dataesr/scientific-paragraphs-categorization huggingface dataset:
Standard triple resonance experiments HNCA, HNCACB and CBCA(CO)NH  52  were recorded at 600 MHz. 15  N relaxation experiments were recorded on a 600-MHz spectrometer at 25 °C. 15 N T 1 and T 1ρ relaxation times were obtained from pseudo-3D HSQC-based experiments recorded in an interleaved fashion with 12 different relaxation delays (21. 6, 86. 4, 162, 248. 4, 345. 6, 432,  518. 4, 669. 6, 885. 6, 1144. 8, 1404, and 1782 ms) for T 1 and 8 different relaxation delays (5, 7, 10, 15, 20, 25, 30, and 40 ms) for T 1ρ. Two delays in each experiment were recorded in duplicates for error estimation. Relaxation rates were extracted by fitting the data to an exponential function using the relaxation module in NMRViewJ  53. Paramagnetic relaxation enhancements (PREs) were recorded at 600 MHz using a sample with concentration of ~200 µM.
UP1(Glu66Cys) was spin-labeled by adding 10-fold excess of 3-(2-Iodoacetamido)-PROXYL to the protein solution (in 100 mM Tris pH 8. 0, 100 mM NaCl) and incubating at 4 °C overnight. Unreacted spin-label was removed by size-exclusion chromatography. Completion of the spin-labeling reaction and attachment of a single spin-label on the protein was confirmed by LC/MS. Control spin-labeling reactions using wild-type UP1 protein showed that the native cysteines at positions 43 and 175 are inaccessible. PRE data were recorded and analyzed as described previously  26, 54. Residual dipolar couplings (RDCs) for 1 H N -15 N bonds were measured on a ~200 µM sample of the UP1/12-mer RNA complex aligned in Pf1 phage (~10 mg/ mL) using the IPAP experiment  55. RDCs were best-fitted to the structure by singular value decomposition (SVD) using PALES  56.
X-ray crystallography. Screening for crystallization conditions was done using commercial screens (QIAGEN and Hampton Research) at 20 °C. UP1 (in 20 mM HEPES pH 7. 0, 100 mM NaCl, and 1 mM TCEP) was mixed with 12-mer RNA in a 1:1 molar ratio (0. 95 mM protein-RNA complex concentration) and incubated on ice for 1 h. Crystallization drops were set-up by mixing 100 nL of complex and 100 nL of reservoir using the sitting drop vapor diffusion method. Crystals with hexagonal plate morphology were obtained in 0. 2 M sodium citrate and 20% PEG 3350 after one week. The crystals were cryoprotected in reservoir solution supplemented with 20% (v/v) ethylene glycol and flash frozen in liquid nitrogen. X-ray diffraction data were collected at the European Synchrotron Radiation Facility (ESRF). All diffraction images were processed by XDS  57.
The CCP4 package  58  was used for all subsequent data analysis. The structure of the UP1/12-mer RNA complex was solved by molecular replacement with the program Phaser 59 using the coordinates of UP1 bound to modified telomeric DNA (PDB code: 1U1R)  60  as search model. Model building was performed manually in Coot  61  and refinement was done using Phenix  62. Restrained molecular dynamics simulations. MD simulations were performed using GROMACS  63  and PLUMED  64. The systems were prepared using the AMBER99SB-ILDN force field 65 in tandem with the TIP3P water model  66. After solvation in a cubic box (100 Å side including 21,300 water molecules and 250 Å side including 531,000 water molecules for the UP1/12-mer RNA and the UP1/primir-18a complex, respectively), a short initial energy minimization of 100 steps was performed to resolve steric clashes between solvent atoms and the complex.
Titrations consisted of 20 injections of 2 µL or 26 injections of 1. 5 µL with a 3-min spacing between each injection. After correction for heat of dilution, data were fitted to a one-site binding model using the Microcal Origin 7. 0 software. Each measurement was repeated at least three times. Static light scattering. Static light scattering (SLS) experiments were performed on a S75 10/300 size-exclusion column (GE Healthcare) connected to a Viscotek Tetra Detector Array (TDA) instrument equipped with refractive index (RI), light scattering, viscosity and photo diode array (PDA) detectors (Malvern Instruments). Sample volume of 100 µL was injected onto the column preequilibrated with NMR buffer and the flow rate was set to 0. 5 mL/min. Calibration was done with BSA (bovine serum albumin) at a concentration of 4-5 mg/mL.
