dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential expression analysis and validation Data were first corrected for batch, age and sex effects with the voom method. 30 DE analysis was performed retaining genes with a false-discovery rate (FDR)-corrected p value <0. 05 and a fold-change (FC) between cases and controls FC|>1. 5. DE analysis was independently performed in the discovery and validation sets and genes significant in both sets were considered as validated. To evaluate to what extent genes selected by univariate feature selection in the discovery set jointly predicted the disease status in the validation set, a random forest model was built (500 trees) whose performance was evaluated via the area under precisionrecall-gain curve (AUPRG)  31  and via the area under receiver operating characteristic (AUROC). Heatmaps were used to display validated genes: data were clustered by individuals' similarity with the k-means algorithm and by transcript similarity, by means of the hierarchical clustering Ward method.
Functional annotation, differential expression pathway analysis and validation Individual functional annotations were performed with the FAIME algorithm  18  considering Reactome pathways 32 mapped by at least 10 genes/transcripts; Entrez Ids without official gene symbols were dropped from the analysis. To allow the comparison of FAIME scores between datasets, values were normalised to the unit interval and cube root transformation applied to unskew the data. Because FAIME scores are a composite measure of transcripts, FC cannot be used to measure the strength of pathway expression and the evaluation of effect size was used instead. calculate the robust effect size (d r )  33  and to perform t-test analysis. Annotations with a FDR p<0. 05 and with a |d r |>0. 62897, corresponding to a moderate effect size 34 were considered as significant.
Pathway analysis based on transcriptome data is potentially useful to discover putative therapeutic targets for drug repurposing. Herein, we describe a framework to find druggable pathways based on the curated database DGIdb 35 and the UpSet visualisation tool. 36 The combination of druggable pathways that can be explored is gargantuan and cannot be summarised, yet the illustrative example we provide shows the potentialities of our approach. Two drugs emerged as potential candidates to tackle immune system and IFN pathway: bortezomib and irinotecan. Bortezomib, a protease inhibitor, is currently under investigation in a phase II clinical trial. 46 Irinotecan is an analogue of camptothecin (CPT), 47 a topoisomerase I inhibitor capable of reducing collagen production in SSc fibroblasts. 48 t could be argued that RNA-seq analysis of specific leucocyte subsets could theoretically have been more informative compared with WB analysis.
Distinct SMC-Fig. 2  A single parS site mediates chromosome folding. a One parS site is necessary and sufficient for wild type-like morphology and nucleoid segregation. Phase-contrast images of exponentially grown cells harboring either all (WT), one (parS 2-10mut , CBK023), or none (parS 1-10mut , CBK024) parS site(s), or lacking parB (ΔparB, CDC003) are shown. DNA is stained with Hoechst (yellow). Scale bar, 2 µm. b Fluorescence microscopy analysis of parB∷parB-mCherry (shown in green) in wild type (CBK006), parS 2-10mut (CBK027), and parS 1-10mut backgrounds (CBK028). Absence of parS leads to diffuse cellular ParB localizations. Scale bar, 2 µm. c ChIP-qPCR for strains described before, normalized to wild-type parS1 signal (mean + SD, n = 3). d ChIP-seq of C. glutamicum parB∷parB-mCherry parS 2-10mut (black) at a 3.
1-3. 2 Mb chromosomal range. Wild-type-like propagation (green) of ParB protein around parS1-4; 0. 5 Kb bin size. Location of parS sites present in wild type or mutant sequences are indicated (gray lines). e Normalized contact maps of ΔparB, parS 1-10mut , and parS 2-10mut mutants centered at oriC (CDC003, CBK024, and CBK023). Color codes as in Fig. 1. f Differential maps correspond to the log2 of the ratio (wild-type norm/mutant norm); color scales indicate contact enrichment in mutant (blue) or wild type (red) (white indicates no differences between the two conditions). g Single-molecule localization microscopy of representative wild-type and parS 2-10mut cells (CBK009 and CBK029). Top: Gaussian rendering of ParB-PAmCherry signals (0. 71 PSF, 1 px = 10 nm), below: color-coded representation of ParB-PAmCherry events within corresponding cells  49  ; all events (light blue), macroclusters (dark blue) and subclusters (yellow) are indicated.
