dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank  Joshua Sarlo  for his artistic contributions to the manuscript. This research was funded by grants from the  National Institutes of Health  ( P50AI150476 ,  U19AI135990 ,  U19AI135972 ,  R01AI143292 ,  R01AI120694 ,  P01A1063302 , and  R01AI122747  to N. J. K. ;  R35GM122481  to B. K. S. ;  1R01CA221969  and  1R01CA244550  to K. M. S. ;  K08HL124068  to J. S. C. ;  1F32CA236347-01  to J. E. M. ); funding from  F. Hoffmann-La Roche  and  Vir Biotechnology  and gifts from The  Ron Conway Family. K. M. S is an investigator of the  Howard Hughes Medical Institute.
1, G  and I ). The exon inclusion observed by reverse transcription PCR (RT-PCR) was also confirmed in publicly available transcriptome data from in vitro myogenic differentiation (Fig. S2  B;  Trapnell et al. , 2014) ; however, exon 31 inclusion is not currently documented at the protein level in proteomic or crystallography databases. We therefore performed a shotgun proteomic analysis enabling the detection of unique peptides covering the exon junctions with or without exon 31 in our myogenic differentiation model (Fig. S2, C  and D ). Quantifying the relative amounts of peptides ± exon 31 confirmed the RT-PCR results at the protein level (Fig. 1 I ), with a replacement of CHC protein without exon 31 by CHC protein including exon 31 during human muscle cell differentiation (Fig. 1 J ). Altogether, our results reveal a correlation between the alternative splicing of exon 31 and the structural organization of clathrin, with exon inclusion associated with plaques and exclusion associated with pits.
Clathrin structure morphometric analysis Clathrin structures were manually delineated on unroofed PM pictures using ImageJ software  (Schneider et al. , 2012 ; National Institutes of Health; https://imagej. nih. gov/ij/) as shown in red in Fig. S3  A. Each object was classified as a CCP or a plaque by the observer; then, the object's area, mean gray value, and minimum and maximum Feret diameters were measured. Each picture membrane area and the membrane background mean gray value (manually thresholded to exclude clathrin structures) were also measured for normalization and background subtraction purposes. Observer manual classification was used for quantification of size and density (object count normalized by membrane area). Each clathrin structure curvature was calculated as its mean gray value minus the mean membrane background, as shown in Fig.
Transferrin assay Cells were incubated in DMEM without FBS for 45 min at 37°C. Alexa Fluor 488 fluorescently labeled human transferrin (40 µg/ml; Life Technologies; T13342) was then incubated 15 min on cells, and endocytosis of this compound was stopped with ice-cold PBS washing followed by 4% PFA fixation and final mounting in Vectashield medium. Fluorescence from myoblasts or myotube areas was acquired using a confocal Nikon Ti2 microscope as stack images with 500-nm Z steps using a 40×/1. 3 NA oil objective at 20°C ambient temperature. Each cell fluorescentpositive surface was quantified on a stack projection using ImageJ software by measuring the integrated density (mean gray value) of the background subtracted from that of the cell and normalizing this value to the total cell area. Structural model and in silico analysis CHC trimers were rendered using VMD software  (Humphrey et al.
, 1996)  and the 3iyv. pdb structure  (Fotin et al. , 2004). Secondary structures were 3D modeled as canonical α-helices with backbone torsion angles ϕ = -60°and ψ = -40 using the 3D Hydrophobic Moment Vector Calculator (3D-HM;  Reißer et al. , 2014) , and surfaces were rendered using University of California, San Francisco Chimera software (RRID:SCR_004097;  Pettersen et al. , 2004). The hydrophobic moment that is a measure of the helix amphiphilicity  (Eisenberg et al. , 1982)  was calculated along the α-helix over 11 residues with a one-residue increment using HeliQuest (http://heliquest. ipmc. cnrs. fr/;  Gautier et al. , 2008). Coiled-coil interaction predictions were performed using the nCoils method  (Lupas et al. , 1991).
Mice were killed by cervical dislocation, and both AAVand control-injected TA muscles were frozen in liquid nitrogencooled isopentane, and cryosections were performed for histological analysis and RNA extraction. Conventional hematoxylin and eosin histochemical staining was performed on 8-µm TA transversal cryosections with final mounting using Eukitt (Sigma-Aldrich), and images were acquired on an Axio Lab. A1 light microscope (Zeiss) at 20°C ambient temperature using a ToupTek 5. 1 MP CMOS camera, a 20×/0. 45 NA objective, and Toupview software (ToupTek). All mice used in this study were housed on a 12-h light/dark cycle and received standard diet and water ad libitum in the animal facility of the Sorbonne University, Paris.