Data were analyzed by the OmniSEC software using refractive index increment (dn/dc) values of 0. 185 mL/g and 0. 17 mL/g for protein and RNA samples, respectively  75. Small angle X-ray/neutron scattering. SAXS data of UP1, pri-mir-18a, and UP1/ pri-mir-18a complex were recorded at the X33 beamline of the European Molecular Biology Laboratory (EMBL) at Deutsches Elektronen Synchrotron (DESY, Hamburg) at 15 °C. The scattering curves were measured with 120-s exposure times (8 frames, 15 s each) for concentrations in the range of 1-10 mg/mL. The scattering intensity was measured covering the momentum transfer range 0. 007 < q < 0. 63 Å -1. Individual frames collected during the exposure time were compared to check for radiation damage before averaging. Scattering of buffer measured before and after each sample was averaged and subtracted from the scattering of the sample.
SAXS data of the UP1/12-mer complex were recorded on a BioSAXS-1000 instrument (Rigaku) equipped with a Pilatus detector using 20 µL of sample in a capillary tube. All SAXS data were processed using the ATSAS software package  76. The radius of gyration (R g ) and the maximum dimension (D max ) values were obtained from the GNOM program, which evaluates the pair-distance distribution function, P(r)  77. For ab initio modeling of the protein-RNA complex, three scattering profiles corresponding to individual components and the complex were used as input for the multiphase modeling program MONSA  78. Theoretical scattering curves were calculated using CRYSOL  74. SANS data were recorded at the large dynamic range diffractometer D22 at the Institut Laue-Langevin (ILL) Grenoble, France, using a neutron wavelength of 6 Å and a detector-collimator set-up of 2 m/2 m.
The scattering intensity was measured covering the momentum transfer range 0. 02 < q < 0. 35 Å -1. Sample and buffer volumes were 200 μL and exposed for 60 min. The 2D detector signals of the samples were corrected for detector efficiency, empty cell scattering, directly calibrated (against water), and azimuthally averaged using ILL in-house software (Gosh, R. E. , Egelhaaf, S. U. & Rennie, A. R. A Computing Guide for Small Angle Scattering Experiments. Technical Report ILL06GH05T, 2006, Institut Laue-Langevin, Grenoble, France). The final 1D scattering curves were further analyzed using the ATSAS software. SHAPE analysis. Pri-mir-17-18a-19a (or pri-mir-18a) in complex with purified hnRNP A1 or UP1 proteins (125 nM) were assembled in folding buffer (100 mM HEPES pH 8, 6 mM MgCl 2 ) using 160 nM RNA prior to treatment with Nmethylisatoic anhydride NMIA (Invitrogen), as the modifying agent, as recently described  79.
RNA was phenol-extracted and ethanol-precipitated and then subjected to primer extension analysis. For primer extension, equal amounts of NMIAtreated and untreated RNAs (10 µL) were incubated with 0. 5 µL of antisense 5′-end 32 P-labeled primers (SHAPE1-3; Supplementary Table  3 ), which were used for the analysis of regions comprising miR-17, miR-18a, and miR-19a, respectively. Primer extension and SHAPE data processing was conducted, as previously described  80. Data availability. Atomic coordinates and structure files for the UP1/12-mer RNA crystal structure have been deposited in the Protein Data Bank (http://www. pdb. org/) with accession code 6DCL. Coordinates for the structural models of the 1:1 complexes of UP1/12-mer and UP1/pri-mir-18a are available upon request. Uncropped gels are presented in Supplementary Fig.
Acknowledgements We thank  Marianne Keith  ( MRC HGU, University of Edinburgh ) for Immunofluorescence data,  Robert Janowski  for collecting the X-ray diffraction data, and  Ralf Stehle  for help with in-house SAXS measurements. We are grateful to the beamline staff at  DESY (Hamburg)  and  Anne Martel  at  ILL (Grenoble)  for support with SAXS and SANS measurements, respectively. We acknowledge the use of the X-ray crystallography platform at the  Helmholtz Zentrum München , NMR access at the  Bavarian NMR Center  and SAXS measurements at the facility of the  SFB1035 at Department Chemie, Technical University of Munich. This work was supported by the  Deutsche Forschungsgemeinschaft  through grants  SFB1035  and  GRK1721  (to M. S. ),  MRC  core funding and the  Wellcome Trust  (Grant  095518/Z/11/Z  to J.
