dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Filtration and Cluster Analysis The transfrags differentially expressed in four testicular cell types were statistically filtered using the annotation, mapping, expression, and network (AMEN) suite of tools  [64]. We first isolated 19 116 transfrags that exhibited a !3-fold difference in expression between averaged cellular conditions (pairwise comparisons). A LIMMA (linear models for microarray data) statistical test  [65]  was then used to identify 14 856 significantly differentially expressed transfrags (F value was adjusted using the false discovery rate method: P 0. 01). The resulting transfrags were then grouped into six expression patterns (P1-P6) using the partitioning around medoids (PAM) algorithm. The ability of the patterns to discriminate between transcripts was verified using a silhouette plot.
Coding Potential Analysis of Novel Transcribed Regions Before analyzing the protein-encoding potential of transfrags corresponding to intronic or intergenic (Cuffcompare classes i and u, respectively) TUT regions, we extracted their DNA sequences and the corresponding open reading frames. We also aligned whole-genome DNA sequences from four mammalian species including human (hg18), mouse (mm8), dog (canFam2), and cow (bosTau2) that were generated by Multiz program  [66]  and downloaded from the UCSC genome browser  [67]. Transfrags were classified as either coding or noncoding by an empirical integrative approach based on four distinct predictive tools: phylogenetic coding substitution frequency (PhyloCSF), HMMER, CPC, and txCdsPredict  [67] [68] [69] [70]. These four tools aimed to predicting the coding potential of a given amino acid or nucleic acid sequence based on: (i) phylogenetic alignments (PhyloCSF); (ii) similarities to known protein domains (HMMER); (iii) a support vector machine-based classifier using several sequence features including similarities to known proteins (CPC); and (iv) a weighting scheme producing a score corresponding to the proteinencoding capacity (txCdsPredict).
Transcripts were considered proteinencoding candidates if they had a PhyloCSF score. 20, an E value ,10 À4 in HMMER (versus Pfam-A and -B), if they were classified as coding by CPC, or if they showed a txCdsPredict score of. 800 (;90% predictive of proteinencoding genes). By combining the results, we were able to organize TUTs into five classes possessing very high (4 of 4 tools predicting protein-encoding potential), high (3 of 4 tools), medium (2 of 4 tools), low (1 of 4 tools), or no (0 of 4 tools) coding potential according to whether transcripts were considered protein-encoding by 4, 3, 2, 1, or none of the four predictive tools, respectively.
We assembled and quantified transcripts using the Tuxedo suite  [61]  (see Materials and Methods). Approximately 80% of reads (;224 million reads) were correctly paired and aligned to the rat genome sequence, which notably covered 911 339 splice junctions for use in transcriptome assembly. Aligned reads were then assembled into cell-specific transcriptomes and subsequently combined into a unique set of 122 262 nonredundant transfrags from 53 409 loci across all cell types. The resulting transfrags were classified by comparison with a comprehensive list of annotated coding and noncoding transcripts. We finally selected those corresponding to novel intronic (loci falling entirely within a reference intron and without exon-exon overlap with another known locus) or intergenic TUTs and compared them to known annotated protein-encoding and noncoding transcripts (see Supplemental Table  S2 ).
3% for TUTs and 48. 8% for known lncRNAs versus 50. 6% for known mRNAs; P , 10 À17 ) (Fig. 2F ). For both meiotic (DE-SC or P4) TUTs (q1 ¼ 524, med ¼ 979, q3 ¼ 2073) and meiotic known lncRNAs (q1 ¼ 571, med ¼ 991, q3 ¼ 2063), the transcript sizes were twice as large as those showing other expression patterns (P1-P3 and P5 and P6; P , 0. 001) (Fig. 2B ). This difference was not due to a greater number of exons but to a significantly longer exon length (median length of 371 bp and 297 bp for meiotic TUTs and known lncRNAs, respectively, versus ;200 bp for all other transcriptional events, P , 0. 01) (Fig. 2, B  and D ). Importantly, this difference was not found for known meiotic protein-encoding transfrags (q1 ¼ 149, med ¼ 203, q3 ¼ 320; P , 0. 001). Sequence conservation. To assess the evolutionary sequence conservation of the transcript isoforms identified, we computed an empirical score by averaging the base-by-base phastCons conservation scores calculated among nine vertebrates as provided by the UCSC genome browser  [59].
