dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction and quality assessment From eligible studies, two investigators independently extracted data using designated forms. Between-study variables included information on study characteristics (author(s), publication year, journal, study design, follow-up duration) and sample characteristics (country, stage of lymphoma, International Prognostic Index [IPI], incidence of transformed lymphoma, number of prior treatments, use of rituximab in prior therapy, sample size). For each study, baseline demographics (mean age, gender), response data (ORR, CR, OS, and PFS), and toxicity data (NRM, median time to ANC engraftment, median time to platelet engraftment, rate of myelodysplastic syndrome) after RIT ACST were extracted if available. Individual data were used where possible. Classical tools (Newcastle-Ottawa Scale [NOS] or Jadad scale) to assess the quality of studies were not utilized in all included studies; therefore, the investigators chose a quality assessment score on compounded items extracted from classical quality assessment scales (type of publication, years, study design, randomization).
Data synthesis and analysis The primary assessment for this study was OS and PFS at 2 years. Secondary assessments were 3-year OS, 3-year PFS, ORR, CR rate, incidence of myelodysplastic syndrome, and NRM. For OS and PFS, mean values were estimated by taking into account censored data. Secondary analyses included estimation of OS and PFS at 2 years in prospective studies according to DLBCL and transformed lymphoma. We determined estimates of the effect of Z-BEAM followed by ASCT by calculating the weighted median proportion (event rate). To account for expected heterogeneity, the mixed-effects model was used. Summary estimates of event rates were obtained using the fixed-effects model method when heterogeneity was not significant. The percentage of variability beyond chance was estimated using the I² statistic  [13, 14].
Study selection and description A flow diagram of the search results is presented in Figure  1. A total of 221 records were identified through electronic databases. Removing search overlap and irrelevant studies based on title and abstract review reduced the results to 26 records. After full-text analysis, 10 studies were chosen for inclusion in the meta-analysis (five abstracts, four articles, one letter). Four of the studies were prospective, multicenter studies  [9, 10, 17, 18]  and six studies were retrospective  [6, [19] [20] [21] [22] [23]. None of the studies contained individual data. The studies were classified according to their design and year of publication. Study characteristics, including design, populations, and outcomes are presented in Table  1. Across the 10 studies, 328 patients with DLBCL received 90 Y-ibritumomab tiuxetan followed by ASCT.
These data are consistent with our in vitro analyses (Fig. 6 ) and indicate that Cys147, but not Cys338 and Cys394, is essential for Atg4 activity in vivo. The functionality of WT and Atg4 mutant forms was also examined through the analysis of GFP-Atg8 processing, a biochemical assay widely used to monitor autophagic activity. 29 No significant difference was observed in the autophagic activity of cells expressing Atg4 WT and the 2 Atg4 C338S and Atg4 C394S mutants. By contrast, Atg4 C147S was unable to promote autophagic activity (Fig. 7A ). To further analyze the role of Cys338 and Cys394 in the control of Atg4 activity, we examined the cellular distribution of GFP-Atg8 in atg4-deficient cells expressing Atg4 cysteine mutants. As previously shown,  8  no GFP-Atg8 was detected as punctate signal in atg4-deficient cells (Fig.
Proteins were resolved on 15% SDS-PAGE gels and stained with Coomassie brilliant blue (Sigma, 27816). For Atg4 activity quantification, gels were scanned with Odyssey (LI-COR, Biosciences, Siemensstrasse, Germany) and the signals corresponding to unprocessed (Atg8) and processed (pAtg8) Atg8 were quantified with the software Image Studio (LI-COR, Biosciences, Siemensstrasse, Germany). The Atg4 activity was considered as the relation between pAtg8/ (Atg8CpAtg8), and it was normalized between 0 and 1, with 0 corresponding to totally oxidized and inactive Atg4 and 1 corresponding to totally reduced and active Atg4. The Atg4 activities (in arbitrary units) were plotted using the software OriginPro 8 (OriginLab).
Redox titration Redox titration of Atg4 activity was performed by incubating the Atg4 protein (4. 5 mM) in 50 mM TRIS-HCl pH 7. 5, 1 mM EDTA at defined E h values imposed by reduced and oxidized DTT (DTTred and DTTox, respectively) in various dithiol/disulfide ratios (20 mM as final DTT concentration). After equilibrating Atg4 samples in 100 ml redox buffers for 2 h at 25 C, 10 ml were added to the reaction mixture containing 5 mM Atg8 and 1 mM EDTA in 50 mM TRIS-HCl buffer at pH 7. 5 for 1 h at 25 C. Atg8 cleavage was visualized by Coomassie blue-stained gel and Atg4 activity was quantified as described above. Titration data were fitted to Nernst equation (n D 2, one component) using nonlinear regression with the CoHort software as described. 42 The midpoint redox potential (E m ) is reported as the mean § standard deviation of 4 independent experiments.
