dataesr/scientific-paragraphs-categorization huggingface dataset:
For vascular plants, we considered the data to be comparable whatever the area sampled (ranging from 600 to 1018 m²) since (i) the observers took care to maintain homogeneous local conditions (phytosociological-type sampling), (ii) in forest types similar to those we sampled, Archaux et al. (2007) highlighted samplings set up on areas above 400 m² are comparable, and (iii) the sampled surface area was constant in each forested area (used as a grouping variable in the models). Fungi were sampled along a gradient of one to three years, with one to three runs per year; we therefore only retained polypore records in our analysis, since their sporophores are long-lasting. Bird data was recorded inside 25, 100, and outside 100 m-radius areas; since environmental variables were recorded on 56 m-radius plots (i.
2018; Jokela et al. 2018 ). However, species composition is slightly more difficult to interpret than species richness, especially in terms of the magnitude of the relationship. Alternative approaches, not pursued here, include using more precise ecological subgroups  (Barbier et al. 2009; Gosselin 2012; Zilliox and Gosselin 2014)  or performing analyses at the species level  (Okland et al. 1996; Bouget et al. 2014b ). However, the practical comprehensive life-trait databases required by such approaches are available for very few taxa (e. g. Syrph the Net for hoverflies;  Speight et al. 2015)  and most life-trait descriptions must therefore be collected from a variety of dispersed sources.
However, we found more numerous significant relationships between full groups and habitat variables than we did for forest subgroups alone. Several reasons may explain this result. First, the life-trait databases or the variables we used to build our forest subgroups may not have been completely relevant. For example, dwelling in cavities or under peeling bark may not be the most relevant feature for forest-specialist bats, very few of which are strict tree-roosters; most also make use of similar microhabitats provided, for instance, by buildings  (Arthur and Lemaire 2009). Secondly, forest specialists may mainly depend on habitat variables we either did not record or recorded too broadly (i. e. our variables may not have focused on features specific enough to definitely select the forest specialist), or which we did not record at the most appropriate spatial scale.
2009; mostly Zilliox and Gosselin 2014). Among bryophytes, forest-specialists do seem to have stronger relationships with forest habitat variables than do the other species in the taxon  (Gosselin et al. 2014 ). Yet a practical life-trait database relevant for France is lacking since the databases available in Europe to date are limited to the UK  (Hill 2007)  and Germany  (Schmidt 2013). 4. 4 From ecological results to forest management guidelines: providing thresholds When we analyzed our species richness models in terms of the magnitude of the relationships (as in e. g. Barbier et al. 2009 and Zilliox & Gosselin 2014) , we found rather strong relationships between habitat variables and species composition, and weak (though sometimes significant) relationships between habitat variables and species richness, with one notable exception (the relationship between the number of strata and hoverfly species richness).
2018). Due to the wide range of taxa and forest types sampled in this study, we believe our findings and recommendations are relevant at the stand scale for temperate European forests. However, further research would be welcome since (i) most of our taxonomic data are related to a single sampling year; ii) habitat variables were only recorded at the stand scale (landscape-level environmental variables should also be recorded); (iii) only one metric was used to evaluate alpha diversity (i. e. species richness); (iv) we used presence/absence data to be consistent among the taxa but adding abundance data could be beneficial; and (v) statistical analyses could be improved (e. g. non-linear approaches, consideration of more random effects). Research should also consider broader temporal  (Favreau et al.
Acknowledgements This research was partly funded by the  French Ministry in charge of the Ecology  through the " Biodiversité, Gestion Forestière et PolitiquesPubliques" (BGF) program  (convention  10-MBGD-BGF-1-CVS-092 , n• CHORUS 2100 214 651 ), and through the  DEB-Irstea convention (GNB-Valo action ). The  French Environment and Energy Management Agency  (ADEME) as well as  European (FEDER)  and  French grants  ( Conseil Régional de Midi-Pyrénées ) also contributed. We are indebted to  B. Nusillard ,  A. Lassauce ,  Y. Dumas ,  Y. Paillet ,  C. Moliard  (Irstea),  T. Barnouin ,  T. Noblecourt ,  F. Soldati ,  H. Voiry  (ONF),  A. Brin ,  H. Brustel ,  L. Valladares  (EIP),  J. Willm ,  L. Burnel  ( INRA ),  J. P. Sarthou  ( INRA ),  V. Sarthou  (Syrphys) and all the local forest managers (private and public, from  ONF  and RNF) for their field and laboratory work and for access to databases.
