dataesr/scientific-paragraphs-categorization huggingface dataset:
Processing of data As quantile normalization is an absolute read countbased approach, any region-specific or technical bias will over/under-represent the read counts and lead to inaccurate downstream analyses. Clonal reads (i. e. , PCR duplicates) constitute one such technical bias. Unfortunately, some level of clonal read contamination is unavoidable in sequencing datasets involving PCR. Epimetheus will remove such clonal reads from the raw alignment data, unless otherwise specified by the user. There are a few alignment and platform-specific biases that should be addressed prior to analysis as these are specific to each data and pipeline. Particularly recommended is to remove reads with more than one perfect alignment and those aligned to repeat and centromere regions. Also, a user can opt to exclude tricky regions from analysis using the respective option available in Epimetheus.
Note that quantile-based normalization cannot be applied to profiles of different histone marks, as the signal distribution and amplitude can be highly dissimilar, which is incompatible with the initial assumption. Similarly, users should not apply quantile normalization in cases where major differences are expected between the datasets. For example, inhibitors of epigenetic enzymes are likely to exert global effects histone mark deposition  [15] ; quantile normalization of such systematically divergent profiles will very likely inappropriately alter the enrichment pattern. In principle, quantile normalization cannot not be applied to profiles with highly divergent patterns. An example is the estrogen receptor, a ligandinducible transcription factor  [16]  which does not significantly bind chromatin in the absence of cognate estrogens; any normalization would artificially alter the compared profiles.
Biological replicates Epimetheus performance has been tested by using biological replicates of H3K4me3 mark datasets from nine different cell lines (GEO file GSE26320)  [18]. The comparison of biological replicates is a standard procedure to reveal the effect of normalization, as the datasets are expected to be highly similar but they may differ, for example, in enrichment amplitudes and signal-noise ratio. Indeed, some of these replicates exhibited significant differences in signal-noise ratios and for the number of enrichment sites. To illustrate the effect of linear and quantile normalization for profiles with variable signalnoise ratio, examples of an enriched region in GM12878 cell replicates and of average RCI plots around transcription start sites (TSSs) for two HMEC replicates with different levels of global enrichment (background, less, medium and high) are shown in the Additional file 1: Figures  S2A  and S2B  (top panels), respectively.
An illustrative MA transformation plot displays the overall transition effect of normalization between the replicates with perfect LOESS line fit (Additional file 1: Figure  S2C ). To evaluate the consequence of quantile normalization on a regular ChIP-seq peak calling approach, MACS peak calling was performed for the H3K4me3 HepG2 dataset with raw and normalized BED files (GSM646364; GSM646365). While normalization led to very small differences in the number of identified peaks (Fig. 1a ), which was generally seen for less enriched sites, the correction of the overall amplitude, particularly towards higher read counts, was very obvious from the LOWESS fit line in the MA transformation plots of the raw and normalized data between replicates (Fig. 1b ). Notably, in TSS plots the few replicate 1-specific peaks were enhanced by normalization both in number and intensity, and exhibited also weak (but apparently not peak calleridentified) signals in replicate 2, while the amplitude of unique peaks in replicate 2, which were invisible in both the raw and normalized replicate 1 TSS plots, got decreased (Fig.
Chromatin state analysis and Peak calling with normalization To illustrate Epimetheus' performance in multi-profile integrative analysis, chromatin state analysis was performed using ChromHMM  [19]  with published data sets for nine histone marks of nine cell-lines (GSE26320) before and after normalization. Biological replicates of each sample were merged into one to increase the coverage. To consider also local background variation, which is ignored in the Poisson distribution-based ChromHMM, peak calling was carried out on raw and normalized data and identified peak regions were used for chromatin state analysis. A comparison of the peaks identified in raw and normalized data sets revealed that seven profiles showed at most 50% peak overlap, while the other profiles had a higher overlap rate (Fig.
