dataesr/scientific-paragraphs-categorization huggingface dataset:
They were carried out under the supervision of certified researchers in accordance with good animal practice, as defined by the French 'Direction des Services Veterinaires'. Mouse imaging. Bioluminescent imaging of inoculated cells was performed using a Xenogen IVIS 100 Imaging System (Xenogen Biosciences, Cranbury, NJ, USA). Before imaging, CB17/SCID mice were anesthetized in a chamber with a 1. 5% (v/v) isoflurane/air mixture and injected i. p. with luciferin (150 mg/kg body weight). Validation of the data obtained by bio-imaging to assess the extent of allografts was performed after autopsy of killed mice. Histological analysis. Histological analysis was performed on tumors fixed in formalin and embedded in paraffin. Sections were stained with hematoxylin, eosin and safran. Statistical analysis.
The results are shown as mean±S. E. M. with the number of experiments indicated. Statistical significance was determined by t-tests or ANOVA using Micrococal Origin 6. 0 (Micrococal Software, Northampton MA, USA). Probability values o0. 05 were considered statistically significant and are marked with a single asterisk, o0. 01 with double asterisks and o0. 001 with triple asterisks.
Acknowledgements. We thank our colleagues, for helpful suggestions and discussions. We are indebted to,  Drs. Pascal Peraldi  and  Didier Pisani , for a careful reading of the manuscript. We acknowledge the expert technical assistance of Agnes Loubat with flow cytometry;  Franck Paput  and  Cendrine Dubaud  with animal care. Our lab receives financial support from  Institut National du Cancer (INCa ; grant number:  CT 30008187 ) and from  Agence Nationale de Recherche sur le SIDA (ANRS ; grant number:  AO-2013-1 ). Pauline Martin  is recipient of a predoctoral fellowship from  La Ligue Contre le Cancer.
Acknowledgments-No potential conflicts of interest relevant to this article were reported. J. -C. M. , M. C. -D. , C. V. , and S. S. wrote the manuscript. J. -C. M. was involved in the management of the patient and wrote the manuscript. M. C. -D. and C. V. did the laboratory analysis and wrote the manuscript. S. S. was involved in the management of the patient and wrote the manuscript. J. -C. M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
A highly significant difference was observed in the expression level of genes involved in transcription, heme synthesis, iron and mitochondrial metabolism and transforming growth factor-b signaling. A comparison with recently published data showed over 1000 genes that as yet have not been reported to be upregulated in the early erythroid lineage. The gene expression level within distinct pathways could be illustrated directly by applying the Ingenuity software program. The results of gene expression analyses can be seen at the Gene Expression Omnibus repository.
PE-Cy7 conjugated anti-CD13, CD38 and human leukocyte antigen-DR (HLA-DR) and Allophycocyanin (APC) conjugated anti-CD34 were purchased from BD Biosciences Pharmingen (San Diego, CA, USA). MoAbs were also obtained from Becton Dickinson (FITC-CD41, PE-CD36 and CD235a, Horizon V500-CD45, Horizon V450-CD105, PerCP-Cy5. 5-CD117), IBGRL Research (Bristol, UK: FITC-conjugated CD173, CD233, CD238 and CD239), Biolegend (San Diego, CA, USA: PE-CD105 and CD318) and DAKO (Glostrup, Denmark: FITC-CD61). FACSDiva software Version 6. 1. 3 (BD Biosciences) was applied to evaluate multiparameter analyses. A minimum of 300 000 MNC were analyzed in each case studied. The gating strategy and stepwise analysis of antibody reactions are presented in the Results section (Figures  1a-g ). The immunophenotypic profile of more mature erythroid cells was established by 3-color FCM analysis.
Cell-sorting experiments Double cell-sorting was performed using the Aria III cell sorter (Becton Dickinson) and FACSDiva software Version 6. 1. 3 (BD Biosciences). Pure (495%) subsets were obtained by FCM sorting with specific marker combinations. Cytospin preparations were stained with Wright-Giemsa in order to demonstrate the morphological properties of the different sorted compartments. A sufficient amount of CD34 þ /CD45 weak cells was obtained in seven cases. Pure CD105 þ /CD45 -cells were sorted in 11 cases. As controls, mature CD14 þ monocytes were sorted in six cases.