F. C. ),  Medical Research Council Career Development Award  ( G10000564  to G. M. ). Wellcome Trust   Center Core Grants  ( 077707  and  092076  to G. M. ). M. M. , A. J. , and C. C. acknowledge the support of the  Technical University of Munich-Institute for Advanced Study , funded by the  German Excellence Initiative  and the  European Union Seventh Framework Programme  under grant agreement  n°291763.
mgc. ac. cn/cgi-bin/DBatVir/main. cgi), in addition to other virus database such as the Virus-Host DB (http://www. genome. jp/virushostdb/; this database currently select 134/10028 items under "bat" query), the NCBI viral genome resources (https://www. ncbi. nlm. nih. gov/ genome/viruses/; this database currently select 84 items under "bat" query) or Virus Pathogen resource, VIPR (https://www. viprbrc. org/brc/home. spg?decorator=vipr). It is worth noting that although bats are found on all continents except Antarctica  [19] , the accumulation of results is very variable from one continent to another. As shown in Figure  1 , Asia is largely in the lead for data accumulation ahead of North America and Africa and next Europe and South America (Figure  1A ). The preponderance of Chinese results for Asia's contribution is even more impressive (Figure  1B ).
It is generally admitted that bats are a source of high viral diversity that may directly or indirectly (following genomic recombination, gene mutations, gene duplication loss/gain) cause a new outbreak. Since the past 20 years, a massive international effort was devoted to the identification of viruses in different families of bats. As shown in Figure  2 , the total number of bat-associated sequences in GenBank has grown exponentially in the last 20 years. A review of articles referring to bat-borne viruses (Figure  3 ) indicates that rabbies (55,000 persons infected each year, case fatality nearly 100%) is the most prominent topic with 2792 articles (33%). Surprisingly, as shown in Figure  3A , the virus family that rank second is Coronaviridae with 2622 articles (31%), while the total number of cases accumulated the different episodes remains relatively low (cumulative cases about 8000 individuals; mean case fatality around 10%).
Indels and structural variants are associated with DNA sequences that can form secondary structures The data described in Fig  2  were further analysed by assessing the relationship between mutation events and putative DNA secondary structures, such as might be targeted by RecQ helicases. First, we calculated the average distance between mutation events and putative Gquadruplex forming sequences (PQSs-the guanine rich motifs that have the potential to form G4s). We have previously reported that recombination events in wildtype parasites are highly associated with PQSs  [9] , and this association held for recombination events leading to SVs in all three parasite lines analysed here (Table  1 ). Indeed, the median distance between a breakpoint and a PQS in wildtype 3D7 parasites was almost identical in our previous analysis and in this new dataset  (16.
I. ) for the null dataset, which comprises distances between PQSs and random simulated breakpoints  [9]. Medians rather than means are used because of the strong skew in these non-normally-distributed datasets. https://doi. org/10. 1371/journal. pgen. 1007490. t001 and the RecQ mutants (Table  2 ). The breakpoints of SVs tended to occur in high-TR regions, suggesting that SVs can have the same mechanistic origin as micro-indels, i. e. impeded polymerase movement through repetitive DNA. However, this relationship was weaker for SVs than for micro-indels and reached significance only in the largest dataset (SVs from PfWRN-k/ d). SNPs, as expected, were largely unrelated to the TR/LCR environment (and since all SNP datasets were very small, any bias in SNP occurrence in wildtype versus mutant parasites may not be meaningful).
Its role in suppressing structural variants and indels is probably particularly crucial for a genome in which repetitive sequences, and thus risks of DNA polymerase slippage, are common. An increased SNP rate was not necessarily expected, since PfWRN is reported to lack the proofreading exonuclease domain found in human WRN  [22] , and importantly the SNP datasets were all very small, making changes in SNP rate only marginally significant. Nevertheless, it is possible that an unidentified, divergent exonuclease domain may exist in PfWRN, or that other defects causing an excess of single-stranded DNA could lead to an elevated SNP rate. Otherwise, the phenotypes seen here are consistent with RecQ functions in other organisms: for example, the yeast helicase Sgs1 is required to prevent indels and fragility in triplet repeat regions  [32] , while metazoan RecQ homologues in species from humans to C.
Southern blotting Genomic DNA was extracted from parasites using the QIAamp DNA Blood Mini Kit (Qiagen) and digested with restriction enzymes. Digested genomic DNA was resolved on a 1% agarose gel, transferred to GenScreen Plus (PerkinElmer) and hybridised with alkaline phosphatase-labelled probe (AlkPhos Direct Labelling and Detection system, GE Healthcare). For confirmation of mutant lines, DNA was digested with SacII, EcoRI and HpaI (ΔPfBLM) or PmlI and SnaBI (PfWRN-k/d). For telomere restriction fragment Southern blotting genomic DNA was digested with AluI, DdeI, MboII and RsaI and the membrane was probed with alkaline-phosphatase labelled probe specific for telomeres  [57]. ImageJ software was used to quantify telomere length.