Scale bar, 0. 5 µm. See Methods and Supplementary Fig. 7  for details. h Comparison of ParB-PAmCherry cluster properties. Only the two biggest clusters per cell were taken into account for analyses; significant differences between conditions are indicated by small letters above datasets. Left: events per macrocluster, medians are indicated as solid lines, and whiskers mark 1. 10C ). These findings suggest that ParB promote condensin loading onto DNA at oriC-proximal parS sites. In addition, ChIP-seq revealed that SMC concentrates at a 13 Kb region upstream parS1 (Fig. 4a ). SMC enrichment in this region was lost following a partial deletion of this locus and its reinsertion at another genomic position (Supplementary Fig. 10D-F ) or following its substitution by a random DNA sequence (Supplementary Fig.
5a ). Moreover, we applied Hi-C to characterize the role of MksB in genome folding in the different mutants (Fig. 3f ). In contrast to smc, deletion of mksB had no effect at large scale on chromosome organization, as shown by the ratio map between the wild type and the mutant (Fig. 3g ). Moreover, Δsmc and ΔsmcΔmksB contact maps were nearly identical (Fig. 3f ), showing that MksB and SMC are most likely not involved in the same process(es). Finally, we applied the software HiCRep on our various Hi-C map, a framework for assessing the reproducibility of Hi-C data  56  (Supplementary Fig. 2A ). Strain backgrounds ΔparB, ΔparS, and ΔparBΔsmc Hi-C maps appear to form a first cluster; Δsmc and ΔsmcΔmksB appear to form a second cluster; finally, wild type, ΔparS2-10, and ΔmksB form a third cluster.
Methods Bacterial strains, plasmids, and oligonucleotides. Primers, plasmids, and strains used in this study are listed in Supplementary Data 1 and 2. For protein-protein interaction screens, genes of interest were amplified via PCR, digested with respective enzymes, and ligated into bacterial two-hybrid vectors  71. E. coli DH5α were utilized for plasmid cloning. Genes divIVA and parB/parB R175A were amplified using primer pairs DivIVA-XbaI-F/DivIVA-BamHI-R and ParB-XbaI-F/ParB-BamHI-R from genomic DNA or pK19mobsacB-ParBR175A, and resulting fragments were digested with XbaI/BamHI. For amplification of scpA, scpB, mksE, mksF, and mksG, primer pairs ScpA-XbaI-F/ ScpA-XmaI-R, ScpB-XbaI-F/ScpB-XmaI-R, MksE-XbaI-F/MksE-XmaI-R, MksF-XbaI-F/MksF-XmaI-R, and MksG-XbaI-F/MksG-XmaI-R were utilized, followed by restriction digests with XmaI/XbaI.
Assembly strategies of multiple consecutive allelic replacements are explained hereafter. C. glutamicum strains CBK002, CBK004, and CBK010 were obtained via transformation of pK19mobsacB-ΔparB, pK19mobsacB-ΔmksB, or pK19mobsacB-parB-eYFP into strain CDC026 lacking smc and strain CBK003 (ΔmksB ΔparB) was constructed using the genetic background of CBK001 (ΔmksB). Further, CBK004 served as parent strain for construction of CBK005 and CBK011 harboring additional mutations ΔparB and parB∷parB-eYFP, respectively. The dual-reporter strain CBK013, expressing ParB-mNeonGreen in combination with SMC-mCherry, was constructed via transformations of pK19mobsacB-smc-mCherry into CBK008; strain CBK014 derives from CBK012 transformed with pK19mobsacB-ΔparB. The complete loss of parS sites in strain CBK024 was accomplished via successive allelic replacements of parS by mutated sequences: the mutation of parS2 (CBK017) followed the mutation of parS3 (CBK016); thereupon, parS4 (CBK018) was mutated followed by parS5 and parS6 (CBK019).