For human CLTC-ex31 PCR as well as for vervet monkey (BS-C-1 cells), forward primer 59-TGCCCTATTTCATCCAGGTCA-39 and reverse primer 59-ATGGGTTGTGTCTCTGTAGC-39 were annealed at 57°C. For mouse and rat Cltc-ex31 PCR, forward primer 59-GAAACCGCATGGAGACATAA-39 and reverse primer 59-AAA CAATGGGCTGTGTCTCTG-39 were annealed at 56°C. Gel electrophoresis of PCR products was performed in 2. 5% agarose, and images were acquired on a Geni2 gel imaging system (Ozyme). The densitometric analysis of PCR bands was realized using ImageJ software, and the PSI value corresponding to the fraction of mRNAs that contained an exon was calculated as the ratio of the density of the exon inclusion band to the sum of the densities of inclusion and exclusion bands, expressed as a percentage.
Normalized collision energy was set at 27 and a dynamic exclusion of 40 s. For identification, the data were searched against the Homo sapiens (UP000005640) UniProt database and the CHC Q00610 ± ex31 sequences using Sequest Proteome Discoverer (version 2. 4; RRID:SCR_014477). Enzyme specificity was set to Glu-C, and a maximum of two missed cleavage sites was allowed. Oxidized methionine, Met-loss, Met-loss-Acetyl, and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 D for MS/MS peaks. The resulting files were further processed using myProMS v3. 9  (Poullet et al. , 2007). False discovery rate calculation was performed using Percolator (RRID:SCR_005040) and was set to 1% at the peptide level for the whole study.
To quantify the specific peptides of CHC with ± exon 31, corresponding to peptides YLTKVDAIKE (+exon 31 393. 892 m/z; charged 3+) and YLTKVDKLDASE (-exon 31 691. 364 m/z; charged 2+), we extracted from the MS survey of the nanoLC-MS/MS raw files the extracted ion chromatogram (XIC) signal using the retention time and m/z values of the well-characterized Glu-C peptide ions using Xcalibur software (manually; RRID:SCR_014593). Specific peptide XIC areas were integrated in Xcalibur under the QualBrowser interface using the ICIS algorithm, and the sum of all clathrin peptide XIC areas were computed for each biological replicate with MassChroQ version 2. 2  (Valot et al. , 2011)  for normalization. The MS proteomics data have been deposited in the Proteo-meXchange Consortium via the PRIDE  (Perez-Riverol et al.
All images are presented at the same 1-µm scale for comparison, with the exception of D and E, which have 500-nm scale bars. All magnification insets are shown at the same scale and are outlined with dashed rectangles in each image. (H) Fluorescent transferrin (Tfn) uptake normalized to cell area, measured in human myoblasts or myotubes differentiated for 8 d. Box plots show median extended from 25th to 75th percentiles, minimum and maximum data point whiskers, and average cross (myoblasts, n = 234; myotubes, n = 73; **, P ≤ 0. 005). (I) Schematic of CHC exon 31 alternative splicing (exon of 21 nucleotides numbered based on National Center for Biotechnology Information mouse RNA reference sequence NM_001356393. 1 from GenBank). Arrows indicate RT-PCR primer positions in bordering constitutive exons (in blue), with expected RT-PCR amplicons shown on the right with or without the inclusion of alternative exon 31 (in orange).
Acknowledgments We thank the MYOVECTOR technical platform of the  Centre of Research in Myology-UMRS974 (Paris, France ) for AAV production, the  Institut de Biologie Paris Seine Electron Microscopy platform  ( Sorbonne University, Paris, France ), and the  Penn Vector Core ,  Gene Therapy Program , ( University of Pennsylvania, Philadelphia, PA ) for providing the pAAV1 plasmid (p0001); and the  MyoBank-AFM  and the platform for immortalization of human cells from the  Institut de Myologie (Paris, France). We also thank  Cécile Martinat ,  Alain Sureau  ( Centre de Recherche en Myologie, UMRS 974, Paris, France ), and  Lorenzo Giordano  ( Centre de Recherche en Myologie, UMRS 974 ) for materials/reagents,  Frédérique Rau  and  Ozgur Ogut  for comments, and  Vanessa Masson  for her technical assistance during the MS study.
