dataesr/scientific-paragraphs-categorization huggingface dataset:
A modeling study, using the MCNP6. 1 particle transport software package  15  was conducted to simulate the response of the plastic scintillator loaded with lithium tetraborate nanoparticles. The aim of this Monte-Carlo study is to provide a description of the response trend of the sensor when confronted to 25 meV, thermalized neutrons. The neutron source is considered homogenous and isotropic, surrounding the sensor. The key parameter appears to be the nanoparticle loading weight. Let us consider a cross-linked plastic scintillator with a suitable composition for fast neutron/gamma discrimination. 16 The density ^. = 1. 07 g. cm * , and the elemental composition (C, H, N, O) are injected into the code. The embedded crystals are of composition Li2B4O7. The scintillator medium was modeled according to the dimensions of the synthetized samples that are experimentally studied in the following section, i.
(Mass: 1. 96 g, Yield: 77%). Nuclear Magnetic Resonance (NMR) Spectroscopy. 1 H and 13 C NMR spectra were obtained at 400. 13 Hz and 100. 62 Hz respectively, on a Bruker Advance III Nanobay 400 MHz spectrometer coupled to the BACS 60 automatic sample changer. All spectra were processed using MestReNova 6. 0 software. The chemical shifts of all 1 H signals were measured relative to the expected solvent peaks of the NMR solvent; 2. 50 ppm (DMSO-d 6 ). The data for all spectra are reported in the following format: chemical shift (integration, coupling constant J (Hz), multiplicity). Multiplicity is defined as; s = singlet, d = doublet, t = triplet, q = quartet, dd = doublet of doublets, dt = doublet of triplets, and m = multiplet. Subsequent abbreviations also include J (Hz) = coupling constant in Hertz.
The 1 H and 13 C NMR spectra and MS data for lumefantrine hydrochloride, lumefantrine dodecyl sulfate, and lumefantrine docusate are provided in the supporting information. Liquid Chromatography-Mass Spectroscopy (LC-MS). LC-MS chromatograms were obtained using an Agilent 6100 Series Single Quad LC/MS coupled with an Agilent 1200 Series HPLC, 1200 Series G1311A quaternary pump, 1200 series G1329A thermostatted autosampler, and 1200 series G1314B variable wavelength detector. The conditions for liquid chromatography were: reverse phase HPLC analysis using a Phenomenex Luna C 8 (2) 5 µm (50 × 4. 6 mm) 100 Å column at a temperature of 30 • C. 5 µL of sample was injected and the sample was run in solvent A of 99. 9% acetonitrile, 0. 1% formic acid with a gradient of 5-100% (v/v) solvent A over 10 min.
Solvent B was 99. 9% water with 0. 1% formic acid. Detection was at a UV wavelength of 254 nm. The conditions for mass spectrometry were: quadrupole ion source with multimode-ES, drying gas temperature 300 • C, and vaporizer temperature 200 • C. The capillary voltage was 2000 V in positive mode, or 4000 V in negative mode and the scan range was 100-1000 m/z with a step size of 0. 1 s over 10 min. High-Resolution Mass Spectrometry. All high-resolution mass spectrometry analyses were performed on an Agilent 6224 TOF LC/MS Mass Spectrometer coupled to an Agilent 1290 Infinity HPLC (Agilent, Palo Alto, CA, USA). All data were acquired and reference mass corrected via a dual-spray electrospray ionization (ESI) source. Each scan or data point on the Total Ion Chromatogram (TIC) is an average of 13,700 transients, producing a spectrum every second.
Mass spectra were created by averaging the scans across each peak and background subtracting against the first 10 s of the TIC. Acquisition was performed using the Agilent Mass Hunter Data Acquisition software version B. 05. 00 Build 5. 0. 5042. 2 and analysis was performed using Mass Hunter Qualitative Analysis version B. 05. 00 Build 5. 0. 519. 13. The MS conditions were: electrospray ionization, a drying gas flow of 11 L/min at a temperature of 325 • C, a nebulizer at 45 psi, a capillary voltage of 4000 V, the fragmentor at 160 V, the skimmer at 65 V, and the OCT RFV of 750 V. The scan range acquired was 100-1500 m/z. The internal reference ions in positive ion mode had a m/z of 121. 0509 and 922. 0098. Chromatographic separation was performed using an Agilent Zorbax SB-C18 Rapid Resolution HT 2.
