensofar Plu Neox white-light scanning confocal microscope housed at the Institució Milà i Fontanals-CSIC (Barcelona, Spain) and employing the SensoSCAN® acquisition software package. For each facet, an area of 150 × 150 μm was selected from images of 255 × 191 μm taken using a 50x (0.
Statistical analysis Dental microwear texture analysis was quantified using two methods in MountainsMap 7®: (1) scale-sensitive fractal analysis (SSFA); and (2) standard roughness (ISO 25178) and flatness (12781) ISO (International Standard Organization;  Leach, 2013). On the one hand, three SSFA parameters were measured: anisotropy (EpLsar), complexity (Asfc), and heterogeneity of complexity (HAsfc9)  (Scott et al. , 2005 (Scott et al. , , 2006)). On the other hand, 34 ISO parameters relating to the height, volume, direction, peak sharpness, and plateau size were obtained (Table  3 ). The information gathered was assigned to five datasets according to the hypotheses: (1) sexual feeding variability; (2) seasonal variability; (3) age variability; (4) discrimination between different wild and domestic goat populations; and (5) discrimination according to status (wild versus domestic).
When the homogeneity of variance test (Levene test) showed significant differences, Welch's ANOVA was used. A linear discriminant analysis (LDA), based on the ISO texture parameters that presented significant differences, was constructed. When Box's M test was violated (p ≤ 0. 05), a quadratic discriminant analysis (QDA) was performed. The significance of the QDA and LDA was assessed using Wilks' lambda. All the statistical analyses were carried out using the IBM SPPS 26® statistics software package.
Acknowledgements This work was funded by a  Fyssen Foundation research fellowship (Paris, France)  and Grant  FJC 2021-046527-I  funded by  MCIN/AEI/ 10. 13039/501100011033  and " European Union NextGenerationEU/ PRTR ". It has also been funded by the  Spanish Ministry of Science  ( PID 2019-105767 GB-I00 ) and is part of the project  PDC 2021-121613-I00 , funded by  MCIN/AEI/10. 13039/501100011033and  the " European Union NextGeneration EU"/PRTR. The authors thank to  Xavier Pons ,  Eco Bosc de Miralles, Zoological Museum of Strasbourg (Strabourg, France ; special thanks are given to  Dr. Marie Meister  and  Dr. Rose-Marie.
While weighted regressions are readily implementable with most of the statistical software available, applying matching algorithms typically requires becoming familiar with specialist tools such as matchIt in R or psmatch2 in Stata. The third step is common to PSM and PSW and consists of testing for balance in covariate distributions between the treatment and the control groups. The general idea of these checks is to compare differences between the treated and the control group before and after matching or weighting. Two common approaches are (1) the standardised bias, which assesses the distance in marginal distribution of the X variables, and (2) a two-sample t-test to check whether there are significant differences in covariate means for both groups  (Rosenbaum & Rubin, 1985). If these tests are not completely successful, some remedial measures are advised, such as including interaction terms in the PS estimation  (Caliendo & Kopeinig, 2008).
This implies that the possibility of selecting a new head coach, let alone each of their different characteristics, in a within-season football turnover is limited in terms of available candidates and time to reach an agreement. Nonetheless, we also explain in an advanced application in Section 4. 7 how to adapt the PSW analysis to deal with the possible endogeneity of the similarity in characteristics between dismissed and new coaches. Our proposed estimation is relatively simple, at least from a programming/software viewpoint, because it is built upon the standard regression analysis. This means that no statistical package specialised in PSA is required for this estimation. The interested reader can find the dataset and R codes used in this analysis in the research data available online.
Data We collected club-match level data from the top tier of the Italian professional football league (Serie A) for seasons 2004/2005-2017/2018, which gives a total of 10,640 observations (5,320 matches). Throughout the season, each club competes against all others, once at their home stadium and once away. In each match, a club is awarded 3, 1, or 0 points for a win, draw, or loss, respectively. At the end of the season, the club with the highest accumulated points wins the championship title, whilst the three lowest-placed clubs are relegated to the lower-tier league (Serie B). The league publishes official match reports. They contain, for example, the names of each club in the match, the respective managers and the outcome of the match. Additional sources used are provided below, together with the descriptions of (1) the treatment variable, (2) variables that explain treatment assignment, and (3) outcome variables and additional control variables associated with the outcome.
DNA extraction, Cryptosporidium species determination and C. parvum subtyping Disruption of oocyst walls was achieved using ten freeze-thaw cycles as previously described  [28]. Then, DNA extraction was conducted using the QIAamp DNA Stool Mini Kit (Qiagen, France), according to the manufacturer's instructions. The Cryptosporidium genus was determined using a nested PCR amplifying a 840 bp fragment of 18S rRNA gene  [29]. PCR products were digested with SspI and MboII endonucleases and restriction fragment length polymorphism (RFLP) analysis was used to determine the Cryptosporidium species  [30]. C. parvum samples were subtyped by nested PCR-sequence analysis of the partial 60 kDa glycoprotein locus (gp60)  [31]  and Sanger sequencing of both strands (Genoscreen) (Genbank numbers in Table  2 ).
They were visualized using gel electrophoresis before Sanger sequencing (Genoscreen). Genbank accession numbers of successful sequencing are provided in Table  2. Additional PCR were carried out using a second set of primers amplifying smaller fragments of the RdRP (RdrpV-F: 5′-TGG GCA TAT GGT GCT CAC TA-3'; RdrpV-R: 5′-GCT AAG AGAT CGT AGA TGT CCA-3) and capsid (CPV-F: 5′-TGG TTC CGA TTT TAC CGG AA-3′; CPV-R: 5′-ACG ACA ATT AGG ACT CAA ATG ACC -3′) as described by Jenkins et al. [33]. CSpV1 was considered detected if at least one of the two PCR approaches returned positive results.