2a ). Interestingly, the datasets that show less overlaps between raw and normalized data were either of poor data quality (as assessed with NGS-QC Generator) or had a low coverage. For example, the H3K27ac profile of H1 cells for which only 25% of normalized data peaks overlapped with raw data peaks was constructed from only 17 M reads and its quality was rated CCD by NGS-QC (AAA is highest and DDD lowest quality). Similarly, several other datasets that showed a poor rate of overlaps were of either poor quality or coverage, highlighting the importance of quality control prior to analysis. In general, the raw and Epimetheus-normalized data revealed the same overall prediction of chromatin states (Fig. 2b  and c ). However, in depth comparison showed that 2-7% of the genomic bins changed chromatin annotation post normalization; a handful of these were located in regulatory or gene regions (Fig.
2d ). While the GM12878, NHEK and NHLF cell lines exhibited very few changes, the other cell-lines showed more than 5% of chromatin annotation changes. Importantly, some of these corrected a false-positive state annotation observed in raw data. For example, the chromatin state annotation at the MYO7A gene in H1 cells changed from "active" to "poised"; this is due to a more prominent enrichment of H3K27me3 after normalization (Fig. 2e ). Importantly, transcriptome data from ENCODE  [20]  confirm that no MYO7A is expressed in H1 cells, which correlates well with the chromatin state annotation assessed after, but not with the one before normalization. Together the above data reveal that normalization in general does not alter chromatin state annotation by ChromHMM but about 5% of them can be affected, thus leading to changes in the prediction of the functional states of a particular gene.
Discussion Here we have demonstrated the effect of quantile normalization on a variety of datasets and show that normalization is imperative when performing a comparative analysis of the relative signal amplitude levels of NGS-profiles. Even though normalization appears to have a minor effect on the global data analysis by peakfinding algorithms, we show here that (i) normalization minimizes amplitude differences in replicates and increases confidence in the temporal evolution of signal amplitude of enrichment patterns and (ii) improves chromatin state annotations when using binarized enrichment data, as with ChromHMM. In contrast to existing normalization tools, Epimetheus provides analytical outputs compatible with a variety of downstream analyses and illustrative plots. Indeed, previously described tools depend on peak caller predictions and/or external control datasets, the latter of which is generally the profile of the "input", i.
e. the whole cell extract (WCE) used for the ChIP. Given the diversity in available peak callerseach of which can be used with different parameters -and the potential bias introduced by a poor quality WCE, this approach could possibly lead to artefactual normalization. Specifically, even though control datasets exhibit in general an "ideal" (i. e. , non-enriched) pattern, a few WCE controls show an artefactual enrichment-like pattern (examples are GSM788366 and GSM768313). Such patterns will significantly influence the normalization outcomes produced by tools like ChIP-norm. In this context we have compared genome-wide and target-specific normalization using Epimetheus for genome-wide and a ChIP-norm (7)-like approach for normalization using a pre-selected bins displaying significant enrichment over the input control.
We compared both the approaches by using datasets with different enrichment patterns (ChIP-seq datasets for the temporal evolution of H3K4me3, H3K27me3 and FAIRE-seq during retinoic acid-induced F9 cell differentiation; for details and dataset IDs, see Additional file 1: Supplementary Note). This comparison included an evaluation of the effect of the enrichmentfactor of the selected population on normalization. Specifically, we asked, if 'fold changes' greater than 0, 1, 2, 3 or 4 between the IP and the input would influence the normalization. Indeed, this analysis revealed significant population-related biases relative to the genome-wide normalization of Epimetheus (Additional file 1: Figure  S3 ); not only the normalization values are changed upon population selection (Additional file 1: Figure  S3A ) but also the resulting changes in signal intensities between samples from a temporal analysis (T48 vs.
T0), which could lead to false interpretation of the temporal epigenome/chromatin changes during cell differentiation (Additional file 1: Figure  S3B ). Thus, while poor quality input samples can bias normalization approaches that use procedures similar to those of ChIP-norm, the genome wide normalization used by Epimetheus exhibited a robust performance across the datasets, as is revealed by MA plots, which display a perfect LOESS fit line (Additional file 1: Figure  S3C , right panel). It is however apparent from this display that also the ChIP-norm-like procedure results in a major improvement of the datasets compared to the raw data (middle and left panels).
