dataesr/scientific-paragraphs-categorization huggingface dataset:
The filtered dataset was then entered into SPSS Statistics (IBM Software, New York, USA, version 22. 0. 0. 1) to perform correlation analysis to select differentially methylated gene promoter regions that associated with gene expression and clinicopathological parameters. Both Pearson and Spearman correlation coefficients were employed to identify associations. In the one CpG site that was highly correlated to survival, different cutoff levels were applied to construct high and low methylated groups, before statistical significance between the groups was assessed using a log-rank (Mantel-Cox) test. A Kaplan-Meier survival plot was created to demonstrate the difference in survival between the high and low expression groups. Linear regression analysis was then performed, to predict determinants of CLDN1 expression, using Stata (StataCorp LP, Texas, USA, version 13.
General description To investigate the DNA methylation status of 24 GC samples and matched controls, more than 27 500 gene promoter sites were examined using the Illumina Infinium HumanMethylation27K platform. First, the methylation status of the entire dataset was considered. Most CpG sites, including both the tumor and normal tissue, demonstrated low levels of methylation, i. e. low β values. Second, there was little variance between the samples within each locus, even between the tumor and normal groups. This is illustrated in the histogram in Fig. 1  and in the colored heatmap in Fig. 2. Then, the Δβ values for each CpG site were calculated. 53. 1 % of all sites demonstrated a net positive value, whereas 46. 9 % demonstrated a net negative value, indicating a slight overall increase in CpG methylation in the tumors.
Rank product testing Of the 27 500 CpG sites on the array, 1 660 CpG sites, corresponding to 1 194 genes, showed statistically significant increased methylation in the tumor relative to normal mucosa, whereas 1 276 CpG sites, corresponding to 1 017 genes, showed significant decreased methylation in the tumor relative to normal mucosa, supporting the trend from the entire dataset. The dataset of all significant CpG sites was then subjected to hierarchical clustering using average linkage and Euclidean distance measure, using the J-express 2012 software package  [19]. There was a tendency for the tumor samples to cluster together, and for the normal samples to cluster together, indicating stronger methylation similarities within each of the two groups, than within each of the sample pairs (Fig.
Clinicopathological correlation The filtered dataset was then compared to post-operative survival, tumor size, Lauren classification, lymph node metastasis, gastric mucosa metaplasia and atrophy and H. pylori, listed in Table  2. Although 44 CpG sites showed relationship with one or more of the factors, the promoter region of the GFRA3 gene showed significant relationship with nearly all the clinicopathological factors. Firstly, an inverse relationship was detected between the GFRA3 promoter Δβ values and post-operative survival (p = 0. 01). High and low methylated GFRA3 groups were constructed using the GFRA3 promoter Δβ mean (p = 0. 017) as the group divider, as demonstrated in the Kaplan-Meier survival plot in Fig. 4. Secondly, a strong, and highly statistically significant positive relationship was identified between GFRA3 Δβ and gastric atrophy (p < 0.
Gene expression correlation The filtered dataset was compared to corresponding gene regulation, imported from our previous study  [17] , to analyze whether the gene promoter methylation status could account for aberrant gene expression. Of the 100 most hypermethylated CpG sites in the tumor relative to the control, four CpG sites showed a statistically significant inverse correlation with gene expression, listed in Table  3. Among the 100 most hypomethylated CpGs, six CpG sites showed significant inverse relationship with gene expression (Table  4 ). The 130 most differentially regulated genes in the tumors  [17]  were then compared against DNA methylation (Table  5 ). Three genes showed an inverse relationship with the methylation status of their gene promoter region. Then, to investigate the association between the most significant genes from our previous study  [17] , and GFRA3 gene methylation from the current study, linear step-wise regression was performed.
Cellular signaling pathways Then the filtered dataset was analyzed for associated KEGG signal pathways using Pathway Express. Significantly impacted pathways and corresponding Impact Factor (IF) are presented in Table  6. None of the hypermethylated CpG sites (in the tumor relative to normal) associated with any KEGG pathways, while many of the hypomethylated CpG sites significantly associated with six cellular signaling pathways; including three cancerrelated pathways.
This contrasts the belief that global DNA hypomethylation is a general hallmark of all cancer. In concordance with other recent studies we found increased DNA methylation in the tumors in the promoter regions of TIMP3, SEMA3B, FBP2, CDH1, CDKN2A, MLH1, and SFRP1,2 and 5  [19] [20] [21] [22] [26] [27] [28] [29] [30] [31]. Hierarchical clustering of the entire dataset illustrated different methylation patterns between the tumor and the control tissue, and this exercise may distinguish tumor from adjacent tissue with relative accuracy. Statistically significant subclustering between the histological subtypes, according to the Lauren classification, was not seen, probably because of the low sample size. However, the aggregation of the intestinal tumor samples towards Fig. 4  Kaplan Mayer survival plot of patients with resected gastric tumors.
