dataesr/scientific-paragraphs-categorization huggingface dataset:
Significance (p value) was determined using the Wald test. Richness was also quantified using the approach described above. Beta diversity among gut microbial communities was assessed using Bray-Curtis dissimilarity analysis of genome-length-normalized count tables after rarefying using phyloseq R package (version 1. 30. 0) [39]. The rarefying depth was set at the depth of the smallest sample. The effect of individuality on variance in the gut microbial communities was quantified using PERMANOVA implemented in phyloseq R package's Adonis function  [39, 41]. The same method was used to quantify the effect of diagnosis on gut microbiota variance; however, rarefied counts were first averaged for each individual prior to genome-length-normalization. Dissimilarity matrices were visualized in two dimensions using uniform manifold approximation and projection for dimension reduction (UMAP) (version 0.
Analysis of Differential Abundance Differences in the abundance of gut microorganisms between different diagnosis groups were assessed using the fitZig function in metagenomeSeq R package (version 1. 30. 0) [44]. First, viral OTUs or MAG clusters that were not present in more than 25% of samples in one of the diagnosis groups were removed. Subsequently, read counts were normalized using the cumulative sum scaling method in metagenomeSeq  [44]. A zero-inflated Gaussian mixture model fitted to the normalized read counts was then used to test for differential abundance between the diagnosis groups at a false discovery rate (FDR) cutoff of 0. 1 and a log 2 fold-change cutoff of 1. The FDR was calculated using the Benjamini-Hochberg method. The model used patient ID as a blocking factor along with the following covariates: disease status, sex, consent age (i.
Although both methods have pros and cons, the current approach was selected for the current study because a large proportion of temperate bacteriophages are yet to be characterized in reference databases. The current study only explored ecological traits (i. e. , alpha diversity, beta diversity, and differential abundances) in temperate bacteriophage communities; thus, no assessments of the functional traits of temperate bacteriophages were conducted. The reason for limiting the design of our study in this way is related to the difficulty involved in recovering full-length temperate bacteriophage genomes from whole metagenome sequence data. Future developments in the metagenome contig binning technique for the viral genomes currently un-represented in reference databases may promote deeper understanding of the relationships between gene functional traits in temperate bacteriophages and IBD.
Acknowledgments: The authors acknowledge the IBDMDB research team, led by Ramnik Xavier and Curtis Huttenhower for generating the phenomenal metagenome dataset and making it available to the public along with its high-quality metadata. We also express gratitude to the NIH Common Fund for supporting Ramnik Xavier and Curtis Huttenhower (grant number 8U54DK102557). Computational time was provided by the SuperComputer System, Institute for Chemical Research, Kyoto University. We thank Tamsin Sheen and Sandra Cheesman for editing a draft of this manuscript.
Experimental Procedure Participants took part in two magnetic resonance imaging (MRI) sessions on separate days (Supplementary Information, details on task versions). We collapsed participants data across two versions of the task (social/non-social) as the presented results did not show differences between versions. The order of task version was counterbalanced across participants. Stimuli used in each version were randomized across participants. Data collection and analysis were not performed blind to the conditions of the experiments. Each session lasted approximately two hours, including one hour of scanning. Participants received £15 per hour and a bonus based on task performance (per session: £5 -£7). Before each scanning session, participants were instructed about the task and performed seven practice trials outside the scanner.
FMRI data acquisition and data processing Imaging data were acquired with a Siemens Prisma 3T MRI using a multiband T2*-weighted echo planar imaging sequence with acceleration factor of two and a 32-channel head-coil. Slices were acquired with an oblique angle of 30 ° to the PC-AC line to reduce signal dropout in frontal pole. Other acquisition parameters included 2. 4x2. 4x2. 4 mm voxel size, TE = 20 ms, TR = 1030 ms, 60° flip angle, a 240 mm field of view and 60 slices per volume. For each session, a fieldmap (2. 4x2. 4x2. 4mm) was acquired to reduce spatial distortions. Bias correction was applied directly to the scan. A structural scan was obtained with slice thickness = 1 mm; TR = 1900 ms, TE = 3. 97 ms and 1x1x1 mm voxel size. Imaging data was analysed using FMRIB's Software Library (FSL)  46.