Methods Study design and setting. This observational prospective cross-sectional study was divided into four stages: (i) information campaign via local radio and newspapers, (ii) mailing of the questionnaires, (iii) data collection and (iv) data analysis. The study was conducted over a 12-month period. It was approved by the Institutional Review Board (IRB 13/02-02). No signed informed consent was required as all questionnaires were anonymous; however, patients had one month after returning the questionnaire to withdraw their participation before data were used. This study was conducted in the Obstetrics and Gynecology department of a tertiary University Hospital in France, for cancer survivor enrolment, and in the local region for control population enrolment. Participants. The cancer survivors (CS) group gathered gynecologic (ovarian, endometrial, cervical) cancer patients treated at the gynecologic cancer Department of our University Hospital.
For secondary analysis, the control group was divided into two subgroups: (i) women with history of simple hysterectomy for benign disease (CGH + ) and (ii) women with neither history of pelvic surgery nor cancer (CGH -). We chose to perform this subgroup analysis due to the lack of data on the role of hysterectomy on PFD incidence in the literature  9, 10. Variables. The survey included general demographic and medical information, as well as three international self-questionnaires both validated in French, the PFDI-20 questionnaire for assessment of PFD and urinary symptoms as well as pelvic pain, the PFIQ-7 questionnaire for assessment of PFD effects on quality of life and the Wexner questionnaire for assessment of fecal incontinence. The PFDI-20 consists in 20 questions on urinary (six questions), digestive (eight questions) and pelvic prolapse (six questions) symptoms.
Statistical methods. Data sources. A database was created manually and validated with datascan by the biostatistics department of our university hospital. It included all completed questionnaires returned. Telephone calls were made to complete missing data for CS patients. Bias. To reduce the geographic bias, women from a unique region were recruited and sampling bias was reduced by sampling the population. Sample size. For the CS group, the expected response rate was of 50%; three phonecalls were made to achieve this rate. For the control group, as most questionnaire surveys have response rates between 15% and 20%  24, 25  , our expected response rate was set-up at 10%. Thus, to obtain 1000 responses, 10000 questionnaires were sent, which required 2. 5 months of screening program mailing, sent over a3-month period to ensure the feasibility of the study.
All estimations are presented with their 95% Confidence Intervals (95% CI). For model interpretation, a risk factor (more symptoms with higher PFDI-20 score or more discomfort with higher PFIQ-7 score) was associated with an OR >1 (increased risk of having a score >0) and/or an IRR > 1 (increase value of the score even non-equal to 0). Although originally planned in the study protocol, individual age matching (±five years) between the groups was not performed to use the entire population as the age difference between the two groups was of only two years. All analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC), with a 2-sided type 1 error rate of 5% as threshold for statistical significance.
750 [95% CI = 1. 111-2. 755], p = 0. 0158), but not in multivariable analysis (ORa = 1. 510 [95% CI = 0. 942-2. 421], p = 0. 0869). The only variable retained for multivariable analysis was BMI. Urge incontinence was significantly different between the two groups in both univariable and multivariable analyses, (ORb = 2. 061 [95% CI = 1. 284-3. 309], p = 0. 0027, and ORa = 1. 672 [95% CI = 0. 014-2. 758], p = 0. 0442, respectively). The only variables retained in the multivariable analysis were age and BMI. Prolapse was significantly more prevalent in the CS group in univariable analysis (5. 88% vs. 0%, p = 0. 0296). Multivariable analysis could not be performed on this dataset due to the data distribution. The Wexner score showed significantly more symptoms of fecal incontinence in CS than in control women in univariable analysis (ORb = 3.
(see Supplementary Information for more details). Regarding TEM characterizations, high resolution (scanning) TEM imaging as well as spatially resolved EELS were performed on a FEI Titan "cubed" microscope, operated at a 200 kV acceleration voltage and equipped with both TEM and STEM aberration correctors. Aberration-corrected TEM imaging was performed under negative Cs conditions (Cs tuned to -12 μm). HAADF-STEM images were acquired using a convergence semi-angle α of 22 mrad and 25 pA probe current, the ADF inner acceptance angle β and the EELS acceptance angle were both equal to 28 mrad. Quantification of the Zr and Ni content was performed with the Gatan Digital Micrograph (DM) software package, using Hartree-Slater cross sections. NBED patterns were acquired on a FEI Titan transmission electron microscope, operating at an accelerating voltage of 200 kV, equipped with a probe Cs corrector and a Gatan US1000XP CCD camera.
Acknowledgements H. Idrissi  is mandated by the  Belgian National Fund for Scientific Research (FSR-FNRS ). This work was supported by the  FNRS  under Grant  PDR -T. 0178. 19. FWO  project  G093417N  (' Compressed sensing enabling low dose imaging in transmission electron microscopy ') and  Hercules fund 'Direct electron detector for soft matter TEM'  from  Flemish Government  are acknowledged.