The Affymetrix GeneChip Fluidics Station 450 was used to wash and stain the arrays with streptavidin-phycoerythrin, according to the standard protocol for eukaryotic targets (IHC kit, Affymetrix). Arrays were scanned with an Affymetrix GeneChip scanner 3000 at 570 nm. Intensity values were determined using GeneChip operating software (Affymetrix). The raw intensity values were then normalized by the Robust Multiarray Average algorithm and analyzed using Partek Genomics Suite software (Partek Incorporated, St Louis, MO, USA). 30 Analysis of variance models were used to detect differences in gene expression between cell populations. Genes with a significantly different expression level (Pp0. 01; fold change X2. 0) were selected. Hierarchical clustering was performed based on normalized expression values of the differentially expressed genes.
Networks were generated by applying Ingenuity pathways analysis (Ingenuity Systems, Redwood City, CA, USA). A stringent cutoff (Pp0. 01; fold change X3. 0) was used to detect differentially expressed genes between CD34 þ and CD105 þ cells for network analyses. These genes were overlaid onto a global molecular network from Ingenuity's knowledge base. Subnetworks enriched by differentially regulated genes were then detected (Figures  5a-c ). Raw and normalized expression data are available at the Gene Expression Omnibus repository.
The PCR program used initial denaturation at 95 1C 10 min followed by 95 1C 10s, 601C 45s, for 50 cycles. Standard curves were prepared for each assay to determine amplification efficiency. Expression values of the transcripts were normalized to the housekeeping gene 18S ribosomal RNA (Life Technologies). A calibrator was additionally introduced to normalize all samples within one run. PCR efficiency correction and normalization to the calibrator and housekeeping gene was performed using LightCycler 480 relative quantification software (Roche Diagnostics).
Gene expression levels of a number of other pathways (heme synthesis, iron and mitochondrial metabolism, erythropoietin receptor) are presented in Supplementary Tables S3,S4. To differentiate erythropoiesis from megakaryopoiesis, this investigation included relevant genes for the latter lineage (Supplementary Table  S5 ). Considerable results were obtained for cell cycle-relevant genes and for genes initiating proliferation or apoptosis (Supplementary Table  S6 ). Distinct genes were found to be orchestral-related as confirmed (Figure  5a ) by using the special program Ingenuity software. This demonstrated the influence of transcription factor Dp-1 (TFDP1) on the upregulation of a large number of genes regulating the cell cycle, including E2F-dependent transcription, cyclin E1, retinoblastoma-like 1, cell division cycle 6 homolog and origin recognition complex subunits 1 and 2.
This hypothesis was tested using the Ingenuity software program, providing graphic information about protein networks and differentially expressed genes. Indeed, a number of genes were shown to interact significantly with the TGF-b receptor/Smad axis in CD34 À /CD105 þ cells. 42 This pathway may also provide a novel therapeutic field, as a soluble receptor fusion protein consisting of the extracellular domain of a modified activin receptor Type IIB linked to human Fc has recently been shown to reduce anemia in patients suffering from multiple myeloma. 43 eside this, the molecular study performed here on sorted BM progenitors confirmed a parallel expression of genes and surface antigens, comforting FCM investigations. Moreover, among 4524 genes differentially expressed between the CD34 À /CD105 þ erythroid compartment and CD34 þ common progenitors, functionally important transcription factors including GATA-1, LMO2, Zinc-finger and BTB domain-containing protein 7A (ZBTB7A) were found to be upregulated, whereas GATA-2, FLI1 and others were found to be significantly downregulated.
The data reported here are consistent for many genes with recent reports on gene regulation within erythropoiesis,  [2] [3] [4] [5] 50  For example, 1,465 of the genes described by Merryweather-Clarke et al. 4  as differentially regulated were also picked up here. Ho ever, some differences emerged, which may be explained by the use of different methodologies. Indeed, Merryweather-Clarke et al. 4  concentrated on erythroid cells at different stages of differentiation, while we compared CD34 þ and CD34 À /CD105 þ cells, therefore concentrating on early erythroid commitment. Furthermore, Merryweather-Clarke et al. 4  used erythropoietinstimulated cells, whereas we used freshly sorted unmanipulated cells. The Ingenuity program further allowed us to focus on selected networks with known functions, such as the GATA-2 and endoglin pathways.