Next, parS7 (CBK020) mutation, parS8 mutation (CBK021), parS9 mutation (CBK022), parS10 mutation (CBK023), and parS1 mutation (CBK024) were accomplished consecutively. CBK090 is a derivative of CBK022. CBK025, CBK027, and CBK029 derive from strain CBK023, which was transformed with pK19mobsacB plasmids coding for parB-eYFP, parB-mCherry2, and parB-PAmCherry, respectively. Accordingly, stains CBK026, CBK28, CBK032, and CBK087 are CBK024 derivatives harboring either endogenous parB-eYFP, parB-mCherry2, smc-mCherry, or parB-PAmCherry, whereas strains CBK030 and CBK031 obtained from CBK022 via transformation of pK19mobsacB-parB-mCherry2 or pK19mobsacB-parB-PAmCherry. CBK091 was obtained by transformation of CBK090 with pK19mobsacB-parB-mCherry2. CBK033 and CBK035 were generated by transformation of strain CBK012 expressing SMC-mCherry with plasmids pK19mobsacB-ΔSMCload or pK19mobsacB-SMCload-r; a further transformation of CBK033 with pK19mobsacB-SMCload-cg0177 yielded CBK034.
To introduce parS sites at different regions within the C. glutamicum genome CBK024 served as parental strain: parS-insertions at chromosomal 9. 5°, 90°, 270°, and 180°positions were achieved via transformation of either pK19mobsacB-parS-cg0108 (CBK036), pK19mobsacB-parS-cg0904 (CBK037), pK19mobsacB-parS-cg2563 (CBK038), or pK19mobsacB-parS-Δint (CBK039). Additional allelic replacements of parB or smc with fluorophore-coupled versions parB-eYFP or parB-mCherry2 and smc-mCherry in the above-named strains resulted in CBK040-CBK045. Second, parS-insertion in CBK037 was combined with a smc deletion by transformation of pK19mobsacB-Δsmc yielding CBK046. Lastly, strains CBK047-CBK051, which express mutant ParB R175A or SMC E1084Q proteins derive from CBK006, CBK027, and CBK012 transformed with plasmid pK19mobsacB-parBR175A or pK19mobsacB-smcE1084Q, respectively.
1% FA on an inhouse-packed chromatography column in a nano-electrospray emitter tip. Eluting peptides were directly infused into the mass spectrometer (QExactive, Thermo Fisher) and detected in positive ionization mode. The operating cycle was programmed to detect peptides in the range from 300 to 1600 m/z and up to 10 precursors were selected for MSMS analysis by CID fragmentation. Precursor ions required a charge state between +2 and +6 and a minimal signal intensity of 6 × 10e4. Protein mapping and quantitative analysis raw LC-MS/MS data were searched against a C. glutamicum database retrieved from Uniprot (vs. 03/2017, 3093 protein entries) using a forward/reversed search by the Andromeda algorithm within the MaxQuant software suite. Peptides hits were searched with 17 p. p. m. precursor mass deviation in the first search and 3 p.
Reads were aligned to the C. glutamicum ATCC 13032 genome sequence (GeneBankID: BX927147. 1), where RES167-specific genome deletions were manually cut using CLC Genomics Workbench. Data of extract and corresponding ChIP sample were each normalized based on read counts and the ratio of the number of reads per 0. 5 Kb bin were determined via the Galaxy web platform  77, 78. Real-time PCR. DNA amplification was performed using a 2× qPCR Mastermix (KAPA SYBR®FAST, Peqlab) according to the manufacturer's instruction, where reaction volumes of 10 µl contained 200 nM oligonucleotides and 4 μl of diluted DNA, respectively. Samples were measured in technical duplicates via an iQ5 multicolor real-time PCR detection system (Bio-Rad) and CT-values were determined via the Bio Rad-IQ™5 software version 2.