Phylogenetic analysis. We conducted a cladistic analysis using morphological data to correctly place crucial fossil taxa and resolve the relationships within Acercaria (Hypoperlidae, Psocodea, Permopsocida, Thripida, and Hemiptera). Therefore, mainly those morphological characters that are also discernible in the fossils have been selected. The data matrix used for the analysis consists of 16 taxa (four outgroup taxa in Polyneoptera and Holometabola, and 12 of the ingroup, see Table  S2 ) and 62 characters (see Table  S3 ). The characters were treated as non-additive and unordered. The matrix was constructed with WinClada ver. 1. 00. 08 (see Table  S4 ) and analysed with the parsimony software package TNT  19. Using New Technology search method with default parameters resulted in a single topology, presented in Fig.
Materials and Methods The amber specimens were ground and polished manually and with polishing machines. The holotype was embedded in Canada balsam to make the inclusion more clearly visible. Pollen was extracted from the gut content of the holotype with a Pasteur pipette, washed with toluene, and then photographed using SEM. Fossil specimens were studied with different stereo microscopes, light microscopes, and laser confocal microscopes, partly with green fluorescence as light source. Microphotographs were made with digital cameras, and focus stacking software was used to increase depth of field. All images were processed with Adobe Photoshop TM. Synchrotron micro-computer tomography (X-ray micro-CT) scans were performed at the TOPO-TOMO beamline of the ANKA Synchrotron Radiation Facility of the Karlsruhe Institute of Technology.
Acknowledgements We thank the  President and Fellows of Harvard College  for permission to use MCZ copyrighted material; the  Willi Hennig Society  for the use of the TNT software; and the  ANKA Synchrotron Radiation Facility  for providing beam time. We are indebted to the following:  Philip Perkins  and  Ricardo Pérez-de la Fuente  ( MCZ, Cambridge, USA ) and  Claire Mellish  ( NHM, London, UK ) for allowing the study and photographing of fossil material and technical support;  Alexander Rasnitsyn  ( Russian Academy of Sciences, Moscow, Russia ) for providing photographs of specimens from the  Palaeontological Institute collections ;  Karin Wolf-Schwenninger  (SMNS) and  Camille Garrouste ,  Marc Gèze ,  Cyril Willig , and  Sylvain Pont  (all  MNHN ) for help in making the figures and images;  Jian-Guo Li  and  Feng Liu  for spore and pollen identification;  Er-Jun Zhuo  for CLSM and  Chun-Zhao Wang  for SEM analyses;  Jie Sun  for reconstruction paintings; and  Bruce C.
Campbell  for commenting on an earlier version of the manuscript. We thank four anonymous referees for their very helpful comments, which improved the manuscript. This research was supported by grants funded by  National Basic Research Program of China  ( 2012CB821903 , DH),  National Natural Science Foundation of China  ( 41222013  and  91114201 , DH),  Outstanding Youth Foundation of Jiangsu Province  ( BK2012049 , DH),  Grant Agency of the Czech Republic  (No. 14-03847J , JP), bilateral project funding " French CNRS-China Academy of Science " for 2015, and  Lebanese University  (team project " Biodiversity: Origin, Structure, Evolution and Geology ", DA).
Strain identification by MALDI-TOF and 16S rRNA sequencing Using the 18 culture conditions of the culturomics concept, the fecal sample was cultivated, and the obtained colonies were identified by MALDI-TOF as described below  [5]. Proteomic analysis of our strain was carried out with MALDI-TOF as previously described  [10, 11]. A Microflex spectrometer (Bruker Daltonics, Leipzig, Germany) was used with a MTP 96 MALDI-TOF target plate (Bruker) on which 12 individual colonies were deposited. Twelve spectra were thus obtained, imported into MALDI BioTyper 2. 0 software (Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 7567 bacteria. Comparison with the BioTyper database spectra enabled the identification and discrimination of the analysed species from those in the database in accordance with the obtained score: a score >2 with a validated species enabled the identification at the species level, and a score <1.