DISCUSSION We report the first outcome of high-resolution transcriptional profiling of three different germ cell populations as well as somatic Sertoli cells in the rat. Large-scale RNA-seq experiments of these four testicular cell types allowed us to reconstruct 20 424 high-confidence coding and noncoding transcript isoforms from 11 116 loci. Notably, we recovered 7168 transcripts already present in RefSeq, Ensembl, AceView, and UCSC databases and identified 11 837 novel isoforms of known loci. We also reconstructed 1419 high-confidence TUTs, with no previous annotations in the databases mentioned above. Finally, we captured some of the dynamic changes in abundance levels of each of these transcripts as spermatogenesis proceeds. In addition, we exploited another transcriptomic dataset (exon array technology; Affymetrix) that we used to validate RNA-seq data.
[75] ). Like those of other genome-wide studies  [2, 76] , our results confirm this observation for protein-encoding loci associated with a meiotic expression pattern and expand it to X-linked noncoding genes. Second, most TUTs and known lncRNAs reconstructed in our dataset accumulate in meiotic and post-meiotic germ cells. These data markedly reinforce previous findings showing an enrichment of lncRNAs observed to coincide with the appearance of spermatocytes and spermatids in mouse  [9, 10]  and human  [23]  testes. Such accumulation of ncRNAs during gametogenesis has also been observed during sporulation (an analogous biological process) in Saccharomyces cerevisiae  [81]. Some TUTs accumulated in postmeiotic germ cells may also belong to the poorly characterized set of transcripts paternally provided by the sperm to the early embryo  [29, 82].
ACKNOWLEDGMENT We thank all members of the SEQanswers forums for helpful advice;  Steven Salzberg  and  Cole Trapnell  for continuous support with the Tuxedo suite; members of the  UCSC  genome browser; and  Bernard Jost ,  Celine Bordet ,  Emmanuelle Becker , Severine  Mazaud-Guittot , and  Nathalie Dejucq-Rainsford  for discussions and helpful comments on the analysis. Sequencing was performed by the  IGBMC Microarray and Sequencing platform , member of the  France Genomique program.
1 Supported by l'Institut national de la santeet de la recherche medicale (1; l'Ecole des hautes etudes en sante ṕublique (EHESP);  INERIS-STORM  grant  N 10028NN  to B. J. ; an  INSERM Young Investigator postdoctoral fellowship grant  to A. L. ; a Rennes Metropole Defis scientifiques emergents-2011 grant to F. C. ; and  INSERM Avenir and Region Bretagne CREATE  grants  R07216NS  and  R11016NN  to M. P. The RNA sequence data files were submitted to  National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA)  and to  NCBI Gene Expression Omnibus (GEO)  under accession numbers  SRP026340  and  GSE48321 , respectively.
Methods Our study was carried out in 6 volunteers on a 3T MR scanner (GE, Signa HDxt) using SMART1Map on one mid-cavity short-axis, in end-diastole, in breath-hold. Single-shot (matrix = 128*224) and two-shot (matrix = 160*224) strategies leading to 5 time points ranging from 50 ms to 2966 ms ± 473 ms were performed. We considered three data-sets: single-shot, two-shot and two-shot plus T∞ from single-shot. Breath-hold duration, chance of inter and/or intra acquisition motions, and spatial resolution were assessed for the different acquisition schemes. Post-processing T∞ was annotated with 9900 ms for fitting. For each data-set, pixel-wise T1 maps were generated. Data were fitted using a 3parameter model. Due to sequence scheme, MR signal is likely to follow theoretical equation S(t) = M0-M0(1cosθ)exp(-t/T1), usually modeled as S(t) = A-B*exp(-t/ T1).
© 2014 Ferry et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Our findings also revealed large differences in acute virulence of French Guiana isolates in the laboratory mouse, and these differ from known genetic mechanisms that have been defined previously. Hence, the ability of some strain types to expand in the environment as a consequence of enhanced transmission may also lead to the spread of virulence determinants. microliter of each diluted PCR product was mixed with 0. 5 mL of a dye-labeled size standard ROX 500 (Applied Biosystems, location) and 23. 5 mL of de-ionized formamide (Applied Biosystems). The mixture was denatured at 95uC for 5 min and then electrophoresed using an ABI PRISM 3130xl automatic sequencer (Applied Biosystems). The sizes of the alleles in bp were estimated using GeneMapper analysis software v4. 0 (Applied Biosystems). Neighbor-Joining trees were reconstructed from the genetic distances among individual isolates using Populations 1.
To be accurate, the calibration between the projector and the camera has to be known. Each point has a corresponding speckle. Their size and shape depend on the distance and the direction to the 3D environment. The depth data are computed by the triangulation of each speckle between the projected pattern and the pattern observed by the camera. While this device is inexpensive, the acquired data are very noisy  [18]  and contain many 'holes' where no depth has been computed. Furthermore, we have to deal with motion blur when the device is moved quickly. The optimal operation range is between 0. 8 and 3. 5 m. This sensor was initially developed for gaming. However, many applications have been developed around this tool. Cui et al. [19, 20]  proposed a method to capture a body model just using the Kinect sensor.