Conclusions To compensate for systematic technical variations between assays, normalization is requisite for any comparative multi-profile analysis of epigenome data provided that the datasets are indeed comparable with respect to their nature and quality (see above). In fact, datasets with systemic modifications of enrichment patterns or major differences in quality should obviously not be normalized. While most of the existing tools focus on normalization only for differential analysis, the present study with biological replicates and chromatin state analyses supports the necessity of normalization for any comparative, integrative or differential analysis. Relative to existing tools, the more robust and sophisticated options in Epimetheus are that (i) it can be customised to variety of requirements, (ii) it can be applied genome-wide or to specific regions (when justified), and (iii) it can exclude specific regions, such as repetitive elements, which would bias global normalization.
Funding Studies in the laboratory of HG were supported by the  AVIESAN-ITMO Cancer , the  Ligue National Contre le Cancer  ( HG; Equipe Labellisée ); and the  Institut National du Cancer (INCa). Support of the  Agence Nationale de la Recherche  ( ANRT-07-PCVI-0031-01 ,  ANR-10-LABX-0030-INRT  and  ANR-10-IDEX-0002-02 ) is acknowledged. The funding bodies did not play any role in the design or conclusions of this study.
Results & Discussion Of 40 participants, data from 19 participants have been analysed to date: 11 trainees and 8 residents, with a mean age of 24. 9. Compared to the WL group, participants of the TC group increased significantly their knowledge level (p = 0. 001) and self-efficacy (p = 0. 038). The actors assessed that the announcement of the bad news was significantly more adequate in the TC group, as compared with the WL group (p=0. 005). There was no difference between groups in terms of stress reduction overtime. All participants were very satisfied about the training (mean = 4. 3/5). The analysis of the videos is currently under study. In conclusion, the training course for bad news delivery using e-learning and role playing appears to offer interesting perspectives in the field, enabling a more feasible approach as regards the acute care settings and concerns.
Introduction & Aims High quality cardiopulmonary resuscitation (CPR) remains essential to improve patient outcome. Several studies have shown that CPR is performed ineffectively, possibly due to irregular training and low skill retention. Feedback on performance is a crucial component of the learning processes associated with simulation and has been shown to improve CPR quality during simulated cardiac arrest on manikins. This study presents a new low-cost feedback device for CPR self-training and compares its efficacy to standard training method. Methods CPR Personal Trainer consists of a standard CPR training manikin instrumented with off-the-shelf sensors connected to a pre-processing unit and information system. The signal is analyzed by extracting relevant data of chest compressions performance and scoring them on three different factors associated with compression quality: hands position, compression rate, and chest recoil.
Results & Discussion A total of 209 surgeons and trainees responded to the survey (Table  2 ). There is poor awareness of HF and NOTSS among junior surgeons (17% and 39% respectively) compared to registrars (35% and 88%) and consultants (41% and 72%). However, 74% of all respondents believed failures in NTS contribute most to adverse events in surgery. Ninety-four percent of respondents believed NTS is equally or more important as technical skills. Forty-eight percent of all trainees rarely or very rarely received feedback on NTS in theatre, while 70% welcome standardised feedback on NTS using NOTSS. Of all respondents, 40% had used NOTSS and 73% of these reported positive or very positive experiences. Qualitative data indicate that trainees were clear about the potential value of NOTSS, especially in the context of patient safety and improving their own practice.
Methods The TracheoSimApp was programmed in HTLM/CSS/JavaScript and made available to Android (Fig. 4 ). The app contains images and texts that guide the user during the execution of the proposed procedure. The images were produced in an operation room scenario, using the TracheoSim simulator. The texts were based on a literature review of the subject. The final product was submitted to testing with a sample of 10 professorsexperts in simulation and tracheostomyof medicine from the Universidade Federal do Paraná, Curitiba, Paraná, Brazil.
Methods We developed an algorithm in LabVIEW and integrated it into a cardiovascular simulator. The algorithm works as it follows: -The clinician inserts patient-specific data as inputs into the algorithm. The translates these inputs into model parameters (i. e. peripheral resistance, ventricular contractility etc. ) that are sent to the cardiovascular simulator, -An automatic procedure refines some parameters, if needed, until the output of the simulator approaches patient-specific condition. We tested the algorithm on 6 end heart failure patients (3 ischemic and 3 dilated cardiomyopathies) whose data were collected retrospectively at the University Hospitals of Leuven. The input collected and provided to the algorithm were: systemic blood pressure (BP), pulmonary arterial pressure (PAP), capillary venous wedge pressure (Pwedge), central venous pressure (CVP), cardiac output (CO), end systolic/diastolic left ventricular volumes (ESV/EDV), left ventricular ejection fraction (EF%), heart rate (HR), body weight and height.
