dataesr/scientific-paragraphs-categorization huggingface dataset:
For each database, estimates generated by C-Model versions with incomplete data (i. e. when a variable was not available in the dataset) were discarded. Random effects meta-analyses were used to generate summary estimates of areas under the ROC curves. Based on the results of these meta-analyses, the C-Model version with largest number of variables was considered able to generate the 'best estimate' of CS probability. The deviation of the observed CS rate (and an uncertainty range) was determined for each database considering the best estimate of CS probability. This uncertainty range was arbitrarily defined by the authors as 20% because differences >20-25% are commonly considered clinically significant or appreciable differences. 24 The ratio between observed and predicted CS (the standardised caesarean section ratio) was determined.
All analysis considered cluster effect at the health facility level and at the country level. STATA Version 13. 0 (www. stata. com/stata13/), PASW Statistics 18, Release Version 18. 0. 0 (SPSS, Inc. , 2009, Chicago, IL, USA, www. spss. com), R package version 3. 1. 1 (http://www. r-project. org/) and MEDCALC Version 11. 6. 1. 0 (MedCalc Software, 2011, Mariakerke, Belgium, www. medcalc. org) were used in the C-Model analysis. The HRP specialist panel (WHO scientific staff and external, independent researchers) on epidemiological research reviewed and approved the WHO MCS study protocol for technical content. The WHO MCS study was approved by the WHO ethical review committee and the relevant ethical clearance mechanisms in all countries. The relevant ethical and administrative clearances for analysis of databases that provided data for C-Model building and testing were obtained.
Supporting Information Table  S1  presents the results of univariate analysis of the relationship of CS and selected characteristics in the Group A; these characteristics were later included in different versions of the C-Model. Random split of the reference population rendered two subgroups: sub-group A1 with 38 324 women and subgroup A2 with 4313 women. The mixed-effects logistic regression modelling was carried out in subgroup A1 and produced four versions of the C-Model, all of them computing the clustering effect. S5-S8 ). The C-Model was tested in 24 independent databases (Supporting Information Tables S3-S9) including data of over 10 million women and 3 million CS performed in 43 countries. Figure  S1  and Table  S4  represent all countries where the C-Model was tested. According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.
832 to 0. 844 (Table  S2 ). Appendix S1 presents areas under the ROC curves for individual databases and meta-analysis details. Table  S3  presents the CS rate, the best estimate of predicted CS rate (with a reference range of CS rate), the absolute deviation of the best estimate, and the standardised CS ratio by database that tested the C-Model. The C-Model was able to generate expected CS rates that are adjusted for the casemix of the obstetric population. In databases with low CS rates and good perinatal outcomes (e. g. Finland) the deviation from observed and estimated CS rates is minimal (i. e. <1. 0%). Hospitals with high CS rates presented larger deviations from what would be expected for the clinical and obstetric characteristics of the population. Detailed results of the studies that conducted external validation analysis are presented in the Appendix S1 (Figures  S6-S9 ) and Appendix S2.
+ (x i b i ) Calculate the probability of caesarean section ProbCS = e Logit /(1 + e Logit ) *Includes both induction of labour and caesarean section before labour. **An electronic calculator is through the link www. who. int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/ Some weaknesses of this analysis should be noted. First, we generated the reference population using a low risk sub-sample of the WHO MCS database. Despite efforts to ensure the best possible quality in this database (considering the scope and magnitude of the original study) minor data heterogeneity could be expected and has been discussed in previous publications  21. For the same reason, we were not able to include in our models all possible predictors of CS. Some important information, such as maternal weight and height, were not available in the WHO MCS database and thus were not considered.
In any case, when the C-Model estimates show appreciable differences from actual CS rates, this could trigger further inquiries and actions to improve quality of care, decision-making, practices, and results. Thirdly, most of the validation data comes from routine databases, which may add some issues concerning completeness and accuracy of the information on clinical risk factors in those databases. In addition, these routine data could be associated to some heterogeneity as some health facilities or countries may not respond uniformly to maternal characteristics and CS predictors; we tried to account for this heterogeneity by suggesting the use of ranges of uncertainty for each estimate. Finally, 'Induction of labour' and 'C-section before labour' were treated as 'provider-initiated childbirth'.