Detection of SNPs, micro-indels and structural variants SNPs, micro-indels and structural variants were called as previously described  [3, 13]. Briefly, sequencing reads were mapped to the P. falciparum 3D7 genome with BWA. SAMtools mpileup detected SNPs, GATK UnifiedGenotyper detected micro-indels, DELLY2 detected structural variants (translocations, inversions, duplications and deletions > 300bp). A downstream analysis using R scripts identified de novo mutations as genetic variants found in a "progeny" sample but not in its parent. All mutations were visualised with Savant  [62]. 563 hits were manually inspected and 393 false hits were discarded (the same false hits were often found in all subclones, hence the relatively large number) (S5A Fig). The False/True call was not biased towards any clone tree (S5B Fig).
Briefly, polyA+ RNA (mRNA) was selected using magnetic oligo-d(T) beads. Reverse transcription using Superscript III (LifeTechnologies) was primed using oligo d(T) primers, then second strand cDNA synthesis included dUTP. The resulting cDNA was fragmented using a Covaris AFA sonicator. A "withbead" protocol was used for dA-tailing, end repair and adapter ligation (NEB) using "PCRfree" barcoded sequencing adaptors (Bioo Scientific, similar to the method of Korarewa et al. [63] ). After 2 rounds of SPRI clean-up the libraries were eluted in EB buffer and USER enzyme mix (NEB) was used to digest the second strand cDNA, generating directional libraries. The libraries were quantified by qPCR and sequenced on an Illumina HiSeq2000. Sequence data have been submitted to the ENA database under accession number ERP021698.
TopHat2  [64]  was used to map reads against the P. falciparum 3D7 reference genome. Read counts and fragments per kilobase of transcript per million mapped reads (FPKM) values were calculated for each gene using HT-seq count  [65]  and Cufflinks  [66] , respectively. Differential expression was analysed using EdgeR  [67] , using a threshold of 1. 5-fold difference in expression from the wildtype 3D7 (See S4 Spreadsheet). Data plots were created in R and Artemis  [68]  or Integrated Genome Browser (IGB) were used to visualise mapped reads.
Analysis of clone tree data The proximity of all mutations found in the clone trees to PQSs (which were located previously using the tool QGRS mapper) was determined as previously described  [9]. Median breakpoint-to-PQS distances were determined for each dataset, together with 95% confidence intervals (C. I. ). A significant difference was reported if a particular median fell outside the confidence interval for the control dataset, i. e. the median distance of random simulated breakpoints from PQSs, taken from our previous study  [9]. The percentage content of tandem repeats and low-complexity regions (TRs and LCRs) in the 1kb surrounding each mutation was calculated after downloading the locations of these two features throughout the genome from PlasmoDB. org. PlasmoDB defines TRs as previously described  [69]  and LCRs according to the DUST algorithm (for details see  [70] ).
Analysis of RNA-seq data To determine the proximity of differentially expressed (DE) genes to PQSs, a similar approach was taken as for the clone-tree data, using the algorithm detailed in Supplementary Methods. In brief, the distances from each end of a gene to all the PQSs that share its chromosome were calculated and the smallest distance was recorded. For each set of DE genes, their mean and median proximity to PQSs was then calculated. As a comparator, the PQS proximity of all genes in the genome was calculated. The statistical significance of differences between the proximity of DE gene sets and the proximity of all genes in the genome was assessed by comparing means via two-tailed t-tests. The mean TR/LCR content of each DE gene set was compared to that of all genes in the genome, as described above for 1kb windows around clone-tree mutations.
Statistical analysis of DNA fibre data Data were analysed as previously described  [23]. In brief, replication fork velocity was estimated on individual forks displaying an IdU track flanked by a CldU track; only intact forks were analysed, as ascertained by DNA counterstaining. Fork asymmetry was calculated as the ratio of the longer track over the shorter track in pairs of progressing divergent forks. A longer fork/shorter fork ratio >1 indicates asymmetry. Inter-origin distances were measured as the distance (in kb, from the stretching factor of 2 kb/μm) between the centres of two adjacent progressing forks located on the same DNA fibre. DNA replication parameters generally do not display a Gaussian distribution  [71]  so statistical comparisons were carried out using the nonparametric Mann-Whitney two-tailed tests.
These gene sets were derived from transcriptomic data from  [74] , available in PlasmoDB  [75]. Timepoints within this dataset were chosen to match the timewindows harvested for RNA-seq in RecQ mutants: 8-16h hpi (R), 24-30h (T) and 40-48h (S). Lines indicate medians, box and whiskers indicate interquartile and full ranges. (B) Table shows mean TR content for each gene set: there was no significant difference between the R, T, and S datasets as assessed by ANOVA. Interestingly, the most highly-expressed genes at all three stages have a higher TR content than the average for all genes in the genome (statistically significant differences as tested by 2-tailed T-test). However, all three differences are only about half as large as the difference between all-genes and the gene sets upregulated in ring stages of RecQ mutant lines (shown for comparison).