Fluorescence microscopy For fluorescence microscopy, yeast cells were grown to OD 600 nm »0. 4 in SD medium and fixed with 4% formaldehyde (Merck, 1. 04003. 1000) in phosphate-buffered saline (10 mM Na 2 HPO 4 , 2 mM KH 2 PO 4 , 137 mM NaCl, 2. 7 mM KCl) for 30 min prior to observation. Samples were examined on a DM6000B microscope (Leica, Wetzlar, Germany) with an ORCA-ER camera (Hamamatsu, Hamamatsu, Japan) and processed with the Leica Application Suite Advanced Fluorescence software package (Leica).
Funding This work was supported in part by  Agence Nationale de la Recherche  Grant  12-BSV5-0019 REDPRO2  and  LABEX DYNAMO ANR-11-LABX-0011  (to CHM and SDL), and  Ministerio de Econom ıa y Competitividad  Grant  BFU2012-35913  and  Junta de Andaluc ıa  Grant  CVI-7336  (to JLC). MEPP was supported by an  IEF   EU Marie Curie Fellowship  ( PIEF-GA-2011-298652-REDOXDYNAMICS ).
MRC analysis and definition of MR semiology MR images were analyzed with Bruker ParaVision 5. 0 MRI software, specially dedicated to Bruker BioSpec 4. 7 Tesla preclinical MRI. This is a software package for MRI data reconstruction, analysis and visualization. This software is dedicated to DICOM (Digital Imaging and Communications in Medicine) image file formats. It permits navigation and visualization with 2D and 3D viewers and basic functionality such as magnification, ROI (region of interest) analysis and geometrical measurement. ParaVision 5. 0 software analyzed MR images. A radiologist (Celine Savoye-Collet) blind to treatment allocation interpreted images. Image quality was assessed by wall and motion artifacts each rated 0 to 3. Quality was considered as excellent without respiratory movement (0/3), good with few respiratory movements responsible for wall artifacts (1/3), and poor in other situations (2-3/3).
Histological assessment of colon fibrosis After formalin fixation (40%), colon samples were embedded in paraffin wax blocks and 5 mm sections were stained with hematoxylin-eosin-safran. A pathologist (Moutaz Aziz) blind to treatment allocation scored these 5 mm sections. Two semiquantitative scores were assessed: a fibrosis score ranging from 0 (no fibrosis) to 3 (severe fibrosis) and an inflammatory score ranging from 0 (no inflammation) to 3 (severe inflammation with ulceration) were performed according to a previously validated scoring system  [20]. Pictures were taken with Leica QWin software (Leica Microsystems, Bensheim, Germany).
Statistical Analysis Statistical comparisons were performed using GraphPad Prism 5 software. Data are expressed as mean 6 SD and number (percentage). Gaussian distribution was checked with Kologorov-Smirnov test. Parametric quantitative data were analyzed with Student t-test and non-parametric quantitative data with Mann Whitney test. Qualitative data were analyzed with chi-square test and Fisher's test for low number  (n,5). Correlation between parametric quantitative data was analyzed by simple linear regression and Pearson's correlation. For correlation between non-parametric data, Spearman rank-order correlation was used and Spearman correlation coefficient r was calculated. A p value equal to or less than 0. 05 was considered significant.
Since SKOV3 cells closely represented the PC expression profile of ovarian cancer cells, we chose them to test the effects of PC silencing using stable gene-silencing shRNA strategy to generate knockdown SKOV3 cells for each expressed PC. In vitro and in vivo assays confirmed the role of PACE4 in the sustainment of SKOV3 cell proliferation, which was not observed with the other three PCs. We also tested PACE4 peptide inhibitors on all three cell lines and observed consequent reduced cell proliferation which was correlated with PACE4 expression. Overall, these data support a role of PACE4 in promoting cell proliferation in ovarian cancer and provides further evidence for PACE4 as a potential therapeutic target.
