dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimation of effective population size Overall, the ABC analysis indicated a mean effective population size across all populations of 117 204 AE 65 853 (Table  S3 , minimum: mode N e_Poland (East) = 50 976, 95% CI: 25 787-364 012; maximum: mode N e_Germany = 277 179, 95% CI: 123 777-732 928). The estimation of N e for the whole dataset was higher than for each population separately (mode N e_all-data = 385 833, 95% CI: 85 225-744 614) and remained within a plausible range given the estimated European corncrake population size of 2. 6-4 million birds (Sch€ affer and Koffijberg 2004; Birdlife International 2013). N e did not exhibit any longitudinal or lati-tudinal pattern (longitude: F 1,7 = 0. 003, adjusted R² = À0. 14, P = 0. 96; latitude: F 1,7 = 0. 0008, adjusted R² = À0. 14, P = 0. 98).
Number of infected corncrakes and prevalence per haemosporidian lineage, for the nine sampling sites across Europe. Sampling sites are ordered from west to east. GenBank accession numbers are provided behind each lineage name. Agriculture intensity: The mean wheat yields per country (2012 data) were downloaded from FAOSTAT (Food and Agriculture Organization of the United Nations, http://faostat. fao. org/, accessed on 11/03/2014) and were used as a proxy for the level of agriculture intensification across Europe. 3 Host population size: We included in our analyses two measures of the corncrake population size, (i) inferred by the national census population sizes of corncrake, obtained from Sch€ affer and Koffijberg (2004), and (ii) the effective population sizes N e calculated here from genetic data.
Acknowledgements The authors would like to thank all people who helped in the collection of corncrake samples:  Michal Budka ,  Serguei Fokin ,  Peter Zverev ,  Susanne Arbeiter ,  Joachim Sadlik ,  Edouard Beslot ,  Gilles Mourgaud  and Emmanuel S echet. We are also grateful to  Tom Jolin  and  Dave Wright  for their contribution to malaria screening. This study was founded by a grant from  Plan Loire Grandeur Nature, R egion Pays-de-Loire , Angers Loire M etropole, Conseil G en eral Maine-et-Loire,  Direction R egionale de l'Environnement, de l'Am enagement et du Logement Pays-de-Loire  (DREAL) and  European Regional Development Fund.
Acknowledgements We thank  Mathias Kölliker ,  Suzanne Foitzik , the members of the team EARWIG at the  JGU Mainz  and two anonymous reviewers for their constructive comments on this manuscript. We also thank  Stefan Boos  and  M. Kölliker  for providing the tested animals and  Maximilian Körner  for English corrections. This study was financed by the  German Research Foundation  (DFG,  ME4179/1-1  to J. M. ). Received: 2 April 2014 Accepted: 3 June 2014 Published: 9 June 2014.
Whole-Genome Microarray An analysis of 55 RNA samples derived from colorectal tissue, consisting of three sample groups (NOR, CRA and CRC) with varying numbers of biological replicates, was performed on 44k Whole Human Genome microarrays (Agilent) that contain 41,093 probes, providing full coverage of human transcripts. Doublestranded cDNA was synthesized from 500 ng of total RNA using the Quick Amp Labeling kit, One-color, as instructed by the manufacturer (Agilent). Labeling with cyanine3-CTP, fragmentation of cRNA, hybridization, and washing were performed according to the manufacturer's instructions (Agilent). The microarrays were scanned and the data were extracted with the Agilent Feature Extraction Software.
Gene Expression Analysis Raw gene expression data were imported into the GeneSpring GX 11. 0. 2 software program (Agilent). Side-by-side comparisons were performed for gene expression alterations: CRC vs. paired NOR, CRA vs. NOR, and CRC vs. CRA. Genes with missing values in more than 25% of the samples were excluded from the analysis. These data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE50114, GSE50115 and GSE50117. A 2fold cut-off difference was applied to select the up-and downregulated genes (P-value #0. 01 by t-test with Benjamini-Hochberg false discovery rate, FDR). Hierarchical clustering of the expression data was performed using Euclidean distance with average linkage.
Gene Set Enrichment Analysis The publicly available software, Database for Annotation, Visualization and Integrated Discovery  [17] , was used to analyze the gene set enrichment in colorectal lesions. A 2-fold cut-off difference was applied to select the list of deregulated genes (Pvalue #0. 01 by t-test with FDR). Only the pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) will be described  [18].
