dataesr/scientific-paragraphs-categorization huggingface dataset:
More importantly, its results are known to be as accurate as those from analytical solutions. Hence we implemented Dual Number Automatic Differentiation  (Yu and Blair, 2013)  in the mocsy package, modifying its routines that compute equilibrium constants and carbonate system variables. By comparing results from derivnum to those from derivauto, we optimized the step size used to compute numerical derivatives in derivnum. Then derivnum, including its optimized step sizes for each derivative, was translated into three other languages, from Fortran 90 (for mocsy) to R (seacarb), MATLAB (CO2SYS-MATLAB), and Visual Basic for Applications (CO2SYS-Excel).
Conclusions We have written software to propagate uncertainties and provided it as add-ons to four commonly used public packages that compute marine carbonate chemistry. These packages include CO2SYS-Excel (Visual Basic), CO2SYS-MATLAB (MATLAB), seacarb (R), and mocsy (Fortran). The last three can be used in interactive Jupyter notebooks, while the last two can be used directly in Python (Appendix D). Results agree across packages and with analytical solutions (for the intermediate sensitivities) to better than 0. 01%. Given the consistent implementation across packages, users may use any of the four updated packages to compute accurate numerical buffer factors and propagate uncertainties. Uncertainties can now be propagated routinely as easily as derived variables can be computed, whether working with preexisting or new data sets.
Generally, it has not been appreciated that the relative combined standard uncertainties for Ω A and Ω C are substantially larger than that for [CO 3 2-] because of the large standard uncertainties in the solubility products for aragonite and calcite. It is hoped that the new software and the new type of diagram proposed here will be used widely and expanded upon so that propagating uncertainties will become standard practice when calculating marine CO 2 system variables. Although the convenience of these tools offers a step forward, more is still required. The uncertainties from the various constants need to be evaluated more rigorously as emphasized above. Scientists will also need to assess their measurement uncertainties more carefully than has usually been the case. To assist with this fundamental effort, there are two valuable resources: the Eurachem Guide on quantifying uncertainty in analytical measurement  (Ellison and Williams, 2012)  and a software package known as the GUM Tree Calculator  (Hall, 2013; Lovell-Smith et al.
To calculate sensitivities, seacarb's derivnum function is called as follows: where only one argument varid has been added to the beginning of the same list of arguments as used for seacarb's carb function to define the input variable for which partial derivatives of computed variables will be taken with respect to. All arguments are detailed and examples given in seacarb's documentation at the CRAN site mentioned above. Further documentation is provided as interactive Jupyter notebooks at https://github. com/jpgattuso/seacarb-git. Those can be downloaded and used locally, after installing the software that is indicated in the HTML version of the notebook file (after clicking on the name of the file on the GitHub site). Alternatively, these notebooks can be used interactively and modified without installing local software by directing one's browser to https://mybinder.
D. 4. Mocsy The mocsy routines including the new add-ons for uncertainty propagation and sensitivity calculations can be downloaded from https://github. com/jamesorr/mocsy/. The errors routine may be called from within Fortran or Python. In Fortran, it may be called as where the first line of arguments is the calculated combined standard uncertainties (output), the second line is the standard input (as in mocsy's vars routine), the third line is for the standard uncertainties (input), and the final line is for the options (also the same as in the vars routine). Similarly, mocsy's derivnum routine is called as where the first two lines are the calculated partial derivatives (output), the fourth line is the input variable for which partial derivatives of computed variables will be taken with respect to, and the third and fifth lines are the input and options as in the errors routine.
In Python, the mocsy errors routine may be called as follows: the first line the combined standard uncertainties (output), the second line is the routine specification, the third line is the standard input, the fourth line includes the standard uncertainties (input), and the fifth line includes the standard options. As with the Fortran version, users mayadd three optional arguments: where r is for the correlation between ealk and edic, epk is for the vector of uncertainties for 7 equilibrium constants (in pK), and ebt, is for the uncertainty in total boron-to-salinity ratio (fractional units). The call in Python for mocsy's derivnum routine takes the following form: The mocsy archive site mentioned above also provides more detailed documentation examples, including Fortran routines, Python scripts, and interactive Jupyter notebooks.