Genome annotation and analysis Open reading frames (ORFs) were predicted using Prodigal  [21]  with default parameters, but the predicted ORFs were excluded if they were spanning a sequencing gap region (contain N). The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COGs) database using BLASTP (E value 1e-03, coverage 0. 7 and identity percentage of 30%). If no hit was found, then it was searched against the NR database using BLASTP with an E value of 1e-03, coverage 0. 7 and identity percentage of 30%, and if the sequence length was smaller than 80 amino acids, we used an E value of 1e-05. The tRNAScanSE tool  [22]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [23]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [24].
ORFans were identified if all the BLASTP performed did not give positive results (E value smaller than 1e-03 for ORFs with sequence size larger than 80 amino acids or E value smaller than 1e-05 for ORFs with sequence length smaller 80 amino acids). Such parameter thresholds have already been used in previous studies to define ORFans. Genomes were automatically retrieved from the 16S RNA tree using Xegen software (Phylopattern)  [25]. For each selected genome, the complete genome sequence, proteome genome sequence and Orfeome genome sequence were retrieved from the FTP site of the National Center for Biotechnology Information (NCBI). All proteomes were analysed with proteinOrtho  [26]. Then for each couple of genomes a similarity score was computed. This score is the mean value of nucleotide similarity between all couple of orthologues between the two genomes studied (average genomic identity of orthologous gene sequences, AGIOS)  [27].
An annotation of the entire proteome was performed to define the distribution of functional classes of predicted genes according to the clusters of orthologous groups of proteins (using the same method as for the genome annotation). To evaluate the genomic similarity among studied Bacillus strains, we determined two parameters: digital DNA-DNA hybridization (dDDH), which exhibits a high correlation with DNA-DNA hybridization (DDH)  [28, 29] , and AGIOS  [27] , which was designed to be independent from DDH. Annotation and comparison processes were performed in the Multi-Agent software system DAGOBAH  [30]  that included Figenix  [31]  libraries that provide pipeline analysis.
Results Strain identification and phylogenetic analyses Strain mt8 T (Table  1 ) was first isolated in January 2015 after 30day preincubation in a blood culture bottle with sheep's blood and cultivation on 5% sheep's blood-enriched Colombia agar in an aerobic atmosphere at 37°C. MALDI-TOF displayed an identification score under 1. 7 for strain mt8 T , suggesting that the obtained spectra was not matched to any spectra in our database. The 16S ribosomal RNA sequence (accession no. LN828943) of strain mt8 T showed a 93% nucleotide sequence similarity with Bacillus halodurans, which is the phylogenetically closest species with a validly published name (Fig. 1 ). Consequently, as this 16S rRNA nucleotide sequence similarity was lower than the threshold of 95% recommended by Stackebrandt and Ebers  [19]  to delineate a new genus, it was classified as a new genus called Massilibacterium, type species Massilibacterium senegalense strain mt8 T.
20 Amino acid transport and metabolism F 138 4. 92 Nucleotide transport and metabolism H 226 8. 06 Coenzyme transport and metabolism I 190 6. 78 Lipid transport and metabolism P 286 10. 20 Inorganic ion transport and metabolism Q 92 3. 28 Secondary metabolites biosynthesis, transport and catabolism R 602 21. 47 General function prediction only S 432 15. 41 Function unknown - 424 7. 31 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on total number of protein-coding genes in annotated genome.
The five HYS structures described here are similar to sinapyl alcohol dehydrogenase (SAD; PDB accession codes 1YQX and 1YQD) and the SAD homologue cinnamyl alcohol dehydrogenase (CAD; PDB accession codes 2CF5 and 2CF6)  [27] [28] [29]  , which reduce the aldehyde moiety of lignin precursors. Indeed, pairwise superpositions of subunits from these structures gave RMSD values of o2 Å (Supplementary Table  4 ). The biological unit is an elongated homodimer, with each subunit divided into a substrate and cofactor-binding domain; the latter also being responsible for forming the dimer interface (Supplementary Fig. 3 ). The overall structure of THAS1, with active site, cofactor and loops highlighted, is shown in Supplementary Fig. 3. The active site cavities of the HYSs are framed by helix a2, the catalytic zinc coordination sphere, and loops 1 and 2, with the NADP(H) co-substrate binding at the base of the active site (Figs 3 and 4a,b,d, , Supplementary Fig.