Alternative Splicing Analysis A pooled RNA, assayed in duplicate, from 3 colorectal normal mucosae and 24 CRA RNA samples were analyzed on Human Exon 1. 0 ST arrays (Affymetrix, Paris, France), which enabled analysis of both gene expression and alternative splicing. Microarray hybridization was performed at the Curie Institute facility (Paris, France). The raw data were analyzed by GenoSplice technology. These data are accessible through GEO Series accession number GSE50592. A 1. 5-fold cut-off difference was applied to select the up-and down-regulated genes and exons (Pvalue #0. 05).
Exon-Level Analysis in Colorectal Adenomas A CRA vs. NOR comparison was performed on Human Exon 1. 0 ST arrays (Affymetrix), and showed that 1,484 genes were deregulated in CRA (590 up-and 894 down-regulated genes; $1. 5 FC, P-value #0. 05; Table  S12 ). A corresponding heat map is shown in Figure  S5. A set of deregulated transcripts in CRA vs. NOR was analyzed by quantitative RT-PCR, and the validation rate of Affymetrix microarray results was 83% (24 out of 29 transcripts, also validated for the Agilent analysis). In addition, the CRA vs. NOR comparison showed extensive changes in alternative splicing profiles: 1,852 exons were deregulated in CRA (862 up-and 990 down-regulated exons; $1. 5 FC, P-value #0. 05; Table  S13 ). A publicly available microarray expression data set from 10 paired tumor-normal CRC samples  [6]  was downloaded from the Affymetrix web site in order to compare alternative splicing profiling in CRA and CRC.
Spike-sorting was performed with the R-package SpikeOMatic  [66]. All E PNs were stimulated twice at five doses from 23 to 1 log ng. For each stimulus, 12 s of post-stimulus activity were recorded. Intracellular recordings ( I PN) were performed with a glass electrode of 150-200 MV resistance filled with Lucifer Yellow or neurobiotin as described in  [67]. I PNs were tested at four doses, 22, 21, 0 and 1 log ng and recorded for 3 s after the onset of stimulation. The brain was then dissected, histologically treated and scanned in a confocal microscope as a wholemount. All I PNs kept for analysis shared the same physiological and (when available) morphological characteristics of PNs. Only a few LNs were impaled in our recording conditions and they never showed phasic response patterns. The distributions of the response frequencies and latencies of I PN and E PN were compared by Kolmogorov-Smirnov tests.
Fitting of response variables to dose For each set of recordings from a neuron, empirical functions were fitted to the experimental points in the dose-response plots C-F (Fig. 6 ) and C-L (Fig. 7 ). Firing rate. The frequencies F at different loads M of any neuron (ORN or PN) can be described by a Hill function of M F (M)~F M 1z M 1=2 M n ð3Þ with F M , asymptotic maximum firing rate at high loads, M 1/2 load at half-maximum response F M /2, and n Hill coefficient. Using dose C = log 10 M as the variable, function (3) becomes F(C)~F M 1z exp { ln (10)n(C{C 1=2 ) À Á , ð4Þ where C 1/2 = log 10 M 1/2. The parameters of the function (4) fitted to data were estimated by an iterative nonlinear least square method (Fig. 6A ). Three characteristics were derived from the fitted parameters. The response threshold C 0 of ORNs and PNs was determined as the dose at which F rises above F 0 = 5 AP/s.
This work was funded by the  Agence Nationale de la Recherche  within the  French-British ANR BBSRC SysBio 006 01 ''Pherosys'' initiative  (http://www. neurochem-project. org/), the European FP7-ICT 2007  STREP Bio-ICT convergence ''Neurochem '' (http://www. informatics. sussex. ac. uk/research/projects/PheroSys/) and the  state program ''Investissements d9avenir '' managed by the  Agence Nationale de la Recherche , grant  ANR-10-BINF-05  ''Pherotaxis'' (http://www. agencenationale-recherche. fr/investissements-d-avenir/appels-a-projets/2010/bioinformatique). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cost evidence was then used to fit the subjective ratings of exhaustion (C) or the decisions to stop effort exertion (D). Models were linear combinations of different possible regressors (main effects, interaction and non-linear effects), as indicated in the bottom chart (tick: included, cross: not included). E-F: Fit of additive and multiplicative models. Data are subjective ratings of exhaustion (E) and probability of stopping effort exertion (F), shown in the duration by difficulty space explored in the Cost Rating and Effort Allocation tasks, respectively. The color code indicates average observed data (left diagram) or predicted data from the additive and multiplicative models that provided the best fit with median parameter values (middle and right diagrams). Note the main difference between additive and multiplicative models is the curvature of iso-value lines (in white), which reflects the interaction between duration and difficulty.