Call for external validation studies vide CS. Between May 2010 and December 2011 the WHO MCS included 314 623 women from Africa, Asia, the East- ern Mediterranean region, and Latin America. (including WHO MCS network, International Network of Obstetric Survey Systems (INOSS) and other experts) C-Model validation 24 databases 10,045,875 women (3,035,465 caesarean sections) (43 countries) Figure 1. Analysis flowchart for model building and validation.
Skeletal muscle biopsies and ethic statement Muscle biopsies were obtained from quadriceps of FSHD1 and healthy control fetuses from spontaneous or legal therapeutic abortions at  14, 15, 16, 18, 20, 22, 24  and 33 weeks of development (Table  1 ). The collection of fetal and FSHD1 muscle biopsies was approved by the "Agence Française de la Biomedecine" of the French Ministery of Health in order to have legal access to the biological material in full accordance with the law (research protocol number PFS12-007). Written informed consent from the donors or the next of kin was obtained for the use of the samples in research. All investigations have been conducted according to the principles expressed in the Declaration of Helsinki and the data were analyzed anonymously. The gender of each fetus was determined by PCR for the gene TSPY1 specific for the Y chromosome (TSPY1-Fwd: AATACAGGGCTTCTCATTCCA; TSPY1-Rev: GTTAGATCCTGCGAAGTTGTG).
Real-time RT-PCR expression analysis The fluorescence accumulation data of real-time RT-PCR reactions of each sample were used to fit four parameters sigmoid curves to represent each amplification curve using the library qPCR  [17] , for the R statistical package version 2. 922 (R Development Core Team. 2009). The cycle of quantification was determined for each amplification by the maximum of the second derivative of the fitted sigmoid curve. The efficiency of each amplification reaction was calculated as the ratio between the fluorescence of the cycle of quantification and the fluorescence of the cycle immediately preceding that. The estimated efficiency of each miRNA was obtained by the mean of the efficiencies calculated for each amplification reaction of that precise miRNA. Normalization between the different amplified samples was achieved by the calculation of normalization factors given by the geometric mean of the expression value of all expressed micro-RNAs in a given sample  [18].
Bioinformatics enrichment analysis of miRNA targets We identified miRNAs potentially involved in FSHD1 onset, based on selective expression or on differential expression between FSHD1 and controls at different stages of fetal muscle development. For these miRNAs, we identified putative target genes based on predictions from five online software programs: Target Scan (http://www. targetscan. org/index. html), miRNAMap (http://mirnamap. mbc. nctu. edu. tw), PicTar (http://pictar. bio. nyu. edu/), Microcosm Target (http://www. ebi. ac. uk/enright-srv/microcosm/htdocs/targets/v5) and miRanda (http://www. microrna. org/microrna/home). Any target gene was considered a putative target if it was predicted in at least 3 out of the five predicting software's. In addition, to identify relevant molecular mechanisms potentially associated with the modulation of miRNAs in FSHD samples, we performed a gene set enrichment analysis (GSEA) with putative target genes  [20].
Finally, we also have made an in silico analysis to look for miRNA putatively regulated by DUX4. We have downloaded the TransmiR database  [39]  v. 1. 2 (last updated in 2013-1-30), and queried for DUX4 (entrezid 22947). Apparently, neither DUX4 nor any gene regulated by it, as described in Geng et al. [54] , is responsible for regulating any of these miRNA. We conclude that the expression of these 8 miRNAs is not linked to DUX4 expression. One possible explanation is the involvement of another gene in FSHD onset which may mis-regulate some miRNAs in fetal samples. We have recently shown that FAT1 is lower expressed in fetal FSHD muscle biopsies compared to age matched control fetal biopsies  [55]. We have also shown that a transgenic mouse down-regulating the planar polarity gene Fat1 exhibits most of the phenotype observed in FSHD patients.
Research reported in this publication was supported by  FSH Society   Delta Railroad Construction Company Fellowship Grant  to JD (FSHS-DRRC-2008; https://www. fshsociety. org), Association Française contre les Myopathies  to JD and FM (AFM-Téléthon, France; www. afm-telethon. fr), the  Agence Nationale de la Recherche  to SR, FM and JD(FSHDecrypt,  ANR-09-GENO-038  and  FSHDecipher ,  ANR-13-BSV1-0004 ; www. agencenationale-recherche. fr) and  CNPq-Inserm French-Brazilian International Laboratory of Cell Therapy  and.