Evaluation of plasma membrane permeability and cell death by staining with propidium iodide Propidium iodide (PI) was used to quantify plasma membrane permeability and cell death  [33]. At the end of reatment with or without 7β-OHC (50 µM)) in the presence or absence of DMF (25 µM), MMF (25 µM) or α-tocopherol (400 µM). 158N cells (adherent and non-adherent cells) collected by trypsinization, were stained with 1 µg/mL of PI (5 min at 37°C) and then immediately analyzed with a Galaxy flow cytometer (Partec, Münster, Germany). Fluorescence was collected through a 590/10 bandpass filter on a Galaxy flow cytometer (Partec): 10,000 cells were acquired. Data were analyzed with Flomax (Partec) or FlowJo (Tree Star Inc. ) software.
Measurement of mitochondrial transmembrane potential with DiOC6(3) The mitochondrial transmembrane potential (∆Ψm) was assessed by flow cytometry with the cationic lipophilic dye 3,3'-dihexyloxacarbocyanine iodide DiOC6(3) (Thermo Fisher Scientific, Courtaboeuf, France). At the end of treatment, adherent and non-adherent cells were pooled, incubated with DiOC6(3) (40 nM, 15 min, 37°C) and then analyzed with a Galaxy flow cytometer (Partec)  [19, 35]. The loss of transmembrane mitochondrial potential (ΔΨm) is indicated by a decreased of the green fluorescence collected through a 520 ± 10 nm band pass filter. For each sample, 10,000 cells were acquired and data were analyzed with Flomax (Partec) or FlowJo (Tree Star Inc. ) software.
Measurement of lysosomal destabilization with Acridine Orange Acridine orange (AO) was used to evaluate lysosomal integrity [49] and or to quantify autophagic vesicular (acidic vesicular) formation [50-52]. AO stock solution was prepared at 1 mg/mL in DMSO and used at 1 µg/mL. After 15 min of incubation at 37°C, cells were washed, and resuspended in PBS for flow cytometry analysis. The fluorescence of AO was collected using a 630 nm longpass filter and measured on a logarithmic scale. Ten thousand cells were acquired on a Galaxy flow cytometer (Partec). Data were analyzed with Flomax (Partec) or FlowJo (Tree Star Inc. ) software.
An antibody directed against β-actin (mouse monoclonal antibody (#A2228, Sigma Aldrich)) was used at a final concentration of 1/10,000. Membranes were washed three times for 5 min, with PBST and incubated for 1 h at room temperature with horseradish peroxidase-conjugated goat anti-rabbit (for caspase-3, LC3-I/-II, and PLP) diluted at 1/5,000 in 1% milk powder in PBST or anti-mouse antibody against MBP or β-actin, diluted at 1/5,000 in 1% milk powder in PBST. The membranes were then washed and antibody binding revealed using an enhanced chemiluminescence detection kit (Supersignal West Femto Maximum Sensitivity Substrate, Thermo Fisher Scientific) and Chemidoc XRS+ (Bio-Rad, Marnes la Coquette, France). Band intensity was determined with Image Lab software (Bio-Rad). The expression of caspase-3 (uncleaved and cleaved) and the ratio LC3-II / LC3-I were calculated with Image Lab software (Bio-Rad).
These data showed that DMF and its main metabolite MMF prevent the decrease of succinate dehydrogenase activity which participates in both the TCA cycle and the electron transport chain at the mitochondrial level. These data lead us to examine the effect of DMF and MMF on 7β-OHC-induced loss of transmembrane mitochondrial potential (∆Ψm) on 158N cells by staining with DiOC6(3). A high value (33%) of DiOC6(3) negative cells, indicating a decrease in ∆Ψm (cells with depolarized mitochondria) was detected under treatment with 7β-OHC (Fig. 6B ). The percentage of DiOC6(3) negative cells was strongly reduced when 158N cells were co-treated with DMF, MMF or αtocopherol (Fig. 6B ). Another fluorochrome (MitoTracker Red) was used to evaluate the mitochondrial mass. Upon staining with Mitotracker Red, an increase in the mean fluorescence intensity (MFI) of this dye was observed when the cells were treated with 7β-OHC compared to control and vehicle-treated cells (Fig.
