dataesr/scientific-paragraphs-categorization huggingface dataset:
Of note, miR-206 was the only miRNA of which the differential expression remained significant after LiMMa moderate t-test and Bonferroni correction (p < 0. 01). The expression level of miR-105, miR-122, miR-135b, miR-206, miR-296, miR-335, miR-492, miR-515, miR-520a and miR-622 in hematopoietic-deficient and -competent hPSCs, at the pluripotent stage, is shown as vertical scatter dot plot with median. B: The expression level of miR-328 and miR-520a in hematopoietic-deficient, poor and high competent hPSCs, at the pluripotent stage, is shown as vertical scatter dot plot with median. MicroRNA expression levels were estimated by a -ΔCt calculation (with ΔCt = Ct miRNA -Ct RNU48), from the TLDA analysis. Human ES cells are represented in red and hiPSCs in black. P-values were calculated by using the LiMMa package from Integromics (+ p < 0,05; ++ p < 0,01; +++ p < 0,001).
Integrative miRNome and Transcriptome Analysis Given that miR-206 displayed the most significant downregulation in hematopoietic-competent hPSCs, we conducted an integrative bioinformatic analysis on miR-206 predicted target genes. To this end, 773 mRNA target transcripts of the broadly conserved (across vertebrates) miR-1-3p/206 family were identified in the TargetScan database and integrated into the global transcriptomic analysis performed by microarray on day-16 EBs. Using supervised ranking product analysis, 62 Since the four hematopoietic-deficient cell lines used in our study were from iPS cells, we removed data from the four hES cell lines and performed the same statistical analyses on the remaining 11 hiPS cells. In this situation, underexpression of miR-206, miR-105, miR-622, and an over-expression of miR-122, miR-520a, miR-296, miR-335, miR-106a (not detected in the global hPSC testing) were observed in hematopoietic-competent hiPSCs (Table  S4 , iPS alone).
Integrative miRNome and Transcriptome Analysis Given that miR-206 displayed the most significant downregulation in hematopoietic-competent hPSCs, we conducted an integrative bioinformatic analysis on miR-206 predicted target genes. To this end, 773 mRNA target transcripts of the broadly conserved (across vertebrates) miR-1-3p/206 family were identified in the TargetScan database and integrated into the global transcriptomic analysis performed by microarray on day-16 EBs. Using supervised ranking product analysis, 62 predicted genes and targets of the miR-1-3p/206 family were found significantly up-regulated in hematopoietic-competent samples (Figure  4A , Table  S6 ), including the hematopoietic transcription factors RUNX1 and TAL1. Hierarchical unsupervised clustering, based on this subset of 62 predicted mir-206 target genes, fully discriminated hematopoietic-deficient from -competent cells (Figure  4B ).
The broadly conserved miR-1-3p/206 family has not been involved in hematopoietic differentiation but is essentially expressed in mesoderm-derived skeletal and cardiac muscle cells  [41, 42]. This could suggest that elevated miR-206 levels may inhibit hematopoiesis commitment of hPSCs by promoting muscle cell differentiation programs. Canonical myomiRs (miR-1, -133 and -206) act as key components of the development of skeletal and cardiac muscles, even though they could also fulfill regulatory functions in non-muscular tissues  [43]. Indeed, it has been shown that overexpression of the miR-206 paralog miR-1 in ES cells promoted mesoderm formation and muscle cell differentiation while repressing non-muscle gene expression  [44]. Interrogation of the TargetScan database identified 773 genes containing predicted conserved sites for miR-1-3p/206 binding.
TLDA Analysis Reverse transcription reaction was performed using the human Megaplex™ primer pools set v3. 0 (Life Technologies, Foster City, CA, USA), which contains RT primers for 754 individual miRNAs, according to manufacturer's instructions. Real-time qRT-PCR was then carried out on an ABI 7900HT real-time PCR machine with the LDA thermal cycler block, using TaqMan ® array human microRNA card set v3. 0 (Life Technologies) and pre-defined TLDA thermal cycling conditions. Quantitative RT-PCR data were extracted with SDS2. 3 and RQ Manager Software (Life Technologies). Thresholds for the determination of Ct values were manually set at 0. 2, 0. 1, or 0. 05 for each miRNA across the 15 samples, depending on the quality of the amplification. Relative fold change was then calculated using the ∆Ct method, normalized to RNU48 expression.