The pH-stat was connected to a PC and operated via Tiamo 2. 0 software (Metrohm ® )  [35]. First, 1. 100 g of formulation was dispersed in 40 mL of bile salt/phospholipid micelles in simulated intestinal fluid (2 mM tris-maleate, 150 mM NaCl, 1. 4 mM CaCl 2 •2H 2 O, 3 mM NaTDC, 0. 75 mM PC, pH 6. 5, 37 • C) and then 4 mL of pancreatic enzyme extract (pancreatin) was added to stimulate digestion. The pancreatic enzyme was prepared by mixing 1 g of porcine pancreatin with 5 mL of lipolysis buffer and 20 µL of 5 M NaOH. After mixing, this was then centrifuged at 2880× g at 5 • C for 10 min (Crown Scientific, Eppendorf AG Centrifuge 5804 R, Sydney Australia) and the supernatant used as the enzyme solution. The lipolysis buffer was prepared by dissolving 0. 474 g of tris maleate, 0. 206 g of CaCl 2 •H 2 O, and 8.
Validation of the lumefantrine HPLC assay was run over two days. Intra-assay accuracy was determined by replicated analysis (n = 5) of three standards at the lowest, middle, and highest concentrations (50, 5000, and 20,000 ng/mL). Inter-assay accuracy was determined on two separate days. The data were expressed as a percentage of the measured concentration over the theoretical concentration. The mean accuracy of the lowest concentration (50 ng/mL) was within ±15% of the theoretical concentration, while the mean accuracy of the middle and highest concentrations (5000 and 20,000 ng/mL) was within ±10% of the theoretical concentration. Intra-assay precision and inter-assay precision were calculated in both runs for each of the three concentrations and expressed as the coefficient of variation.
The rats were fed 8 h after dosing. The general approach to the doses chosen for the in vivo studies was based on the desire to provide evidence of utility of the IL approach and to provide a comparison between solution and suspension formulations. In general, the potential drug load that could be dissolved using the IL was much higher than that that could be achieved with the free base, and so to compare exposure at a fixed dose (and typically a relatively high dose since this was possible with the IL technology), the IL formulations were solutions whereas the free base formulations were suspensions. However, data were also collected (where possible) at a fixed dose when both free base and IL form were in solution. These studies had to be conducted at a lower dose to allow the free base to be in solution.
Data acquisition and processing was performed using LC-MS Solutions software (Shimadzu, Kyoto, Japan). The desolvation line (DL) and heat block were kept at 250 • C and 400 • C, respectively. The nebulizing gas flow and drying gas flow rates were 3. 0 L/min and 10. 0 L/min, respectively. Mobile phase A was Milli-Q water containing 0. 1% w/v formic acid, and mobile phase B was acetonitrile containing 0. 1% w/v formic acid. The mobile phase flow rate was 0. 5 mL/min with the following gradient elution: mobile phase B was first held at 30% for 1. 25 min, then linearly increased to 80% over the next 0. 75 min. Mobile phase B was then held at 80% for 0. 5 min, followed by a linear decrease to 30% over the next 0. 5 min after which conditions were held for another 0. 5 min. Each sample (1 µL) was injected onto a Phenomenex Kinetex ® C18 column (2.
6 µm, 100 Å, 50 × 2. 1 mm, Sydney, NSW, Australia). The retention times of lumefantrine and the internal standard (halofantrine hydrochloride) were 2. 1 and 1. 9 min, respectively. Lumefantrine detection was achieved using positive electrospray ionization with multiple-reaction monitoring (MRM) of the 530. 2 > 512. 2 mass/charge ion peak (m/z) at a collision energy of -25. 0 V. The internal standard halofantrine hydrochloride was monitored at 500. 2 > 142. 0 m/z at a collision energy of -25. 0 V. Sample concentrations were determined by comparison to a calibration curve obtained by fitting the peak area ratio of lumefantrine to internal standard (halofantrine hydrochloride) versus concentration data to a linear equation with a weighting factor inversely proportional to the standard concentration.
Validation of the lumefantrine LC-MS plasma assay was run over two days. Intra-assay accuracy was determined by replicate analysis (n = 5) of three standards at the lowest, middle, and highest concentrations  (50, 2000 , 5000 ng/mL). Inter-assay accuracy was determined on two separate days. The data were expressed as a percentage of the measured concentration over the theoretical concentration. The mean accuracy was within ±10% of the theoretical concentration. Intra-assay precision and inter-assay precision were calculated for both runs for each of the three concentrations and expressed as the coefficient of variation. Precision was within ±10% for all three concentrations. Linearity was performed on the standard curves for each analysis and linearity was accepted when the correlation coefficient (r 2 ) of the regression line was >0.