Acknowledgments This effort was proposed by the  SOLAS-IMBER Ocean Acidification Working group (SIOA)  and the  Advisory Board  for the  Ocean Acidification International Coordination Centre (OA-ICC)  as an effort to promote best practices. It was funded by  OA-ICC , a project of the  Peaceful Uses Initiative of the International Atomic Energy Agency (IAEA) , which supported the consultancy of JME. We thank  Lina Hansson  for managing this project within the  OA-ICC  and  Cathy Nangini  for developing the interactive interface that produces error-space diagrams. JCO was supported by the  EU H2020  Project  CRESCENDO  (grant # 641846 ) and the  French ANR  project  SOBUMS. AGD was supported, in part, by the  US National Science Foundation  under grants  OCE-1233648  and  OCE-1657799. JPG was supported by the  EU-H2020  project  INTAROS  (grant # 727890 ) and  IPEV , the  French Polar Institute.
DISCUSSION In order to verify the reliability of the proposed simulator, this section aims to compare the emotional predictions to real feedbacks. The experimentation is carried out in the context of a programming course in an engineering school. It is based on the SG "Co-deCombat" which is a multi-players game designed for learning programming languages. Given the purpose of this experimentation work, it is composed by two parallel steps: a simulation of the considered learning process based on the proposed tool and a real performing of a programming learning session based on "CodeCombat". Once the two steps are achieved, their results are compared. After the learning session, we proposed a specially designed questionnaire to all the participants in order to report the emotions felt by each player and to verify their conformity to the simulator results.
statistical analysis Unsupervised hierarchical clustering analysis was performed using RStudio™ Version 0. 98. 501 (RStudio, Inc. , USA). The global profiling results were analyzed using one-way ANOVA on SPSS version 21 (IBM, SPSS Inc. , USA). Quantitative RT-PCR results were processed using SDS 2. 4 (Life Technologies, USA, v2. 4). Differential expression of microRNAs in skin biopsies between all groups (controls, pre-transplant, grades 0-I, and grades II-III/IV) was determined using Kruskal-Wallis one-way analysis of variance with Dunn's post hoc test on GraphPad Prism 5 (GraphPad Software, Inc. , USA). Significance was set at p < 0. 05. Differential expression of microRNAs in serum samples between two groups was assessed using Mann-Whitney U test on GraphPad Prism 5. To determine trends in the expression levels for aGvHD severity, the Jonckheere-Terpstra test (SPSS version 21) was also performed for the microRNAs in the signature list.
Generalized linear models (ordinal logistic regression) (GLMs) were performed to find the significance and statistical interactions between microRNAs in the signature list using SPSS version 21 (IBM, SPSS Inc. , USA). The main effect is statistically defined as an effect of an independent variable on the outcome, while an interaction is a measure of two or more independent variables on the dependent outcome  (21). In this scenario, the main effect was the effect of the individual miRNA expressions or significant clinical risk factors on aGvHD outcome and the interaction was a measure of the effect of one independent miRNA expression level as it differed at every condition in comparison to the other independent miRNA expression levels  (21). In order to predict aGvHD outcome from a list of covariates, GLMs were built using the binary and/or ordinal logistic regressions.
The equation for the binary or ordinal regression models that was used for determining aGvHD grade (outcome) was aGvHD grade (cumulative logit function) = β0 + β1 (miRNA expression 1) + β2 (miRNA expression 2) + β3 (miRNA expression 1 × miRNA expression 2). β0 = intercept coefficient and β1-3 = the coefficient of each miRNA expression. Rotational 3D scatterplots were generated using JMP ). OS was calculated using the time in months from transplant to death or last follow-up (October 2014). Survival plots were generated using the Kaplan-Meier (K. M) method and differences in outcome were assessed for significance using the Log-Rank (L. R) test (SPSS v21). The threshold to determine dichotomies for microRNA expression (low and high expression) was evaluated by ROC analysis. resUlTs.