Methods Residents and nurse anesthetists from an internship in pediatric anesthesia were included to participate in a simulation session in pediatric anesthesia. They were informed that it was a laryngospasm case just before the simulation. The students were randomized in two groups: group A was given a simple algorithm based on data from the literature to manage laryngospasm in four steps before the simulation and group C was invited to do exactly as usual to manage a laryngospasm. We analyzed the videos recording of their performances during the simulation session. Our primary endpoint was the evaluation of the management of laryngospasm using 10 technical items the videos. We did statistical analysis on SPSS. Results & Discussion 71 participants, 35 anesthetist residents and 36 nurse anesthetists in training were included in this study.
Introduction & Aims Globally confidential enquiries into maternal deaths identify substandard care in around 70% of cases. These enquiries further pinpoint failures of teamwork and communication with failures to recognize, act and refer unwell women appropriately. Post-Partum Haemorrhage (PPH) is one of the main causes of maternal mortality globally. MedNav is a platform based software service that guides, scribes and prompts teams managing emergencies. In the way that satellite navigation devices have replaced maps MedNav is replacing notes and protocols. It has been designed for use on a tablet or smart phone. Initially MedNav has been designed for maternity teams. We report our study using MedNav in simulated environment to manage PPH. Methods Multidisciplinary (MDT) maternity teams (Chelsea and Westminster Hospital, UK) were randomised from clusters of staff grade to using the decision support tool or control during PPH full platform simulations.
Market chain study The market chain study largely followed the methodology described by Wandschneider et al  [13]. Once study locations were identified, local government representatives facilitated the rapid market appraisal consisting of combination research methodologies including qualitative participatory appraisal and quantitative surveys. Focus group discussions conducted with village participants and traders aimed to i) understand the motivations and constraints for smallholder pig production and trade, ii) track pig movements along the market chain, and iii) identify potential future access to profitable markets. Structured questionnaires were administered to a total of 66 farmers, 14 traders and the eight village heads in order to collect quantitative data on household characteristics, pig production systems and constraints, and marketing practises.
burnetii (FQS-MS-2P-664 IDVET, Grabels, France). Pig sera were tested for presence of antibodies against hepatitis E (HEV ELISA 4. 0v kit, MP Diagnostics Singapore), Trichinella (Priocheck Trichinella Ab ELISA, Prionics, Schlieren, Switzerland), JE IgM (non-commercial ELISA, AAHL, Australia), brucellosis (non-commercial ELISA, AAHL, Australia) C. burnetii (FQS-MS-2P-664 IDVET, Grabels, France), PRRS (PRRSX3 Antibody Test IDEXX Laboratories, Westbrook, ME, USA) and CSF (PrioCHECK CSFV 2. 0 Prionics, Lelystad, Netherlands). All questionnaire and serological data was stored in a secure database. Questionnaire and laboratory data were entered into a spreadsheet (Excel; Microsoft, Redmond, WA, USA) and analysed using the SPSS software (IBM, USA version 16. 0, New York, NY, USA). Initial exploratory data analysis was performed using multiple correspondence analysis (MCA), after which cross tabulation and Chi-Square tests were used to determine significant associations between serological results and variables such as gender, ethnicity, pig production and consumption habits.
ACKNOWLEDGMENTS The project implementers would like to thank the  Lao PDR Ministries of Health and Agriculture , in particular the district and provincial offices of the Xayaboury and Phongsaly provinces, for their support during this study. We would also like to acknowledge the support of the  International Livestock Research Institute (ILRI) , and for the support of  Stuart Blacksell  by the  Wellcome Trust of Great Britain. Last but not least we thank the communities including the traders, farmers and households for their trust and cooperation; it is greatly appreciated. This study was funded by the  Australian Centre for International Agricultural Research  (grant number  AH2009-001  and  AH2009-016 ).