1. Primer efficiencies were estimated by calibration dilution curves and slope calculation  79  ; data were analyzed via the 2 -ΔCT method  80  accounting for dilution factors and sample volumes used for DNA purification. qPCR data of ChIP samples were normalized according to the ParB-mCherry2 signal obtained at locus parS1 in the wild-type background, serving as reference in each experiment. Protein purification. ParB protein production was performed in E. coli BL21 pLysS via the pET-16b vector-based system. Cells were grown in LB at 37 °C; gene expression was induced adding 1 mM IPTG following growth for 12 h at 18 °C. Subsequently, cells were suspended in washing buffer (50 mM Tris-HCl pH 7. 4; 100 mM NaCl; 5 mM; MgCl 2 ; 1 mM dithiothreitol) containing EDTA-free proteinase inhibitor (cOmplete TM , Sigma) and DNAseI, and lysed using a high-pressure cell homogenizer.
DNA-ParB binding was assayed using purified protein and double-stranded DNA fragments of ~1100 bp length with or without two parS sites. Fragments were generated by PCRs of a C. glutamicum genomic locus surrounding parS9 and parS10 using primer pairs parS9mut-Hin-dIII-up-F/parS10mut-EcoRI-D-R. ParB concentrations of 0. 05-25 µM were incubated with 100 ng DNA for 30 min at 30 °C, following sample separation in native gels (3-12% polyacrylamide, ServaGel TM ). DNA was stained using SYBR® Green I (Invitrogen). Photoactivated localization microscopy. C. glutamicum cells were fixed with 3% of formaldehyde prior to super-resolution imaging using a Zeiss ELYRA P. 1 microscope with laser lines HR diode 50 mW 405 nm and HR DPSS 200 mW 561 nm, and an Andor EMCCD iXon DU 897 camera. Cellular ParB-PAmCherry signals were further analyzed using Fiji software  75  and identification of distinct protein clusters was carried out by applying the OPTICS algorithm in R  49, 50.
Super-resolution imaging was performed on a Zeiss ELYRA P. 1 microscope (laser lines HR diode 50 mW 405 nm and HR DPSS 200 mW 561 nm). Cellular PAmCherry-tagged proteins were detected via an Andor EMCCD iXon DU 897 camera as described before, using a long-pass 570 nm filter (LP570) and an alpha Plan-Apochromat ×100/1. 46 Oil DIC M27 objective for imaging. Further, 100 nm TetraSpeck microspheres and the implemented drift correction tool served for drift correction; the Z-axis was stabilized via the "definite focus" system. PALM image calculation was performed applying the 2D x/y Gaussian fit (Zenblack software, Zeiss) using a peak mask size of 9 pixels, where one pixel corresponds to 100 nm and a peak intensity to noise ratio of 6. To exclude background and events resulting from the co-emission of co-localizing molecules, events were filtered for photon numbers between 70 and 350, and PSF (point spread function) width at 1/e maximum (70-170 nm) were applied.
Reachability distance thresholds were then chosen to highlight such phenomenon. Specifically, 50 and 35 nm were chosen as thresholds, as, in the tested conditions, they were able to consistently identify and separate subclusters from the macroclusters they were lying into (Supplementary Fig. 7 ). Documentation is available at Github (https://github. com/GiacomoGiacomelli/ ParB-clustering-protein-profiling). Chromosome conformation capture libraries. 3C/Hi-C libraries were generated as previously described by Val et al. 48 with minor changes. Briefly, cells were grown in 200 ml of BHI medium at 30 °C to an OD 600 of 3 and rediluted to a final concentration of ~1 × 10 7 cells/ml. Cells were crosslinked using fresh formaldehyde for 30 min at RT (3% final concentration; Sigma Aldrich Formalin 37%) followed by 30 min at 4 °C.
81. We then bin the genomes into regular units of 5 Kb to generate contact maps and normalized them using the sequential component normalization procedure  81. Contact maps were then generated using Pyplot library and a saturation threshold at 99. 5% of the maximum value. Contact map comparison. Ratio between contact maps was computed for each point of the map by dividing the amount of normalized contacts in one condition by the amount of normalized contacts in the other condition and by plotting the Log2 of the ratio. The color code reflects a decrease or increase of contacts in one condition compared to the other (blue or red signal, respectively). No change is represented by a white signal. To further compare the Hi-C data, we applied the HiCRep software 56 at a resolution of 5 Kb with a smoothing index of 3.