Thus, patient groups were dichotomized according to skin stage 0 versus 1-3 or overall grade 0 versus I-III for subsequent analysis. miR-503-5p (p = 0. 001), miR-34a-5p (p = 0. 005), and miR-34a-3p (p = 0. 004) were expressed at a higher level in patients at D28 post-HSCT who developed both skin and overall aGvHD (stages 1-3, grades I-III) (n = 17), compared with patients with no skin or overall aGvHD (n = 13) (Figure  5A ). When microRNAs were assessed in relation to individual skin aGvHD stage or clinical aGvHD grade, there was no significant difference in expression between disease stages or grades (p > 0. 05, data not shown), indicating the microRNAs to be associated with skin and overall aGvHD incidence, but not severity (data not shown). Following identification of differential expression of candidate microRNAs in patients who developed skin and overall aGvHD, ROC analysis was performed to identify time pointspecific microRNA prognostic ability.
MULTI-TURN SIMULATIONS There is no standard simulation package that integrates the detailed study of the target phenomenology and the accelerator performance simultaneously, therefore, the multi-turn simulation has been divided in two parts: particle tracking and positron interaction with target. Particle tracking in the ring is performed with AT and MAD-X PTC, while positron interaction with the target is done either with GEANT4  [9]  or FLUKA  [10]. The effect of the target on the positron beam has been studied for different locations through the cell and at the IR, with different materials and thicknesses. Results from AT/GEANT and MAD-X PTC/GEANT or MAD-X PTC/FLUKA are compared and described in the following. Further studies are needed to optimize the scheme. Figure  5  shows the number of particles as a function of machine turns.
Migration assay Migration was assessed by wound closure assay as previously described  [5]. Briefly, Cells cultured at confluence in 24-well plates were scratched with a small tip along the ruler. Cells were then cultured for 6, 24 or 48 h in starvation media with or without MPs. The distances between the edges of the scratch were measured at 0 h and 6, 24 or 48 h after scratching. Data are represented as rate of wound closure.
Flow cytometry Fluorescence (FL) was quantified on a SORP FACSAria2 (BD Biosciences). Data were processed with FACS Diva 6. 3 software (BD Biosciences) as previously described  [19]. Doublets were excluded by FSC-W × FSC-H and SSC-W × SSC-H analysis; calcein-AM and Annexin V were acquired with 488 nm blue laser and 510/50 nm emission, PI was acquired 488 nm blue laser and 670/14 nm emission. Charts display the median of fluorescence intensity (mfi) relative to control. Single stained channels were used for compensation and fluorophore minus one (FMO) controls were used for gating. 20,000 events were acquired per sample.
Western blot analysis Western blot were carried out as previously described  [7]. Immunostaining was carried out using a rabbit monoclonal caspase3, caspase9, actin and PhosphoAKT antibody (1/1000, Cells signaling) and a secondary polyclonal mouse anti-rabbit antibody HRP conjugated (1/2000, cell signalling). Blots were developed using HRP and chemiluminescent peroxidase substrate (#CPS1120, Sigma). Data were collected using Geliance CCD camera (Perkin Elmer), and analyzed using ImageJ software (NIH).
V. , France). Standardized quantitative analysis of IL8 immunostaining was based on Image J software (National Institutes of health, USA). Briefly, a grid delimitating 10,000 μm 2 squares was applied to each photograph, and 3 randomly selected squares were analyzed. We used the Image J cell counter plugin to count the stained an unstained tumor cells within each square. IL8 expression was defined as the rate of stained tumor cells.
Antibodies for surface markers were added for the last 30 min of labeling, using the following combination: CD3-eF780-APC, CD20-Viogreen, CD14-Viogreen, CD8-BV785, CD4 PE-CF594, CD45RA-BV421, CCR7-PE-Cy7, CXCR5-AF488, PD-1-PE, and Viability-eF506. Cells were washed twice with PBA (phosphate-buffered saline [PBS], 1% bovine serum albumin [BSA], 0. 09% azide) and fixed using 2% paraformaldehyde (PFA) in PBS. Fluorescence was collected on an LSR Fortessa flow cytometer (BD Biosciences). All flow cytometry experiments were analyzed with FlowJo v8. 8 software (Tree Star Inc. ). Analysis was performed on singlet viable single cells, gated according to morphological criteria and viability dye expression. For cTfh phenotyping, 1 ϫ 10 6 cells were stained in a total volume of 100 l PBA with the same antibody panel as described above for 30 min at 4°C.