Participants The study was approved by the Pitie-Salpetriere Hospital ethics committee. All subjects were recruited via email within an academic database and gave inform consent prior to participating in the study. There was no restriction of handedness, excepted for the original (Implicit) Effort Allocation Task, in which participants were all right handed for neuroimaging purposes. Other inclusion criteria were: age between 20 and 39 years, absence of self-reported psychiatric or neurological history and of current psycho-active substance consumption. In all studies, participants were told that they would win the money accumulated during the task. In the previous study (Implicit task), the payoff was eventually rounded up to a fixed amount (100J) credited by bank transfer. In all new studies participants were paid in cash at the end of the experiment.
Experimental set up We used homemade power grips composed of two plastic or wood cylinders compressing an air tube when squeezed. The tube was connected to a transducer converting air pressure into voltage. Thus, grip compression resulted in the generation of a differential voltage signal, linearly proportional to the force exerted. The signal was amplified and digitized by a signal conditioner (CED 1401, Cambridge electronic design, UK) for Implicit, Explicit and Dissociation tasks, and by a homemade device for the Adaptation Tasks and Cost Rating task. The digitized signal was read by a Matlab program (The MathWorks Inc. , USA).
Pre-processing of force data In the Adaptation Tasks (1 to 3), the effort onsets were identified on-line and used to update the screen displayed to the participants. The effort onset was determined as the first sample exceeding 20% of the participant maximal force. In the Effort Allocation Tasks ('implicit', 'explicit' and 'dissociation'), effort onsets and offsets were identified off-line with an algorithm using the same two criteria for all conditions: 1) force temporal derivative higher than one standard deviation and 2) force level lower (for effort onset) or higher (for effort offset) than half the maximal force. The first rest period started with coin presentation and the subsequent effort and rest periods were defined by force onsets and offsets.
Behavioral tasks All tasks were presented on a computer screen, and were programmed with Matlab using Cogent 2000 (Wellcome Department of Imaging Neuroscience, London, UK) for the Implicit and Explicit Tasks, and Psychtoolbox (http://psychtoolbox. org) for the Dissociation Task, Adaptation Tasks, and Cost Rating Task. Adaptation Tasks. The display was quite similar in all adaptation tasks, which included a total of 8 sessions. The exerted force level was always displayed as a fluid moving up a thermometer, the target bar on the top of the thermometer indicating 60% of the participant maximal force. The 60% level was chosen to ensure that effort could be maintained by all subjects and for all the imposed durations. All trials included a first effort (with imposed duration), a rest period, and a second effort (with free duration).
E-F: Fit of additive and multiplicative models. Data are subjective ratings of exhaustion (E) and probability of stopping effort exertion (F), shown in the duration by difficulty space explored in the Cost Rating and Effort Allocation tasks, respectively. The color code indicates average observed data (left diagram) or predicted data from the additive and multiplicative models that provided the best fit with median parameter values (middle and right diagrams). Note the main difference between additive and multiplicative models is the curvature of iso-value lines (in white), which reflects the interaction between duration and difficulty. doi:10. 1371/journal. pcbi. 1003584. g004.
The study was funded by a  Starting Grant (BioMotiv)  from the  European Research Council  to MP. This work also benefited from the '' Investissements d'Avenir'' program  ( ANR-10-IAIHU-06 ). FM and LS were financially supported by the  French Ministere de la Recherche. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Genomic and Gene Expression Data 2. 3. 1. RNA and DNA Extraction. Total RNA was extracted using the RNeasy Lipid Tissue Mini Kit (Qiagen), and DNA was extracted using the QIAamp DNA Mini Kit (Qiagen) following the manufacturer's instructions. Both the RNA and DNA were assessed for integrity and quantity, following stringent quality control criteria (CIT program protocols http://cit. ligue-cancer. net/). The genomic and gene expression analyses were performed using R software (http://www. R-project. org/).
Gene Expression Arrays. The gene expression arrays were performed using the IGBMC microarray platform (Strasbourg, France). Total RNA was amplified, labeled, and hybridized to the Affymetrix Human Genome U133 plus2 GeneChip, following the manufacturer's protocol (Affymetrix, Santa Clara, CA, USA). The microarrays were scanned using an Affymetrix GeneChip Scanner 3000, and the raw intensities were quantified from the subsequent images using GCOS 1. 4 software (Affymetrix). The data were normalized using the robust multiarray average method implemented in the R package affinity  [9]. Unsupervised hierarchical clustering analysis was performed using the Pearson correlation metric. Only probesets with an Affymetrix annotation class A and located on autosomes were considered. Differences between the sample clusters were tested using the Chi-squared test, and genes differentially expressed between the tumors of responder and nonresponder patients were assessed using the -test followed by Benjamini and Hochberg correction.
