dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular genetics The 14 coding exons and the adjacent intronic junctions of the human ADCK3 gene were amplified and sequenced as detailed previously  [4]. Sequences were analysed using the Seqpilot software version 2. 0 (JSI medisys, Kippenheim, Germany) or the Seqscape 2. 5 software (Applied Biosystems). Conservation of amino-acids involved in missense mutations was analysed with the blastp software of the NCBI platform (http://blast. ncbi. nlm. nih. gov/Blast. cgi) with the non-redundant protein sequences (nr) database. The splice site mutation was analysed with the SplicePort software (http://spliceport. cbcb. umd. edu/) and by RT-PCR as detailed previously  [4]  using primers located in exons 3 and 8 (forward 5′-TCTTTGCAAA CCCCAGAGAC -3′, reverse 5′-CCCAGGTCGTTGTT GAGAGT -3′). Partial gene deletion was assessed with the Illumina CytoSNP-12 arrays which contain 300,000 markers including 200,000 SNPs according to the manufacturer's specifications (Illumina, San Diego, California, CA, USA).
Molecular analysis of the ADCK3 gene All patients carried biallelic alterations of ADCK3 (Additional file 1: Table  S1 ) with a total of nine different mutations. Mutations inducing aminoacid substitutions were c. 811C > T (p. The mutation c. 1081-1_1082dupGTA is a 3-nucleotide duplication overlapping the acceptor splice site of exon 9. It does not alter the splice site but results in a 3-nucleotide insertion at the beginning of exon 9 which immediately creates an in-frame stop codon (p. Gln360_Tyr361ins*). The homozygous c. 589-3C > G mutation lying in intron 3 and found in sisters #8 and #9 affects the acceptor splice site of exon 4. It reduces the splicing score predicted with the SplicePort software from 3. 02 to 1. 53. ADCK3 transcript analysis from the patients fibroblasts, who are homozygous for the mutation, indicates that usage of the normal exon 4 acceptor splice site is completely abolished and replaced by the usage of a cryptic splice site located 23 nucleotides upstream, causing a frame-shift and termination 20 codons after the last codon of exon 3 (p.
Indeed, data from the literature suggest a favorable ataxic outcome, since 14/17 patients were still ambulatory when reported after a mean ataxia history of 20. 8 years (range 5-34 years, median 27. 5 years). Among the three previously reported patients who became wheelchair bound, one had a SLE  [5] , like the significantly disabled patient #13 of our series, and two other had a global neurological regression from the ages of 1. 5 and 3 years (patients 3 and 4 in  [3] ), like patient #7 reported here. The mean SARA score in the 10 evaluated ARCA2 patients reported here is 10. 7 after a mean duration of 19. 4 years (ratio SARA/disease duration = 0. 55 point/ year). Most other genetic progressive ataxias are associated with a more unfavorable outcome. As an example, patients with Friedreich ataxia (whose first signs occur later than in ARCA2) have a mean SARA score of 22.
Paraclinical tests in ARCA2 We report for the first time polymyographic recordings of patients with ARCA2. Our data show that movement disorders in ARCA2 are variable and include subcortical myoclonus, dystonia and tremor. It is worth mentioning that myoclonus in ARCA2 are of low amplitude and less disabling than cortical myoclonus recorded in progressive myoclonic epilepsies, with which ARCA2 shares some features  [16, 17]. Cerebellar atrophy was present in all reported ARCA2 patients to date and global brain atrophy in two patients with severe involvement (patient #7 and patient 3 in  [3] ). As discussed above, cerebellar atrophy is expected from the patients of our series screened from an ataxic cohort. The finding of stroke-like lesions on brain MRI in a patient with cerebellar atrophy (like in patients #5 and #13 also reported in  [5] ) is undoubtedly a clue for the diagnosis of ARCA2.
real rNa-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. this result highlights the influence of the normalization step on the differential expression analysis. real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. the group including the novel method named "median ratio Normalization" (mrN) gives the lower number of false discoveries. Within this group the mrN method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down-and upregulated genes and the gene expression levels. the newly proposed mrN method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes.
Evaluated Normalization Methods The present paper evaluates a number of normalization methods designed to resolve (directly or indirectly) the bias induced by the relative size of studied transcriptomes. First, methods directly addressing this issue are taken into consideration. The Trimmed Mean of M-values (TMM) method (developed in ref. 17 ) deals with estimating the "relative RNA production of 2 samples" under the assumption that the majority of genes are not differentially expressed. This method is implemented in the edgeR package available from the Bioconductor website at http://www. bioconductor. org (see refs. Sixteen and 7). Similarly, Anders and Huber (ref. 1) , proposed a normalization method based on "size factors" that renders counts from different samples comparable. This method is called "Relative Log Expression" (RLE) and is implemented in edgeR and DESeq packages (available from the Bioconductor website).