The table also shows the TR content of var genes, grouped into all vars, and vars containing a PQS on the sense or antisense strand: in no group is the TR content different from that of all ring-stage genes. (C) Box-plot as in (A), showing the percentage low-complexity-region (LCR) content of the same gene sets. (D) Table shows mean LCR content for each gene set: there was no significant difference between the R, T, and S datasets as assessed by ANOVA. The most highlyexpressed genes at all stages have a lower LCR content than the average for all genes in the genome. The table also shows the LCR content of var genes, which is lower than that of all ring-stage genes. (PDF).
(D) Box-plot showing the percentage tandem-repeat (TR) content of differentially expressed genes in RecQ helicase mutants at rings (R), trophozoites (T) and schizonts (S). Genes are divided into Up and Down regulated subsets. Lines indicate medians, box and whiskers indicate interquartile and full ranges. Shaded boxes represent a statistically significant difference in the mean from that of the null dataset (all genes in the genome) by 2-tailed t-test: Ã , p<0. 05; ÃÃ , p<0. 01; ÃÃÃ , p<0. 001. (E) Box-plot as in (A), showing the percentage low-complexity-region (LCR) content of differentially expressed genes in RecQ helicase mutants. (F) Box-plot as in (A), showing distances between PQSs and genes differentially expressed in RecQ helicase mutants. https://doi. org/10. 1371/journal. pgen. 1007490.
BSF metabolise glycerol as efficiently as glucose To investigate the capability of BSF to use glycerol, we measured the rates of glycerol consumption in cells pre-adapted or not to CMM_Glyc/GlcNAc and compared these data to the rates of glucose consumption. Cells were adapted for one month in either CMM_Glyc/GlcNAc or CMM_Glc and then were transferred to fresh CMM medium containing either glycerol/ GlcNAc, glucose or glycerol/glucose to measure the consumption of the carbon sources. The rates of glucose and glycerol consumption were determined by enzymatic assay of the carbon sources remaining in the CMM medium. Since glucose contains 6 carbons and glycerol only 3, data were expressed as carbon molecules equivalent (mol. C) for comparison purpose. Cells adapted to CMM_Glyc/GlcNAc consumed glycerol at the rate of 57.
NanoLC-MS/MS analysis Online nanoLC-MS/MS analyses were performed using an Ultimate 3000 RSLC Nano-UPHLC system (Thermo Scientific, USA) coupled to a nanospray Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Scientific, USA). 500 ng of each peptide extract was loaded on a 300 μm ID x 5 mm PepMap C 18 precolumn (Thermo Scientific, USA) at a flow rate of 10 μl min -1. After a 3 min desalting step, peptides were separated on a 75 μm ID x 25 cm C 18 Acclaim PepMap RSLC column (Thermo Scientific, USA) with a 4-40% linear gradient of solvent B (0. 1% formic acid in 80% ACN) in 108 min. The separation flow rate was set at 300 nl min -1. The mass spectrometer operated in positive ion mode at a 1. 8 kV needle voltage. Data was acquired using Xcalibur 3. 1 software in a data-dependent mode.
Database search and results processing For protein identification, Sequest HT and Mascot 2. 4 algorithms through Proteome Discoverer 1. 4 Software (Thermo Fisher Scientific Inc. ) were used for protein identification in batch mode by searching against a T. brucei protein database (9,594 entries, TriTrypDB-33_Tbrucei-TREU927, release 33). This database was downloaded from http://tritrypdb. org website. Two missed enzyme cleavages were allowed. Mass tolerances in MS and MS/MS were set to 10 ppm and 0. 02 Da. Oxidation of methionine, acetylation of lysine and deamidation of asparagine and glutamine were searched as dynamic modifications. Carbamidomethylation on cysteine was searched as static modification. Peptide validation was performed using Percolator algorithm and only "high confidence" peptides were retained corresponding to a 1% False Discovery Rate (FDR) at peptide level  [72].
A minimum of three peptides matched to a protein was used as the criteria for identification as a differentially expressed protein. Univariate one-way analysis of variance (ANOVA) was performed within Progenesis to calculate the protein p-value according to the sum of the normalized abundances across all runs. Only protein with a p-value cutoffs � 0. 05 were validated. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [73]  partner repository with the dataset identifier PXD009763.