Methods Selection and cloning of candidate MDRs. The nucleotide and the protein sequences of THAS1 were subjected to a BLAST search against the C. roseus Sunstorm Apricot V1. 0 Transcript sequences (http://medicinalplantgenomics. msu. edu) and the MDR sequences with the highest identity to THAS1 at the active site and which showed non-negligible expression levels in young and mature leaves were selected as candidates for cloning and expression. The protein sequence of SAD was blasted against the same database and MDRs were also selected based on their active site similarity to that of SAD. The genes coding the candidate MDRs were amplified from C. roseus leaf cDNA and cloned into the E. coli expression vector pOPINF using the In-Fusion cloning kit (Clontech Takara)  41  by using primers designed based on the transcript sequences (Supplementary Table  1 ).
1 M MMT buffer, pH 5 and 15% w/v PEG 3350. All crystals were cryoprotected by soaking in crystallization solution containing 25% v/v ethylene glycol before flash-cooling in liquid nitrogen. Data collection and structure determination. X-ray data sets were recorded on one of three beamlines at the Diamond Light Source (Oxfordshire, UK) (2FI3, I04; 2FI5, I03; 5H81 I04-1; 5H82, I04-1; 5H83, I04-1) at wavelengths of 0. 9000-0. 976 Å (2FI3, 0. 900 Å; 2FI5, 0. 976 Å; 5H81, 0. 920 Å; 5H82, 0. 920 Å; 5H83, 0. 920 Å) using either a Pilatus 6M or 2M detector (Dectris) with the crystals maintained at 100 K by a Cryojet cryocooler (Oxford Instruments). Diffraction data were integrated using XDS  42  and scaled and merged using AIMLESS  43  via the XIA2 expert system  44  ; data collection statistics are summarized in Supplementary Table  3.
Initially the THAS1-NADP þ data set was automatically processed at the beamline by fast_dp  45  to 1. 12 Å resolution and a structure solution was automatically obtained by single wavelength anomalous dispersion phasing using the SHELX suite  46  via the fast_ep pipeline (Winter, manuscript in preparation). Despite being collected at a wavelength somewhat remote from the zinc K X-ray absorption edge (theoretical wavelength 1. 284 Å), the anomalous signal was sufficient for fast_ep to locate four zinc sites and calculate a very clear experimentally phased electron density map (Fig. 4a ). This was available to view at the beamline in the ISPyB database  47  via the SynchWeb interface  48  within a few minutes of completing the data collection. The map was of sufficient quality to enable 94% of the residues expected for a THAS1 homodimer to be automatically fitted using BUCCANEER  49.
Compound characterization. High-resolution electrospray ionization MS spectra were measured with a Shimadzu IT-TOF mass spectrometer. NMR spectra were acquired using a Bruker Advance NMR instrument operating at 400 MHz for  1  H equipped with a BBFO plus 5-mm probe. The number of scans depended on the concentration of the sample. The 1 H,  15  N-HMBC experiment was acquired with a spectral width 6,009 Hz in the F2 ( 1 H) dimension and 30,410 Hz in the F1 ( 15 N), with an acquisition time of 0. 09 s and 360 scans per increment. The long range delay was optimized after a series of experiments with [4-15 N]-strictosidine using a range of different mixing times and finally was adjusted for a coupling of 5 Hz. The relaxation delay was 2. 5 s, the data collection matrix was 1024 Â 64, the t1 dimension was zero filled to 1k real data points and a p/2 square sine bell window was applied in both dimensions.
In parallel, a chemical reducing agent, NaBH 4 (3 mM final concentration), was added to deglycosylated strictosidine as a control reaction. All reactions were carried out in triplicate. An end-point sample (10 ml) was taken for each assay and prepared for UPLC-MS by addition of 10 ml of 100% MeOH to stop the reaction, and then diluted 1:5 with H 2 O, and centrifuged for 10 min at 4,000 r. p. m. UPLC-MS and data collection were performed as described above for heteroyohimbine separation and quantification. CD spectra and analysis. Far ultraviolet CD spectra of the wild-type enzymes THAS1 and HYS, as well as the loop mutants of THAS1 and HYS were recorded on a Chirascan Plus spectropolarimeter (Applied Photophysics) at 20 °C in 10 mM potassium phosphate buffer pH 7. 0. Samples were analysed from 180 to 260 nm using a 0.
5-nm step at a speed of 1 s per step. Four replicate measurements were performed on each sample and baseline correction was applied to all data. Spectra are presented as the CD absorption coefficient calculated on a mean residue ellipticity basis. Melting curves of HYS and the HYS loop 2 swap mutant were also acquired by CD. The samples were subjected to temperature ramping at the rate of 1 °C min À 1 from 20 to 90 °C. Data collection was done from 260 to 201 nm using a 1-nm step and 0. 75 s time per point. Data were analysed using the Global 3 software. HYS melting point was measured as 61. 0 ± 0. 1 °C; enthalpy 351. 5 ± 3. 6 KJ mol À 1. HYS loop 2 swap melting point was measured at 62. 0±0. 1 °C; enthalpy 535. 8 ± 4. 5 KJ mol À 1. Protein sequence alignments and phylogenetic tree. Protein sequence alignment was generated using ClustalW algorithm with Geneious v.
