dataesr/scientific-paragraphs-categorization huggingface dataset:
Neutronics and code validation The 14MeV neutron rates issued from the D-T fusion reaction should be accurately measured for the scientific exploitation of the JET and ITER D-T experiment (code validation, performance optimisation), for the accurate measurement of the fusion power (including tritium burn and tritium accountancy), and, for a precise estimate of the machine activation within the available neutron limit. On JET, an accurate calibration procedure of neutron detectors at 14 MeV neutron energy ( 235 U fission chambers and the in-vessel activation system) has been developed to measure the fusion power during the future D-T experiments. The 14 MeV neutron calibration is designed using a 14 MeV neutron generator to be deployed in JET by remote handling. The calibration layout and strategy have been developed taking into account the source characteristics, the safety requirements and the limitations imposed by the remote handling capabilities.
Dedicated neutron measurements around JET have been performed to validate the various codes used in ITER to predict quantities such as the neutron flux along streaming paths, the activation of materials, as well as the resulting shutdown dose rates  [212, 213]. For this purpose, several streaming experiments have been carried out, consisting of measuring the neutron fluence and dose-rates in the torus hall and along its ducts. It was demonstrated that the neutron fluence measurements along penetrations of the JET torus hall biological shield are well reproduced by the Monte-Carlo codes over six orders of magnitude of the neutron fluence as shown in figure  27  and reported in  [211]. Calculations have been carried out using ITER relevant Monte-Carlo codes such as MCNP, a validated geometrical model of JET and neutron cross-section data from the ITER reference nuclear data library (FENDL-3).
Benchmark against the AutomateD VAriaNce reducTion Generator (ADVANTG) software, which generates variance reduction parameters for Monte Carlo simulations and which is now increasingly used in ITER to speed up neutronics analyses, has also been performed showing a good agreement between this code and the full MCNP calculations. Shutdown dose rates have been also calculated with various codes (Advanced D1S, R2Smesh, MCR2S and R2S-UNED). A good agreement is found among the codes that reproduce the measured gamma dose rates along the mid-port and in several cells outside the vessel. The measured D-D neutron fluence and gamma dose rates have been compared with simulations performed with the codes used for ITER nuclear analyses. A good agreement is obtained which is an important step to gain confidence in ITER safety assessment calculations  [212, 213].
Acknowledgment The corresponding author would like to warmly acknowledge the impressive collective work of the  JET Task Force Leaders , the  JET  Project Leaders, the JET contributors, the JET operator, the  JET Exploitation Unit  and the  EUROfusion Programme Management Unit  for achieving the scientific results reported in this overview paper. In addition, the corresponding author would like to acknowledge valuable scientific discussions and inputs from the JET international partners, and, in particular from his colleagues at  ITER Organization Central Team. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Circulating tumor cells (CTCs) isolated from blood can be probed for the expression of treatment targets. Immunofluorescence is often used for both the enumeration of CTC and the determination of protein expression levels related to treatment targets. Accurate and reproducible assessment of such treatment target expression levels is essential for their use in the clinic. To enable this, an open source image analysis program named ACCEPT was developed in the EU-FP7 CTCTrap and CANCER-ID programs. Here its application is shown on a retrospective cohort of 132 metastatic breast cancer patients from which blood samples were processed by CellSearch ® and stained for HER-2 expression as additional marker. Images were digitally stored and reviewers identified a total of 4084 CTCs. CTC's HER-2 expression was determined in the thumbnail images by ACCEPT.