To derive a "domain general prediction difference", we calculated the mean across absolute uncertainty and accuracy prediction differences: ((chosen -unchosen uncertainty) + (chosen -unchosen accuracy)) (Supplementary Figure  3A ) and calculated a conjunction between both cluster-corrected maps of accuracy and uncertainty prediction differences with a cluster-correction of z>2. 3 and p<0. 05 (Supplementary Figure  3B ). For the conjunction analysis, we used the provided FSL script 'easythresh_conj' with z>2. 3 and p<0. 05. The confidence phase was defined from the onset of circle and interval presentation (Figure  1A ) until a decision about the interval size was made. It included a constant and the following parametric regressors: fMRI-GLM 1, confidence phase: chosen uncertainty, chosen accuracy, block time, chosen uncertainty x block time, chosen accuracy x block time.
Acknowledgements NT was funded by a  DTC ESRC studentship  ( ES/J500112/1 ), JS was supported by a  MRC   Skills Development Fellowship  ( MR/NO14448/1 ), MCKF by a  Sir Henry Wellcome Fellowship  ( 103184/Z/13/Z ), MFSR was funded by a  Wellcome Senior Investigator Award  ( WT100973AIA ). We would like to thank all members of the  Rushworth lab  for great discussions on this project.
Data availability We have deposited all choice raw data used for the analyses in an OSF repository. The accession code is: https://osf. io/d5qzw/?view_only=037ea3b875914623a06999cef97ac57f. We have deposited unthresholded fMRI maps of all contrasts depicted in the manuscript on Neurovolt. The accession code is: https://identifiers. org/neurovault. collection:8073. The source data underlying Figure  3 ,6,7 and Extended Data Figure  1 ,2,3,5 are provided as a Source Data file.
Methanol and isopropanol both cause an increase in mutation rate (Fig. 2d ). Methanol is metabolized by yeast cells to formaldehyde, a known mutagen. Isopropanol on the other hand cannot be metabolized by yeast cells. These data indicate that alcohol metabolism is not required to increase mutation rate. We next performed additional fluctuation assays using a total of five genetically different yeast strains, four acetaldehyde concentrations, and two mutation reporters (URA3 and CAN1). Our results show that in some, but not all of the yeast strains   tested, acetaldehyde increases mutation rate (Supplementary Fig. 4 ). We also noticed that the highest acetaldehyde concentration used in our assays (0. 1%) was borderline lethal, suggesting that acetaldehyde clearly affected the cells. These results indicate that the lack of mutagenic effect of acetaldehyde observed in some strains is not due to a technical problem and provide additional support that the mutagenic effect of ethanol observed in yeast is not solely due to acetaldehyde.
As expected, exposing cells to hydrogen peroxide increased oxidant levels (p = 0. 0029, unpaired t-test with Welch's correction) (Supplementary Fig. 5 ). Ethanol-exposed cells on the other hand do not show an increase in fluorescence. These results indicate that ethanol exposure does not cause an increase in ROS. Mutations indicate that ethanol affects replication. If the mutagenic effect of ethanol is caused by specific mutational mechanisms, this could result in a specific mutational spectrum. To investigate this, we sequenced the CAN1 ORF of canavanineresistant colonies, isolated from fluctuation assays performed in 0 and 6% ethanol conditions. A total of 234 sequences were analyzed (121 for 0% and 113 for the 6% (v/v) ethanol condition; see also "Methods," Fig. 3a , and Supplementary Data 1).
For 0% EtOH, a total of 121 colonies were analyzed; for 6% EtOH, a total of 113 colonies were analyzed. Source data for this figure are provided as a Source Data file. fluorescent protein (YFP)-tagged ssDNA-binding Rfa1 protein (Rfa1-YFP) to foci as a measure for ssDNA  50. Cells not exposed to ethanol display mostly a diffuse nuclear localization of Rfa1, consistent with no ssDNA accumulation (Supplementary Fig. 7a ). When cells go through S phase in the presence of methyl methanesulfonate (MMS) (a DNA alkylating agent known to cause the formation of ssDNA), cells progressively accumulate bright Rfa1 foci (Supplementary Fig. 7c ). However, when cells go through S phase in the presence of 6% ethanol, we do not observe an increase of Rfa1 foci (Supplementary Fig. 7b ), indicating that ethanol-exposed cells do not contain more ssDNA regions than untreated cells.
S-phase progression was delayed in cells exposed to 6% ethanol, suggesting a replication defect in ethanol-exposed cells. To further confirm whether DNA replication is affected upon ethanol exposure, we analyzed replication progression by DNA combing from asynchronous cell cultures. This allowed us to measure replication fork progression (track length). Ethanolexposed cells showed significantly shorter track length compared with cells that were not exposed to ethanol (Fig. 5d, e ). This data indicates that the cell cycle delay observed in ethanol-exposed cells is caused by altered DNA replication and suggest that ethanol affects replication fork progression. EtOH affects Mrc1 localization. As replication rate is highly dependent on Mrc1, the homolog of metazoan Claspin  30, 57, 58  , an evolutionary conserved component of the replisome that links the replicative helicase with DNA polymerase activities  59  , we next investigated the effects of ethanol on Mrc1.