This work was funded by  Sorbonne Université ,  Institut National de la Santé et de la Recherche Médicale ,  Association Institut de Myologie , the  Agence Nationale de la Recherche  (grants  ANR-14-CE12-0001-01  to  S. Vassilopoulos ,  ANR-14-CE12-0009  to  M. Bitoun , and  ANR-16-CE17-0018  to D. Furling),  Tokyo National Center of Neurology and Psychiatry  (funding  29-4  to  M. Nakamori ), and  Région Ile-de-France  and  Fondation pour la Recherche Médicale  grants (to D. Loew). The authors declare no competing financial interests. Author contributions: G. Moulay: conceptualization, formal analysis, investigation, methodology, visualization, writingoriginal draft, review, and editing. J. Lainé ,  M. Lemaître ,  M. Nakamori ,  I. Nishino ,  G. Caillol ,  F. Dingli ,  D. Loew , and  C. Leterrier : formal analysis, investigation, review, and editing.
We discuss in Sect. 4 our spectral disentangling procedure, yielding a refined orbital solution as well as a disentangled spectra for each component. The disentangled spectra are analyzed to determine stellar parameters as described in Sect. 5. Finally, we present in Sect. 6 simultaneous reconstruction of the global magnetic field maps for the two components using ZDI, the analysis of the X-ray data in Sect. 7, and discuss our results in Sect. 8.
OBSERVATIONS Our study of V1878 Ori is based on the high-resolution optical spectropolarimetric observations obtained in the context of the BinaMIcS large programme  (Alecian et al. 2015)  between 2014 and 2016. The data were acquired with the Echelle SpectroPolarimetric Device for the Observation of Stars (ESPaDOnS) instrument mounted at the 3. 6-metre Canada-France-Hawaii Telescope (CFHT). ESPaDOnS  (Donati 2003; Donati et al. 2006 ) is a highresolution (R ≡ λ/∆λ ≈ 65000) cross-dispersed spectropolarimeter with a spectral coverage from 370 to 1000 nm that can record circularly and linearly polarized spectra as well as intensity spectra. Each polarized observation consists of a set of four sub-exposures during which the configuration of the polarimeter is changed to swap the position of the two orthogonal polarization beams through the instrument optics and on the detector.
This beam-switching technique  (Semel et al. 1993 ) efficiently removes instrumental polarization and effects arising from inhomogeneities between the two optical paths. We obtained a total of 22 Stokes IV observations taken with exposure times of either 4 × 1715 s (for the observing date 2014-02-21) or 4 × 1355 s (all other observations). The main dataset is comprised of 21 observations obtained over 16 consecutive nights in January 2016. Our data were reduced on the fly at the telescope with the Upena pipeline 1 that runs the LIBRE-ESPRIT routines  (Donati et al. 1997 ). The median S/N of the reduced data per spectral pixel at λ = 566 nm is 128. Information on individual observations can be found in the observing log given in Table  1. We complemented our study of the magnetic properties of V1878 Ori by analyzing also a long XMM-Newton observation.
Stokes I Stokes V × 25 on 2016 March 21 near the periastron passage. Neither the spatial nor spectral resolution of XMM-Newton allows to separate the two components. X-ray data were therefore analyzed for the whole binary system. We analyzed X-ray data gathered with the three EPIC instruments (PN, M1, and M2). EPIC data were first processed using the SAS V15. 0 standard tasks. X-ray source events were extracted from a circle with a radius of 37. 5 ′′ centered on the target position. A similar nearby region, free from other X-ray sources, was used to extract background events. We considered only events with energy ranging between 0. 3 and 7. 9 keV. During the two observations some time intervals were affected by significantly high background levels. We therefore discarded these high-background intervals, slightly reducing the actual exposure times.
LEAST-SQUARES DECONVOLUTION Cool stars generally exhibit very weak Zeeman polarization signals in individual spectral lines. Combining the signal from a large set of spectral lines is a widely used solution in order to obtain a high S/N mean line profile. Here we applied the least squares deconvolution technique (LSD,  Donati et al. 1997; Kochukhov et al. 2010)  to compute high S/N mean Stokes IV profiles for each observation. LSD effectively performs a weighted co-addition of a set of spectral lines, defined by a line mask, into a mean profile. To construct the line mask, we retrieved a line list from the VALD3 database  (Ryabchikova et al. 2015)  using a MARCS stellar atmosphere  (Gustafsson et al. 2008)  with T eff = 4750 K and log g = 4. 0. After having removed spectral regions where tellurics or particularly strong and broad spectral lines were present, we obtained a line mask containing 5304 atomic lines deeper than 20% of the continuum.
Date We then fitted the RV measurements to derive an orbital solution. We used both our own measurements, which sample around half of the orbit, and the data from  Covino et al. (2001b) , which, though less precise, has the benefit of covering the entire orbit. We present our orbital solution in Table  2  alongside the previously determined solution by  Covino et al. (2001b). The orbital parameters derived from the RV fit include the orbital period P orb , the time of periastron passage T 0 , the eccentricity e, the radial velocity of the mass centre of the system γ, the radial velocity semi-amplitudes K A,B , and the longitude of periastron ω. We also determined the minimum stellar masses M A,B sin 3 i and the projected semi-major axes a A,B sin i. The RV measurements and the best-fitting model are shown in Fig.