To address this issue a CTC image analysis algorithm for identification and characterization of CTC is being developed in the EU funded CANCER-ID & CTCTrap programs. Here we introduce the first version of the Open Source program named ACCEPT (Automated CTC Classification Enumeration and PhenoTyping) that allows for the quantification of treatment targets on annotated CTCs. ACCEPT is a toolbox in which a novel efficient parameter-free multiscale segmentation method is used to identify objects in images captured by several CTC platforms  [16]. Here we investigate a retrospective set of images generated from CTCs detected in metastatic breast cancer patients' blood by the CellSearch 1 system in which the expression of HER-2 is assessed. Images generated by the CellSearch 1 system were used as this system is standardized and in use in multiple centers.
fact that during data export an metadata xml file is created which contains patient information and machine identification. This metadata can of course not be shared but is required by our toolbox to process the images. Therefore we cannot share the raw datasets that contain patient specific information but prepared an excel table where all measurements that we extracted are listed. This data can be used to reproduce all graphs in the publication. The important contribution in our paper is the open-source toolbox that can be used by interested researchers on any CellSearch dataset for reproduction.
Patients In this study CTC images generated by the CellSearch 1 system (Menarini Silicon Biosystems Inc) from 132 patients with metastatic breast cancer were used. 80 patients (36 recruited in the Department of Gynecology and Obstetrics-Ludwig-Maximilians-University, Munich and 44 in the Department of Gynecology and Obstetrics-Heinrich-Heine-University, Duesseldorf) were enrolled in the Detect III study (NCT01619111). The other 52 patients were enrolled in the BEVERLY02 study from Paris (NCT00717405). All patient identifying information is maintained at the clinical sites and no access to this information was available for our analysis. Approval for HER-2 CTC analysis was obtained by the independent ethics committee for both, the Detect III study (Ethical Committee of the Heinrich-Heine University Duesseldorf) and the BEVERLY02 study (Comitede Protection des Personnes Sud Mediterranee I), and all patients provided written informed consent.
Image analysis The images were reanalyzed using the newly developed image analysis toolbox for CTC analysis ACCEPT (Automated CTC Classification Enumeration and PhenoTyping). The software is written in Matlab 2016a (Mathworks, Natick, MA). Next to the source code a compiled standalone executable version, that includes the MATLAB Runtime and allows royalty-free deployment for users who do not need MATLAB, is also available. In ACCEPT objects of interest are segmented based on shape contours and intensity. The underlying algorithm to automatically detect multiple objects with different scales is described in detail elsewhere  [16, 18, 19]. The main tool of the ACCEPT toolbox is a fully automated detection and classification approach for blood samples but in this paper we introduce a second tool, called the Marker Characterization tool, that reproducibly evaluates the marker expression of prescored cells and can aid users to manually score or quantify cells and their fluorescent signals.
This results in a much lower correlation coefficient (r = 0. 51). To compare the number of manually and automatically scored cells to a "ground truth" solution, further experiments are necessary to obtain images of cells where the HER-2 expression is known. Yet, in this work, we concentrate on retrospectively studying samples that were investigated before. The main goal of the Marker Characterization tool of the ACCEPT toolbox is to reproducibly quantify manually scored cells and aid users to unify their scoring results. Yet, any approach that is still to some part manual will not give fully reproducible and unified results. This highly motivates the use of a fully automated approach, as it will be possible in another tool of the ACCEPT software, especially in multi-center studies to unify the scoring results.
The lack of an automated unified approach to designate a cell as a CTC and determine what is and what is not expressed is however impeding progress of the field. To address this issue, an open source image analysis program ACCEPT is being developed in a consorted European effort enabling comparisons between platforms and providing accurate and reproducible information. Here we introduce the first applications of ACCEPT and demonstrate the ability to extract relative expression of antigens expressed by CTCs. In addition, we show that the use of scatterplots and expression levels of the antigens next to the fluorescence images helps to reduce inter reader variability in terms of scoring of HER-2 positivity. Increased concordance in the expression of targetable biomarkers will optimize the performance and results of clinical multicenter studies such as the DETECT trial  [24].