Various PC substrates have recognized roles in cell growth, tumor angiogenesis, invasion, and metastasis, including secreted growth factors, matrix metalloproteinases, and adhesion molecules  [8]. The implication of the different PCs in ovarian cancer is controversial, as PACE4, in contrast to prostate cancer studies, has been reported to be epigenetically silenced  [9]  and furin to be highly expressed in ovarian cancer cells  [10]. These data contrast with those published in open sources such as Oncomine databases (Compendia Bioscience, Ann Arbor, MI) where PACE4 expression varies significantly according to data sets but tends to increase in tumor tissues, just like furin and PC7. Thus, the functional roles and redundancies of PCs in ovarian cancer context remain unclear. In the present study, we used molecular silencing [i.
e. , lentivirusdelivered small hairpin RNAs (shRNAs)] to knock down each endogenously coexpressed PC in the SKOV3 cell line and then test for cell proliferation and tumor progression response. SKOV3 cells are the most studied models for serous ovarian cancer and display strong expression of furin, PACE4, PC5/6, and PC7, similar to ovarian cancer tissues and metastases. Our molecular silencing approach method is highly specific and permits a better distinction in regards to PC functional redundancy. We also examined the effects of our recently developed specific PACE4 inhibitor, namely, the Multi-Leu (ML) peptide and some peptidomimetic analogs in SKOV3 cells, as well as two other cell lines, OVCAR3 and CAOV3 cells. The sum of our data confirms that PACE4, and no other PCs, has an important role in ovarian cancer cell proliferation and further suggests that PACE4 is a potential therapeutic target.
IHC of Tissue Markers in Xenograft Model Tumors IHC were performed on 5-μm tumor sections in the Department of Pathology of the Centre Hospitalier Universitaire de Sherbrooke, Québec (CHUS) (Sherbrooke) using a standard streptavidin-biotinperoxidase immunostaining procedure with a Ventana NexES autostainer and the solvent-resistant DAB Map detection kit (Ventana Medical Systems, Tucson, AZ) using ready-to-use solutions (Ki67 and E-cadherin) purchased from Dako, Burlington, Ontario, Canada. Ki67-positive cells were manually counted in up to five × 400 light microscope representative fields per tumor (containing an average of 150 cells). Total counts were reported as total cell number per field. E-cadherin protein levels were quantified using the yellow channel of a cyan, magenta, yellow, key (CMYK) color model with pictures taken with a Super Coolscan 9000 scanner (Nikon, Tokyo, Japan) using Fiji software (Open Source)  [13] , and quantification was performed using Image-Pro software (Media Cybernetics, Bethesda, MD).
To avoid quantification of any nontumoral area (e. g. , skin and fat), the xenograft sections were counterstained using hematoxylin and eosin in addition to staining the estrogen receptor, a positive marker of SKOV3 cells. Pictures with × 100 and × 400 magnifications were acquired using an Axioskop 2 phasecontrast microscope (Carl Zeiss, Thornwood, NY) and processed using Image-Pro software.
PC Expression Pattern in Ovarian Cancer Stages and Cell Models On the basis of the data available in Oncomine databases, PCs trend toward a global overexpression in ovarian cancer when compared to normal tissues. We first decided to investigate this trend in PC expression using RT-PCR in tissue samples originating from normal ovaries, ovarian tumors, ascitic cells, and distant metastases. As shown in Figure  1A , furin, PACE4, PC5/6, and PC7 were all upregulated in the cancerous stages (primary tumor, ascites, and metastases), confirming the Oncomine databases. The expression of endogenous PCs was further examined in wellknown ovarian cancer cell models, including SKOV3, OVCAR3, and CAOV3. Reverse transcription-and real-time PCR (RT-qPCR) analyses were performed for each PC, and the expression was normalized using β-actin mRNA levels.
Down-Regulation of Endogenously Expressed PCs Because the overall expression of furin, PACE4, PC5/6, and PC7 is increased in ovarian cancer tissues compared to normal tissues from Oncomine databases and our analysis further validated this result (Figure  1A ), we chose SKOV3 cell line as the best model to examine the role of each PC in cell proliferation and tumor progression, because this cell line coexpresses high levels of these PCs. A lentiviral delivery strategy was used to generate stable shRNA-expressing cells for each of these PCs  [11, 12]. Consistent with the previously determined gene silencing efficiency for human PC shRNA sets  [11] , the two most efficient sequences were used to knock down these PCs in SKOV3 cells, and the most efficiently silenced cell line was further used for the cell-based assays.
