dataesr/scientific-paragraphs-categorization huggingface dataset:
The data were searched using the Mascot search engine (version 2. 5) with fully tryptic protease cleavage, using methionine oxidation as variable modification, with mass tolerances of 10 ppm for precursors and 0. 2 Da for MS2 fragment ions, a minimum peptide mass of 400 Da, and a false-discovery rate of 0. 05 at both peptide and protein levels. The taxonomy was set to Homo Sapiens and the SwissProt database was used. Mapping of A2ML1 glycosylation pattern. Recombinant A2ML1 was digested with trypsin alone or both trypsin and chymotrypsin in solution. Digested samples were purified using homemade C18 stage tips before analysis by LC-MS/MS. Online RP-HPLC fractionation using an EASY-nLC 1200 (Thermo Fisher Scientific) was performed with a 5-45% gradient of acetonitrile (ACN) over 30 min. Mass spectrometry was performed on an Orbitrap Eclipse Tribrid (Thermo Fisher Scientific) running a glycan-triggered MS2(HCD)_MS2(EThcd) data-dependent acquisition method with a resolution of 120,000 on the MS1 level and 60,000 on both MS2 levels.
HCD fragmentation used stepped collison energies (27%, 30%, 32%) whereas EThcD used automatically adjusted ETD fragmentation augmented with 10% HCD. Data were searched with the Byonic search engine (Version 3. 7. 13. Protein Metrics) with a standard human glycosylation database and a PEP 2D score threshold/cut-off of 0. 05. Proteolytic digests of A2ML1. Digestions with chymotrypsin, PPE, and trypsin were done with up to a 1:1 molar ratio of protease to A2ML1. All of the reactions were carried out in HBS with and without 50 mM BAPN. The reaction mixes were incubated for 15 min at 37 °C, after which digestion was stopped by inhibiting with 2 mM phenylmethanesulfonyl fluoride (PMSF) for 15 min before SDS-PAGE analysis. Digests with thermolysin, chymotrypsin, and HNE were carried out with a molar ratio of 1:10 of protease to A2ML1 in the presence of a 0-0.
The protease may escape at any step between bait region cleavage and conjugation; steps 7-8 show one scenario where the protease escapes at a point late in the conformational change. 1:1 molar ratio of trypsin, thermolysin, HNE, cathepsin G, V8, PPE, or chymotrypsin were separated by reducing SDS-PAGE and transferred to a polyvinylidene difluoride (PVDF) membrane. The C-terminal bait region cleavage bands were cut out and applied to TFA-treated glass fiber membranes. Automated Edman degradation was performed using a PPSQ-31B protein sequencer (Shimadzu Biotech) with in-line phenylthiohydantion analysis on an LC-20AT HPLC system. Data were obtained using Shimadzu PPSQ-31B software and the sequences were determined manually from the UV 269 nm chromatograms. To identify bait region cleavage sites by different proteases using LC-MS/MS, A2ML1 was incubated with a 1:2 molar ratio of trypsin, thermolysin, HNE, cathepsin G, V8, PPE, or chymotrypsin to A2ML1.
The liberated peptides were purified using homemade C18 stage tips and analyzed by LC-MS/MS on a Q Exactive Orbitrap (Thermo Fisher Scientific), using the same methods as described for the identification of A2Ml1 in skin extract. The data were searched using Proteome Discoverer v. 2. 5 with no enzyme in the case of PPE, thermolysin, and cathepsin G, while the relevant protease specificity was selected for GluC, chymotrypsin, and HNE. Up to two missed cleavages were allowed, and mass tolerances of 10 ppm for precursors and 0. 02 Da for MS 2 fragment ions were selected; the false-discovery rate was 0. 05 at the peptide and protein level. The following variable modifications were considered; methionine oxidation, proline oxidation, and glutamine/asparagine deamidation along with the following variable modifications of the N-terminal; acetylation, met-loss, and acetylation combined with met-loss.
The final optimal reservoir condition contained 0. 1 M potassium phosphate dibasic, 10% PEG 3350 pH 9. 2 in drops of 0. 9 µl A2ML1 mixed with 0. 1 µl seeds and 1 µl reservoir buffer. Crystals were soaked into the reservoir solution added 25% (v/v) ethylene glycol as cryoprotectant followed by flash cooling in liquid nitrogen. Data collection with MXCUBE3 from flash-frozen crystals was performed at BioMAX (MAX IV, Lund) at 100 K and processed with XDS 47 built 20180808. Phenix programs from the 1. 19. 2-4158 version used for crystallographic and cryo-EM structural work. The structure was determined in phenix. phaser  48 using two complementary density search models covering MG1-6 and CTM8, respectively, excised from a preliminary 3. 8 Å cryo-EM map. After density modification with phenix. density_modification  49 , a near complete EM structure of native A2ML1 was placed domain-wise in the two molecules within the asymmetric unit present in the crystallographic map.