Experimental procedure Every subject participated in one collective and six individual sessions, for a total of seven sessions of approximately 1 h. The collective session aimed at determining the individual swallowing point of both products. Participants were asked to consume the product mouthful (20 cm 3 ) in a natural manner and were recorded on video while doing it. They were asked to point out the swallowing moment by raising their hand. Total chewing duration was calculated as the time elapsed from the placement of the food inside the mouth and the swallowing point, which was defined right after the first swallow. The number of chewing cycles were determined from this recording as well and one chewing cycle was defined after a complete sequence of opening-occlusion. Chewing frequency was calculated from this data by dividing the number of chewing cycles by the chewing duration.
Regarding bolus properties, in order to take account for variability over time, a three-way ANCOVA model was applied by adding total chewing time as explanatory variable (chewing time + stimulated salivary flow + dental status*stimulated salivary flow). For every statistical procedure, a significance level of α=0. 05 was used. Pearson correlation coefficients were calculated when needed between bolus properties, oral comfort scores and chewing parameters. Finally, Principal Component Analysis (PCA) was used to study the relationship between all of the variables cited above. All statistical analyses were performed with XLSTAT software (v. 2016 18. 06, Addinsoft, USA).
Analysis of pooled international data, obtained using consistent methodologies with agreed definitions, could provide more reliable information on these associated factors and hence provide the potential to develop appropriate evidence-based preventive strategies and guide best practice. The International Network of Obstetric Survey Systems (INOSS) is a collaboration of organisations in over 15 countries conducting prospective population-based studies of uncommon and severe complications in pregnancy and childbirth using comparable surveillance systems  [10]. Five INOSS members in Australia, France, the Netherlands, Slovakia, and the UK have collected data on women with AFE, and two of the INOSS members in Australia and the UK have also collected information on a sample of control women. The aim of this study was to pool these data in order to describe risk factors, management, and outcomes of AFE, as well as identify whether there are specific factors that are associated with poor maternal outcomes.
Study design and data collection A multicountry, population-based cohort and nested case-control study was conducted using the INOSS. We pooled anonymous individual-level data on women with AFE that were collected prospectively in five population-based studies conducted by members of INOSS in Australia, France, the Netherlands, Slovakia, and the UK. The studies were conducted in Australia between 1 January 2010 and 31 December 2016, using the Australasian Maternity Outcomes Surveillance System (AMOSS)  [11] ; in France between 1 May 2012 and 30 November 2013, using the E ´pidemiologie de la MorbiditeMaternelle Sevère (EPIMOMS)  [12]  study; in the Netherlands between 1 September 2013 and 31 August 2016, using the Netherlands Obstetric Surveillance System (NethOSS); in Slovakia between 1 January 2012 and 31 December 2016, using the Slovak Obstetric Survey System (SOSS); and in the UK between 1 February 2005 and 31 January 2018, using the UK Obstetric Surveillance System (UKOSS)  [13].
We also pooled anonymous individual-level data on a sample of control women without AFE that were collected in Australia and the UK. In Australia, the control women were identified by AMOSS reporting clinicians as the two women delivering immediately before other AMOSS study cases identified between January 2010 and December 2011  [18]. The AMOSS reporting clinicians were asked to complete a data collection form for the control women that was identical to that used to capture general non-case-specific information about all conditions, including AFE, studied by AMOSS. In the UK, the control women were identified by UKOSS reporting clinicians as the two women delivering in the same hospital immediately before other UKOSS study cases identified at various periods between February 2005 and June 2014  [19] [20] [21] [22] [23] [24] [25] [26] [27].
Statistical analysis All analyses were prespecified as described in detail in the methods section and outlined in the study protocol (S1 Study Protocol), with the exception of adjusting, when possible, the analysis of maternal outcome in relation to the timing of when interventions were used for cardiac arrest. This was performed in response to peer reviewer comments. As stated in the study protocol, we had originally planned to include AFE cases collected in New Zealand and Denmark but did not include these cases because data sharing with INOSS members in these countries was not possible in the timeline of this study. Incidence rates for AFE with exact Poisson 95% confidence intervals (95% CIs) were calculated using as a denominator the available data on the number of maternities recorded regionally or nationally during each study period.
