dataesr/scientific-paragraphs-categorization huggingface dataset:
An NGS-based strategy for CNV detection will be of great value for increasing the diagnostic yield in patients affected by mendelian muscle diseases. Current technology does not adequately capture repeat expansion diseases such as myotonic dystrophy or diseases related to other repetitive elements. Long-read sequencing technologies may further help the identification and mapping of CNVs as well as of repeat expansions. However, the clinical interpretation of CNVs remains challenging, in particular for CNVs identified in genes without previously reported disease causing deletions or duplications. The inclusion of CNV data in public databases, e. g. , ExAC, could help in pathogenicity assessment of CNVs.
ACKNOWLEDGMENT The authors thank  Merja Soininen  and  Helena Luque  for their technical help and  Sini Penttilä ,  Tiina Suominen , and  Sara Lehtinen  for acquisition of samples. They also thank all the patients and family members as well as the clinicians who provided samples. L. Sagath,  M. Arumilli , and  K. Kiiski  report no disclosures. A. Sáenz  has received research support from  Health Research Fund  ( PI13-00722 ) of the  Spanish Ministry of Economy and Competitiveness , the  European Unión (European Regional Development Fund). A. Lopez De Munain  has received travel funding from  Sanofi. A. Cobo  and  K. Pelin  report no disclosures. B. Udd has served on the editorial board of  Neuromuscular Disorders  and has received grants from  Finska Läkaresällskapet  ( 20 , 000  euros  2017 ), the  Sigrid Juselius Foundation  (general grant for the study of myopathies), the  Jane and Aatos Erkko Foundation , and the  Vasa Central Hospital Research Foundation  (grant partly for myopathies).
Setting the boundaries and context of the evidence map In order to framework our mapping project, we consulted the 2013 World Health Organization (WHO) classification  [15] , and the related clinical guidelines, combined with the consultation to an oncologist with expertise in sarcomas. With this information we established the eligibility criteria for study inclusion. We selected SRs assessing therapeutic interventions in patients diagnosed with GIST, summarizing randomized controlled trials (RCTs) as well as phase I and II clinical trials, observational studies (including cohorts studies, case control studies, cases series), or case reports. We used a broader definition of SR in order to obtain the largest possible number of documents. The systematic reviews that conducted a search in at least two databases were considered eligible.
Searching and selection of systematic reviews We conducted searches in PubMed, EMBASE, The Cochrane Library, and Epistemonikos from 1990 to March 2016; the former was updated in November 2016. The lower date boundary was chosen taking into account that biological mechanisms were discovered in 1998 and opened the use of biological agents as the key therapeutic approach of GIST that completely changed the management of the disease  [17]. "However, we extended our retrospective search until 1990 for establishing a reasonable period of time to guarantee a higher sensitivity". We combined keywords and medical subject headings (Mesh terms) for all types of sarcoma according to the WHO 2013 classification  [15]. We adapted the search strategy in accordance with the specific characteristics of each database.
We did not limit searches by language. In addition, a clinical expert (AL) was consulted to help in identifying any other relevant reviews. Likewise, we reviewed all references in the relevant articles to identify potential additional reviews. Detailed search strategies are reported in Additional file 1. We managed the search results with the reference manager software COVIDENCE  [18]. After removing duplicates, two reviewers (MB, NM) independently screened all titles and abstracts to exclude irrelevant reviews. Full texts of potentially relevant reviews were obtained for a final decision. Disagreements were solved through discussion and consensus; if necessary, an additional reviewer (GU) was consulted. Reasons for exclusion are clearly stated.
Data analysis We built a data extraction form to register the main characteristics and quality of included systematic reviews. We tested a pre-defined data extraction form to ensure consistency among reviewers in a pilot study with 20% of eligible SRs. Two authors extracted data (MB, NM). Disagreements were solved by discussion with a third author (AL). We collected data at three levels: a) General characteristics from systematic reviews: authors, year of publication, type of systematic review (with or without meta-analysis), objective, search methods, design and number of included studies, type and number of patients included, and quality of the systematic review. Two researchers (MB, NM) independently assessed the methodological quality of the included reviews with the AMSTAR tool. Disagreements were discussed until consensus was reached.