The analyses of the gene sets using KEGG and Biocarta pathways and Gene Ontology terms, Molecular Signature Database gene sets, and Stanford Microarray Database gene sets were performed on the 1000 most differentially expressed genes (500 genes upregulated in responders and 500 genes upregulated in nonresponders) using hypergeometric tests  [10]. We used the published centroid-based classifier of Verhaak et al. to classify our samples according to their system  [7]. Samples were assigned to one of the six molecular subtypes of gliomas (called intrinsic glioma subtypes (IGS)) described by Gravendeel et al. [8]  using ClusterRepro (an R package; http://crantastic. org/packages/clusterRepro) [11].
Genomic Arrays. The genomic arrays were performed using the IntegraGen Platform (Evry, France). DNA was hybridized to Illumina SNP Human CNV370 chips according to the instructions provided by the array manufacturer (Illumina, San Diego, CA). The raw fluorescent signals were imported into Illumina BeadStudio software and normalized as previously described  [12]  to obtain the log R ratio (LRR) and B Allele Frequency (BAF) for each SNP. A supplemental normalization procedure tQN  [13]  was applied to correct for dye bias. The genomic profiles were then segmented using the circular binary segmentation algorithm (DNAcopy package, Bioconductor)  [14]  into the LRR and BAF data separately, as previously described  [13, 15]. The absolute copy number and genotype status of the segments were then determined using the genome alteration print (GAP) method  [15].
RT-PCR. The gene expression of NPTX2, EPHA7, SOCS2, PDGFD, PRKCZ, and ENPP4 in the tumors and nontumor control tissue were analyzed using UPL probe real-time quantitative polymerase chain reaction (QPCR) analysis. The reference gene was PPIA. The sequences of the primers and probes are listed in additional Table  2. The real-time QPCR reactions were performed as follows: 1X LightCycler 480 Probes Master (Roche Applied Science), 4 pmoles each primer, 2 pmoles Universal ProbeLibrary Set, Human, and 8 ng cDNA. The real-time QPCR cycles were as follows: initial denaturation at 95 ∘ C for 110 minutes and 45 cycles of 95 ∘ C for 10 seconds and annealing at 60 ∘ C for 30 minutes. The 2-DeltaDeltaCT method was used to determine the relative expression levels. The calculation of the relative amounts of the studied transcript compared to the reference transcript was performed using the LightCycler 480 Software (Roche applied science).
Independent Data Set. An independent series of 33 GBMs from the Salpêtrière database treated with the bevacizumab/irinotecan combination at recurrence (31 out of 33 were treated at first recurrence) was used to assess the impact of the CDKN2A homozygous deletion and EGFR amplification. These alterations were assessed in the initial tumor using CGH arrays as previously described  [16]. The response according to RANO criteria was assessable in 29 of the patients. RNA was available for 7 of the responders and 11 nonresponders and was used to study NPTX2, EPHA7, SOCS2, PDGFD, PRKCZ, and ENPP4 gene expression using RT-PCR.
This independent series comprised 33 GBMs from the Salpêtrière database treated with the combination of bevacizumab/irinotecan at recurrence and for whom the CDKN2A locus homozygous deletion and EGFR amplification status were available in the initial tumor. The patients' characteristics are shown in Table  2. However, we did not observe any significant association between EGFR amplification and/or CDKN2A deletion status and the response rate to bevacizumab/irinotecan, the PFS, or the OS after bevacizumab/irinotecan initiation.
Acknowledgments The authors thank  Fabien Petel  for his help in managing the microarray database of the CIT3. They thank  Pim French  for his help in assigning their samples to IGS. This work is part of the  Cartes d'Identité des Tumeurs (CIT) national program  funded and developed by the  Ligue Nationale Contre le Cancer  and was also supported in part by the  Institut National du Cancer (INCa ; ref  2009-126 ). Di Stefano  is supported by an investigator Fellowship from  Collegio Ghislieri, Pavia, Italy. Pr Figarella-Branger  is supported by a Grant INCa-DGOS-Inserm 6038. Frozen specimens from the AP-HM institution were stored and then provided by the AP-HM tumor bank (authorization no. AC-2013-1786 ). Frozen specimens from  Lyon were stored in NeuroBioTec, Bron, France.