Cryo-EM. Copper grids CF-1. 2/1. 3-3Cu-50 (Protochips) were glow discharged with a current of 15 mA for 45 s using a GloQube (Quorum Technologies) system. Cryo-EM samples were prepared with a Mark IV Vitrobot system (Thermo Fisher Scientific). Briefly, 3 µL of sample were applied to each grid, the temperature during grid preparation was 4 °C and the relative humidity in the chamber was set to 100%. The blot force was 0 or 1 and the blot time 4. 5 s. Data collection using the EPU 2. 7 software was done on a Titan Krios G3i (Thermo Fisher Scientific) equipped with a K3 detector (Gatan) with a Bioquantum (Gatan) energy filter (slit width of 20 eV) and a C2 aperture with a 50 μM diameter, spot size 3 and a beam diameter of 0. 98-1. 12 μm. Data were collected in counted super-resolution mode with gain correction on the fly followed by 2x binning in EPU resulting in movies output at physical pixel size (0.
647 Å/pixel at the magnification used). For processing of all the datasets, the collected movies were imported into CryoSparc v3 (Structura Biotechnology Inc), with the following parameters: 300 kV accelerating voltage, 2. 7 mm spherical aberration, 0. 1 amplitude contrast fraction, 130,000 nominal magnification (calibrated pixel size of 0. 647 Å/pixel). All subsequent processing steps were carried out in CryoSparc v3. Key parameters and statistics for data collection are presented in Supplementary Table  1. In general, the workflow for image analysis and 3D reconstruction was the same for all four data sets. The specific steps are described in Supplementary Fig. 3A  for native A2ML1, Supplementary Fig. 6A  for A2ML1-CE, Supplementary Fig. 7A  for A2ML1-CC, and Supplementary Fig. 8A  for the A2ML1-CA dimer.
For each movie, motion correction and CTF parameters were estimated in patches. Particles were picked through blob picker followed by 2D classification to generate templates for template picker. Template-picked particles were cleaned with another 2D classification, although this step was skipped for A2ML1-CC. For further cleaning of particles ab initio volumes were generated by 3D classification, followed by heterogenous refinement. Eventually, non-uniform refinement was applied on the cleaned particle stacks. In the three datasets with TEV-P cleaved A2ML1 where both monomers and dimers are present, these were separated by 2D classification, multi-class ab initio reconstructions and heterogeneous refinement as indicated in Supplementary Figs. 6, 7, and 8. An atomic model for A2ML1-CC was generated by homology modeling starting from the structures methylamine-treated A2M in pdb entry 4ACQ (22).
After overall fitting in Chimera-X, each domain of the starting model was placed by rigid-body refinement in Coot and manually rebuilt to the density. The model was refined in phenix. real_space_refine using rigid-body refinement, positional refinement and grouped B-factor refinement  52  and rebuilt with Coot in an iterative manner until convergence. Starting models for the A2ML1-CE monomer and the A2ML1-CA dimer were generated from the structure of A2ML1-CC and first fitted by rigid-body refinement with phenix. real_space_refine into the relevant map after which the model was fitted in Coot and refined again in an iterative manner until convergence. A starting model for native A2ML1 was constructed from domains taken from the A2ML1-CC structure combined with a homology model for the TE domain based on the structure of native C3 (pdb entry 6RU5  53  ) and the crystal structure of the isolated MG8 domain of bovine A2M (pdb entry 1AYO  54  ).
elegans (Caenorhabditis elegans). Only full-length coding sequences were considered. Using CLC Main Workbench 21. 0. 5 (QIAGEN), the sequences were aligned with Clustal Omega  59. A maximum-likelihood phylogeny was reconstructed on the multiple sequence alignment. Bootstrap values from 1000 bootstrap replicates were computed. To investigate A2MFs in Porifera, tblastn searches limited to nucleotide entries assigned to Porifera taxonomy (taxid: 6040) were conducted using the Trichoplax CD109-like protein (NCBI: XP_002111588. 1) as query. Searching non-redundant (NR) and whole genome shotgun contigs (WGS) databases gave no results, whereas transcriptome shotgun assembles (TSA and the database assembled by Manousaki et al. 60  returned several hits (TRINI-TY_DN158914_c0_g1_i1, ACY74611, GIUN01026505.