Integrative miRNome and Transcriptome Analysis Predicted target genes of miR-206 were retrieved from the TargetScan database (release 7. 2, March 2018)  [47]. They were then merged with the Agilent transcriptome matrix. Microarray supervised ranking product analysis was performed with MeV 4. 9. 0 software  [48]. Bioinformatics analysis was conducted in R programming environment version 3. 4. 3. Expression heatmap was performed with R Bioconductor package made4  [49]. Integrative networks were obtained with network software Cytoscape version 3. 6. 0 [50].
Statistical Analysis TLDA data were analyzed with StatMiner Software (Integromics, Armilla, Spain). To identify differentially expressed target miRNAs, the LiMMa (Linear models for microarrays) moderate t-test was used adjusting or not the p-value with the Bonferroni correction. TLDA and qRT-PCR data were also analyzed with the nonparametric 2-tailed Mann-Whitney U-test applied to the ∆Ct values. Results with a p-value < 0. 05 were considered as statistically significant. Statistical tests and graphical representations were carried out using GraphPad Prism version 7. 0 (GraphPad Software, La Jolla, CA, USA).
Recently, combining NMR and biochemical data through data-driven docking, we proposed an atypical compaction mechanism of DNA by MC1  29. We describe here the solution structure of an MC1-DNA 15bp complex, uncovering the first structure of a monomeric protein bound to the concave side of a strongly bent and distorted DNA (PDB code 2NBJ). This new data allowed us to establish that MC1, as a monomer, is able to induce a V-turn on DNAs. The existence of such a conformation opens new insights into MC1-related and MC1-based biological processes, including transcription, replication and repair.
67 mM. NMR experiments and structure calculations of the MC1-DNA 15bp complex. All NMR experiments were performed at 298 K on a 700 MHz Bruker Avance III HD spectrometer equipped with a cryoprobe. All data were processed using Topspin Bruker and assignments were performed with the CcpNmr suite  57. Interproton distances were derived from NOESY data sets run at 120 ms mixing time. 1 D NH RDCs were measured by using 2D IPAP 1 H-  15  N and 1 H-  13  C HSQC experiments acquired on a 600 MHz Varian UNITY INOVA spectrometer for both the isotropic and anisotropic conditions  32. Structures were calculated with NOE distance, hydrogen-bond, and RDC constraints using the HADDOCK web server based on restrained MD/SA (XPLOR/CNS)  58. The H-bond restraints used for the calculation of the 3D structures (Table  1 ) were deduced from the cross peaks observed in the NOESY spectrum.
36). RDC restraints were fitted and analyzed with the MODULE program  59. Docking was started from the whole ensemble of the 15 lowest-energy MC1 free structures (2KHL. pdb) and a B-standard DNA structure obtained with 3D-DART  60. The first step of the protocol 58 described for a protein-DNA complex which consisted of a rigid-body docking (1000 models) followed by a refinement stage (200 models). Both protein and DNA were successively defined to be fully flexible on all their length (first run), then fully flexible for residues 63-80 (MC1) and 12-15/16-18 (DNA 15bp ) and finally automatically semi-flexible throughout the different runs, 40 in total. The final step of the structure refinement was performed in explicit water (200 models). The twelve structures with the lowest interaction energies and lowest constraints violations were selected for further analysis.
Protein analysis was performed with Procheck. The figures were prepared with PyMOL 61 and Molmol 62. Relaxation experiments. 1  H-  15  N CPMG experiments were recorded with ν CPMG = 0, 50, 100, 150, 200, 250, 300, 350, 400, 500, 600, 700, 800 and 900 Hz, and a CPMG constant time delay, T cp of 80 ms and 40 ms for MC1 and MC1-DNA 23bp , respectively  63. Peak intensities were plotted as effective relaxation rates and relaxation dispersion curves were fitted using the NESSY software  64. Model construction of the MC1-DNA 23bp complex. DNA 23bp was constructed with 3D-Dart starting from bound DNA 15bp structure and a canonical B-DNA of 8 bp was added to the terminal A 15 T 16 base pair. By substituting DNA 23bp in the MC1-DNA 15bp complex structure and minimization, we obtained a model of the MC1-DNA 23bp complex.