Data represent average fluorescence intensity; error bars represent 95% confidence intervals. Statistical significance was assessed using a two-tailed unpaired t-test with Welch's correction. ***P < 0. 001. Specifically, p-value < 0. 0001. AU, arbitrary units. c Cell cycle progression is slower in ethanol-exposed cells. Wild-type cells were arrested in G 1 with α-factor and were released synchronously into S phase with the addition of pronase, in medium containing 0 or 6% ethanol. Cells were collected at the indicated time points. DNA content was assessed using flow cytometry after PI staining. The 1C peak corresponds to cells in the G 0 /G 1 phase. The 2C peak corresponds to cells in the G 2 /M phase. d, e Replication fork progression is perturbed by ethanol. Fork speed, measured after pulse incorporation of EdU and DNA combing, was analyzed in asynchronous cell cultures (strain PP2226) exposed for 2 h to 0 or 6% ethanol.
For each strain and condition, 54 cultures were analyzed. Data represent mutation rate estimates, as determined by fluctuation assays on canavanine. Error bars represent 95% confidence intervals. Statistical significance of differences in mutation rates was assessed using a likelihood ratio test. ***P < 0. 001, ns nonsignificant. Specifically, p-values are 0 for WT, 9. 99 × 10 -1 for pol30 mutant, 0. 25168 for rev1Δ, 0. 1036 for rev7Δ, 1. 8445 × 10 -6 for rev3Δ and 1. 1643 10 -9 for rad30Δ. Source data for this figure are provided as a Source Data file. counted. Six cultures were diluted and plated on YPD to determine total cell counts. Fluctuation assays using URA3 as a mutation reporter were performed similarly, except that cultures were plated on FOA (Sigma-Aldrich) containing plates for mutant selection and incubated at 30 °C for 2 days, after which colonies were counted.
Colony counts on selective and non-selective plates were used to estimate mutation rates using the rSalvador R package (v1. 7)  79. Likelihood ratio methods, as implemented in rSalvador, were used to calculate 95% confidence intervals, as well as to assess the statistical significance of differences between mutation rate estimates. can R mutant frequency after short ethanol exposure. Precultures were grown as for standard fluctuation assay. Cell count of precultures was determined using BioRad CellCounter and 10 5 cells per culture were used to set up the experiment. Cells were incubated for 2 h in the presence of 0 or 6% ethanol and then plated on can R mutant frequency after acetaldehyde exposure. This protocol is modified from ref. 80. Briefly, cells were grown as for standard fluctuation assay.
Each colony sequence was then compiled into a final multiple sequence alignment. Mutations were analyzed by a perl script, which categorized them into indels or silent/missense/nonsense mutations, and checked whether they were transitions/transversions. All canavanine-resistant colonies carried at least one mutation in the CAN1 gene. Sequencing data have been deposited at GenBank, accession codes MT509124-MT509357. Determination of viability. Cellular viability was determined using methylene blue staining protocol. Briefly, cells were grown exactly as for performing fluctuation assays. Cultures were diluted 1/100 in SC medium and 100 µL of a filter sterilized 0. 1% (w/V) methylene blue solution (Sigma-Aldrich) was added to 100 µL of this diluted culture. Samples were incubated at room temperature for 1 min and subsequently loaded on a counting slide (KOVA Glasstic Slide 10).
All samples were taken in biological duplicates. Details on analysis can be found in Supplementary Information. The RNA-seq dataset generated and analyzed has been deposited in Sequence Read Archive, as Bioproject PRJNA632734 (http://www. ncbi. nlm. nih. gov/bioproject/ 632734). Spot assays. Cultures were grown overnight to saturation in YPD. Tenfold serial dilutions (OD 600 of start dilution = 1. 0) were spotted on agar plates. To test the effect of proteasome inhibition, cultures were also spotted on agar plates containing the proteasome inhibitor MG-132 (100 µM final concentration, Sigma-Aldrich)). Plates were sealed with parafilm and incubated at 30 °C. YPD plates were scanned after 2 days incubation and YPD Ethanol plates were scanned after 3 days incubation. Yeast live-cell imaging and fluorescence.