To evaluate the relative cancer-sustaining functions of each PC in ovarian cancer, we used a gene-silencing method to generate individual cell lines, each lacking an endogenously expressed PC member. Because pharmacological compounds selectively targeting each member of the PC family are limited, this method represents the best option allowing for the direct comparison of the implication of PCs in cell proliferation both in vitro and in vivo  [12]. On the basis of the observation that ovarian tumor tissues, and also ascites cells and metastases, display variable levels of PC expression (Oncomine databases; Figure  1A ), we opted for the SKOV3 cells to explore the relative implication of each PCs, as they coexpressed the four relevant PCs: furin, PACE4, PC5/6, and PC7 (see Figure  1B ). Using in vitro proliferation assays, we observed the effects of PACE4 and PC7 molecular silencing through proliferation and colony formation assays in these cells.
The role of PACE4 in ovarian homeostasis has already been documented  [34] , and its expression has also been shown to be decreased in ovarian cancer tissues  [9]. However, this latter study is in contradiction with gene expression databases such as Oncomine. This may be due to results that suggest that expression is also linked to various tumor grades  [10]. As our gene silencing studies indicate that the inhibition of PACE4 might be beneficial in ovarian cancer, we then tested the application of pharmacological inhibitors of PACE4. In a recent work, we developed a peptide-based inhibitor targeting PACE4, named the ML peptide inhibitor. Using ML peptide inhibitors, we provided evidence of their effectiveness on prostate cancer cell lines  [15]. These peptides were designed on the basis of the observation that PC7 prodomainbased peptides with nonpolar amino acid residues at P6 and P7 positions have better inhibition potency toward PACE4 than furin  [35].
Motivation: Experimental spatial proteomics, i. e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. Results: Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative massspectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.
AVAILABLE FUNCTIONALITY pRoloc makes use of the architecture implemented in the MSnbase package  (Gatto and Lilley, 2012)  for data storage, feature and sample annotation (meta-data) and data processing, such as scaling, normalization and missing data imputation. We also distribute 16 annotated datasets in the pRolocdata package, which are used for illustration of different pipelines as well as algorithm testing and development. Algorithms for (i) clustering, (ii) novelty detection and (iii) classification are proposed along with visualization functionalities.
Classification Since the development and refinement of spatial proteomics experiments, several classification methods have been used: partial least-square discriminant analysis  (Dunkley et al. , 2006) , SVMs  (Trotter et al. , 2010) , random forest  (Ohta et al. , 2010) , neural *To whom correspondence should be addressed. networks  (Tardif et al. , 2012)  and naive Bayes  (Nikolovski et al. , 2012) , all available in pRoloc. In addition, other novel algorithms are proposed, such as PerTurbo  (Courty et al. , 2011). We have compared and contrasted these algorithms using reliable marker sets and demonstrate in the package documentation that the driving factor for good classification is reflected in the intrinsic quality of the data itself, i. e. efficient cellular content separation, accurate quantitation  (Jakobsen et al.
CONCLUSIONS The need for statistically sound proteomics data analysis has spawned interest in the proteomics community  (Gatto and Christoforou, 2013)  for R and Bioconductor  (Gentleman et al. , 2004). pRoloc is a mature R package that provide users with dedicated data infrastructure, visualization functionality and state-of-the-art machine-learning methodologies, enabling unparalleled insight into experimental spatial proteomics data. It is also a framework to further develop spatial proteomics data analysis and novel pipelines. Multiple organelle proteomics datasets illustrating various and diverse experimental designs are available in pRolocdata. Both packages come with thorough documentation and represent a unique framework for sound and reproducible organelle proteomics data analysis.
Sedimentation Velocity Experiments The experiments were carried out at 40,000 rpm and 20 °C on a Beckman (Fullerton, CA, USA) Optima XL-A analytical ultracentrifuge equipped with absorbance optics, using a four An55Ti and a eight holes An50Ti rotors and 1. 2 cm Epon double-sector centerpieces. Samples of cleaved tubulin were prepared in PG buffer (i. e. , 20 mM NaPi, 0. 1 mM GTP, pH 6. 5). Apparent sedimentation coefficients, Sapp, were determined by the sedimentation coefficient distribution C(S) generated by SEDFIT program  [54]  and corrected to standard conditions with SEDNTERP program.