The data were digitally sampled at 10 kHz and acquired on a PC using the IOTech acquisition system (Wavebook, IOTech Inc. ). Lateral olfactory tract (LOT) electrical stimulations were performed via bipolar stainless steel electrodes, which were stereotaxically positioned in the LOT (bregma coordinates: anteroposterior, 3. 7 mm; lateral, 3. 4 mm). Optimal placement was determined by the observation of field potentials evoked in the OB by electrical stimulation (constant current square pulses, 100 s; amplitude range, 0. 1-0. 5 mA). The respiration frequency was used to monitor the depth of anesthesia, and the injection of urethane was performed accordingly when the frequency increased. The respiration signal was recorded using a homemade flowmeter based on a fast response time thermodilution airflow sensor (bidirectional microbridge mass airflow sensor, AWM 2000 family, Micro Switch Honeywell; described in detail in the study by  Roux et al.
Interestingly, the overall CSD pattern did not change across respiratory phases except for its amplitude, indicating that evoked recurrent inhibition and GC-evoked centrifugal excitation were not constant at each respiratory phase. To quantify this change, we measured the amplitude of the current source in the EPL as a function of the respiratory phase. We observed a significant (mean Ϯ SEM, 22 Ϯ 11%) modulation of the EPL current source amplitude (Fig. 4C , top; n ϭ 180 LOT stimulations; Kruskal-Wallis test, p Ͻ 0. 01), which peaked at the initial part (1. 1 ) of the expiration phase and dipped at the end of expiration (E/I; 0 , unpaired Wilcoxon test, p Ͻ 0. 01; n 0 ϭ 18, n 1. 1 ϭ 10). In contrast, we did not find any significant changes in the amplitude of the EPL current sink around the peak of the LOT-evoked response (from Ϫ2 to 10 ms; data not shown; n ϭ 180 LOT stimulations; Kruskal-Wallis test, p ϭ 0.
This work was supported by the  Centre National de la Recherche Scientifique  and the  LABEX Cortex  (Grant  NR-11-LABX-0042 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency. Acknowledgments: We thank  Gloria Fleming  from the  Emotional Brain Institute  for her careful reading of the manuscript.
Lepidopteran stem borers are among the most important pests of maize in East Africa. The objective of the present study was to predict the impact of temperature change on the distribution and abundance of the crambid Chilo partellus, the noctuid Busseola fusca, and their larval parasitoids Cotesia flavipes and Cotesia sesamiae at local scale along Kilimanjaro and Taita Hills gradients in Tanzania and Kenya, respectively. Temperature-dependent phenology models of pests and parasitoids were used in a geographic information system for mapping. The three risk indices namely establishment, generation, and activity indices were computed using current temperature data record from local weather stations and future (i. e. , 2055) climatic condition based on downscaled climate change data from the AFRICLIM database.
Stem borer data Field surveys were carried out in maize fields along the two-altitudinal gradients for two years in 2012 and 2013. No specific permissions were required for these maize fields; however permissions to conduct research were asked to the small-scale farmers owners of the maize plots. The field studies did not involve endangered or protected species. A total of 6 localities along each altitudinal transect were selected on the basis of the annual mean temperature, whereby each locality differed from the closest one by 1°C. In addition these localities belong to some of the maize agro-ecological zones defined by Hassan et al. ,  [53]. The lowland tropical and dry mid altitude zones situated mainly below 1300 m. a. s. l belong to low potential zones, whereas the moist transitional and highland tropics located above 1300 m.
Climate data The temperature data required for carrying out spatial simulation under current climatic conditions were obtained from local weather stations. At each study site across each transect, 24 automatic onsets HOBO data loggers were installed, within a minimum distance of 600m from each others, to keep track of daily minimum and maximum temperatures, and monthly mean minimum and maximum temperatures (°C). The following localities were considered: Kipusi, Dembwa, Josa, Mbengonyi, Kighala and Vuria in the Taita Hills, and Miwaleni, Uparo Iwaleni, Uparo, Kopachi, Nduoni and Marua in the Mount Kilimanjaro transect. At each locality, the geographical coordinates (longitude and latitude) and altitudes were recorded using a Global Positioning System (GPS). For simulations under future climatic conditions, downscaled data of the Representative Concentration Pathways Scenarios, Fifth Assessment Report (RCPs-AR5)  [3]  under future climate change scenarios were used.