Acknowledgments: This work was presented as a poster at the 8 th  ENOR Symposium "Oxysterols and Sterols: from Lipidomics to Food Sciences ", September  20-21 , 2018,  University of Bologna, Bologna, Italy  (https://www. oxysterols. net/). This work was supported by grants from:  Univ. Bourgogne (Dijon, France) ,  Univ. Monastir (Monastir, Tunisia ), and  Univ. Manouba  ( Tunis, Tunisia ). We acknowledge  Dr Delphine Meffre  ( Inserm UMR 1124, Paris, France ) for her valuable advice on the choice of anti-PLP and and anti-MBP antibodies, and for the conditions of analysis of these proteins by Western blotting.
MATERIALS AND METHODS The study was approved by a local ethics committee (CPP Sud-Est 6), ANSM 2013-A00971-44 and registered in www. Clinicaltrials. gov (NCT02438163). All patients provided written, informed consent. The trial was conducted in the Hospital Le Vinatier, University Department for treatmentresistant depression, University of Lyon, France. All patients were consecutively recruited from March 2014 to October 2017.
AMT was assessed in the setting phase described above and was defined as the lowest intensity to obtain at least five MEPs of 200 µV over 10 stimulations in the FDI contracted at 20% of maximal strength  (Huang et al. , 2005). This strength was measured using a dynamometer (Hand Dynamometer Vernier HD-BTA, driven by the software Logger Pro 3); a continuous audio-visual EMG feedback was available to evaluate participants' relaxation or their level of muscle contraction. The experimental design is illustrated in Figure  1.
TBS-induced modulation of MEP size across the five time points (baseline, 5, 10, 20 and 30 min) in both condition (iTBS and cTBS) were also investigated as the maximum peak amplitude at the individual level. Number of responders and non-responders after TBS according to  Hamada et al. (2013)  classification were also calculated and compared across groups using Fischer exact test. Responders and non-responders were defined according to the grand average of TBS responses below and above 1 for cTBS and iTBS, respectively  (Hamada et al. , 2013). SPSS 21 was used for all analyses, and the level of significance was set at p < 0. 05.
TBS Induced Changes in Neural Plasticity At baseline, there was no significant difference in the mean 1 mV MEP between the groups. Before the iTBS session, the mean amplitude of 1 mV MEP in the MDD group was 969. 7 (SD 243) vs. 1055. 7 (129) µV in the control group (p = 0. 317). Before the cTBS session, the mean amplitude of 1 mV MEP in the MDD group was  1206. 3 (385) vs. 975. 4 (131)  µV in the control group (p = 0. 074). The individual data illustrating the modulation of MEP amplitudes induced by iTBS and cTBS are displayed in Figure  2. A large interindividual variability was observed in the two FIGURE 1 | Experimental design. MRI, magnetic resonance imaging (T1-weighted image acquired to be used with the neuronavigation system); AMT, activity motor threshold; 1 mV MEP, transcranial magnetic stimulation (TMS) intensity to obtain an MEP with a mean amplitude of 1 mV; MEP, motor evoked potential; iTBS, intermittent theta-burst stimulation; cTBS, continuous theta-burst stimulation.
Germination experiments Both the greenhouse and the natural experiments had a randomised block design with seven blocks (corresponding to sampling month) and four treatments (corresponding to animal vector). Over a 15month period, we recorded the germinated seedling species and then removed them. To obtain seedling species richness and abundance for each sampling season (spring, summer, autumn), we pooled the monthly data from May-June, July-August and October-November for each site and each animal vector.
Greenhouse data analysis We used generalized linear mixed models (GLMM) to compare seedling abundance and the number of species among dispersal vectors, season and habitat type (dung sample as the statistical unit). Negative binomial and Poisson regression models were respectively assigned for seedling abundance and species richness (count response variables) including additional over-dispersion in the model. Animal species (4 species), sampling season (spring, summer and autumn) and habitat (forest and prairie-forest ecotones) were xed factors, and site within habitat was a random effect. The log-transformed weight of each dung sample was taken as an offset to account for differing sample weights. We used the lsmeans package to obtain the predicted values for each combination of factors. We then performed a Tukey post-hoc test for pairwise comparisons.
Acknowledgements We are grateful to  Hossein Akhani  and  Atefeh Ghorbanalizadeh  for their help in the eld and their assistance with identify plant species. We also thank  Esmail Ghadimi ,  Mojtaba Ghadimi  and  Javd Pourrezaei  for assisting with data collection. We would like to nally thank  Vicki Moore  for the English revision of the manuscript.
MS identification of proteins interacting with GFP-fusion proteins Proteins from GFP pulldown experiments were essentially processed and analyzed as previously described  (37). In brief, proteins enriched by GFP-Trap beads were eluted and digested by adding 25 l 2M urea, 1 mM DTT and 5 ng/l trypsin. The resulting peptides were alkylated by adding 5 mM iodoacetamide and then desalted using StageTips  (45). Desalted peptides were analyzed using an EASY-nLC 1200 system connected to a QExactive HF mass spectrometer (Thermo Fisher Scientific) operated in a data dependent acquisition mode  (46). Label-free quantification of proteins from pull-down experiment was performed using the MaxLFQ algorithm  (47)  embedded in the MaxQuant package  (48).