Validation (VS Kinect) We acquired a plastic mannequin of a woman torso that had realistic dimension. We manually introduced a defect on the left breast that is similar to what might be observed with a significant size lumpectomy on a small breast (see Figure  8 ). We did a 3D reconstruction of this mannequin with both our system and a Kinect. For the Kinect, we used RecFusion, an open-source software implemented by a German company. It offers a solution to generate a 3D reconstruction. Their software is based on the KinectFusion approach with some modifications detailed in  [17]. The depth images acquired from the Kinect are registered to the current 3D reconstruction using the a b iterative closest point (ICP) algorithm. Once registered, the current 3D reconstruction was updated using the new depth measurements.
The expression of Bcl-2 was analyzed using a monoclonal mouse anti-Bcl-2 antibody conjugated to FITC (F7053, DAKO) after permeabilization of the cells as recommended by the manufacturer. All apoptotic markers were used together with anti-CD4 and anti-CD25 antibodies as described above. Acquisition was performed on Becton Dickinson cytometers (FACS Caliburacquisition software Cell Quest, FORTESSA II-acquisition software FACS Diva, FACS Verse-acquisition software FACS Suite) and subsequent analysis was performed using FlowJo software (Treestar, Olten, Switzerland) or the FACS Suite (Becton Dickison, Le-Pont-de-Claix, France). All cytometry experiments except those with the results shown in Figure  5  and Figure  6  were performed as follows: after gating on living cells (according to their forward-and side-scattered light, 50 c.
After 20 min of fixation with acetone, the primary antibodies were incubated for 1 h at room temperature followed by three phosphate buffered saline (PBS) washes before incubation with the secondary antibody (1 h at room temperature) and mounting. Slides were mounted in Faramount fluorescent mounting media (DAKO). Images were acquired with a Zeiss (Marly Le Roi, France) Axio Observer Z1 Inverted microscope with a Â 10 eyepiece objective and a Â 10 or Â 20 objectives, with a Zeiss AxioCam MRm camera. The acquisition software was Axiovision (Zeiss). In Figure  7 , fluorescent staining was performed on acetone-fixed (10 min), cytospin mTECs after 8 days of culture. The following primary antibodies were detected: rabbit polyclonal anti-human keratin 14 and anti-human keratin 5 (Eurogentec, Angers, France), and mouse monoclonal anti-human ICOSL (R&D Systems).
The acquisition software was Axiovision. ELISA. ELISA analyses were performed following the manufacturer's instructions for the different cytokines tested (IL-7, Peprotech, Neuilly sur Seine, France; CD40s, R&D Systems; ICOSL, Antibodies-online GmbH, Paris, France) on 100 ml of cell culture supernatant. Measurements were performed on a MRX Revelation microplate reader from Dynex (Thermolab System, Chantilly, VA, USA). qPCR analyses. For RNA extraction and further qPCR analysis experiments (Figures  7a  and b ), cytokines were added for 24 h to CD4 þ cultures at the following concentrations: IL-7 (0. 75 ng/ml, Peprotech), CD40S (25 ng/ml, R&D Systems), and ICOSL (0. 125 mg/ml, Antibodies-online GmbH). Extraction of total RNA from CD4 þ T cells and synthesis of cDNA was performed as previously described in the literature.
49, 51 We performed qPCR using reagents and a LightCycler 480 system provided by Roche SAS (Boulogne-Billancourt, France) with FOXP3 primers,  49  CD25 primers (F: 5 0 -ATCAGTGCGTC CAGGGATAC-3 0 ; R: 5 0 -GACGAGGCAGGAAGTCTCAC-3 0 ), and IL-2 primers (F: 5 0 -ACCTCAACTCCTGCCACAAT-3 0 ; R: 5 0 -GCCTTCTTGGGCATGTAAAA-3 0 ). Acquisition was performed with the Roche LightCycler Software version 1. 5. The results are normalized to 28S. 49, 52 tatistical analyses. Differences between groups were evaluated by twoway ANOVA and parametric or non-parametric t-tests for paired or unpaired data (InStat, GraphPad Software, San Diego, CA, USA), with the significance level set to Po0. 05. In some experiments, the 'normalize' function of GraphPad was used to limit the inter-experiment variability owing to the use of different FACS machines.