STELLAR PARAMETERS We used the disentangled stellar spectra, corrected for the continuum dilution, to determine stellar parameters for the two components by fitting synthetic spectra to observations. To calculate theoretical stellar spectra and optimise free parameters, we used the Spectroscopy Made Easy (SME) spectrum synthesis code  (Piskunov & Valenti 2017). Adopting the solar abundances from  Asplund et al. (2009) , we downloaded absorption line lists from the VALD database  (Ryabchikova et al. 2015)  using the Extract Stellar mode with T eff = 4750 K and log g = 4. 0. We used 1D model atmospheres from the MARCS grid  (Gustafsson et al. 2008 ). We fitted a set of wavelength intervals sensitive to different stellar parameters, mostly in the wavelength regions 516-519 nm and 600-620 nm, as used in  Valenti & Fischer (2005).
ZEEMAN DOPPLER IMAGING We carried out reconstruction of the large-scale magnetic field geometry of the two components simultaneously using the binary Zeeman Doppler imaging code InversLSDB code described by  Rosén et al. (2018) , which is based on the single star ZDI code InversLSD code  (Kochukhov et al. 2014). InversLSDB has previously been used in the studies of the double-lined spectroscopic binary systems σ 2 CrB  (Rosén et al. 2018 ) and YY Gem  (Kochukhov & Shulyak 2019). Only the main dataset acquired in 2016 was used for ZDI inversions. The single observation from 2014 was discarded due to lack of precise rotation periods necessary for phasing together data taken two years apart and a high likelihood that magnetic field topologies have evolved during that time period. In our analysis, we computed local Stokes IV profiles using the Unno-Rachkovsky solution of the polarised radiative transfer equation, assuming a Milne-Eddington atmosphere.
003 cts s -1 , significantly higher than 0. 477 ± 0. 003 cts s -1 , observed during the periastron. V1878 Ori A V1878 Ori B ℓ = 1 70. 0 % 89. 4 % ℓ = 2 23. 0 % 7. 6 % ℓ = 3 4. 1 % 1. 7 % ℓ = 4 1. 3 % 0. 4 % ℓ = 5 0. 7 % 0. 4 % ℓ = 6 0. 5 % 0. 3 % ℓ = 7 0. 3 % 0. 1 % ℓ = 8 0. 1 % 0. 0 % ℓ = 9 0. 0 % 0. 0 % ℓ = 10 0. 0 % 0. To derive the characteristics of the coronal plasma we inspected the PN, M1, and M2 X-ray spectra collected during non-periastron and periastron, respectively. To increase the signal-to-noise ratio we rebinned each spectra to obtain at least 30 counts in each channel. To better constrain the Xray emitting plasma properties we simultaneously fitted the spectra of the three EPIC instruments. The spectral analysis was performed using XSPEC V12. 10. We adopted a model of an optically-thin plasma emission (using the APED atomic database,  Smith et al.
2008; Boro Saikia et al. 2018). It is also important to note that the strength of the field recovered using ZDI is a small fraction of the total magnetic field strength at the surface of the star  (Kochukhov & Shulyak 2019; Lavail et al. 2019; Kochukhov et al. 2020). This fraction depends on the magnetic field complexity, as (potentially strong) local tangled fields can cancel out in circular polarization. Analysis of Zee-man broadening in intensity spectra would be needed to obtain a reliable estimate of the total magnetic field strength but was not carried out with this dataset as Zeeman broadening is relatively small in the optical. Self-consistent Zeeman broadening measurements and ZDI inversions, as well as spot coverage measurements using molecular bands are becoming more feasible using high-resolution near-infrared spectropolarimeters such as SPIRou at CFHT or the upcoming CRIRES+ instrument at VLT.
ACKNOWLEDGEMENTS O. K. acknowledges support by the  Knut and Alice Wallenberg Foundation  (project grant ' The New Milky Way '), the  Swedish Research Council  (projects 621-2014Council (projects  621- -5720 ,  2019-03548)-03548 ), and the  Swedish National Space Board  (projects  185/14 ,  137/17 ). E. A. and the  BinaMIcS Collaboration acknowledge financial support from  ' Programme National de Physique Stellaire' (PNPS)  of  CNRS/INSU (France). This research has made use of  NASA 's Astrophysics Data System. Based on observations obtained at the  Canada-France-Hawaii Telescope (CFHT)  which is operated from the summit of Maunakea by the  National Research Council of Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France , and the  University of Hawaii.