Boxes indicate qPCR amplicons location at ARS305 (black box), an early-firing origin, as well as non-origin sites -3 kb (pink box) and +3 kb (yellow box) from ARS305. Primers for the late-firing origin ARS501 are used to monitor late origin activation and background signals. Origins are described in refs. 83, 84. ChIP was performed as described in "Methods" on myc-tagged Pol2 and FLAG-tagged Rev1-3-7. G 1arrested cells (strain ML996-2D) were released into media containing 0 or 6% ethanol for the times indicated. Values are average of three biological replicates, with two technical repeats each; error bars represent SD. Data represent the real-time PCR signal as fold increase of the IP over the beads-only control for Pol2 and Rev1-3-7. Statistical significance was assessed using a two-tailed unpaired t-test with Welch's correction.
Ethanol Fluorophores were cyan fluorescent protein (clone W7), YFP (clone 10C), and red fluorescent protein (clone yEmRFP or mCherry). Fluorophores were visualized on a Deltavision Elite microscope (Applied Precision, Inc. ) equipped with a ×100 objective lens (Olympus U-PLAN S-APO, NA 1. 4), a cooled Evolve 512 EMCCD camera (Photometrics, Japan), and an Insight solid-state illumination source (Applied Precision, Inc. ). Images were acquired using softWoRx version 7. 0. 0 (Applied Precision, Inc. ) software and fluorescence intensities measured with Volocity software version 5. 4 (PerkinElmer). Cell cycle progression analyses. Cells were grown at 25 °C in SC medium supplemented with 2% glucose and synchronized in G 1 using 8 µg/ml α-factor (Biotem, France) for 150 min. Cells were released in S-phase from the G 1 arrest by αfactor degradation using 75 µg/ml Pronase and 20 mM phosphate buffer.
Samples from the time-course experiments were processed with standard methods for subsequent flow cytometry analysis. Data were acquired on a MACSQuant Analyser (Miltenyi Biotec) and analyzed with FlowJo software. Three independent biological replicates have been performed for each cell cycle progression analysis. Cells (10,000) were gated on PI-Height/PI-Area. Haploid and diploid strains were used for calibration. Analysis of flow cytometry profiles was performed in FlowJo version 10. Profiles can be found in Supplementary Fig. 10. Protein extraction and Rad53 western blotting. Approximately 5 × 10 8 cells were collected at each relevant time point and were washed with 20% trichloroacetic acid to prevent proteolysis, then resuspended in 200 µl of 20% trichloroacetic acid at room temperature.
Fold increase of the IP over the beads-only control was calculated 66 using the formula: fold increase = Statistical analyses. Statistical significance of differences in mutation rates was assessed using a likelihood ratio test, as implemented in the R package rSalvador. Statistical significance of differences in mutant frequency was assessed using an unpaired t-test with Welch's correction. Statistical significance of differences in Mrc1 foci was assessed using a one-tailed Fisher's exact test. Statistical analyses were performed with GraphPad Prism 7, with the exception of statistical analyses of differences in mutation rates. Additional methodology information can be found in Supplementary Methods. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Patricia T. N. Van Dam  ( TU Delft ) for technical assistance with the acetaldehyde HPLC measurements, as well as all CMPG members for discussions,  Valmik Vyas  for plasmid pV1382 and sharing the protocol for CRISPR,  Judith Frydman  for sharing the pESC-VHL-mCherry plasmid, and  Boris Pfander  for sharing plasmid pBP81. This work was financially supported by a grant from  Research Foundation -Flanders (FWO) -grant number  G090618N. K. V. acknowledges financial support from  FWO  by a postdoctoral fellowship (1249117 N). L. H. was supported by a PhD fellowship from  FWO  ( 11B6720N ). J. K. was supported by a  KU Leuven  F + fellowship. M. L. is supported by  The Independent Research Fund Denmark (FNU)  and the  Villum Foundation. Work in the lab of P. P. is funded by grants from the  Agence Nationale pour la Recherche (ANR) , the  Institut National du Cancer(INCa) , and the  Ligue contre le Cancer  (équipe labellisée).
Data availability The RNA-sequencing dataset generated and analyzed during the current study has been deposited in Sequence Read Archive, as Bioproject PRJNA632734 (http://www. ncbi. nlm. nih. gov/bioproject/632734). The CAN1 sequencing data generated and analyzed during the current study has been deposited at GenBank, accession codes MT509124-MT509357. All data are available from the authors upon reasonable request. Source data are provided with this paper.