ACKNOWLEDGMENTS This study was carried out in the frame of the  ANRS EP36 HIV Controllers Study Group. We are grateful to patients who participated to the study. We thank the clinicians who recruited patients for this study:  Huguette Berthé ,  Dominique Bornarel ,  Olivier Bouchaud ,  Patricia Honoré ,  Philippe Genet ,  Juliette Gerbe ,  Olivier Patey ,  Laurent Richier ,  Katia Bourdic ,  Pierre-Marie Girard ,  Bénédicte Lefebvre ,  Michèle Pauchard ,  Jean-Michel Molina ,  Sylvie Parlier ,  Caroline Lascaux-Combe ,  Samuel Ferret ,  Valérie Garrait , and  Isabelle DeLacroix-Szmania. We thank  Véronique Avettand-Fenoel  for discussions. We acknowledge the  Cytometry and Biomarkers UTECHS  at the  Pasteur Institute Center for Translational Science  for support in conducting this study. MHC-II tetramer reagents were provided by the  NIH Tetramer Core Facility at Emory University.
The recruitment of these two unusual RNA processing pathways to the replacement plastid from a lineage with no known history of either, and the genomic changes associated with plastids in a dinoflagellate cell, represent a fascinating system for studying the organellar interactions involved in endosymbiosis. As molecular genetic techniques have not yet been successfully applied in dinoflagellates these studies have been restricted to sequence-based analyses, based on relatively small selections of plastid genes, with only two full genomes that include extensive plastid editing analyzed from one fucoxanthin and one peridinin dinoflagellate (Karl. veneficum and S. minutum, respectively)  (Mungpakdee et al. 2014; Richardson et al. 2014). In this study, we have focused on the evolution and function of RNA editing across peridinin and fucoxanthin dinoflagellate lineages in an effort to answer two questions: what are the observable effect(s) of editing, and why is the pathway maintained in extant taxa, including in serially acquired fucoxanthin plastids? To this end, we use bioinformatic tools to analyze properties of RNA editing at genome-wide scales, and apply these to a comprehensive data set spanning the diversity of dinoflagellates and including novel plastid transcriptomic and genomic data from the peridinin dinoflagellate Pyrocystis lunula and plastid genomic data from the fucoxanthin dinoflagellate Kare.
They were sequenced on one lane for 100 cycles from each end on an Illumina HiSeq2000 platform to produce 100 nt reads. The gDNA library yield was 69,846,936 paired end reads and the RNA library was 141,434,468 paired end reads. Transcriptomes were assembled from read data using SOAPdenovo v1. 0. 3  (Luo et al. 2012 ), Trinity v2. 2. 0  (Grabherr et al. 2011), and Velvet v1. 2. 10 (Zerbino and Birney 2008). For each assembler, the default k-mer size was used  (63, 25, and 31, respectively). Putative chloroplast transcripts were identified from the assemblies using standalone tBLASTx  (Altschul et al. 1997)  to compare predicted proteins to all Genbank-archived plastome records (accessed April 2013). Contig translations that matched an existing coding region with an E-value of 0. 001 or less were considered significant and binned.
Bacterial contaminants resulting from the xenic P. lunula cultures were removed by analyzing BLAST searches against the nr database (NCBI; downloaded June 25, 2013). Here, a more stringent E-value cutoff of 1Â10 À10 was employed. If the taxon label for the most statistically significant hit contained the string "bacter," the contig was removed. Finally, the resulting contigs were sorted and separated into files based on the gene name of the top BLAST hit. Putative mRNA sequences were selected from Trinity contigs using a pipeline of three Python scripts (available from https:// github. com/DacksLab/RNAediting; last accessed  March 21, 2018). The first extracts any potential ORF with a minimum length of 40 amino acids from the contig data. The second script executes a BLASTp search on each ORF found, comparing it to a custom database of 42 dinoflagellate plastid protein sequences, including all 12 proteins encoded in peridinin dinoflagellate plastids  (Dorrell et al.