In this study, we used advanced mathematical approaches to identify objects in the images and to extract features from the identified objects  [16]. More importantly, we make the program available for all researchers with an interest in identifying and characterizing CTCs or other objects and enable the comparison of fluorescence signals generated with different imaging platforms. By using the tools provided in ACCEPT differences in the effectiveness of different treatments targeting the HER-2 receptor can be assessed. The ACCEPT program and manual can be downloaded from https://github. com/LeonieZ/ACCEPT. Besides a more reliable and reproducible quantification of therapeutic marker expression, our ultimate goal is to develop a common definition of a CTC. The large variety of cancer and phenotypes makes it of course very challenging but using the input of a variety of users applying a variety of platforms together with the current breakthroughs in the field of machine learning and imaging may help to understand differences and similarities in the CTCs between different cancer and phenotypes in the future and enables us to find a common CTC definition spanning at least a wide range of cancer and phenotypes.
Until then, a unified approach to the analysis of fluorescent images across CTC isolation platforms and CTC phenotypes may increase robustness and lead to a higher reproducibility of results as we have seen it in this study. In its current version, the software can load images from several CTC isolation platforms. Moreover, it contains a general tiff-loader if no specific sample information should be loaded. Loaders for different microscope system can be easily integrated and are work in progress. Further features and tools will be available throughout the IMI CANCER-ID program.
The tool is relatively complex with potentially high data requirements but is modular, and so can be simplified to focus purely on FP costs and benefits. Much further detail on the OneHealth Tool is available on its website. 20 Additional analysis was conducted using Excel. The year 2013 was chosen as the baseline for the analysis as this was when the study was commissioned and 2013 was the latest year for which data were available -including key data from the latest DHS. Our analysis modelled costs and benefits over two time frames: a shorter-term time frame from 2013 to 2017, chosen to align with the Agenda for Prosperity poverty reduction strategy, and a long-term time frame from 2013 to 2035, chosen to align with the country's time frame to become a middle-income country. 21 The longer-term time frame also coincides with that of the Lancet Global Investment Framework, which aims to show what could be achieved in a generation.
5 percentage points a year are possible. 23 ur 'high' scenario modelled a gradual shift towards greater use of long-acting and reversible methods and permanent methods, based on expert advice from United Nations Population Fund (UNFPA) and the Ministry of Health and Sanitation. These scale-up scenarios were compared to a 'no-change' counterfactual of the CPR and method mix remaining constant at their 2013 values. Our analysis should therefore be interpreted as the comparisons between the scale-up and counterfactual scenarios. Table  1  shows the key input data and data sources for the estimation of costs and benefits and modelling of demographic and health impact. How these data were used in our cost-benefit model is explained in subsequent sections.
17  This When provided as an outreach service, minutes per case is assumed to be 9 min longer per case to allow for health worker travel time from the clinic to the community. c Salaries include costs of 34% of basic salaries. Salaries were used to cost the medical personnel requirements from the OneHealth Tool. This analysis is necessarily conducted outside the tool. We used the same assumptions as in (2) below, whereby we assume that medical personnel work on average 8 h a day for 220 days per year and spend 80% of their working time directly on service delivery (as opposed to admin, downtime, etc. ). d For MOHS recurrent, non-salary expenditure, FY2013 estimates are used as the basis for the calculations. For MOHS salary expenditure, 2013 gross monthly salaries are used. e IMF data on end of period consumer inflation were used to inflate 2008 USD costs from WHO-CHOICE to 2013 USD prices.
In the medium scenario, unmet need increases less rapidly but takes longer to peak. These scenarios are compared to the counterfactual scenario of unmet need remaining constant at 25. 0%. In terms of mortality impact, there are modelled to be fewer maternal and infant deaths in the FP scale-up scenarios. The maternal mortality rate (MMRate) (The modelling approach of the FamPlan module within the OneHealth Tool means that the maternal mortality rate is more suitable for this analysis than the maternal mortality ratio because it captures the demographic impact of FP on maternal deaths. ) is projected to decrease from 176 maternal deaths per 100,000 women age 15-49 years to an MMRate of 122 in 2035 in the medium scenario and 92 in the high scenario in 2035. This is compared to an MMRate of 151 by 2035 in the counterfactual scenario.