Materials and Methods This section is divided into 4 independent parts. The first part is devoted to the description of the MRN procedure. We explicitly show, in this part, how the MRN procedure overcomes the bias due to the relative size of studied transcriptomes. The second part presents the pipeline used in this paper for the DE analysis. The third part describes the algorithm used to simulate RNA-Seq data sets that will be used to compare the normalization methods described above. Finally, the last part describes the tomato's RNA-Seq data set studied in this paper. real data sets. The newly established method named "Median Ratio Normalization" (MRN) is then compared with normalization methods that have been described in the literature (refs. 17, 1, and 2).
Computations of MRN, FPKM and ToCo normalization methods were implemented by us and provided within a supplementary file (see Supplementary Materials). For the DE analysis, an error model based on the Negative Binomial distribution was used, with variance and mean linked by local regression. This model is well suited for modeling count data dispersion (refs. 3, 6, and 10) and is implemented in edgeR and DESeq packages. In this study, we used the 2 functions esti-mateDispersions() and nbinomTest() of the DESeq package. The R program for the DE analyses is also provided in a supplementary file (see Supplementary Materials). Simulation of RNA-Seq data. Simulated RNA-Seq data sets for K = 2 different conditions and R = 3 biological replicates were generated by the following 2-steps procedure. Step I.
This is the reason why M-values of upregulated, non DE and downregulated genes are all downshifted from 0. The full R program of these simulations is provided on a supplementary file (see Supplementary Materials) with parameters from simulation type 1. These parameters can obviously be changed in order to simulate different biological conditions. Tomato's RNA-Seq data set. To investigate the tomato transcriptome dynamics of fruit set, RNA were isolated from flower buds and flowers at Anthesis and Post-Anthesis stages. For each stage, cDNA libraries were generated from 3 biological replicates and subjected to Illumina mRNA-Seq technology sequencing. We generated 30-40 million high-quality sequence reads for each replicate. A total of 287. 5 millions of 101 bp paired-end 5. Calculation of gene expectations from Equation (  1 ): E gkr = P gk N kr.
Quantitative results. Table  2  summarizes, for each method, the results obtained from the 20 simulations (from the 4 simulation types with 5 independent repetitions for each one). The high quality reads: 94 M reads from Bud, 91M reads from Anthesis and 102M reads from Post-Anthesis. Mapping of these reads to the Tomato genome sequence Sly2. 40 was performed by the TopHat software with default parameters, at most 2 mismatches and no indels allowed  (refs. Twenty and 18). More than 90% of the reads were aligned to the genomic sequence. Expressed loci were assessed with Cufflinks software with default parameters and using the tomato gene annotation file iTAG2. 30 (refs. 21, 18).
Results This section is devoted to the comparison of results of DE analyses obtained with the various normalization methods described before on both simulated data sets and the tomato's RNA-Seq data set. Results on simulated data sets. The impact of normalization methods on the DE analysis of the 4 different simulation types from Table  1  is presented in Figures  2  and 3 , and in Table  2. In order to compare the variability of the studied methods, 5 data sets have been simulated and compared for each simulation type. As described below, obtained results are remarkably stable for the MRN method from one repetition to another and, consequently, no more than 5 repetitions seem to be needed. Figure  2  shows the number of false discoveries produced by each of the normalization methods for each of the simulation types.
62%) followed by the MRN method (about 1. 17%). Globally, the MRN method seems to give the best results. Results on the tomato fruit set data set. Results of DE analyses performed on the tomato RNA-Seq data with the various normalization methods described above are shown on    1 , scatter plots of above panels show the mSE of each normalization method for 5 independent simulation repetitions. For each simulation type, each repetition has the same symbol for each normalization method. we made is less restrictive than the one made in reference 17, which assumes that "the majority of genes are non DE. " The same issue has been also addressed by the RLE normalization method (ref. 1) , which uses also another computational workflow. The processing procedure of RNA-Seq data described in the present study clearly outperforms comparatively to previous methods.