3, 44. 5 mM boric acid, 0. 5 mM EDTA). Electrophoresis was run at 14 V/cm, for 2 hours at 10 °C. After drying, gels were scanned with a β-scanner (Typhoon Trio) and quantified using ImagQuant software. The binding curves were fitted to a single binding site model using the equation Y = [MC1]/([MC1] + K Dapp ) where Y is the fraction of bound DNA, [MC1] is the concentration of free protein, and K Dapp is the apparent dissociation constant. Under our experimental conditions, the free protein concentration is reasonably approximated by the total protein concentration. The obtained values correspond to the mean (±SD) of three independent experiments. Assession numbers. NMR restraints and atomic coordinates for the MC1-DNA 15bp complex have been deposited with the RCSB PDB under the accession code PDB ID: 2NBJ.
Study Population The study was carried out in accordance with French legislation on biomedical research and the Declaration of Helsinki. The reports of all kidney allograft biopsies performed between September 1, 2004, and September 1, 2012, at Edouard Herriot Hospital or Jules Courmont Hospital, the two university hospitals in Lyon, France, were screened (2,024 biopsies in 938 patients) by means of the pathology department's computer database (DIAMIC). All kidney transplant recipients who displayed DSAs during the same period were identified through the immunology department's computer database. Information from the two databases was compared to identify patients with microvascular inflammation (defined as g+ptc Banff scores ≥2) and concomitant DSA, thus fulfilling the diagnosis criteria of AMR according to Banff classification.
2), anti-CD19 PerCP-Cy5. 5 (Clone HIB19), anti-CD14 PerCP Cy5. 5 (Clone M5E2), anti-CD4 PerCP Cy5. 5 (Clone SK3) all from BD and a viability Dye eFluor 506 (BD). Then the cells were washed and permeabilized with Cytofix/Cytoperm (BD) to allow for staining of MIP-1β (anti-MIP-1β V450, Clone D21-1351; BD). Flow cytometry analysis was performed with a FACS Canto II and data were analyzed with FlowJo R software.
FUNDING The study was funded by the 2012 AOR clinical research grant from the  Agence de la Biomédecine  and the  Etablissement Français du Sang. TB and AK received a financial support from the  HCL  (année médaille d'or) and INSERM (poste accueil). OT is supported by a Subvention Transplantation et Thérapie cellulaire 2018 FRM; the  Joint Program  of  Activities  ( PAC7 ) of the Fondation CENTAURE, l' Agence Nationale pour la Recherche  ( ANR-16-CE17-0007-01 ). SD, VM, VD, and OT are supported by the  Etablissement Français du Sang  and the  Société Francophone d'Histocompatibilité et d'Immunogénétique. EM, SD , and OT are member of the  CENTAURE Transplantation Research Network  (https://www. fondation-centaure. org).
Our focus in this paper is on proof checking. What we report on here would still have been worth doing, even if there were no gaps or errors in Euclid. The details of our proofs, and a discussion of the errors in the original proofs of Euclid, will be published separately, with a focus on the geometry and on the correspondence between the axioms and proofs of Euclid and those of our formal development. That will necessitate a longer discussion than is possible here. The formal proofs themselves, as well as the PHP and ML scripts that we used, are available on the Web. Look for links to them at http://www. michaelbeeson. com/research/CheckEuclid/index. php They are also available as ancillary files to the version of this paper posted on ArXiv.
In the case of the HOL Light system (one of the incarnations of HOL), the correctness of this kernel has even been formally proved (using the HOL4 system, another incarnation of HOL), which gives an extremely high guarantee that the system will not have any logical errors. Our procedure for proof-checking Euclid was thus as follows: • Write formal proofs in the Euc language, simultaneously checking and debugging them with our custom proof debugger. • Translate these proofs into HOL Light or Coq syntax by means of simple scripts. • Check the resulting proofs in HOL Light and Coq Was it necessary to use the Euc language? No, but it is closer to Euclid than either HOL Light or Coq, and allowed us to write the proofs only once, and moreover, has a better chance of being readable a thousand years from now.
Checking the proofs in HOL Light To ensure the correctness of the Euclid formalization from the language Euc to HOL Light, we built a very small custom checker on top of HOL Light, and used that to check our work for correctness in HOL as well. The source of this proof checker, proofs. ml, has about a hundred lines, which then is used to check a translation of the formalization into syntax that HOL Light can process of almost twenty thousand lines, michael. ml. This last file is created from the original proof files described above by two small ad hoc scripts, a PHP script called FreekFiles. php and a Perl script called FreekFiles. pl. In the HOL system, all input (even the proofs) always consists of executable ML source code. The proofs of each lemma in our case is checked by calling a function run_proof (implemented in proofs.