Isolation and Characterization of Acorenone B Acorenone B was isolated by column chromatography on silica gel G60 (1 kg). The mixture (5. 0 g) was eluted in isocratic condition with hexane-ethyl acetate 90:10. The compound (1. 48 g) was obtained as a pale yellow oil, with optical rotation [∝] = -17. 6 (CHCl3; c = 11. 4). Results similar to ours were reported by Zalkow  [24]. The molecular structure of acorenone B (Figure  2 ) was confirmed by 1 H NMR, 13 C NMR, and MS analysis, and compared with data present in the literature  [24] [25] [26] [27]. Results similar to ours were reported by Zalkow  [24]. The molecular structure of acorenone B (Figure  2 ) was confirmed by 1 H NMR, 13 C NMR, and MS analysis, and compared with data present in the literature  [24] [25] [26] [27]. EI-MS m/z (%): 220 (M+, 58), 177 (76), 149  (45) , 135 (100), 121 (60), 109 (100), 93  (46) , 82 (84), 69  (28) , 55  (22) , 41  (29).
4 and used at 25 mU/mL for the assay. After 10 min of pre-incubation, the substrate acetylthiocholine iodide (1. 5 mM) was added to start the reaction. During 1 h of incubation at 30 • C, 96-well microtiter multiplates were read on a PherastarFS (BMG Labtech) detection system. All measurements were made in triplicate. When possible, the IC 50 values were calculated using the GNUPLOT package on line (www. ic50. tk, www. gnuplot. info). Donepezil was used as reference ChE inhibitor with an IC 50 = 100 nM for AChE and 8500 nM for BChE. In this assay, we did not exclude the possibility of false-positive inhibition results previously described for high concentrations (>100 µg/mL) of amine or aldehyde compounds  [45, 46] , but the lack of inhibition observed for the essential oil versus the AChE strongly minimized this possibility.
The chemical composition of the samples was studied using energy-dispersive X-ray analysis (EDX) on a Zeiss Supra 55VP FEG scanning electron microscope with EDAX GENESIS software. Single-crystal X-ray diffraction measurements of the grown crystals were performed using an Oxford diffraction Gemini diffractometer equipped with a Mo K source. The experimental setup for second harmonic generation (SHG) measurements is based on that of  Kurtz & Perry (1968)  and is described fully in  Jones & Thomas (2002). A Nd-YAG laser with a wavelength of 1064 nm and a modulated frequency of 1 Hz was used. The beam was initially split with a glass slide, with one beam sent to a photodiode and the other directed at a sample held inside a furnace with two holes to allow light to pass through. The SHG signal with a wavelength of 532 nm was measured using a photomultiplier tube (PMT) positioned behind a green filter to ensure only the secondharmonic signal was detected.
An oscilloscope was used to measure the pulses from the photodiode and the PMT and a computer running LabVIEW was used to record the data from the oscilloscope and calculate an average signal from 10 pulses. Birefringence imaging microscopy (BIM;  Glazer et al. , 1996 ) measurements were made every 1 K, while heating and cooling the crystal at a rate of 60 K h À1 between 300 and 873 K using a Linkam THMS600 hot-stage. In the BIM setup, the measured intensity of the rotating polariser and circular analyser is given by I ¼ ðI 0 =2Þ½1 þ sinð2' À 2Þ sin ; ð2Þ where I 0 is equivalent to the transmitted light intensity, ' is the angle measured anticlockwise between the horizontal axis of the microscope (given by the top and bottom of the CCD array of the detector) and the slow axis of the indicatrix, is the angle of the polariser from a predetermined zero, and is the phase shift of the light given by ¼ 2 Ánt: ð3Þ Án is the observed birefringence, t is the sample thickness and is the light wavelength.
The BIM measurements were analysed using the Metripol software, which produces falsecolour images of |sin|, ' and the transmitted intensity. The measured |sin | oscillates between 0 and 1, with a peak when ¼ ð2i þ 1Þ=2 and a trough when ¼ i, where i is an integer. To determine which trough corresponds to the material being zero-birefringent (i = 0) usually requires separate measurements of the refractive indices, however, this is not required when the variation in the birefringence as a function of wavelength is small. In this case, the zero-birefringence (i = 0) troughs will be at the same temperature at each wavelength. Troughs that occur when is , 2 etc. will not be at the same temperature for different wavelengths, allowing the trough corresponding to zero-birefringence to be determined.