To date, 57 GS enzymes have been biochemically characterized and three-dimensional structures are available for only four glucansucrases  [4] [5] [6] [7] [8] [9]. These structures were obtained by crystallization of recombinant truncated forms of GS from Lb. reuteri 180 (GTF180-ΔN; PDB: 3LK), S. mutans (GTF-SI; PDB: 3AIE), Ln. mesenteroides NRRL B-1299 (ΔN 123 -GBD-CD2; PDB: 3TTQ) and Lb. reuteri 121 (GTFA-ΔN; PDB: 4AMC). These four enzymes present different linkage specificity but share a common U-type fold, organized into five domains (A, B, C, IV and V). All the domains except domain C, are built up from discontinuous segments of the polypeptide chain. The catalytic domain A in GH70 enzymes adopts a (β/α) 8 barrel fold, which is circularly permuted relative to the related (β/α) 8 barrel of GH family 13 and 77 enzymes (belonging to the same GH-H clan as family GH70).
7 mm; Bio-rad, Hercules, CA, USA), with ultrapure water as eluent at a flow rate of 0. 6 mL. min -1. The temperature of the column oven was set at 80°C. The initial velocities were calculated directly from fructose production rates and are expressed in µmol of fructose produced per minute and per gram of enzyme. The SigmaPlot "Enzyme kinetics" module (version 3. 1) was used for curve fitting of the data and determination of kinetic parameters.
NMR spectroscopy Freeze-dried polymer samples (15 mg) were exchanged twice with 99. 9 atom % D 2 O, lyophilized and dissolved in 600 μL of D 2 O. 1D 1 H NMR spectra were recorded on a BrukerAvance 500 MHz spectrometer using a 5 mm z-gradient TBI probe at 298 K, an acquisition frequency of 500. 13 MHz and a spectral width of 8012. 82 Hz. The 1 H-signal from D 2 O was used for automatic lock and a gradient shimming was performed with each sample. Before Fourier transformation, the FIDs were multiplied by an exponential function with a line broadening of 0. 3 Hz. Spectra were processed with a 64 k zero filling, baseline correction and were referenced using the TSP-d4 signal at 0 ppm. All NMR data were acquired and processed using TopSpin 2. 1 software. The various signals were assigned as previously described  [15, 16, [29] [30] [31].
Three-dimensional molecular modeling The sequence of DSR-S vardel Δ4N catalytic domain (region N119 to K973) was submitted to the I-TASSER server  [32] [33] [34] , an online service for automated protein structure and function prediction, using as restraint the structure of the Lactobacillus reuteri N-terminally truncated glucansucrase GTF180 (PDB accession code: 3KLK). The 3D model of DSR-S vardel Δ4N predicted by I-TASSER was then further refined using the CFF91 force-field implemented in the DISCOVER module of the Insight II software suite (Accelrys, San Diego, CA, USA). For the minimization, the CFF91 cross terms, a harmonic bond potential, and a dielectric of 1. 0 were used. An initial minimization with a restraint on the protein backbone was performed using a steepest descent algorithm followed by conjugated gradient minimization steps until the maximum RMS was less than 0.
5. In a subsequent step, the system was fully relaxed. A 3D model of the D460M:H463Y:T464M:S512C mutant was constructed by introducing, in silico, the divergent amino acids in this mutant into the corresponding positions in the parental enzyme, using the Biopolymer module of the Insight II software package (Accelrys). The conformations of the mutated residue side chains were optimized by manually selecting a low-energy conformation from a side-chain rotamer library. Steric clashes (van der Waals overlap) and non-bonded interaction energies (Coulombic and Lennard-Jones) were evaluated for the various side-chain conformations. The mutant model was subsequently minimized following the same energy minimization procedure as described for the parental enzyme. Sucrose was manually docked into the active site of DSR-S vardel Δ4N and the D460M:H463Y:T464M:S512C mutant using the structure of the Lactobacillus reuteri N-terminally truncated glucansucrase GTF180 in complex with sucrose (PDB code: 1HZ3) as a model.
Three other positions, located in a non-conserved region of GH70 enzymes, emerged as possibly significant from structural analysis of GTF180-ΔN glucansucrase in complex with maltose (PDB code: 3KLL)  [5]. In this structure, four residues corresponding to D460, H463, T464 and S512 of DSR-S vardel Δ4N interact with maltose in subsite +2, either directly or through water molecule mediation. It has been suggested that these residues participate in the correct positioning of the acceptor for driving the formation of α(1→6) linkage  [5]. By analogy, these residues were assumed to participate in linkage specificity and were thus selected as targets for mutation. Consequently, engineering efforts were focused on eight residues: D306, F353, N404, W440, D460, H463, T464 and S512. The ISOR method  [44]  was used to generate two libraries named "LibA", targeting amino acids D306, F353, N404, W440, and "LibB", targeting amino acids D460, H463, T464 and S512.