MALDI mass spectrometry/tandem mass spectrometry Total RNA was hybridized to a DNA oligodeoxynucleotide complementary to position 4201-4250 of human 28S rRNA, followed by digestion with mung bean nuclease and RNase A as described previously  (49). The desired 28S rRNA sub-fragment was purified by polyacrylamide gel electrophoresis and digested with RNase T1 for analysis on a Bruker UltraFlex MALDI mass spectrometer  (50). 1-2 pmol rRNA sub-fragment were RNase digested to completion and analysed directly using 3-hydroxypicolinic acid as matrix. The observed masses were compared to theoretical masses calculated using GPMAW software (Lighthouse Data, Denmark). Tandem mass spectrometry to confirm the exact position of the observed modification was performed on a Waters Q-TOF MALDI Premiere instrument (Waters, Manchester, UK) in positive ion mode as previously described  (51).
Biotinylated RNA oligonucleotides in test samples were recovered by binding to Dynabeads M280 Streptavidin (Thermo Fisher Scientific) equilibrated in B&W Buffer (50 mM Tris-HCl pH 7. 4, 0. 5 mM EDTA, 1 M NaCl), according to the manufacturer's instructions. Dynabeads with bound oligonucleotides were washed three times with B&W Buffer, resuspended in 200 l of 0. 1 M KOH, and transferred to scintillation vials filled with 4 ml of Ultima Gold scintillation liquid (Perkin-Elmer). The incorporated radioactivity was measured in 1900 TR liquid scintillation analyzer (Packard). The experiment was performed two times, in triplicates. All data points were included in the statistical analysis.
ZCCHC4 --domain architecture, evolutionary distribution and key motifs Bioinformatics analysis indicated that the uncharacterized human protein ZCCHC4, which carries CCHC zinc finger (Pfam PF00098), also contains a MTase domain, which belongs to the seven ␤-strand (7BS, also referred to as 'Rossmann-fold') class of MTases  (35). The MTase domain is found in the middle portion of ZCCHC4, flanked by N-and C-terminal regions that are rich in Cys and His residues (Figure  1A ). The C-terminal region contains the classic CCHC zinc finger, as well as several additional non-canonical CCHC zinc fingers (see below), while the Cys/His-rich N-terminal part lacks annotated motifs (Figure  1A ). Putative ZCCHC4 orthologues are present in a wide range of animals, and sequence alignments reveal several interesting conserved motifs (Figure  1B, C ).
ACKNOWLEDGEMENTS We thank  Anette Lund ,  Ingrid Fadum Kjønstad  and  Claudia Gräf  for technical assistance. We would like to thank the  PRO-MS Danish National Mass Spectrometry Platform for Functional Proteomics  and the  CPR Mass Spectrometry Platform  for instrument support and assistance. We thank  Oslo NorMIC Imaging Platform  at  Department of Biosciences, University of Oslo  for the use of cell imaging equipment.
FUNDING Norwegian Cancer Society  [ 107744-PR-2007-0132 ];  Research Council of Norway  [ FRIMEDBIO-240009  to P. Ø. F. ];The  Max Planck Society  and  DFG Grant  [ LE 3260/3-1  to S. A. L. ]; PROMEC is funded by the  Faculty of Medicine at NTNU  and  Central Norway Regional Health Authority ; The  Lundbeck Foundation  [ R231-2016-2682  to M. E. J. ];  Novo Nordisk Foundation  [ NNF16OC0022946  to  M. E. J. ,  NNF14CC0001  to J. V. O. ]. Agence nationale de la recherche  [ ANR-16-CE11-0029-01  to P. E. G]. The Vermeulen lab is part of the  Oncode Institute , whichis partly funded by the  Dutch Cancer Society (KWF). Funding for open access charge: The  Norwegian Cancer Society.
DATA AVAILABILITY Proteome data for the ZCCHC4-pulldown (Figure  2 ), m  6  A-RNA-pulldown (Supplementary Figure  S4 ), and polysome proteomics (Supplementary Figure  S6 ) experiments are available at ProteomeExchange (http://www. proteomexchange. org/) with accession numbers PXD012346, PXD013354 and PXD015084 respectively. The ribosome profiling experiments (Figure  6 ) are available at the Gene Expression Omnibus with accession number GSE128994.