1, GIUN01023177. 1, HBWD01391903. 1). Using these hits as queries in blastx searches of the entire NR database revealed that these sequences most likely originate from other taxa (cnidaria, rat, and mussels) and therefore do not support the presence of A2MFs in Porifera. No Porifera reference genome has been sequenced to date. To investigate A2MFs in fungi and plant kingdoms, tblastn searches limited to nucleotide entries assigned to Fungi (taxid:4751) and green plants (taxid:33090) were conducted, again using the Trichoplax CD109-like protein (NCBI: XP_002111588. 1) as query. No hits were returned for fungi. The five hits returned for plants (XP_030540977. 1, XP_030549320. 1, XP_002499844. 1, GHP02778. 1, XP_007512798. 1) were then used as queries in blastp searches of the entire NR database.
Acknowledgements J. J. E. was supported by the  Danish Council for Independent Research  ( DFF-4004-00471 ), the  LEO Foundation  ( LF18039 ), the  VELUX FONDEN  ( 00014557 ), and the  Novo Nordisk Foundation (BIO-MS)  ( NNF18OC0032724 ). We thank the  EMBION Cryo-EM Facility at iNANO, Aarhus University , for time under application ID 68 and the staff for assistance during EM data collection. We also thank the beamline staff at  MAX IV BioMAX  for assistance during collection of the diffraction data. G. R. A. received financial support from Danscatt and the  Lundbeck Foundation BRAINSTRUC center  ( R155-2015-2666 ). A. Z. acknowledges the  Danish Council for Independent Research  ( DFF-1025-00015B ) for funding.
Data availability All the relevant data have been deposited or are available from the corresponding authors upon request. Source data are provided in the accompanying Source Data file. Sequences for the recombinant A2ML1 proteins are given in Supplementary Note 7. All mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  61  with the dataset identifier PXD032010. New atomic structures for the EM and X-ray structures are deposited at the Protein Data Bank as entries 7Q1Y (X-ray native A2ML1), 7Q5Z (EM native A2ML1), 7Q60 (EM A2ML1-CE monomer), 7Q61 (EM A2ML1-CC monomer), and 7Q62 (EM A2ML1-CA dimer). Previously published structures used in this article are available at the Protein Data bank: 4ACQ (A2M-MA), 7S63 (native A2Moo), 6RU5 (native C3), 1AYO (bovine A2M MG8).
Using the molecular timetable method we found that this pattern would fit a global phase delay of cycling genes in response to long days, which is similar to what has been observed in other plants such as Arabidopsis  [10, 11]  and Panicum hallii  [9]. In contrast, while our method based on a single time point estimates an average phase delay of 0. 8 h (Fig. 3D ), full time-course datasets performed in Arabidopsis and Panicum hallii  [9, 10]  estimate this phase change to be around 4 h. It is very likely that this discordance in the estimation of phase differences induced by photoperiod between our study and those of others is due to the low accuracy of our method, which is based in a prediction from a single time point, while the other studies are based on datasets with multiple samples collected at regular intervals during one or various days  [9, 10].
5 using Bowtie2 version 2. 3. 4. 2 with default parameters  [45]. Previous to variant calling, reads with mapping quality lower than 5 were discarded using samtools v1. 7  [46] , duplicated reads were removed using Picardtools (http:// broad insti tute. github. io/ picard) and indels were realigned using GATK Indel-Realigner  [47]. We called variants in all three alignments simultaneously using GATK UnifiedGenotyper  [47]. Presence/absence of S. pimpinellifolium alleles in the F1 hybrid was scored at 3,396,011 biallelic positions where Moneymaker and LA1589 were homozygous and different from each other, and the hybrid was heterozygous.