445. 120025 ions were used as lock mass for internal calibration. The 10 abundant precursor ions were selected and collision-induced dissociation fragmentations were performed in the ion trap on the 10 most intense precursor ions measured to have maximum sensitivity and maximum amount of MS/MS data. 500 counts was set as signal threshold for an MS/MS event. Charge state screening was enabled to exclude precursors with 0 and 1 charge states. Dynamic exclusion was enabled with a repeat count of 1, exclusion list size 500 and exclusion duration of 30 s. Bioinformatic analysis. Raw files generated from MS analysis were processed with Proteome Discoverer 1. 4 (Thermo Fisher Scientific). This software was used to search data via in-house Mascot server (version 2. 4. 1; Matrix Science Inc. , London, UK) against the mouse subset (16,718 sequences) of the SwissProt database (2015_06).
Database search were done using the following setting: a maximum of two trypsin miscleavages allowed, methionine oxidation, N-terminal protein acetylation as variable modifications, and cysteine carbamido-methylation as fixed modification. A peptide mass tolerance of 6 ppm and a fragment mass tolerance of 0. 8 Da were used for search analysis. Only peptides with high stringency Mascot score threshold (identity, false discovery rate (FDR) <1%) were selected and used for protein identification (unique peptides). Relative intensity-based label-free quantifications (abundance scores) were processed using Progenesis LC-MS software (version 4. 1; Nonlinear Dynamics, Newcastle, UK), according to the manufacturer's instruction (Nonlinear Dynamics, Newcastle, UK). First, raw LC Orbitrap MS data were imported and LC-MS heatmap of retention time and m/z were generated.
Features from these LC-MS were automatically aligned and filtered to retain signals crossing the following parameters; retention time 10-110 min, m/z 300-1700, charge state 2-6 and 3 or more isotopes. MS-MS spectra were exported into peak list as Mascot generic files (MGF) to allow protein identification using the following inclusion options: the three highest intensity precursors for each feature and precursor intensity over 25%. MGF files were used to search data via in-house Mascot server version 2. 4. 1 (Matrix Science Inc. , London, UK) against the Mouse database subset of the SwissProt database (version 2015_06). Only peptides (unique peptides) adjusted to 1% FDR (identity) and with ion score cut-off of 30 were exported from Mascot results and imported back to Progenesis LC-MS for protein grouping and quantification.
Total ion intensity signal from each of the individual peptides generated protein quantification. Any conflicting peptide identifications were removed from the measurements of the quantified proteins. Univariate one-way analysis of variance (ANOVA) was performed within Progenesis LC-MS to calculate the protein p-value according to the sum of the normalized abundances across all runs. We analyzed the enrichment of gene ontology (GO) terms, using the tool GoTermFinder  50  , and protein domains by protein families database, using the tool SMART  51  and python in-house scripts (Supplementary Data 3). The majority of Ago2 interactors identified were RNA-binding proteins. Although, our data clearly demonstrated an enrichment of RNA-related terms and domains for both RNAdependent and RNA-independent interactors of Ago2, the proteins showing RNAdependency were particularly enriched in post-transcriptional regulation terms and molecular functions, such as mRNA stabilization and mRNA binding (Supplementary Fig.
PCR products represented cDNA inserts of 20-50 nt. Libraries were quantified using both bioanalyzer (Agilent) and Qubit (Invitrogen) and were subjected to sequencing by Ion Proton sequencer (Life Technologies). Bioinformatic analysis. Reads were mapped onto the mm10 genomes using STAR  54  , then unmapped reads were mapped with bowtie2  55. Total number of reads mapped are reported in Supplementary Data 5. The reproducibility of the replicates for each condition was assessed by performing principal component analysis using the R package "htSeqTools" (R package version 1. 14. 0). Afterward, replicates were merged together to assess bioinformatics analysis. Ago2 peaks specific of let-7a-transfected cells condition were found by comparing let-7atransfected samples with the control using dCLIP program  56.