LN and LT crystals are clear, which suggests there could be some V contamination, which is below the detection limits of EDX. The collected diffraction data were indexed and integrated using the CrysAlisPro software and the absorption corrected with the analytical method of  Clark & Reid (1995). The corrected intensities were analysed using SHELX and the refined structures from the powder diffraction measurements were used as a starting point. Initial refinements were made allowing the Nb and Ta occupancies to vary, but these were not in agreement with the values determined by EDX and varied inconsistently as a function of composition. Following this, the refinements were made with fixed Nb and Ta occupancies similar to the Rietveld refinements. Attempts were also made to refine the data using the Li-deficiency models but these were also unsuccessful, so the refinements were made with the occupancies fixed at their stoichiometric values.
Acknowledgements The PANalytical MPD diffractometer used in this research was obtained through the  Science City Energy Futures Project :  Hydrogen Energy , with support from  Advantage West Midlands (AWM). The Oxford Diffraction Gemini CCD and Panalytical MRD diffractometers used in this research were obtained through the  Science City Advanced   Materials Project: Creating and Characterizing Next Generation Advanced Materials , with support from  AWM  and part funded by the  European Regional Development Fund (ERDF).
The reprocessing of actinides with long half-life has been non-existent except for plutonium (Pu). This work looks at reducing the actinides inventory nuclear fuel waste meant for permanent disposal. The uranium oxide fuel (UOX) assembly, as in the open cycle system, was designed to reach a burnup of 46GWd/T and 68GWd/T using the MURE code. The MURE code is based on the coupling of a static Monte Carlo code and the calculation of the evolution of the fuel during irradiation and cooling periods. The MURE code has been used to address two different questions concerning the mono-recycling of americium (Am) in present French pressurised water reactors (PWR). These are reduction of americium in the clear fuel cycle and the safe quantity of americium that can be introduced into mixed oxide (MOX) as fuel.
The MURE (MCNP utility for reactor evolution) code The MURE code performs nuclear reactor time evolution using the particle transport code MCNP. It is written in an object-oriented framework, and a user can define their own way to interact with the evolution. It can also couple neutronics (with or without fuel burnup) and thermal-hydraulics  [4, 5]. The MURE code can be used to 'easily' generate MCNP input files using the C++ interface. It constructs a nuclear tree network of radioactive decays and nuclear reactions of all nuclei involved in the evolution. This evolution of nuclear materials is achieved by solving the corresponding Bateman equations  [6]  (1) produced essentially during the cooling period after irradiation by the decay of Pu-241. Am-241 is responsible for the high mid-term residual heat.
A. The MURE (MCNP utility for reactor evolution) code The MURE code performs nuclear reactor time evolution using the particle transport code MCNP. It is written in an object-oriented framework, and a user can dene their own way to interact with the evolution. It can also couple neutronics (with or without fuel burnup) and thermal-hydraulics  [4, 5]. The MURE code can be used to 'easily' generate MCNP input les using the C++ interface. It constructs a nuclear tree network of radioactive decays and nuclear reactions of all nuclei involved in the evolution. This evolution of nuclear materials is achieved by solving the corresponding Bateman equations [6] �� � �� � �� � ��� �� � + ∑ � ��� ��� �� � � � � � � + � � � ��� � � (1) The Bateman equations are solved in the interval ∆t with a 4th-order Runge-Kutta method using the ux and reaction rates calculated by MCNP to create new MCNP le at � � + � � � � + ��.
Each time MCNP is called, the reactor fuel composition will have changed due to the ssion/capture/decay process, which has occurred in the material [6]. The Bateman equations are solved in the interval Δt with a 4th-order Runge-Kutta method using the flux and reaction rates calculated by MCNP to create new MCNP file at t i + 1 = t i + Δt. Each time MCNP is called, the reactor fuel composition will have changed due to the fission/capture/decay process, which has occurred in the material  [6]. The MURE code was used to simulate various configurations of the UOX, MOX and MOXAm as- Principle of fuel evolution in  MURE [6]  Fig. 2  Linear curve fitting of MOXAm assembly burnup.