0326 DexYG (GenBank accession no. ABC75033. 1), Lb. parabuchneri 33 GTF-33 (GenBank accession no. AAU08006. 1), S. mutans GS 5 GTF-D (GenBank accession no. AAA26895. 1), S. gordonii str. Challis substr. CH 1 GTF-G (GenBank accession no. AAC43483. 1), S. salivarius ATCC 25975 GTF-K (GenBank accession no.
This work was funded by the  French National Institute for Agricultural Research (INRA)  and the  French National Center for Scientific Research (CNRS). Grant number : CEPIA-AIC2007-2010, http://www. inra. fr/,http://www. cnrs. fr/. ICEO is supported by grants from the  Region Midi-Pyrénées, France , the  European Regional Development Fund , and  INRA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
0326 DexYG (GenBank accession no. ABC75033. 1), Lb. parabuchneri 33 GTF-33 (GenBank accession no. AAU08006. 1), S. mutans GS 5 GTF-D (GenBank accession no. AAA26895. 1), S. gordonii str. Challis substr. CH 1 GTF-G (GenBank accession no. AAC43483. 1), S. salivarius ATCC 25975 GTF-K (GenBank accession no. AAA26896. 1), Lb. fermentum Kg3 GTF-kg3 (GenBank accession no. AAU08008. 1), Lb. sakei Kg15 GTF-kg15 (GenBank accession no. AAU08011. 1), S. salivarius ATCC 25975 GTF-M (GenBank accession no. AAC41413. 1), S. sanguinis ATCC 10556 GTF-P (GenBank accession no. BAF43788. 1), S. downei MFE 28 GTF-S (GenBank accession no. AAA26898. 1), S. oralis ATCC 10557 GTF-R (GenBank accession no. BAA95201. 1), S. sobrinus B13N /OMZ176 GTF- T (GenBank accession no. AAX76986. 1), S. sobrinus B13N GTF-U (GenBank accession no.
BAC07265. 1), Ln. citreum HJ- P4 LcDS (GenBank accession no. BAF96719. 1), S. mutans GS5 GTF-I SmGS (GenBank accession no. AAA88588. 1), S. mutans UA159 GTF-I SmUA (GenBank accession no. AAN58705. 1), S. criceti GTC242/ HS-6 GTF-I Sc (GenBank accession no. BAF62338. 1), S. downei Mfe 28 GTF-I Sd (GenBank accession no. AAC63063. 1), S. sobrinus ATCC 33478 / OMZ176 GTF-IS sob (GenBank accession no. BAA02976. 1), S. salivarius ATCC 25975 GTF-IS sal (GenBank accession no. AAA26896. 1), S. salivarius ATCC 25975 GTF-L (GenBank accession no. AAC41412. 1), Lb. reuteri ML1 GTF- ML1 (GenBank accession no. AAU08004. 1), Lb. reuteri 121 GTF-A (GenBank accession no. AAU08015. 1), Lb. reuteri ATCC 55730 GTF-O (GenBank accession no. AAY86923. 1), Ln. citreum B-1355 ASR (GenBank accession no. CAB65910. 2), Ln.
citreum NRRL B-1299 DSR-E (GenBank accession no. CAD22883. 1) and DSR-S vardel Δ4N. 1), Ln. citreum NRRL B-1299 DSR-B (GenBank accession no. AAB95453. 1), Ln. mesenteroides NRRL B-1299CB4 DSR-BCB4 (GenBank accession no. ABF85832. 1), Ln. citreum B-1355 DSR-C (GenBank accession no. CAB76565. 1), Ln. mesenteroides Lcc4 DSR-D (GenBank accession no. AAG61158. 1), Ln. citreumB/110-1-2 DSR-F (GenBank accession no. ACY92456. 2), W. cibaria LBAE-k39 DSR-k39 (GenBank accession no. ADB43097. 3), Ln. mesenteroides IBT-PQ DSR-P (GenBank accession no. AAS79426. 1), W. cibaria CMU DSR-wC (GenBank accession no. ACK38203. 1), Ln. citreum KM20 DexT (GenBank accession no. ACA83218. 1), Ln. mesenteroides.
The DNA MMS BGTC and GAL DNA MMS BGTC were rapidly accumulated in the liver. This accumulation persisted during the whole observation period and was similar for both DNA nanocarriers. From the raw data (data not shown), the calculated fluorescence intensities in the liver were three to four times higher than in the bladder and about two times higher than in the lungs. DNA MMS DOSP showed an accumulation in the liver, but less important than for DNA MMS BGTC. A fluorescence signal was also observed in the urinary system, and in the lungs during the whole period with similar intensities than in liver and bladder. Interestingly, DNA MMS DOSP had an increased circulation time compared with the other DNA MMS tested, with fluorescence signals four to five times higher than for GAL DNA MMS DOSP and an increase at 24 hours.