Acknowledgements. We acknowledge  Professor A Serraf  ( Hopital Jacques Cartier, Massy, France ) and  E Belli  ( Centre Chirurgical Marie Lannelongue ) for providing the thymic samples, and  Catherine Blanc  and  Benedicte Hoareau  from the  Flow Cytometry Core CYPS, Pierre & Marie Curie University, Pitie-Salpe  ˆtrie  `re Hospital, Paris, France , for their help with Fortessa use. We also acknowledge  Dr P Nancy  for the cytokine array experiments presented in Supplementary Table S1. We would like to thank our colleagues  R Le Panse  and  N Dragin  for a critical reading of the manuscript. The work was supported by the 7th framework program of the  European Union FIGHT-MG  (grant number  242210 ) and by the  Association Franc¸aise Contre les Myopathies.
In case of MAR mechanism, the T -optimal estimator seems to have the better behavior. Finally, relative efficiencies of the WMC estimator are very large which confirms that this estimator should not be used in this case. With the aim to apply our estimators, we have artificially created two new datasets with some causes of death missing. The first one is obtained from the WMC dataset after the application of a MCAR mechanism with α = 0. 4 and is called "MCAR dataset". The second one is also obtained from the WMC dataset but after the use of MAR mechanism with probability of missingness given by the logistic regression model logit(π(X, Z)) = -1 + 0. 01X + Z. Let us call it the "MAR dataset". The different percentages of events for the three datasets are listed in Table  6. Except for the WMC dataset where only the WMC estimator is used, we have applied our three estimators (WMC, T -optimal and DR) on these datasets.
Hodgkin's disease dataset  (Pintilie, 2006). Estimates of the baseline incidence of second malignancy (solid) and death without malignancy (dotted) specific cumulative hazard functions using the WMC method (red), the T -optimal method (green) and the DR method (blue) under MCAR (left) and MAR (right) assumptions. 2001 ) and, thus, we take into account two binary covariates: the presence of at least four positive nodes and the oestrogen receptor status (ER-negative) of their primary tumor, that were considered as significantly associated with overall survival. A cause-specific survival analysis study using the additive hazards model is carried out for these data. Of the 169 eligible patients, 79 had died at the time of analysis and cause-of-death information was incomplete. Of these 79, cause of death was available for 61, of whom 44 were classified as dying from their disease and 17 from other causes.
Simulation results for the three estimators (WMC, T -optimal and DR) of the baseline cause-specific cumulative hazard rate functions Λ 01 (•) and Λ 02 (•) under MCAR and MAR assumptions. Monte Carlo estimates of MISE (×10 3 ). Let us first consider the real dataset presented in Pintilie (2006) of 616 patients treated with radiation therapy for a Hodgkin's disease at the Princess Margaret Hospital between 1968 and 1986. The event of interest is the incidence of second malignancy, the death without malignancy is seen as a competing risk. The question is whether the younger subjects (30 years old or younger) have the same risk of a new malignancy than the older ones. In this study there are 84 patients with incidence of second malignancy and 195 who died without a second malignancy. There is only one covariate, age, with 308 young patients and 308 older patients.
There is only one covariate, age, with 308 young patients and 308 older patients. The rate of censoring is about 54%. Let us call this dataset the "Without Missing Cause (WMC) dataset". Estimator WMC T -optimal DR Data n Λ 01 (•) Λ 02 (•) Λ 01 (•) Λ 02 (•) Λ 01 (•) Λ 02 (•) 100 11. 69 15. 89 11. 31 15. 19 10. 85 14. 59 MCAR 400 3. 05 4. 02 2. 94 3. 84 2. 87 3. 67 1000 1. 29 1. 63 1. 23 1. 52 1. 17 1. 37 100 16. 22 23. 56 12. 29 15. 9 13. 28 16. 43 MAR 400 7. 30 12. 35 4. 17 5. 61 3. 52 4. 18 1000 5. 35 8. 69 2. 39 2. 97 1. 39 1. 44 7. 2. Applications on real datasets 7. 2. 1. Hodgkin's disease dataset.
Now, let us consider a dataset from a clinical trial in stage II breast cancer involving a population of elderly women. At the opposite of previous dataset, some Method β 1 β 2 WMC WMC Estim. (SE) 0. 00558 (0. 00198) 0. 02435 (0. 00305) 95% CI [0. 00169, 0. 00946] [0. 01837, 0. 03034] WMC Estim. (SE) 0. 01064 (0. 00325) 0. 02681 (0. 00463) 95% CI [0. 00426, 0. 01701] [0. 01773, 0. 03588] MCAR T -optimal Estim. (SE) 0. 00827 (0. 00300) 0. 02166 (0. 00376) 95% CI [0. 00239, 0. 01142] [0. 01430, 0. 02902] DR Estim. (SE) 0. 00877 (0. 00258) 0. 02069 (0. 00358) 95% CI [0. 00370, 0. 01383] [0. 01367, 0. 02772] WMC Estim. (SE) 0. 01083 (0. 00287) 0. 03882 (0. 00427) 95% CI [0. 00521, 0. 01645] [0. 03044, 0. 04720] MAR T -optimal Estim. (SE) 0. 00576 (0. 00272) 0. 02601 (0. 00360) 95% CI [0. 00043, 0.