Determination of the Binding Parameters Energy transfer between fluorescent microtubules and tau derivatized with 5(6)-TAMRA was followed by monitoring the decrease in fluorescence emission at 520 nm upon addition of labeled tau. After correction for the inner filter effect (Equation (  5 ))  [15] , the normalized fluorescence intensity values at 520 nm were plotted versus ligand concentrations. where F corr is the fluorescence intensity after correction, F obs , the experimental fluorescence intensity, A exc and A em refer to the absorption of the ligand at the excitation and the emission wavelengths, respectively (the A em was corrected to account for the 0. 2 cm pathlength). The binding parameters of Equation (6) were fitted as described  [34] , using nonlinear least-square curve fitting program Sigmaplot ®.
Acknowledgments The authors are grateful to the plateau proteomic Timone for mass spectrometry experiments of cleaved tubulin. They would like to thank  José M. Andreu  for generously donating Flutax-2 used in these experiments and  J. Fernando Diaz  for providing the molecular model of bound Flutax-2 to tubulin dimers. This work was supported in part by the  French National Research Agency  grant: No. NT05-3_41703  (to Vincent Peyrot). Isabelle L. Di Maïo  is a postdoctoral fellow funded by the same grant. We acknowledge the financial support of  INCa (Institut National du Cancer) ;  Inserm  ( Institut National de la Sante' et de la Recherche Médicale );  AMU (Aix-Marseille Université)  and  DGOS [SIRIC label (Site de Recherche Intégré sur le Cancer) ].
, 2011). This neuronal motility seems to involve axonemal dynein motors  (Göpfert and Robert, 2003; Senthilan et al. , 2012) , and, judging from electron microscopy, the sensory cilia of JO neurons are endowed with dynein-like arms  (Kavlie et al. , 2010). conserved domain of unknown function is also found in the human orthologue, KIAA1430, and is referred to as the KIAA1430 domain in the Pfam database (Figure  1A ). The Drosophila domain shares only 31% identity with the human KIAA1430 domain and 24% with the one found in C. reinhardtii. Structural predictions based on the DSC algorithm  (King et al. , 1997) , however, suggest a strong conservation of the secondary structure, with two helices separated by a spacer (Figure  1B ) that belongs to the family of coiled-coil domains. Outside the coiled-coil domain, the HMW proteins of different species do not show high conservation.
, 2006). To deduce thresholds of the corresponding nerve responses, their amplitudes were plotted against sound intensity. Thresholds were deduced from Hill fits, using 10% of the maximum as the threshold criterion  (Effertz et al. , 2011 , Senthilan et al. , 2012). Data analysis and statistical data evaluation were performed using Polytec-VIB (Polytec, Waldbronn, Germany), Spike 2 (Cambridge Electronic Design, Cambridge, UK), Excel 2007 (Microsoft, Redmond, WA), and SigmaPlot 10 (Systat Software, San Jose, CA).
Edde, Centre de Recherche de Biochimie Macromoléculaire, CNRS, Montpellier, France); and rabbit anti-Glu-Tub (anti-detyrosinated tubulin; AbCys, Paris, France; ABC0101) antibody (1/500). Secondary antibodies were diluted at 1/1000: goat anti-mouse Alexa Fluor 488-conjugated and 546-conjugated, and goat antirabbit Alexa Fluor 488-conjugated and 555-conjugated (Molecular Probes). Slides were analyzed at room temperature on a Zeiss Imager Z1 microscope equipped with 5× Plan-Neofluar, 20× Plan-Neofluar (0. 5 numerical aperture  [NA] ). Epi fluorescence images were acquired with a charge-coupled device camera (CoolSNAP HQ2; Roper Scientific, Saratosa, FL) and MediaView software (Molecular Devices, Sunnyvale, CA). Confocal stacks were acquired with a confocal microscope (LSM 510 META; Carl Zeiss, Oberkochen, Germany) equipped with 63× Plan-Apochromat (1.
ACKNOWLEDGMENTS We thank  Jérôme Schmitt  and  Patricia Morales for Drosophila  medium and stock maintenance. Electron microscopy was performed at the  Center Technologique des Microstructures of the University of Lyon. We thank  Gilbert Deléage  for help with structural analysis of HMW protein structure. We thank  Renata Basto  for helpful discussions. This work was supported by grants to B. D. from the  Fondation pour la Recherche Médicale  (Equipe FRM 2009), the  Agence National de la Recherche (Ciliopath-X) , and the  Région Rhône-Alpes  (Cible 2008), as well as by grants to M. C. G. from the  Bundesministeriums für Bildung und Forschung Göttingen Bernstein Center  ( A1 ) and the  German Science Foundation  ( SFB 889 ,  C2 ). F. S. and D. P. were supported by doctoral fellowships from the  Région Rhône-Alpes  and the  German National Academic Foundation , respectively.