Study population and blood sample collection The EPIC cohort is a large, multicenter prospective study, designed to investigate the associations between nutritional, lifestyle, metabolic and genetic risk factors and cancer. It was initiated in 1992 in 10 European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden and United Kingdom) and involved about 370,000 women and 150,000 men. Study population and baseline data collection have previously been described in details. 29  In brief, questionnaires included data about diet, reproductive history, use of exogenous hormones, tobacco smoking and alcohol consumption, education level, occupational history, physical activity and history of selected diseases. Anthropometric variables were measured according to standardized protocols.
Follow-up for cancer incidence and vital status Incident cancer cases were identified through record linkage with regional cancer registries in most countries and through health insurance records, cancer and pathology registries and active follow-up of study subjects in France, Germany and Greece. Data on vital status were obtained from mortality registries at the regional or national level, in combination with data collected by active follow-up (Greece). For each EPIC center, closure dates of the study period were defined as the latest dates of complete follow-up for both cancer incidence and vital status (dates varied between centers, from June 2008 to December 2013).
T1 5 0. 79, 95% CI: 0. 58-1. 07, P trend 5 0. 13), whereas a positive significant association with IL-10 was confirmed (OR T3vs. T1 5 1. 61, 95% CI: 1. 18-2. 21, P trend 5 0. 005) (Supporting Information Table  3 ). No statistically significant heterogeneity was observed in the inverse association between adiponectin and TC risk in women by age at blood collection, national TC incidence, education, smoking, BMI, waist circumference, menopausal status, histologic type, TNM stage (Fig. 1 ) and country (data not shown). The negative association with adiponectin was, however, absent among women in whom the interval between blood collection and TC diagnosis was <6 years (OR: 1. 07, 95% CI: 0. 81-1. 43). Finally, we explored the effect of adiponectin (middle and highest tertile vs. lowest tertile) according to BMI (<25 or 25; data not shown).
Functional imaging and electrophysiology Bioluminescence imaging was performed in open brain preparations of tethered flies. GCaMP6 imaging and electrophysiology were carried out on isolated brains maintained in saline solution. Whole cell current clamp recordings were performed using glass electrodes with 8-12 MΩ resistance filled with intracellular solution and an Axon MultiClamp 700B amplifier, digitised with an Axon DigiData 1440A (sampling rate: 20kHz; filter: Bessel 10kHz) and recorded using pClamp 10 (Molecular Devices, CA, USA).
Behavioural analysis Single, 3-5 day old flies were placed in an open-field arena, and their behaviour was recorded with a Logitech c920 camera at 10 frames per second. 36 flies, 12 of each genotype, were recorded simultaneously for 1 hour. After 30 minutes, mechanical stimulation was delivered through shaft-less motors (Precision Microdrives) controlled by custom-made DART software  70. These behavioural experiments were carried out in a temperature-controlled chamber at either 25°C or 31°C. Thermal preference behaviour was carried out using a linear temperature gradient from 18°C to 31°C.
ACKNOWLEDGEMENTS This work was supported by the  UK Medical Research Council  ( G070149 ;  MR/L010666/1 ), the  UK Biotechnology and Biological Sciences Research Council  ( BB/N001230/1 ), the  Royal Society  ( Hirth2007/R2 ),  Parkinson's UK  ( G0714 ),  King's College Medical Research Trust  ( JRC 19/2007 ), the  MND Association  ( Hirth/Mar12/6085 ;  Hirth/Oct07/6233 ),  Alzheimer Research UK  ( Hirth/ARUK/2012 ), and the  Fondation Thierry Latran (DrosALS)  to F. H. N. J. S. received support from the  Air Force Research Laboratory  ( FA86511010001 ); G. V. and J. R. M. from the  French ANR  ( FlyBrainImaging / ANR-11-BSV04-003-01 ); E. B. , C. C. , R. S. and J. H. from the  BBSRC  ( BB/J017221/1  and  BB/J018589/1 ), and R. D. holds a  Wellcome Trust Senior Investigator Award  ( 098362/Z/12/Z ).