ITS region 1 was targeted and PCR amplified using the primer pair ITS1F  (Gardes and Bruns, 1993)  and ITS2  (White et al. , 1990). All samples were barcoded on the forward primer to allow for multiplex sequencing. Sequences were analysed using QIIME v 1. 8. 0  (Caporaso et al. , 2010). Sequences were demultiplexed according to barcode identity and were quality filtered according to QIIME default parameters, with exceptions for the ITS dataset. For the ITS dataset, the p score applied was altered to 0. 45 to ensure that all reads >200bp were included in the dataset, provided that they met the other default quality parameters (i. e. q 19, r 3, etc. ). Chimeras were detected both de novo and against the Greengenes reference library  (DeSantis et al. , 2006)  for 16S rRNA genes and the UNITE reference database for the ITS region  (Kõljalg et al.
, 2013)  using usearch61  (Edgar et al. , 2011). Operational taxonomic units (OTUs) were clustered at a similarity threshold of 97% using the uclust method and the Greengenes and UNITE reference database for 16S rRNA gene and ITS region datasets, respectively. 16S rRNA gene OTUs were assigned a taxonomy using the Greengenes  (DeSantis et al. , 2006)  reference database. Taxonomy was assigned to ITS region OTUs using a combination of the UNITE database in QIIME and the Warcup Fungal ITS training set in the RDP classifier  (Wang et al. , 2007; Kõljalg et al. , 2013; Deshpande et al. , 2016). For the RDP classifier, taxonomic classifications were kept at the default confidence threshold of equal to or greater than 80% for classification to the root only, after which the default confidence threshold was decreased to 50% for all subsequent assignments.
Statistical analysis All statistical analyses were performed using R statistical software version 3. 2. 2. (R Core Team, 2013). Alpha diversity was calculated using the vegan package  (Oksanen et al. , 2011). Statistically significant differences in diversity measures between each treatment group were determined via the aov function (analysis of variance, ANOVA) in vegan and the post-hoc TukeyHSD test. Microbial community structure was visualised by non-metric multidimensional scaling (nMDS) ordination plots with the metamds function of vegan using Bray-Curtis dissimilarity and visualised using ggplot2. Significant differences in microbial community structure were determined via PERMANOVA (permutational analysis of variance) using the adonis and betadisper functions in vegan. The aov and tukeyHSD functions of vegan were used to determine significant differences between treatment groups for each of the enzyme activities.
Correlations between taxon abundances, enzyme activities and pH were calculated using Spearman's rho, via the rcorr function of the Hmisc package. Relationships between pH and enzyme activities, bacterial, archaeal and fungal diversity and taxon abundance were modelled using linear regressions (lm function). Fungal diversity relationships with pH were non-significant (p-value > 0. 5), and therefore no regression line is shown. Topological roles of OTUs are indicated either as peripherals (Zi ≤ 2. 5, Pi ≤ 0. 62), connectors (Zi ≤ 2. 5, Pi > 0. 62), module hubs (Zi > 2. 5, Pi ≤ 0. 62) and network hubs (Zi > 2. 5, Pi > 0. 62). The abundance of peripherals, connectors and module hubs between the three treatment groups is indicated in (b), where the control group is the centremost donut ring, the medium group the middle ring and the low group the outer donut ring.
ACKNOWLEDGEMENTS We are grateful to the  National Research Foundation (NRF)  (Grant  ID 100052  SZdS) the  South African National Antarctic Programme  ( SANAP 110717 ), and the  University of Pretoria  for funding. TPM also wishes to acknowledge the  Fulbright Visiting Scholar Program  for providing sabbatical funding. We wish to acknowledge  Mr. A. van der Walt  and  Mr. M. Clayton  for useful discussions on the content of the manuscript.
Study sites and species data We studied the distributions of plant species along 18 elevation gradients in the French Alps. The gradients belong to the long-term observatory ORCHAMP (<www. orchamp. osug. fr>, Supplementary material Appendix 1) and were selected according to the following criteria: 1) continuous gradient from about 900 m to 3000 m (Supplementary material Appendix 1 Fig. A1-A ), 2) exposure and slope along the gradient is homogeneous, 3) vegetation is typical for the elevation stages with forests dominating the lower parts and alpine meadows the higher parts of the gradient and 4) all gradients together are representative for the environmental and topographical variability of the French Alps (Fig. 1 , Supplementary material Appendix 1 Fig. A1-B ). Between 2016 and 2018, a minimum of five sampling plots (30 × 30 m, see Fig.