0 using hisat2 v2. 1. 0 [51] , allowing a maximum intron length of 115400 bp (the largest intron in the tomato genome annotation ITAG4. 1). An average of 92. 9% of the reads aligned to the reference (minimum of 89. 8%). The number of reads per transcript in the ITAG4. 1 annotation was counted with the featureCounts function in the Rsubread R package with default parameters  [52]. Only transcripts that presented more than 10 reads in all samples together were used in downstream analysis, leaving us with 23,226 transcripts out of the 34,688 transcripts present in the annotation. Sample homogeneity was surveyed the with the plotPCA function in the DEseq2 package in R  [53]. Differential expression between each genotype and condition was calculated with DEseq2 using two different models. The first model aimed to obtain lists of differentially expressed transcripts between each genotype and S.
lycopersicum cv Moneymaker in each photoperiod separately, and contained a unique variable with 8 factors grouping the genotype and condition for each sample ("MM_LD", "MM_SD","LNK2_LD", "LNK2_SD", "EID1_LD", "EID1_SD", "LE_LD" and "LE_ SD"). The second model was conceived to test the effect of photoperiod in each genotype and included the three variables "photoperiod", "genotype at EID1" and "genotype at LNK2", as well as all its possible interactions ( pho top eriod + EID1 + LNK2 + photoperiod:EID1 + phot operiod:LNK2 + EID1:LNK2 + photoperiod:EID1:LNK2 ). From this model we extracted the effect of photoperiod in each genotype. In both models we considered as differentially expressed those transcripts with a q-value lower than 0. 01. A dataset with normalized values for each sample used in all downstream analyses and graphical representations was generated using the vst function in the DEseq2 R package  [53].
For functional analysis of differentially expressed genes, GO terms in the ITAG4. 1 genome annotation were assigned to the 23,226 transcripts that presented more than 10 reads in all samples. Overrepresentation analysis was performed with the GOseq R package based on the Wallenius non-central hyper-geometric distribution  [54]. GO categories with q values smaller than 0. 05 (adjusted using the Benjamini and Hochberg's method) were considered as significantly overrepresented.
Homologs of the PSEUDO-RESPONSE REGULATOR and REVEILLE gene families in tomato To obtain the homologs of PRRs and RVE genes in tomato, we compared the protein sequences from the following Arabidopsis TAIR10 ids: AT1G01060. 1 (LHY), AT1G18330. 2 (RVE7), AT2G46830. 1 (CCA1), AT3G09600. 1, (RVE8), AT5G02840. 1 (RVE4), AT5G17300. 1 (RVE1), AT5G37260. 1 (RVE2) AT5G52660. 2 (RVE6), AT2G46790. 1 (PRR9), AT4G18020. 1 (APRR2), AT5G02810. 1 (PRR7) , AT5G24470. 1 (PRR5), AT5G60100. 2 (PRR3) and AT5G61380. 1 (TOC1) onto the tomato protein sequences from annotation ITAG4. 1 using standalone BLAST +  [55]. For the RVE family, we selected all matching tomato proteins with a bit score greater than 90, and for the PRR family, we selected those with a p value lower than 1e-40. Only tomato transcripts whose expression showed oscillation during the diel cycle were considered homologs.
Determination of cycling genes in tomato Short reads from a time course experiment performed in 7-day old S. lycopersicum var. M82 and S. pennellii seedlings were obtained from the SRA database (www. ncbi. nlm. nih. gov/ sra, project number PRJNA295848). Although the original experiment consisted in duplicate samples every 4 h during one diel cycle and two circadian cycles, only reads corresponding to the diel cycle were used, corresponding to samples from time-points 12, 16, 20, 24, 28 and 32 (consecutive SRA numbers SRR2452525 to SRR2452536, and SRR2452572 to SRR2452586). Short reads from these 24 libraries were aligned to the tomato genome reference sequence v4. 0 using hisat2 v2. 1. 0 with default parameters  [51]. The number of reads per transcript in the ITAG 4. 1 annotation was counted with the featureCounts function using the Rsubread R package with default parameters  [52].
Low expressed transcripts, that contained less than 10 reads across all samples were discarded. The number of reads in the 24,330 remaining transcripts was normalized using the vst function in the DEseq2 R package  [53]. To improve detection of cycling genes, we split the two replicates from each time-point taken in a single day into two consecutive days by adding 24 h to the collection time of the second replicate. Cycling genes were identified independently in S. lycopersicum and S. pennellii using the meta2d function in the R package MetaCycle with parameters minper = 20, maxper = 28, cycMethod = "ARS", adjustPhase = "predict-edPer", combinePvalue = "fisher", ARSmle = "auto" and ARSdefaultPer = 24  [58]. We considered as cycling a total of 5740 genes that presented an adjusted p value < 0.