The RCPs provide spatially resolved data sets of land use change and sector-based emissions of air pollutants, and it specifies annual greenhouse gas concentrations and anthropogenic emissions up to 2100  [3]. The downscaling of the data was obtained from regional climate models (RCMs). RCMs better capture local climate feedbacks, especially in biologically rich and highly populous mountain and coastal regions. These grids were bias-corrected and change-factor downscaled to 30" (1km) spatial resolution, using the WorldClim grids as baselines. The data are well documented in Platts et al. ,  [55] , and are freely accessible at http://www. york. ac. uk/environment/research/kite/resources/. For studying the effect of future climate conditions on stem borers and their natural enemies, the year 2055 was selected because of the time horizon of 30 years, which is more realistic than 2080 and beyond.
This approach implemented the index interpolator in a sub-module of the Insect Life Cycle Modeling (ILCYM) software  [57] , and is freely accessible at https://research. cip. cgiar. org/confluence/display/ilcym/Home. Using an index interpolator module, the following steps were considered: the digital elevation model (DEM), defined as co-variables, was inputted into ILCYM; DEM was obtained from the Shuttle Radar Topography Mission (SRTM). Phenology model and temperature data from each station for a year were also inputted into the tool. The inbuilt thin plate algorithm was selected for the interpolation of the indices on the surface of DEM. The indices were generated as ASCII files format and were transferred to Arc-GIS software version 10. 1 for enhancing the visualization. Furthermore, to understand the effect of future climate, the difference between the current (2013) and future (2055) climate condition were obtained by using ILCYM's inbuilt raster calculator.
Discussion The predictive mapping of future maize stem borer pest risks at small scale generated through point-by-point analysis, using climatic data obtained from data logger dispatched along the two altitudinal gradients, confirm and complete results recorded at country scale generated from temperature extrapolated from available historical records by Khadioli et al. ,  [51]. It confirms the precision of the temperature process based phenology model achieved with ILCYM software compared to other methods like rule based methods  [62]  and multivariate statistical techniques  [63]. The results indicate a worsening of the impact of the two stem borer pests on maize production along the two East African mountain gradients studied. The aggravation can be attributed to three main changes occurring simultaneously: range expansion in higher altitude areas; increase of the abundance, thus damage potential, of the pests at all altitudes; disruption of the biological control due to a mismatch of the geographical distribution between the pests and their main larval parasitoids.
Acknowledgments We would like to thank the  Ministry of Foreign Affairs of Finland  for their sponsorship through the  Climate Change Impacts of Ecosystem Services and Food Security in Eastern Africa (CHIESA)  project. We also would to thank  Fritz Schulthess  for his numerous constructive comments on a previous version of the manuscript. The authors are grateful to the entire members of the  Noctuid Stem Borer Biodiversity (NSBB) team of the African Insect Science for Food and Health (icipe) , the  Institut de Recherche pour le Développement (IRD) ,  University of Dar es Salaam , through the  Department of Mathematics, and Dar es Salaam Institute of Technology (DIT)  for their support.
006) and the LR group (median EFS: 76. 4 vs. 42. 5 months, P < 0. 001; median OS: NA vs. 63. 3 months, P < 0. 001) (Figure  3 ). As optimal cut-offs for IGFBP7 expression were different between the cohorts (see methods section), we applied the same fraction of patients defined as "high" IGFBP7 expressers in the HM-cohort (46/274, 18. 6%) to the LR-cohort (131/701, 18. 6%), confirming the association with EFS and OS (Additional file 3: Figure  S1 ). High expression of IGFBP7 was likewise associated with poor risk cytogenetics, most notably translocation t(4;14)(P < 0. 0001) and gain of 1q21 (P = 0. 039). No significant correlation was observed with the translocation t  (11;14)  and deletion 17p (Additional file 4: Figure  S2 ). The association between IGFBP7 expression levels and t(4;14) was further validated by an in-silico analysis of two additional publically available GEP-datasets  [28, 29].