The standard way to state such a lemma in Coq is to split the lemmas in two parts. But, we did not want to modify the original formalization of the lemmas. Hence, to circumvent this limitation, we verified these proof steps using a tactic based on forwardchaining. The case distinction tactic allows to distinguish cases on previously proved disjunctions or disjunctions which are classical tautologies. The tactic can deal with n-ary disjunctions. The proof script is structured using curly brackets and indentation. Each proof step of the original proof corresponds to one proof step in Coq, except that steps for which there is no justification in the original proofs. Those steps correspond to the natural deduction rule for introduction of implication and are implemented using the standard Coq tactic intro.
All tactics are designed such that the names of the geometric objects are preserved from the original proofs but the names of the hypotheses are automatically generated by Coq and not used explicitly in the proofs. Figure  10  displays the proof of Playfair's axiom of uniqueness of parallels in Coq's language enriched by the tactics to ease the verification. Some of the predicates have been renamed into longer names to enhance readability and to match the names used in the GeoCoq library. The proofs are verified using classical logic. The proofs can be checked by Coq in about 90 seconds using an Intel(R) Core(TM) i7-7700 CPU @ 3. 60GH with 32Go RAM.
Modified Microdosimetric kinetic model (MKM) In the modified MKM  [22, 23] , for any radiation quality, RBE α is expressed as a function of the saturation-corrected dose-mean specific energy of the domain delivered in a single event z Ã 1D divided by the (α/β) x ratio: RBE α ¼ 1 þ α β -1 x •z Ã 1D ð6Þ z Ã 1D depends on z, the specific energy, and z sat , the saturation-corrected specific energy which accounts for the decrease of RBE due to the overkilling effect for high specific energy values  [27]. z depends on the radius of the domain (R d ) while z sat depends R d and the radius of the cell nucleus (R n )  [22]. MKM input parameters (R d and R n ) have been tuned in a previous work  [22]  to reproduce an in vitro experimental biological database of Table  1  Photon parameters applied during the in silico investigations.
The D t parameter is required for LEM calculations only Table  2  Clonogenic cell survival LQ fit parameters for photon (α x and β x ) and helium ion beam (α and β) irradiation using the Renca cells in vitro with corresponding LET d derived from MC simulation. Data for both experiment A (pristine peaks) and experiment B (SOBPs) are provided α x [Gy -1 ] β x [Gy -2 ] ( α/β) x [Gy] D t [ Exp. α x [Gy -1 ] β x [Gy -2 ] ( α/β) x [Gy] α [Gy -1 ] β [Gy -2 ] LET d [keV/μm] A 0. R β ¼ 1: ð7Þ Local effect model (LEM) The LEM-version IV developed by the GSI Helmholtz Centre for Heavy Ion Research (Darmstadt, Germany)  [21]  relates the biological response directly to the double-strand breaks pattern and has been benchmarked by its developers in various publications  [10, 21]. The LEM intrinsic α z tables are obtained using the PT RBE Generator software by Siemens which is available at HIT, while for β z , we have used the approximation β z = ( s max -α z )/(2D t ), with negative values found at high LET forced to zero  [25].
Patient studies and validations Retrospective study: patient treatment planning and forward computation of D RBE In this work, the MC-based treatment planning tool (MCTP)  [32, 33]  is employed to create biologically optimized treatment plans and to perform forward dose calculation for retrospective study. The MCTP relies on FLUKA's capability to describe the interaction and transport of radiation with matter for 4 He ion beams and is coupled with both biophysical and phenomenological RBE models for 4 He. FLUKA has been benchmarked against dosimetric data, demonstrating overall a satisfactory agreement  [11]. The MCTP uses dosimetrically commissioned scanned pencil beams as available at HIT  [34]. The data-driven RBE model has been used for treatment plan optimization. The MCTP tool relies on externally generated databases for each biological effect model to calculate RBE and D RBE values  [37, 38].