We filtered out peaks with score less than 7 and have identified 4202 Ago2-differential binding peaks (here called Ago2-let-7a peaks, Supplementary Data 5). To identify let-7a canonical-binding sites, we screened the identified Ago2-let-7a peaks sequences in the 3′ UTR looking for the reverse complement of the seed sequence (UACCUC). Identical approach was used to identify the canonical-binding sites for the 21 most endogenously expressed miRNAs (Supplementary Data 5). To map Sfpq-binding sites we used dCLIP program by comparing Sfpq HITS-CLIP in siSfpq-transfected P19 cells with siCtr-transfected cells. We only selected peaks with a score greater than 7. To investigate the potential role of Sfpq to promote let-7a targeting, we checked whether the identified Ago2-let-7a peaks were reduced in absence of Sfpq.
In Supplementary Data 7 is shown the recruited Ago2-let-7a or Ago2-miRNA peaks in siSfpq-transfected P19 cells. Spatial correlation of the Sfpq-dependent recruited Ago2 peaks (for both let-7a and endogenous miRNAs) compared to the decreased ones was performed with the R package GenometricCorr  58. Briefly, we found that the two sets of Sfpq-dependent recruited and reduced Ago2 peaks are spatially correlated across the genome, calculated the significance as a deviation from a non-uniform distribution of one set of peaks over to the other one. All analyses were performed with custom python scripts where not specified elsewhere. User-friendly website for Ago2 and Sfpq CLIP data sets. We created a database in MySql language with a web interface developed in php language to easily access to the entire mapping of Sfpq, Ago2-let-7a, or Ago2-miRNA peaks identified by HITS-CLIP analysis.
The database can be interrogated by the following four different keys: (1) "gene identifier", which provides all peaks on the requested gene; (2) "genomic category", which provides all peaks that map in specific genomic category, including 3′UTR, 5′UTR, CDS, introns, TTS, promoters, intergenic, and ncRNA; (3) "miRNA name", to access to all peaks bearing the signature of let-7a or the 21 most expressed miRNAs in P19 cells (Supplementary Data 5); and (4) "peak category", which provides all peaks that belong to a specific category as assessed by this study, including Sfpq peaks, Ago2 peaks, Sfpq-dependent decreased Ago2 peaks localized within 500 nt from Sfpq peaks, Sfpq-dependent decreased Ago2 peaks localized between 500 and 7000 nt from Sfpq peaks, and Sfpq-dependent decreased Ago2 peaks localized more than 7000 nt from Sfpq peaks.
One or more keys can be combined to explore the data from different points of view. The database is freely accessible at http://trabucchilab. unice. fr/SITO/index. php#. Search for Sfpq-binding motifs and clusterization. To identify the putative binding motif(s) of Sfpq that would play a role in miRNA targeting in the 3′UTR, we divided the data set of Sfpq peaks from the HITS-CLIP analysis in the following three subsets: (i) all Sfpq peaks; (ii) Sfpq peaks located in the 3′UTR; and (iii) Sfpq peaks located in the 3′UTR within a distance of 500 nt from the Sfpq-dependent reduced Ago2-let-7a peaks. Then, we launched de novo motif discovery with a length between 4 and 6 nt. We used two motif finding programs: Homer 59 and DREME  60. We observed that all these analyses reported significantly enriched motifs with a core composed by UGU.
Thus, we performed motif clusterization with the program "Gimmemotifs" 41 (Supplementary Fig. 10a ) and found two consensus motifs of 4 and 5-nt long, respectively (Fig. 6d ). Each motif corresponds to many different words that belong to the motif with different probabilities. Hence, to set up a threshold on the number of words to look for, we calculated the occurrences of the two motifs in the sequences of the three Sfpq peak subsets previously selected. We used the tool FIMO 61 using different p-value cutoffs associated to the motif likelihood established by the program. For cutoffs higher than 0. 01 for motif#1 and 0. 005 for motif#2, we found many occurrences superimposed. Thus, to avoid superimposition, we established the aforementioned cutoffs, which correspond to two words for the motif#1, namely, CUGU and CUGA, and four words for motif#2, namely, CUGUA, AUGUA, UUGUA, and GUGUA.