The GO and KEGG enrichment analyses showed that genes involved in ''cell/cell part", ''catalytic activity", ''metabolic process" (Table  S16 ), and ''metabolic pathways" (Table  S17 ) were prominent. To validate the RNA-seq transcript profiling, the relative expression levels of ten key genes involved in the mevalonate (MVA) pathway were measured by RT-qPCR. The relative expression of seven of these marker genes was consistent with the RNA-seq data (Table  S18 ). Alternative splicing (AS) events are widespread in the transcriptome of W. cocos. Intron retention (IR) and exon skip-   S19  and S20 ). In addition, a total of 3539 and 3809 novel transcript units (TUs) were predicted in the transcriptomes of the mycelium and sclerotium, respectively (Table  S21 ).
Genes related to cell wall degradation To identify enzymes involved in cell wall degradation, W. cocos genes were searched against the carbohydrate-active enzymes (CAZymes) database  [38]. A total of 321 genes encoding CAZymes were predicted in the W. cocos (IMPLAD) genome, including 139 glycoside hydrolase (GH) genes, 63 glycosyl transferase (GT) genes, 58 carbohydrate esterase (CE) genes, 20 carbohydrate-binding module (CBM) genes, 38 auxiliary activity (AA) genes, and 3 polysaccharide lyase (PL) genes (Table  S31A ). Most CEs, GHs, and AAs are involved in the hydrolysis of carbohydrate and noncarbohydrate substrates and the oxidative degradation of lignin-based components of the plant cell wall. The presence of multiple gene copies for GH5, GH16, GH18, and GH28, which were involved in the degradation of cellulose, b-glucan, chitin, and pectin, respectively, and the absence of GH11 (encoding a xylanase) and CE12 (encoding an acetylesterase) in W.
The overlapping reads (read1 and read2 overlap at least 10 bp and mismatch less than 10%) and duplicate reads (the sequence of read1 was equal to read2, which means duplication) were also excluded. After removal of the vector sequence from fosmid clones, the reads generated from the HiSeq 2000 were used in assembly. The short reads were first assembled into fosmid clone sequences using SOAP de novo  [24]. After removing the remaining vector sequences and other low-quality bases, the fosmid clone sequences were assembled into contig sequences using Celera software. The redundant sequences were first removed via BLAST by alignment to the National Center for Biotechnology Information (NCBI) microbial nucleotide database for any alignment query contigs. Then, the redundant sequences based on the K-mer frequencies obtained from the short library reads were removed.
Gene prediction and annotation De novo gene prediction was executed using Augustus, GeneMark-ES, and SNAP based on the Hidden Markov Model (HMM). Additionally, the homolog-based method was used to weight and calibrate gene structure. The combination of de novo gene prediction and gene structure prediction was then used to obtain the GLEAN gene groups. The GLEAN data were corrected by RNA-seq data to accomplish gene prediction. Genes were functionally predicted by BLAST against the public databases, including NCBI nonredundant protein database (Nr), NCBI nonredundant nucleotide database (Nt), InterProScan, GO database, KEGG database, Clusters of Orthologous Groups (COG) database, and TrEMBL/Swissprot database. The PFAM families of TFs in fungi were selected to search the W. cocos genome  [58].
Identification of TEs and noncoding RNA TEs were searched in the W. cocos genome using RepeatMasker and ProteinMask software based on Repbase (16. 03) data. The de novo genome repeat sequence database was then constructed by PILER-DF, RepeatScout, and LTR-Finder, and redundant sequences were removed. TEs were identified with the RepeatMasker software in the clean de novo genome repeat sequence database. tRNAs were identified by tRNAscan-SE software. rRNAs were identified by BLAST against the rRNA database of S. commune.
Transcriptome sequencing and analysis Transcriptome analyses were performed on the materials obtained from the 7-day-old mycelia and mature sclerotia of W. cocos. Frozen samples were ground to powder in liquid nitrogen for RNA isolation. Total RNA from each sample was extracted using an RNeasy Plant Mini Kit (Qiagen, Dusseldorf, Germany) according to the manufacturer's instructions. These total RNA samples were also reversetranscribed into cDNA to validate the RNA-seq data by RT-qPCR method. Genomic DNA was eliminated from the total RNA using DNase I (NEB, Ipswich, MA). RNA integrity and quality were tested using the RNA 6000 Nano II kit on a Bioanalyzer 2100 (Agilent Technologies, Lexington, MA). The RNA samples were sequenced using an Illumina HiSeq 2000 (Illumina). RNA-seq data from the mycelial and mature sclerotial tissues were assembled into transcripts using Trinity.