Observed differences in frequency and height of expression are most likely due to different amplification protocols used (double vs. single amplification)  [42] [43] [44]. IGFBP7 expression in MM cells was linked to prognostically adverse chromosomal aberrations such as translocation t(4;14) and amplification 1q21. The multiple myeloma SET domain (MMSET) protein, specifically overexpressed in t(4;14) myeloma, is a histone methyl transferase shown to modulate DNA methylation, thereby inducing the activation of specific target genes  [45]. Interestingly, IGFBP7 expression was found to be regulated by MMSET in myeloma cells  [45]  which is in line with our observation of higher IGFBP7 transcript levels in t(4;14) cases. The association between MMSET expression and expression of IGFBP7 was confirmed by in-silico analysis of two open-source GEP datasets.
Gene expression profiling Gene expression profiling was performed as previously published  [44]  using U133 2. 0 plus arrays according to the manufacturer's instructions (Affymetrix, Santa Clara, CA, USA). Expression data are deposited in Array Express under the accession numbers E-MTAB-317 as well as E-TABM-1138 and Gene Expression Omnibus GSE24080 (the latter two for the LR group). When different probe sets were available for the same gene, we chose the probe set yielding the maximal variance and the highest signal.
Statistical analysis Correlations (Spearman and Pearson) and survival analysis (Cox regression analysis) were performed using SPSS statistics 17. 0. For the analysis of in vitro experiments, two-tailed unpaired t test was performed using Prism 5 (GraphPad Software Inc. , La Jolla, CA, USA). P-values <0. 05 were considered to be statistically significant. Gene expression analyses were performed on GC-RMA preprocessed data sets. To assess presence ("expressed") or absence ("not expressed") of gene expression, the "Presence-Absence calls with Negative Probesets" (PANP) algorithm was used  [53]. For the analysis of the prognostic value of IGFBP7, patients with high and low IGFBP7 expression in the HM cohort were delineated using maximally selected rank statistics as implemented in the maxstat R package (http://cran.
r-project. org/ web/packages/maxstat/index. html). The survival function was assessed by means of the Kaplan-Meier method (rms package in R). The log rank test was used to test for statistically significant survival curve differences. An effect was considered as statistically significant if the P-value of its corresponding statistical test was not higher than 5%. All computations were performed using R 3. 1. 0 (http://www. r-project. org/) and Bioconductor 2. 14. For validation of the impact of IGFBP7 expression on survival, cel-files from 701 patients treated within the total therapy 2 or 3 protocol (LR group), were preprocessed as described above (GC-RMA), and the same analysis strategy followed to obtain an optimal cut-off (maxstat), i. e. the optimal cut-off was derived as the mean of the cut-offs derived for EFS and OS.
Acknowledgments This work was supported by a grant from the  Fellinger Krebsforschungsverein  and the  Austrian Forum against Cancer , as well as in part by grants from the  Deutsche Forschungsgemeinschaft  ( SFB/TRR79 ), and the  Bundesministerium für Bildung und Forschung (CAMPSIMM and CLIOMMICS), Bonn, Germany. The funders had no role in study design, data collection and analysis, preparation of the manuscript as well as decision to publish. The authors would like to thank  Elsa Thagian  for excellent technical assistance.
Materials and Methods Histone marks, H2AZ, CTCF, CHD1, NANOG and OCT4 ChIP-seq data ChIP-seq data were retrieved for the following cell lines: an ESC line (H1hesc), an immature myeloid cell line (K562), a monocytes-CD14+ (monocd14ro1746), a lymphoblastoid cell line (Gm12878), a mammary epithelial cell line (Hmec), an adult dermal fibroblast cell line (Nhdfad). For all ChIP-seq data, we downloaded data in the Encode standard format "broadpeaks" (http://genome. ucsc. edu/FAQ/FAQformat. html). Broadpeaks format is a table of significantly enriched genomic intervals. The signal value associated with each enriched intervals is the fold enrichment compared to a uniform distribution of reads  [100]. For all cell types, we downloaded the broadpeak tables for the following antibodies: CTCF, H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me2, H3K4me3, H3K9me3, H2AZ, H3K79me2, H4K20me1.