Development and validation of an analytical biological dose calculation engine: FRoG Once patient case dose calculation was established via biological dose models coupled with FLUKA, validation of the fast (GPU-based) analytical dose engine, FRoG, was performed  [44, 45]. Physical and biological parameter database generation took place using FLUKA MC simulation. Corresponding biological parameters for DDM (α He and β He ), LEM (α He and β He ), and MKM (z Ã 1D ) were scored as a function of depth, along with the necessary physical parameters (dose and LET d ). The physical and biological tables were incorporated into the FRoG platform, enabling multi-tissue (variable (α/β) x ) dose calculation for the three biological dose models. The glioma patient plan was executed in FRoG for comparison with the gold standard FLUKA MC.
These findings suggest that systematics in RBE predictions in the high dose region for clinical 4 He ion treatment fields due to different choices of RBE modelling approaches can be restricted to be mostly within 10% to 15% when tuning the parameters of the RBE models to the same (or a similar) set of the available in vitro cell data for 4 He ions. In turn, this may imply that systematic uncertainties in the prediction of RBE for helium ions for clinical scenarios are not primarily dominated by the choice of the RBE model, but instead dictated by the choice of the in vitro dataset and methodology used for tuning the RBE model parameters. Similar conclusions might hold true for RBE models of higher Z ion species. Additional systematic RBE uncertainties arise from differences between in vivo and in vitro data; however, due to their scarcity, in vivo and clinical data are hardly used to tune RBE models, but rather for validation of commonly established RBE models  [52] , exception being the neutron-equivalent scaling point used for carbon ions  [53, 54].
Results This study utilized summary statistics of the phase-1 GWAS data from the International FTD-Genomics Consortium (IFGC), comprised of 2,154 clinically diagnosed FTD spectrum cases and 4,308 controls and a total of 6,026,384 SNPs. Of the 2,154 cases diagnosed with FTD, 361 were diagnosed with the svPPA subtype (referred to as "semantic dementia" in the original study). Cases within the cohort were diagnosed according to the Neary criteria for FTLD  12. For additional cohort details, please see Ferrari et al. 6. We generated gene-based p-values for 17,466 genes with data available in the svPPA cohort using the tool versatile gene-based association study (VEGAS). We next generated protein interaction networks (PINs) for the significant genes (VEGAS p < 0. 05) using the protein interaction network-based pathway analysis (PINBPA) package.
The background PIN database used in our analyses contained 8,960 proteins and 27,724 interactions. The largest network generated (in terms of both nodes and edges) contained 64 nodes (genes) and had 81 edges (protein interactions) (Fig. 1 , Supplementary Table  S1 ). We evaluated only the largest and most significant network to avoid false positive findings. Notably, TARDBP (the gene encoding TDP-43) was absent from our network, but many genes implicated in cell death (e. g. SMAD3, SMAD4), nuclear trafficking (e. g. RANGAP1), and stress responses (e. g. HNF4A) were present. The svPPA network was within the top 10 th percentile for both nodes and edges based on permutation testing. The size of a node corresponds to closeness centrality (a metric that describes a node's nearness to other network nodes).
The thickness of edges in the network corresponds to edge betweenness (a metric that describes the number of paths going through an edge in the network). We next explored the gene expression patterns of the svPPA network genes in pathologically confirmed cohorts of FTLD cases versus controls. Fifty-eight of 64 svPPA genes in the dataset (GSE13162) had expression data available. Fifteen of the svPPA network genes (HNF4A, NR5A1, TAL1, SLC2A4, PSEN1, KRT81, MYBL2, UBE2I, EBI3, BATF, ARFRP1, NR6A1, PACS1, PELP1, and TEF) were significantly differentially expressed at an FDR-corrected p < 0. 05 in cases when compared to controls (Table  1 , Supplementary Table  S2 ). For each gene in the svPPA network we provide a detailed summary of our results with respect to VEGAS results, the top 3 regions expressing each gene in healthy human brain tissue (from the Braineac cohort, http://braineac.
org), OMIM biological process implicated for each gene, and known neurodegenerative disease associations in Supplementary Table  S3. To better understand the biological and functional implications of the svPPA gene network, we performed two separate ontological analyses. The first analysis utilized two common and publicly available databases of gene ontologies (Reactome and Gene Ontology [GO] portals)  13, 14. For the second analysis, we used a recently developed and independent analytical pipeline called weighted protein-protein interaction network analysis (W-PPI-NA) pipeline, which was recently developed by our group  15, 16. Sixty-four genes were included in the first svPPA ontological pathway analysis (Supplementary Table  S1 ); genetic enrichment was seen in pathways involved in RNA metabolism, development, immunity, and cell signaling (Table  2  and Supplementary Data S1).