Surprisingly, a distinct part of the cell cycle regulation was the pathway with the highest ranking for upregulated genes when comparing CD34 À /CD105 þ cells to the CD34 þ common   progenitor compartment. This included transcription factor DP-1 (TFDP1), cyclin E (CCNE1), cell division cycle 6 homolog, origin recognition complex 1, retinoblastoma-like 1 and aurora kinase B. In summary, this study establishes the immunophenotypic features of erythroid progenitors along three stages of maturation. Freshly obtained and separated CD34 À /CD105 þ early erythroid cells, evaluated for their gene expression profile in comparison with the CD34 þ common progenitor compartment, allowed to observe a high number of differentially regulated genes. Although the function of some of these genes is as yet unknown, relevant functional pathways were nonetheless illustrated directly with the Ingenuity software program.
Effects of SSR128129E on atherosclerosis In order to determine whether a FGF receptor antagonist could affect atherosclerosis development, one month-old apoEdeficient mice were treated with SSR128129E (50mg/kg/d) for 3 and 5 months. After a 3-month period on normal diet, apoE-deficient mice develop fatty streak lesions. Treatment with SSR128129E in the diet slightly reduced these primary lesions (16. 4%, ns; data not shown). After 5 months of treatment, control mice showed extensive lesions, intimal thickening and smooth muscle proliferation, as well as appearance of cholesterol clefts (Figure  2A ). SSR1282129E-treatment significantly reduced the lesion size (42. 9% inhibition, p<0. 01, n=8-10, Figures  2B  and 2C ). The amount of smooth muscle cells within 6 month-old lesions was determined by IHC as described in the previous paragraph.
CFU/mL) on a chocolate polyvitex agar plate, and the strain showed susceptibility to ampicillin/clavulanate, ceftriaxone, LETTERS gentamicin, and ciprofloxacin. The bacterium was hemolytic and required factor V, but not factor X, for growth. The bacterium was identified in the lab- oratory by matrix-assisted laser desorp- tion ionization time-of-flight (MALDI- TOF) mass spectrometry with the Bruker Biotyper software database (Bruker Daltonics, Bremen, Germa- ny), with a good score (>2. 0) (8). The identification was confirmed by PCR amplification and sequencing of the 16S rRNA gene (size of sequence was 1,387 bp, and it had 99. 6% homology with sequence AJ295746 in GenBank). Blood culture results were negative for both bacterial species. The final diag- nosis was septic shock associated with ARDS, due to aspiration pneumonia.
Results 12 Rabs are expressed in the tachyzoite stage Previously, 15 genes encoding Rab-like proteins have been identified in the genome of T. gondii, whereas analyses of other apicomplexan genomes indicated the presence of 9 Rab-GTPases in Theileria, Cryptosporidium and Babesia and 11 in Plasmodium  [14]  (Table  S1 ). We excluded 3 of the previous identified putative Rab-GTPases in T. gondii from our analysis based on the absence of Rab-family specific motifs or expression evidence in the available database (www. toxoDB. org) and our inability to amplify cDNA for the respective genes (Table  S2 ). We amplified full-length cDNA for each Tgrab and verified their predicted amino acid sequence (Figure  S1 , Table  S1&S2 ).
Immunofluorescence analysis HFF monolayers grown on 24 well coverslips were infected with tachyzoites of the strains to be analysed and grown in presence or absence of 1 mM Shield-1 for 12-24 hrs. Immunofluorescence analysis was performed as described  [23]. Z-stack images of 0. 15 mm increment were collected on a PerkinElmer Ultra-View spinning disc confocal Nikon Ti inverted microscope, using a 1006 NA 1. 6 oil immersion lens or DeltaVision Core microscope. For deconvolution Huygens or softWoRx software was used. For image processing ImageJ 1. 44 and Adobe Photoshop CS4 were utilised. The Pearson's correlation coefficient was calculated using ImageJ or softWoRx software.
Two excitation lasers at 570 nm +/2 5 nm (for ATTO 594) and 647 nm +/2 5 nm (for ATTO 647N) originated from a single supercontinuum laser source while two additional high-power STED lasers at 711 nm +/2 3 nm and 745 nm +/2 3 nm, respectively, emerged from the same laser source (SC-450-PP-HE, Fianium, Ltd. , Southampton, UK). A pulse-interleaved acquisition scheme was used to image both colour channels in a quasisimultaneous recording mode (25 ns time-shift). The fluorescent signals were detected by two high-sensitive avalanche photodiodes at 620 nm +/2 20 nm (ATTO 594) and 670 nm +/2 15 nm (ATTO 647N). STED images were deconvolved by a linear deconvolution algorithm using the Software ImSpector (www. imspector. de). The Pearson's correlation coefficient (Figure  6C ) was calculated using Image J.