Full-length genome sequencing CSpV1 genome was obtained from a pool of samples B44 and B45, using RNA sequencing. cDNA libraries were prepared using an Ion total RNAseq Kit (Life technologies, Carlsbad, CA, USA) according to the supplier's instructions. The cDNA libraries were sequenced using   Sequence reads were cleaned and trimmed for adapter removal using fastP version 0. 20. 1  [34]  and sequence quality was verified using FastQC version 0. 11. 8  [35]. Reads were assembled with rnaSPADES de novo assembler as implemented in SPAdes assembler version 3. 10. 0 [36]. Resulting contigs were aligned on local nt database with Megablast version 2. 10. 1 to identify viral references. Then, both sequence reads and assembled contigs were aligned using Burroughs-Wheeler Aligner (BWA, version 0. 7.
Thus, IIaA15G2R1 C. parvum subtype was the most prevalent, and was found in 11 departments on all four successive years (2018 to 2022). IIaA17G1R1 and IIaA17G2R1 were found in one department in 2020 and 2021, while IIaA16G3R1 and IIdA18G1R1 were found only once, in one department. The full-length genome of CSpV1 from France was obtained from a pool of C. parvum of subtype IIaA15G2R1 collected from cattle in 2021 (Samples B44 and B45; Genbank acc numbers OQ686777 and OQ686778 for dsRNA1 and dsRNA2 respectively). The representative dsRNA1 and dsRNA2 FR strain genome was obtained with a coverage depth of 50 and 70 reads per position and with 0. 6 and 0. 8% iupac codes for dsRNA1 and dsRNA2, respectively. The genome sequence was compared to the three available full-length Cryspovirus genomes from Iowa (Iowa), Kansas (KSU) and China.
The FR strain dsRNA1 shows a maximum of 97. 3% nucleotide (nc) identity to the Iowa strain and a minimum of 96. 4% nc identity to the KSU strain from North America. The RdRP protein shows 99% and 97% amino-acid (aa) * Spl: Sample. Letter (B, bovine / cattle; C, Caprine / goats and O, ovine / sheep); Dpt: Department; Cryspovirus PCR was carried out using primers RdRPV and CPV from Jenkins et al. [33]  primers VL and VS from Murakoshi et al. [25]. GP60 C. parvum subtyping and VL/VS Cryspovirus amplicon were followed with Sanger sequencing. Genbank accession numbers are provided. identity to the Iowa and KSU strains, respectively. The FR strain dsRNA2 shows a maximum of 98. 4% nc identity to the Iowa strain and a minimum of 98% nc identity to KSU strain and was more distantly related to the related viruses discovered in Cryptosporidium meleagridis (85.
6% nc identity), Cryptosporidium felis (86. 7% nc identity), and Cryptosporidium hominis (90. 8% nc identity). The capsid protein shows 98. 6% and 99. 1% aa identity to the Iowa and KSU strains, respectively. Using two different sets of primers per virus genome fragment, we confirmed the presence of CSpV1 within all but two isolates of C. parvum (Table  2 ), including in the sample for which the C. parvum subtype could not be determined. This high prevalence was in accordance with previous reports  [21, 25]. Good quality partial sequences of CSpV1 virus dsRNA1 encoding the RdRP, and dsRNA2 encoding the Capsid proteins were obtained for 30 and 42 samples, respectively (Genbank accession numbers are provided in Table  2 ). CSpV1 partial genome sequencing provided evidence of co-infection by two different genotypes of CSpV1 in C.
Objective Ovarian pregnancy is a rare but well-known pathology. However, pathophysiology, diagnosis and treatment are not clearly established. Data sources. All case reports published in Pubmed from Nov 2011 till Nov 2022. Study eligibility criteria. A systematic review and 2 case reports of ovarian pregnancy Study appraisal and synthesis methods. Not applicable. Results: Ovarian pregnancies occur in 8% of women without or blocked oviducts and in 23% on the other side than the corpus luteum and the symptoms of ovarian pregnancies are not specific. Therefore ovarian pregnancy has to be suspected in all women with abdominal bleeding. As for extrauterine pregnancies, the presence of an intrauterine pregnancy does not rule out an ovarian pregnancy. Surgical excision is the preferred treatment.
Data extraction These case reports were searched for age and antecedents of the woman such as gravity, parity, abortions, ectopic pregnancies, in-vitro fertilisation (IVF), intrauterine insemination (IUI), tubal or endometriosis or pelvic surgery, pelvic inflammatory disease (PID) and the use of an intrauterine device (IUD). For the ovarian pregnancies, we retrieved the duration of the pregnancy, presenting symptoms (pain, vaginal bleeding, shock), HCG concentration, ultrasound and MRI exams, treatment and outcome.
Conventional data processing is carried out by splitting the scattered intensity into form and structure factors. Such an approach is very suitable for dilute suspensions, with the form factor being restricted to simple morphology and the structure factor being restricted generally to hard-core repulsion. However, for microstructures such as heterogeneous catalysts, conventional data processing may not be relevant. For instance, the multiscale models proposed by  Beaucage (1995)  give parameters that are easily interpretable only for fractal morphology, which is seldom encountered in heterogeneous catalysis. The program MIXTURE  (Konarev et al. , 2003)  fits the experimental scattering curve by modeling the multicomponent system with different form factors considering interparticle interactions.