2017). The final script parses the output from the BLASTp searches and selects the most likely protein coding contig for a given gene based on e-value and contig length. Once a best candidate was identified, a second draft mRNA sequence for each gene was produced using the best candidate transcript for a template-based assembly using Geneious v8. 1  (Kearse et al. 2012). Second draft mRNAs were then used as template sequences to assemble gDNA contigs. After assembly of DNA reads to each transcript, the transcript template was removed and the consensus sequence saved as a first draft gDNA assembly. Each gDNA first draft was used as a template to repeat the assembly process and create gDNA second draft assemblies. Final gDNA and mRNA assemblies were translated using Virtual Ribosome  (Wernersson 2006)  or Geneious.
tRNA screens were performed using tRNAScan and ARAGORN  (Lowe and Eddy 1997; Laslett and Canback 2004). Details for each P. lunula gene, including mapped genomic and transcript reads, as well as the determined location of genes on minicircles, are provided (supplementary table  S1 , Supplementary Material online). Karenia mikimotoi RCC1513 was grown in modified f/2 medium under a 12/12 h cycle of 30 mE m À2 s À1 as described in  Dorrell and Howe (2012). Cells were harvested for DNA extraction 4 weeks after inoculation from $200 ml late exponential phase culture, and DNA was isolated using phenol chloroform extraction as described in  Barbrook et al. (2012). About 400 ng crude DNA, as quantified using a Qubit fluorometer (Invitrogen) was used to generate a sequencing library with a NexteraXT tagmentation kit (Illumina), and sequenced over 500 cycles using a MiSeq sequencer.
Reads were trimmed using the MiSeq reporter version 2. 0. 26, and assembled into 574,711 contigs using ELAND (Illumina). Contigs of probable plastid origin were identified using reciprocal BLASTn and tBLASTx searches, using as queries transcript sequences encoded within the Kare. mikimotoi plastid, previously confirmed experimentally  (Dorrell et al. 2016) , and a BLAST cutoff value of E-05. Genomic sequences were confirmed by alignment against the corresponding transcript sequences using the built-in alignment programme within Geneious v4. 76  (Kearse et al. 2012 ) using default settings. The sequences of 16 plastid genes, for which contigs spanning > 700 bp were obtained through the next-generation sequencing approaches, were verified by PCR using primer sequences designed against the contig in question, and Pfu High-Fidelity Polymerase (Thermo), following previously defined methodology  (Dorrell et al.
PsbA Alignment and Phylogeny gDNA and mRNA psbA sequences for 15 peridinin dinoflagellate species were extracted from a previously constructed sequence library  (Dorrell et al. 2017) , alongside plastid genomic and transcript sequences for Kare. mikimotoi and Karl. veneficum. PsbA was selected as the number of species for which sequences are available is vastly greater than other dinoflagellate plastid-encoded genes, allowing a phylogenetically sensitive appraisal of the distribution of editing sites  (Dorrell et al. 2017). gDNA and mRNA sequences from the same species were first aligned to one another, trimmed, and then globally aligned using the translation sequence, with Geneious v 4. 76  (Kearse et al. 2012). A tabular form alignment, including annotations of all editing events observed, is provided (supplementary table S5, Supplementary Material online).
Automated Analysis of Editing Events Automated editing event detection was carried out using custom Python scripts (available from https://github. com/ DacksLab/RNAediting; last accessed March 21, 2018). Sequence translation used the standard genetic code, as there is no evidence for systematic differences in the genetic code applied to internal residues in plastid transcript sequences in previous studies of peridinin and fucoxanthin dinoflagellates  (Dorrell et al. 2016 (Dorrell et al. , 2017)) , from plastid genome sequences of free-living haptophytes  (Puerta et al. 2005; Hovde et al. 2014) , or in our data set (supplementary table  S2 , Supplementary Material online). Alignments were built from our initial sequences using MUSCLE v. 3. 8. 31  (Edgar 2004 ) with default settings. Nucleotide alignments were scanned from the first aligned base to the last, whereas amino acid alignments were taken to start and end when at least five of ten overlapping bases were identical.
Simulations to Assess Cluster Significance For each N-length gene, a sequence of equal length comprising a random sequence of nucleotide bases was generated using the Python random module. For each generation in the simulation, the same number of editing events as observed in the relevant gene was applied randomly across this sequence. Editing events were not allowed to occur in the same position multiple times. Clustering of editing events would lead to larger deviations from the mean rate of editing across the length of the sequence, and hence a larger variance in observed values between simulated and real data. About 1,000 simulations were run for each gene and variance in observed editing rates across all windows (W ¼ 60) was calculated using the levene function of the SciPy library v0.