Phylogenetic analysis Rab-GTPases from apicomplexan parasites have been previously identified  [14]. Orthologue groups were obtained from OrthoMCL DB (http://www. orthomcl. org/cgi-bin/OrthoMclWeb. cgi). From each orthologue group sequences were collected if they were derived from the following species groups: Canonical speciesrepresentative species from the major groups of the eukaryotes (Baldauf) [except for Rhizaria -no genomic data set] Homo sapiens (Opisthokonts), Dictyostelium discoideum (amoebazoa), Arabidopsis thaliana (archaeplastidae,viridiplantae), Plasmodium falciparum (alveolates), Thalassiosira pseudonana (stamenopiles), Trypanosoma brucei (discicristates), Giardia lamblia (excavates), Species drawn from the Alveolate group: Plasmodium bergei, Cryptosproridium parvum, Neospora caninum, Theileria anuulata, Toxoplasma gondii, Red algae (C.
merolae). Candidate sequences were assessed for the presence of ras domains using the hmmsearch option of the HMMER package using the profile PF00071 from Pfam. Sequences were checked for the presence of potential prenylation or myristilation sites in the Cterminal region. The ras domains from the conformant sequences were identified by hmmalign option of the HMMER package and extracted as described previously  [65]. Blastp searches of the conformant sequences were performed agains the appropriate Uniprot KB species specific proteome sets. Multiple sequence alignment of the set of ras domains was performed by three independent methods; ClustalW  [66] , t-coffee  [67]  and hmmalign  [68]  guided by the appropriate profile. The alignments used the default settings for each method. Alignments were combined under t-coffee, and quality of alignment assessed -columns displaying low consistency (score , 5) or significant numbers of gaps (.
15%) were removed. The phylogeny was visualised as an unrooted neighbour joining tree by the Splits-Tree program. Major clades containing T. gondii sequences were identified, and the alignments of sequences within these clades extracted. The phylogenies were visualised as rooted Neighbour Joining phylograms, using an appropriate C. merolae sequences as outgroup. The robustness of the phylogeny was established by bootstrap analysis (1000 iterations). Figure  S2  Rooted neighbour joining phylograms of 3 major clades (A,B,C) as described in Figure  1. Phylogenetic analysis of apicomplexan Rabs demonstrates that they belong to the major families highly conserved in other eukaryotes. Only Rab1A, Rab5B and Rab11B can be classified as alveolate or apicomplexan specific sub-class. The accession numbers can be downloaded as supporting information.
Acknowledgments We are indebted to  K. Hager  ( University of Alabama-Birmingham, USA ),  B. Striepen  and  S. Moreno  ( University of Georgia, USA ),  D. L. Sibley  ( Washington University, USA ),  G. Ward  ( University of Vermont, USA ),  C. J. Beckers  ( The University of North Carolina, USA ),  D Soldati  ( University of Geneva, Switzerland ) for sharing antibodies and fluorescent markers.
This work was funded by the  BioFuture-Programm  (Grant number:  0311897 ) of the  German ministry of science and education (BMBF) , by a  Wellcome Trust   Senior Fellowship for MM  (Grant number:  087582/Z/08/Z ), MALSIG and EviMalAr (European  FP7 ). DJPF was supported by a  Wellcome Trust   equipment grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In response to spatio-temporally exponentially correlated noise and natural movies, cell type had a modest effect on output correlations (Fig. 3A  and 3B ). Cells with opposite ON-or OFF-polarities (gray points) tended to have negative correlations, whereas cells of the same polarity (black and colored points) generally had positive correlations. Moreover, pairs with opposite polarity showed a greater-than-average excess correlation, particularly in response to natural movies. Under natural movies, opposite-polarity pairs had an excess correlation of 1. 5; under the spatio-temporal expo- - - - - low-contrast white 1 49 - - - - - low-contrast 1 49 - - - - - multiscale a For our measurements of output correlation (Fig. 3C ), we include additional data from experiments performed as part of other studies in which receptive field structure was not probed.