C. The quadratic approach The linear assumption did not strictly t burnups with low Pu quality and high Pu concentration of the MOX(Am) fuel, hence the quadratic approach. semblies to achieve burnups of 46GWd/t and 68GWd/t. Cooling periods of 5 years and 30 years of spent UOX fuel assemblies were considered for the reprocessing of the americium of the spent UOX fuel. These years were chosen to simulate early reprocessing and late reprocessing of the spent UOX fuel. The radiotoxicity of various fuel configurations was investigated using the MURE code and analysed.
RNA high-throughput sequencing We reanalyzed six RNA-seq libraries used by Jaquiéry et al. [34]. Briefly, these six libraries correspond to three male libraries and three parthenogenetic female libraries of clone LSR1. Details regarding aphid rearing, RNA extraction, libraries preparation and sequencing are provided in Jaquiéry et al. [34]. Libraries were mapped on the version 2 of the pea aphid genome assembly using TopHat2 (RRID:SCR_013035) default parameters  [65]. The number of reads covering each CDS of the gene prediction v2. 1 was then counted using HTSeq-count (RRID:SCR_011867)  [66]  with the following parameters: -m intersection-strict -s no -t exon. The numbers of mapped reads per library ranged from 7. 3 to 20 million, with an average over libraries of 14. 3 million reads. Raw read counts were normalized in R (RRID:SCR_001905)  [67]  with edgeR (RRID:SCR_012802)  [68]  package by sequencing depth using the TMM method and by genes length (RPKM and TPM calculation).
Phenol chloroform extraction steps were performed using Sigma phenol chloroform. Subsequently to its extraction, the FAIRE DNA was purified the using ZYMO ChIP DNA Clean & Concentrator. Finally, FAIRE and Control DNA were quantified using a Quantus with the Quantifluor dsDNA kit. The reproducibility of the replicates was assessed before sequencing by calculating the FAIRE/Control ratio described in Simon et al. [40]. FAIRE and Control DNA were sequenced using the Illumina Hiseq 2000 instrument and ChIP TrueSeq kit. Three FAIRE samples for each morph were sequenced, while only a pool of the three samples from the same morph individuals was sequenced for control DNA. The eight different samples (six FAIRE and two Control samples) were 100-pb paired-end sequenced on a single lane. The raw sequenced data for these eight samples are available online at the NCBI under the BioProject accession number PRJNA348188.
DeepTools2  [45]  was used to calculate and represent the FAIRE signal as the ratio between pooled FAIRE reads over Control reads on bins of 10 pb on whole genome. FAIRE signal data were first retrieved along every gene and represented in heatmaps with deepTools2  [45]. The mean FAIRE signal was calculated using R and represented using the ggplot2 package  [67, 75]. In order to estimate a confidence interval for each mean FAIRE signal calculated, bootstrap was done using a custom R script that performed 1000 random resampling of the genes by taking into account the number of genes in each resampled set. 99% confidence interval, which depends on gene number and FAIRE signal values, was then calculated and represented using the ggplot2  [75]  package in the R software  [67].
Funding Gautier Richard 's PhD is funded by a Young Scientist Contract from  INRA, The Division for Plant Health and Environment (SPE) Department. This project was funded by The  French National Research Agency  Project  MiRNAdapt  ( ANR-11-BSV6-01701 ) and by a  French National Institute for Agricultural Research (INRA) ,  The Division for Plant Health and Environment (SPE)   Department Project Grant.
The study protocol included a formal request to participate, which explained the objectives of the study that was delivered by mail before the first telephone call. Informed consent was obtained at the start of the telephone interview, in accordance with the guidelines of the French Data Protection Authority (CNIL). The interviews were conducted using a computer-assisted telephone interview system. In order to obtain adequate statistical power for measuring associations between variables and changes in participation rates at smaller levels, a sample size of between 3500 and 4000 was deemed appropriate. The 2005 Cancer Barometer sample consisted of 4046 participants aged over 16 years interviewed between April and June 2005. 18 There were 226 individuals with missing observations in the 2005 Cancer Barometer sample, notably for all 3 of the dependent variables, and 7 out of 10 covariates and independent variables.