Fluorescence accumulation and luciferase quantification in different organs 24 hours after intravenous injection. Twentyfour hours after the last administration of DNA nanocarriers (two administrations for PEG DNA LNCs and DNA MMS DOSP; one administration for DNA LNCs, DNA MMS BGTC, GAL DNA MMS BGTC, and GAL DNA MMS DOSP; and no injection for the control group), the animals were killed and the fluorescence localization was determined in different organs (liver, lungs, kidneys, spleen, and heart) to confirm BF images since BFI is affected by tissue depth 8 (Figure  7 ). For all DNA nanocarriers, the major intensity was observed in the liver, followed by some fluorescence in the lungs and almost no fluorescence intensity in the heart, the spleen, and the kidneys. In mice receiving the two injections, the luciferase expression was also quantified but revealed little luciferase expression (<2 relative luciferase unit/mg proteins) (data not shown).
Acknowledgments. The authors thank  Caroline Denis  ( Inserm U1078, Brest, France ) and  Pauline Resnier  ( Inserm U1066, Angers, France ) for their help with in vivo experiments as well as the platform SynNanoVect. The authors are also very grateful to  Emilie Goudeau  ( Inserm UMR1087, Nantes, France ) for excellent technical expertise in synthetic transfection. This work was supported by special grants from the  "Association Française contre les Myopathies" (Evry, France) ,  "Vaincre la Mucoviscidose" (Paris, France) ,  "Région Pays de la Loire" (CIMATH), "Biogenouest", "Région Bretagne ", " Ligue contre le cancer 29 ", and the " Canceropole Grand Ouest ". The authors declared no conflict of interest.
PCR assays RNA was extracted from cells and tissues with guanidinium thiocyanate (TRI Reagent, Sigma). RNA integrity was assessed by denaturing gel electrophoresis and RNA concentration with a NanoDrop-ND1000 spectrophotometer (Isogen Life Sciences). One µg of total RNA was reverse transcribed with the iScript cDNA synthesis kit (Bio-Rad). The cDNA preparation was diluted 1:10 (v/v) with milliQ water prior to addition to the PCR mixtures. Quantitative PCR. PCR was performed using iQTM SYBR® Green Supermix (Biorad 170-8882) in an iQ5 real-time PCR detection system (Biorad). The extension temperature was 60 °C for all primer pairs. cDNA samples were diluted 60-fold before use for mRNAs and 600-fold for 18S rRNA. Primary fluorescent data were exported and analyzed using the Lin-RegPCR program  [28].
LPV induces apoptosis in CSCs. Oct-4-expressing CSCs or cells from the total cancer cell population were treated with individual PIs for 48 h and apoptosis induction was assessed by the loss of plasma membrane asymmetry, as measured by annexin-V-labeling using flow cytometry. A 48-h treatment with LPV (1 mg/ml, i. e. , 1. 6 mM) induced an increase in annexin-V-fluorescein isothiocyanate (FITC) labeling in 77. 5 ± 12. 4% (n ¼ 4) of the cells, while a similar treatment did not induce significant labeling in the total cancer cell population (Figure  4a ). In this latter population, 48-h treatment with 20 mg/ml (i. e. , 32 mM) LPV was required to significantly increase the percentage of labeled cells to 31±6% (n ¼ 3) as compared with non-treated cells (data not shown). CSC treatment with NFV (1-3 mM) increased the percentage of annexin-V-FITC-positive cells to 69.
They were diluted in Tris-buffered saline (pH 7. 6) containing 0. 1% Tween 20 and 5% nonfat dry milk, as indicated in Supplementary Table  1. The membranes were incubated with the diluted antibody overnight at 4 1C. The bound primary antibody was detected by horseradish peroxidase-conjugated secondary antibody and visualized using an ECL detection kit (Millipore). Chemiluminescence was observed and quantified using a molecular imager ChemiDoc XRS system (Bio-Rad, Marne la Coquette, France). The band intensity was measured using Bio-Rad Quantity One software. Effect of LPV on allograft formation. Allograft induction was performed as previously described. 14 Briefly, single-cell suspensions were prepared in a PBS-Matrigel (BD Bioscience) mixture (v/v) and injected in a 100 ml volume subcutaneously in the back of CB17/SCID mice (Charles River Laboratories, L'Arbresle, France) anesthetized by i.