For model correlations and other analyses, we only used the subset of retinae and cells for which we obtained robust receptive field estimates. b We used a stringent requirement that receptive fields (RFs) be of high quality for any analyses in which we used receptive field estimates. c Cells were only divided into subtypes if they had high-quality receptive fields and were recorded in response to stimuli chosen for detailed analysis. Left and middle bars show excess correlation when scrambled natural movies and intact natural movies, respectively, are compared to white noise in the data and in a population of non-adapting model neurons. Right bars show excess correlation when responses to natural movies are compared to scrambled natural movies directly. A non-adapting model predicts larger output correlations in response to the correlated natural input than seen in the data.
(B) Output correlations under the spatio-temporal exponential stimulus compared with white noise as predicted by LN models with parameters fit to the data. The two leftmost bars ("data" and "WN model (no adaptation)") reproduce the spatio-temporal "data" and "model" bars in Fig. 3D. (Note the difference in scale. ) For the other bars, we simulated a population of neurons using linear filters measured from each stimulus but gains measured only from white noise ("filter adaptation model") or using experimentally derived estimates of both linear filters and gains for each stimulus ("filter + gain adaptation model"). In the fully adapted model, excess correlations are consistent with the data. (C) Pairwise output correlation as a function of the distance between receptive field centers in the natural movie (top) and spatio-temporal exponential (bottom) datasets.
Each dataset contained responses of the same cell population to white noise (left) and to a correlated stimulus (middle). The difference in output correlation in the correlated stimulus over the white noise stimulus is also shown for each cell pair (right). Each point corresponds to one simultaneously recorded cell pair; the blue lines are the median correlation within bins chosen to contain 30 cells each. Solid lines are median correlations for same-polarity cell pairs; dashed lines are for opposite-polarity pairs. nential stimulus their excess correlation was 0. 38. Within same-type pairs, slow-ON and slow-OFF pairs (blue and yellow) tended to show a greater excess correlation than fast-ON and fast-OFF pairs (red and green). Pairs of slow cells had an excess correlation of 0. 29 in the natural stimulus and 0.
Adaptation of temporal filters We next sought to determine whether receptive fields adapt to stimulus correlations and whether this adaptation can explain the observed pattern of output correlations. As noted above, we were able to obtain STAs from responses to white noise. STAs computed in response to correlated stimuli, however, will be artificially blurred by the stimulus correlations. To obtain a better estimate of the spatio-temporal receptive field (STRF), we used maximum likelihood estimation to fit a LN model separately for the white and exponentially correlated stimuli  [24]. Examples of STRFs obtained in this way for one cell are shown in Fig. 2C. The strongly correlated structure of the multiscale stimulus and the natural movies precluded robust, unbiased STRF estimation with limited data (see Methods).
Discussion Our principal finding is that the retina maintains a moderate, and relatively constant, level of output correlation across a wide range of spatio-temporally correlated stimuli ranging from white noise to natural movies. Our data also suggest a differential effect of spatial vs. temporal correlations on the functional properties of the retinal output. We focused here on spatial variations in our control stimuli, but it would interesting to design future studies to explore the space-time differences more systematically. In response to exponentially correlated noise, where the receptive fields could be estimated, we showed that the relative invariance of output correlations is largely accounted for by the observed changes in the linear receptive field (faster temporal kernels and slightly stronger surround inhibition for more correlated stimuli) and by changes in the nonlinear gain (anti-correlated to changes in effective contrast).
Methods Ethics statement. All procedures were in keeping with the guidelines of the University of Pennsylvania, the NIH, and the AVMA. Neural recording. We recorded retinal ganglion cells from Hartley guinea pig using a 30electrode array (30 µm spacing, Multi Channel Systems MCS GmbH, Reutlingen, Germany). After anesthesia with ketamine/xylazine (100/20 mg/kg) and pentobarbital (100 mg/kg), the eye was enucleated and the animal was euthanized by pentobarbital overdose. The eye was hemisected and dark adapted. The retina was separated from the pigment epithelium, mounted on filter paper, and placed onto the electrode array, ganglion cells closest to the electrodes. Extracellular signals were recorded at 10 kHz. Spike times were extracted with the spike-sorting algorithm described in  [16] ; briefly, a subset of data was manually clustered to generate spike templates that were then fit to the remaining data using a Bayesian goodness-of-fit criterion.