In short, exponentially growing cultures were treated with 25 µg/ml chloramphenicol for more than 4 h, to induce replication runouts. Cells were fixated in 70% ethanol (1:9 v/v) and washed once in PBS. Cellular DNA was stained using SYBR® Green I (Invitrogen, 1:10,000 dilutions) for 15 min in the dark. Flow cytometry analysis was carried out subsequently using a BD Accuri C6 (BD Biosciences) equipped with a 488 nm laser. At least 200,000 events were collected per sample at a slow flow rate of 10 µl/min measuring <5000 events per second, applying an acquisition threshold of 650 set on the green channel FL1-H. Data analysis was performed using the BD Accuri C6 Plus software (BD Biosciences). At first, events derived from cell aggregates were identified in plots of SSC vs. width and excluded from DNA content analysis.
Remaining events were plotted as histograms vs. DNA amount (FL1-A, EM BP 533/30) at log scale, where chromosome numbers were assigned in accordance to calibration standards described before  41. All experiments were performed in biological triplicates. Comparison of contact signals with transcriptional data. RNA-sequencing data for C. glutamicum were recovered from ENA (Project PRJEB4788). Only reads with a mapping quality above 30 were conserved. Raw signal was then binned to match the binning of the corresponding contact maps and plotted along the genome. Both contact and transcription signals were smoothed with a Savitzky-Golay filter as previously described in Lioy et al. 38. Statistics and reproducibility. Correlation coefficients, linear regressions and analyses of nearest neighbor distance distributions were calculated using Excel 2019, Graph Pad Prism (GraphPad Software), and R (R-studio v1.
Acknowledgements This work was funded by grants to M. B. from the  Deutsche Forschungsgemeinschaft  (project  326464754  in  TRR174  and  BR2815/6-2 ) and the  Ministry of Science and Education (BMBF :  031A302  e:Bio-Modul II: 0. 6 plus). This research was supported by funding to R. K. from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  260822 ), and from  Agence Nationale pour la Recherche  ( HiR-esBac   ANR-15-CE11-0023-03 ) and  JPI-EC-AMR STARCS ANR-16-JPEC-0003-05  all to R. K. We thank our colleagues from R. K. and M. B. labs for discussions, feedback, and comments on the manuscripts, and the interpretation of the data. We are thankful to  Professor Dr Stephan Gruber  ( Department of Fundamental Microbiology, University of Lausanne ) for inspiring discussions and for his help with ChIP-seq.
Data availability C. glutamicum ATCC 13032 genome sequence was obtained from GeneBank (GeneBankID: BX927147. 1). RNA-seq data for C. glutamicum were recovered from ENA (Project PRJEB4788). Proteomic data are available via ProteomeXchange with the project identifier PXD008916 (ref. 82 ). Genome-wide sequencing reads of ChIP-seq and chromosome conformation capture assays generated in this study are available in the Sequence Read Archive (SRA) under accession numbers PRJNA529385 and PRJNA525583. Flow cytometry raw data results were deposited in the FlowRepository database (accession number FR-FCM-Z2DJ)  80. The source data underlying Figs. 2c, h, 3b, d, e , and 5b, c, e, f and Supplementary Figs. 2B, C , 3B-D, 5A, D, 7C-E, 8A-C, 9B-D, 10A, B, D, 12A, B, D, and 13A, C are provided as a Source Data file.
5 4 6 54 Fig. 2A single parS site mediates chromosome folding. a One parS site is necessary and sufficient for wild type-like morphology and nucleoid segregation. Phase-contrast images of exponentially grown cells harboring either all (WT), one (parS 2-10mut , CBK023), or none (parS 1-10mut , CBK024) parS site(s), or lacking parB (ΔparB, CDC003) are shown. DNA is stained with Hoechst (yellow). Scale bar, 2 µm. b Fluorescence microscopy analysis of parB∷parB-mCherry (shown in green) in wild type (CBK006), parS 2-10mut (CBK027), and parS 1-10mut backgrounds (CBK028). Absence of parS leads to diffuse cellular ParB localizations. Scale bar, 2 µm. c ChIP-qPCR for strains described before, normalized to wild-type parS1 signal (mean + SD, n = 3). d ChIP-seq of C. glutamicum parB∷parB-mCherry parS 2-10mut (black) at a 3.