Acknowledgements We gratefully acknowledge support from the  ERC  ( 311363 ) and a  BBSRC Institute Strategic Programme grant  ( MET ;  BB/J004561/1 ) to S. E. O'C. and from the  Region Centre (France , ABISAL grant) to V. C. A. S. is supported by a  BBSRC   DTP studentship. The Diamond Light Source provided access to beamlines I03, I04 and I04-1 (proposal MX9475).
Data availability. The atomic coordinates and structure factors of the five X-ray structures described in this manuscript have been deposited in the Protein Data Bank (http://www. pdb. org/), with accession codes 5FI3, 5FI5, 5H81, 5H82 and 5H83. Accession numbers: THAS1 (AKF02528. 1); THAS2 (KU865323); THAS3 (KU865322); THAS4 (KU865324); HYS (KU865325); Cro_017994 (KU865326); Cro_011702 (KU865327); Cro_030442 (KU865328); Cro_006840 (KU865329); Cro_022770 (KU865330); Cro_033537 (KU865331); Cro_027234 (AHK60846); Cro_033062 (KU865332); Tabersonine-3-reductase (AKM12281). Data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding author upon reasonable request.
Transcriptomic analysis To identify altered pathways in the 16p11. 2 mouse models, we performed transcriptomic analysis of three brain regions (hippocampus, striatum, and cerebellum) and of one peripheral tissue (liver) in Del/+, Dup/+ and wt animals (data accessible at NCBI GEO database  [30] , accession GSE66468). We assessed expression of the genes encompassed by the Sult1a1-Spn region boundaries and found that the majority of them showed mRNA levels proportional to the CNV copy number  (5). The expression of the genes in the Sult1a1-Spn region was largely affected by the CNVs in all three brain regions and the liver, with the following exceptions: Kif22, Qprt, Spn, and Zg16 appeared to be under dosage compensation in all tissues studied, whereas Aldoa, Coro1a, Fam57b, Prrt2, and Tbx6 expression levels were only compensated in the liver (Fig 6A  -6D ).
Furthermore, principal component analysis suggested that the effect of the CNV on the expression of Sult1a1-Spn interval genes was stronger in the Del/+ animals (Fig  6E ). At a 5% false discovery rate (FDR), between 7 and 14 probe sets mapping elsewhere on the genome were associated with the CNV dosage (9-32 at 10% FDR). We then performed gene set enrichment analysis (GSEA) on the genes ranked by the results of the differential expression analysis to identify the sets of genes that were positively or negatively associated with the Sult1a1-Spn copy number. Our GSEA analysis considers the pre-ranked gene list according to the LIMMA results based on the dosage model. We used the MSigDB C2 gene set database (c2. all. v4. 0. symbols. gmt), which collects curated gene sets from online pathways, and publications in PubMed.
Body composition analysis by qNMR was performed to give a precise assessment of the body composition for fat content, lean tissues and free body fluid by nuclear magnetic resonance apparatus and Minispec+ analyser (Bruker, Germany). The test was conducted during the light period in conscious, fed mice. Energy expenditure was evaluated by indirect calorimetry that measured oxygen consumption with an open flow respirometric system (TSE System, Labmaster, Germany). Sensors measured the difference in CO 2 and O 2 concentrations in the air volumes flowing through control or animal cages. The amount of oxygen consumed over a given period of time could be calculated, as long as the air flow through the cage was known. The gas exchange data are expressed in Kcal/h/kg 0. 75. The system also monitored CO 2 production, and we calculated the respiratory exchange ratio (RER = VCO 2 /VO 2 ), which defines fuel preference between glucose vs lipid metabolism and heat production (Kcal/h/kg 0.
Craniofacial analysis Craniums of 13 week-old female mice (n = 10 wt and 10 Del/+; n = 8 wt and 8 Dup/+) of C57BL/6N genetic background were stored in 100% ethanol. Three-dimensional coordinates of 39 relevant cranial landmarks were recorded using Landmark software, and posterior comparisons were performed using the Euclidean distance matrix analysis (EDMA) with the WinE-DMA software (version 1. 0. 1 beta). Further details are provided in supplementary information (S1 File).