Molecular cloning and transient expression in N. benthamiana DNA with sequences corresponding to GenBank accession YP_959486 (hypothetical protein Maqu_2220 [M. aquaeolei VT8]), GenBank accession ABO21021 (wax ester synthase [M. hydrocarbonoclasticus ATCC 49840]) and a fusion of both coding sequences was produced by gene synthesis. A transit peptide sequence was accessed by PCR from Arabidopsis FAR6 clone At3g56700 long  (Doan et al. , 2009). The amplified transit peptide sequence corresponded to 76 aa in the N-terminal of AtFAR6. The synthetic genes and the AtFAR6 transit peptide sequence were amplified in a two-step procedure using a Phusion ™ High-Fidelity PCR (Finnzymes) essentially according to the manufacturer's instructions. Primers for amplification were designed to introduce attB-sites for Gateway cloning at the 5 0 -end of FAR6 transit peptide sequence and 3 0 -end of the synthetic gene fragments.
The specificity of the reactions and the amplicon identities were verified by melting curve analysis. Analyses of the data were carried out by a comparative C T method  (Livak and Schmittgen, 2001)  with PCR efficiency correction, which was determined based on the slope of standard curves. The normalization of gene expression level by qRT-PCR was carried on by using the Actin (ACT) and Ubiquitin (UBI3) genes  (Liu et al. , 2012) , and eventually the fold-differences in the transcript levels and mean standard error were calculated as described earlier  (Livak and Schmittgen, 2001). Three different RNA isolations from independent biological replicates and cDNA synthesis products were used for quantification. 2. 5. Ultrastructure N. benthamiana leaves infiltrated for transient expression were sampled, at early day after dark period and late day before dark period, 5 days after infiltration (size of the pieces was about 2 Â 3 mm 2 ), in a drop of fixative (4% paraformaldehydeþ2.
Acknowledgments We thank  Ingela Fridborg  ( presently at Cavida, Uppsala ) for molecular cloning assistance,  Salla Martilla  and  Kerstin Brismar  at  Plant Protection Biology, SLU, Alnarp ,  Anders Ahlander , at  Rudbeck Laboratories, UU, Uppsala  for their assistance with ultrastructural images. This work was supported by  EU   FP7 project  " ICON ", the  Swedish Research Council Formas  and the  Swedish Governmental Agency for Innovation Systems, VINNOVA, Sweden.
Two well defined, modifiable risk factors for kidney allograft failure are acute rejection and poor graft function at one year post-transplant. Regulatory bodies and expert panels in the USA and Europe have recognized that both acute rejection and one-year graft function should be assessed when evaluating immunosuppressive regimens. TRANSFORM (Clinicaltrials. gov NCT01950819) is one of the first trials to adopt this approach and the first that applies a novel combined clinically meaningful endpoint to take the first step towards changing the paradigm for immunosuppression in kidney transplant patients. Everolimus with reduced-exposure calcineurin inhibitor (CNI) therapy is a strategy designed to reduce the risk of chronic nephrotoxicity and other dose-dependent complications associated with CNI therapy.
By capturing follow-up to 5 years, TRANSFORM will provide substantial data on the incidence of graft loss, graft dysfunction, cancer, cardiovascular events, and other patient-relevant outcomes. TRANSFORM will determine whether de novo CNI reduction with an everolimus-based regimen achieves short-term outcomes compared with standard CNI. As the largest clinical trial undertaken to date in kidney transplantation, recruiting more than 2,000 patients, and with extended follow-up to 5 years, TRANSFORM will provide critical data required to help maximize long-term outcomes.
35 nimmunosuppressive effects The indirect advantages of CNI reduction are complemented by the pleiotropic effect and other nonimmunosuppressive actions of everolimus. In terms of cardiovascular function, experimental evidence indicates that mTOR inhibition can lead to regression of cardiac hypertrophy,  36, 37  and a remodeling benefit has been shown in kidney transplant patients receiving everolimus therapy. 38, 39 mTOR inhibitors inhibit atherosclerotic plaque development and rupture in animal models,  40  consistent with evidence showing that their antiproliferative effect ameliorates the progression of transplant vasculopathy in heart transplant patients. 41, 42 Further, preliminary data suggest that mTOR inhibitors may attenuate the development of arterial stiffness,  43, 44  a known risk factor for post-transplant cardiovascular events.