Acknowledgements We thank  Antonio Monforte  for sharing the S. pimpinellifolium introgression line population used to construct the near isogenic lines. This work has benefited from the support of IJPB's Plant Observatory technological platforms, and we thank specifically  Michel Le-Brusq ,  Patrick Grillot ,  Michel Burtin  and  Lilian Dahuron. We thank  Nazneen Badroudine ,  Maria Jesus Lacruz ,  Adrien Léonardi ,  Magali Nawrocki-Serin ,  Vanessa Pacé-Faria ,  Stéphane Raude  and  Stéphanie Zimmermann  for help with administrative tasks. We thank  Niels A. Müller  and  Paloma Mas  for discussions and critical reading of the manuscript. The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Surprisingly the influence of elections on access to credit has never been empirically investigated in spite of the huge literature devoted to access to credit. In this paper, we shed light upon the question if the occurrence of elections affects access to credit for firms. In order to scrutinize this question, we perform an empirical investigation on firm-level data from a large cross-country dataset of firms of developed and developing countries. We use data from the World Bank Enterprise Survey (WBES), containing surveys regularly performed in various countries since 2005 which includes information on credit constraints. We combine this information with data on elections to investigate whether electoral episodes affect access to credit. To achieve this objective, we examine whether election years but also pre-election and post-election years are associated with changes in access to credit.
Our work has several limitations. First, we do not have information about the lenders for each firm. It would have been of interest to know the characteristics of the lending banks in line with the hypotheses. For instance, state-owned banks or banks in bad financial situation under the threat of losing their license may relax more access to credit in election times. This limitation does not prevent us however to investigate for the first time the impact of elections on access to credit. Second, our dataset only includes 51 elections from 44 countries. However this number of electoral episodes is sufficient to perform our analysis and is related to the difficulties to get data on access to credit for a large cross-country sample of firms. From this perspective, WBES data are the best dataset to our knowledge to investigate our research question.
By elections, we refer to the presidential election for presidential systems and parliamentary election for parliamentary systems. For countries classified as assembly-elected president, we consider presidential and parliamentary elections. Political regime is obtained from the Database of Political Institutions and completed by ours for missing information (notably in Balkans or in recent years). For pre-and post-election dummies, we consider not only elections displayed in Table  A2  but also other elections occurring in the country. Let's illustrate with the example of Turkey. For this country, data on firms are available in  2007 , 2012 , 2013 , 2017 , and 2018. Elections in Turkey occurred in 2007 , 2011 , 2015 and 2018. Therefore, Election dummy for election takes value one for firms in 2007 and in 2018, Pre-election dummy equals one for firms in 2017, Post-election dummy for firms in 2012.
Macroeconomic variables are collected from World Development Indicators and Global Financial Development Database. Finally, we include a set of firm-level variables (  ) to control for observable heterogeneity. We add firm size measured by the log of the number of employees (Employees) and firm age defined as the log of the age of the firm (Age). We also include dummy variables equal to one if the firm is owned by foreign investors (Foreign), owned by the government (Government), operates in manufacturing (Manufacturing), construction (Construction) or services (Services), and if the firm is audited (Audited), is privately held (Private) or is a partnership (Partnership). These variables are extracted from the WBES and the precise definition is displayed in the Appendix. Table  1  indicates that firms have on average more than 90 employees and are 16-year old.
This could weaken the influence of uncertainty on their behavior. We therefore examine the effect of foreign ownership by splitting the sample between domestic and foreign firms. This information is provided in WBES database for each firm. We observe that election years exert the same negative impact on domestic and foreign firms. During these years, access to credit and credit demand are reduced for both types of firms. However the detrimental effect of pre-election years is observed only for domestic firms. No reduction in credit demand is found for foreign firms in the year preceding elections. These findings support the view that uncertainty in election times affects more domestic firms than foreign firms.
Put together, a larger financial system and a more competitive banking system limit possibilities for the authorities to influence the behavior of banks. For these reasons, we expect that the impact of elections on credit supply should be lower in countries with larger financial systems and more competitive banking systems. We measure the size of the financial system with the ratio of domestic credit to the private sector to GDP (variable Fin Dev), and the degree of bank competition with the Boone indicator (lower values indicate higher competition). Data for both indicators come from the Global Financial Development Database. Figure  2  displays the impact of the size of the financial system, while Figure  3  presents the effect of the degree of bank competition. We find evidence supporting the hypothesis that banking system characteristics influence the impact of elections on credit access.