These individuals terminated the survey prematurely, and were thus removed from the analysis as their data were not contributive, leaving 3820 participants in the sample population. Females (51. 5%) responded more often than males (48. 5%) and mean age of interviewees was 46. 7 years. The 2010 Cancer Barometer sample consisted of 3727 participants aged 15-85 years interviewed during the first semester of 2010. 19 The mean age was 44. 6 years and the majority of participants were also female (52. 0% vs 48. 0%). The response rates for the 2005 and 2010 Cancer Barometers were 51. 2% and 47. 0%, respectively.
For colorectal cancer screening, participants aged 50-74 years were asked if they had undergone an FOBT within their lifetime. Covariates included gender, age, smoking status, alcohol consumption, region, living as a couple and having a close relative with cancer. For the calculation of screening participation rates, we added filters to select the target population eligible for each of the three different screening programmes. Breast screening by mammography (n=1546): female gender and 49<age<75. Cervical screening by cervical smear (n=3085): female gender and 24<age<66. Colorectal screening by FOBT (n=2647): both genders where 49<age<75. The weighting was based on the data of the 1999 and 2008 Employment Survey of the French population,  21  taking into account age, gender, region, education level and number of persons per household.
statistical analysis We created a pooled dataset of the two surveys conducted in 2005 and 2010. We calculated age-adjusted screening rates (AAR) for each stratum using the weighting provided by the INPES. The temporal evolution in the participation rate within each stratum between 2005 and 2010 was examined by adding an interaction term for the year of the survey. The disparity within each socioeconomic variable was calculated as the absolute difference between the AAR for the highest and lowest group within an ordinal or binary variable for the given year. ORs, derived from multiple logistic regression of screening participation on each socioeconomic variable were used as a measure of participation likelihood for each stratum of the six socioeconomic variables. The model was adjusted for the covariates: age, gender (colorectal screening only), region, alcohol, smoking, living as a couple and close relative with cancer.
All statistical analysis was performed using SAS V. 9. 2. results Table  1  presents the demographic and socioeconomic characteristics of the study populations. The overall participation rates among the eligible populations for breast, cervical and colorectal cancer screening are shown in table 2. χ 2 tests for the change in participation rates within each socioeconomic stratum between 2005 and 2010 are also included. For mammography, participation rates increased significantly (P<0. 05) among all socioeconomic strata, with the exception of farmers, managers, manual workers, unemployed, those with basic health insurance and education level superior to the baccalauréat. For FOBT, participation rates increased significantly among all socioeconomic strata between 2005 and 2010, with the exception of the unemployed or those with an occupation classified as other.
It is the duty of doctors to organise and falls to the individual to pay for opportunistic screening via a cervical smear test. The lack of a nationally organised screening programme may impose significant financial, educational and cultural barriers to screening uptake among certain sections of the French population. The financial costs for a consultation and laboratory processing of the screening test may deter those with only basic health insurance, as public reimbursement covers only 70% of the cost. 28 This may account for the persistence of the observed differences in participation rates and large RIIs. Improving the awareness, affordability and access to cervical cancer screening should be prioritised in order to increase participation rates and reduce socioeconomic disparities. limitations and strengths of the study Our study used two almost identical datasets to construct a temporal analysis of participation in screening programmes in France between 2005 and 2010.
Acknowledgements This article uses data collected from the 2005 and 2010 Barometre cancer studies, provided by the  National Institute for Prevention and Health Education ,  INPES  (now Santé Publique France). Special thanks to the department of screening at the  Institut National du Cancer (INCA). contributors HN conceived the study, advised on methodology, reviewed the results of statistical analyses and supervised the final edit of the manuscript. DK reviewed the background literature, run statistical analyses and drafted the.
In addition, wound photographs will be taken at each study visit and evaluated via a central database by independent outcome assessors Fig. 2  SPIRIT figure for the IOWISI trial (according to SPIRIT 2013  [22] ). AE adverse event, ALAT alanine aminotransferase, ASAT aspartate aminotransferase, Cr creatinine, Glu glucose, IOWI intraoperative wound irrigation, NNIS National Nosocomial Infection Surveillance, PHX polyhexanide, POD postoperative day, SAE serious adverse event, SSI surgical site infection, ITT intention-to-treat at spatially separated participating study sites. Unblinding of the local principal investigator is permissible if significant hazards for subjects' safety or welfare occur.