1-3. 2 Mb chromosomal range. Wild-type-like propagation (green) of ParB protein around parS1-4; 0. 5 Kb bin size. Location of parS sites present in wild type or mutant sequences are indicated (gray lines). e Normalized contact maps of ΔparB, parS 1-10mut , and parS 2-10mut mutants centered at oriC (CDC003, CBK024, and CBK023). Color codes as in Fig. 1. f Differential maps correspond to the log2 of the ratio (wild-type norm/mutant norm); color scales indicate contact enrichment in mutant (blue) or wild type (red) (white indicates no differences between the two conditions). g Single-molecule localization microscopy of representative wild-type and parS 2-10mut cells (CBK009 and CBK029). Top: Gaussian rendering of ParB-PAmCherry signals (0. 71 PSF, 1 px = 10 nm), below: color-coded representation of ParB-PAmCherry events within corresponding cells 49  ; all events (light blue), macroclusters (dark blue) and subclusters (yellow) are indicated.
Scale bar, 0. 5 µm. See Methods and Supplementary Fig. 7for details. h Comparison of ParB-PAmCherry cluster properties. Only the two biggest clusters per cell were taken into account for analyses; significant differences between conditions are indicated by small letters above datasets. Left: events per macrocluster, medians are indicated as solid lines, and whiskers mark 1. 5 IQRs (interquartile ranges); clusters wild type : n = 130, clusters parS2-10mut : n = 143. Right: subcluster numbers per macrocluster shown as overlay bar chart for both strains. Number of subcluster per macrocluster (two-tailed Kruskal-Wallis rank-sum test: χ 2 = 12. 284, df = 1, p = 0. 0004569) and macroclusters size (two-tailed Kruskal-Wallis rank-sum test: χ 2 = 27. 582, df = 1, p = 1. 506e -07) differ significantly between.
Two different analysis methods are developed, corresponding to the signal with or without jets in the final state. For the events without jets (ll H and νν H ), the signal identification strongly relies on the event multiplicity information. For the events with jets (qq H ), a dedicated tau-finding algorithm TAURUS has been developed (see in Appendix A ), and the signal is identified using the measurements from both the di-tau system and the recoiling di-jet system. The results of all those channels are combined, leading to a relative accuracy of 0. 8% of the objective signal strength at the CEPC. Extrapolated to the ILC, this analysis yields an accuracy of 1. 1%/1. 2% for the nominal left/right-hand polarization setting. This paper is organized into five sections. Section 2 describes the detector model, the software, and the samples used in this study.
The latter is denoted as Z ZorW W and Z orW process since the final state fermion combination allows multiple intermediate states and their interferences. The cross sections for signal and backgrounds are summarized in Table  2. The detector model used in the simulation is the CEPC baseline detector  [2] , a Particle Flow Oriented detector. It is composed of a low-material tracking system, a high granularity calorimeter system, and a 3-Tesla large radius solenoid that hosts both ECAL and HCAL inside. A baseline CEPC simulation-reconstruction software has been established. It uses the Whizard as the generator  [8] , the MokkaPlus  [9]  for the full detector simulation, the Clupatra  [10]  for tracking, and the Arbor  [11]  for the PFA reconstruction. Using the CEPC baseline geometry, an official Monte-Carlo sample production is performed, corresponding to the nominal setting of the CEPC Higgs runs.
Acknowledgements The authors would like to thank  Chengdong FU ,  Gang LI , and  Xianghu ZHAO  for providing the simulation tools and samples. This study was supported by the  National Key Programme for S&T Research and Development  (Grant NO. :  2016YFA0400400 ), the  Hundred Talent programs of Chinese Academy of Science  No. Y3515540U1. The work is supported by the  Beijing Municipal Science & Technology Commission , project No. Z191100007219010. Y. Xu  and  X. Chen  is supported by the  National Natural Science Foudation of China  (Grant NO. : 11675087 ) and  Tsinghua University Initiative Scientific Research Program.