01109] [0. 01894, 0. 03307] DR Estim. (SE) 0. 00567 (0. 00278) 0. 02399 (0. 00368) 95% CI [0. 00022, 0. 01111] [0. 01678, 0. 03120] 7. 2. 2. Breast Cancer dataset imsart-ejs ver. 2013/03/06 file: Bordes_Dauxois_Joly_R2. tex date: January 9, 2014.
We experimentally validated the testicular expression pattern of two of these novel protein-coding gene candidates, both highly conserved in mammals: one for a vesicle-associated membrane protein we named VAMP-9, and the other for an enolase domain-containing protein. This study confirms the potential of PIT approaches for the discovery of protein-coding transcripts initially thought to be untranslated or unknown transcripts. Our results contribute to the understanding of spermatogenesis by characterizing two novel proteins, implicated by their strong expression in germ cells. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium under the data set identifier PXD000872.
As a consequence, it is now possible to establish extensive, indeed near-exhaustive, protein repertoires that unsurprisingly include several thousand nonredundant proteins from a total cell lysate and in a single run  [31, 32]. Recently, Evans and collaborators described a novel approach that significantly improves the power of proteomic exploration: they termed this strategy ''proteomics informed by transcriptomics'' (PIT)  [30]. The PIT approach is based on the assumption that established sequence databases used for protein identification are incomplete. The query of mass spectrometry data using sequence databases such as UniProt  [33]  inevitably leads to a loss of information deriving from the MS/MS data itself. The importance of such losses cannot be anticipated: if the sequence from which they are derived is not present in databases, MS/MS spectra that cannot be assigned to any theoretical peptide derived from virtual trypsin digestion of these sequences will not lead to any protein identification.
RNA-Sequencing Analysis Comprehensive database of known transcripts. Transcript annotations from public databases (Ensembl  [42] ; National Center for Biotechnology Information [NCBI] release RGSC3. 4 [43] ; AceView  [44] ; and mRNA data from University of California at Santa Cruz [UCSC] rn4  [45] ) were merged into a combined set of nonredundant known transcript annotations using Cuffcompare  [46]. Read mapping. Briefly, and as previously described  [13] , RNA-seqderived reads from each sample replicate were aligned independently to the Rattus norvegicus genome (rn4, downloaded from the UCSC genome browser website  [45]  with TopHat (version 1. 4. 1) [47]  using published approaches  [29, 46]. The database of known transcripts (see above) and expressed sequence tag (EST) alignments (from UCSC) was used to define an additional junction set (AJS) for each TopHat run.
The junction outputs from individual TopHat runs were pooled and added to the AJS to allow TopHat to use junction information from all samples. TopHat was run again for each sample using the resulting AJS. The output of this second run comprised the final alignment. Ab initio transcriptome assembly. Individual sample alignments for each testicular cell type were pooled. The transcriptome of each individual cell type was assembled with Cufflinks (version 1. 2. 0) by finding a parsimonious allocation of reads of the transcripts within a locus using default settings  [46, 48]. Next, the Cuffcompare program was used to merge the individual transcript fragments (transfrags) into a combined set (nonredundant ''union'' of all transfrags that share all introns and exons) of 99 438 assembled transcripts; this set was named the rat nonredundant reference transcriptome.
Transfrag quantification. The isoform-level abundances (expression level) were assessed using Cuffdiff  [46, 48]  for each sample with upper quantile normalization. Abundance was measured in fragments per kilobase of exon model per million reads mapped (FPKM). A matrix of FPKM values was then prepared from the results of transcriptome quantification. The data were quantile normalized to reduce systematic effects and allow direct comparison between the individual samples. Transfrag classification. The Cuffcompare program  [46, 48]  was used to classify the 99 438 transfrags belonging to the rat nonredundant reference transcriptome according to the known transcript annotation database. All long (cumulative exon length !200 nucleotides [nt]) transcripts that were annotated automatically as complete match (Cuffcompare class ''¼'') or potentially novel isoform (''j'') of annotated noncoding genes and all novel intronic (''i,'' i.