Continuous variables were therefore treated as continuous linear terms when adjusting for them in the analysis but were presented as categorical variables for ease of interpretation. The pooled data on women with AFE from each of the five countries were used to describe the management and outcomes of AFE. Factors associated with poor maternal outcomes (fatality and composite of fatality or permanent neurological injury, including persistent vegetative state, anoxic/hypoxic brain injury, or cerebrovascular accident, with the composite hereafter referred to as severe outcome) amongst women with AFE from each of the countries were investigated using unconditional logistic regression or Wilcoxon rank-sum test as appropriate. In all analyses, only factors that were collected in a comparable way between countries were included.
As well as having information on timing, our study represents, to the best of our knowledge, the first study to have pooled population-based international data on AFE obtained using consistent methodologies with agreed definitions and cross-checking against case criteria. It thus is able to provide a clearer more robust picture of the factors associated with the occurrence of AFE, although we cannot rule out the possibility of residual confounding or confounding by indication. Limited studies have examined factors associated with severe outcomes amongst women with AFE. A study of 227 cases ascertained from a coded national hospital admissions database and a study of just 20 cases ascertained from a regional database with additional criteria to exclude false positive cases both found no significant association between various sociodemographic or obstetric-related characteristics and fatality  [9, 36].
A study that reviewed 44 case reports of AFE found that treatment with factor VIIa was associated with an increased risk of death or permanent disability, while a recent study that reviewed 177 case reports of AFE only found a significant association between oxytocin use during labour and fatality amongst typical cases of AFE defined as having the classical triad of symptoms (cardiac arrest/cardiovascular collapse, respiratory failure, coagulopathy)  [37, 38]. Another study of 180 cases identified from a coded hospital admissions population database found a crude association between induction of labour and fatality  [9]. More recently, a study of 54 cases of AFE with coagulopathy reported that transfusion with a fresh-frozen plasma/red blood cell ratio of 1 or more was associated with higher survival rates  [39].
Incorporation of amino acids and cell size measurement To quantify the incorporation of amino acids, NRVMs were incubated with [ 3 H]-leucine for the last 24 h with (1. 0 μCi/ml, Perkin Elmer, USA). After three washes with PBS 1×, cells were treated for 2 h at 4 °C with trichloroacetic acid (10% w/v) followed by solubilization in 0. 1 N NaOH (0. 2 M) for 2 h under agitation at 37 °C. The radioactivity was determined by scintillation counting and expressed as percentage of control cells. To perform cell size measurements, cells were fixed in 4% PFA and then stained with anti-alpha actinin antibody (Sigma Aldrich) and Hoescht staining for nucleus. Pictures were recorded using a fluorescent microscope (Zeiss Imager Z1, France). A minimun of 10 fields per condition was recorded at ×20. Cardiomyocytes areas were determined using Image J Software.
Cryptococcus The tRNA charging activity of aaRSs is essential in both cytosol and mitochondria to support translation in each compartment, and examples of alternative localization of two aaRS isoforms of a single gene have been observed in fungi, plants, and animals  (44) (45) (46). This implies that a eukaryote with a single genomic homolog of an aaRS is likely to make distinct localized isoforms from that locus. Thus, we examined predicted aaRS localization in fungi. We assembled gene lists of aaRSs in diverse fungi from homology databases OrthoDB  (47)  and PANTHERdb  (48) , adding a mitochondrial SerRS (CNAG_06763/CNB00380) to the list of Cryptococcus aaRSs analysed by Datt and Sharma  (49). In C. neoformans and C. deneoformans, eleven aaRSs are each expressed from a single genomic locus, including the homologs of all five S.
The reads were then cleaned using cutadapt/1. 18 (84)  and trimmed for polyA sequence in their 3'end. PolyA untrimmed and trimmed reads were mapped to the adapted Cryptococcus and to the S. cerevisiae genomes with Tophat2 (83) with the same setting as for RNA-Seq. To eliminate the polyadenylated reads corresponding to genomic polyA stretches, we considered only the reads that aligned to the genomes after polyA trimming but not before the trimming. The 3'end position of these reads were considered as potential PAS. As for the TSS, for each condition we kept only the positions that were present in all three replicates. Similarly, the PAS dataset was normalized using the spike in normalization factor and the PAS positions were clustered using the same strategies.
Ribosome profiling data analysis Ribosome profiling and matched RNA-seq reads were demultiplexed on BaseSpace (Illumina) and then analyzed essentially with the RiboViz pipeline v. 1. 1. 0 (86). In brief, sequencing adapters were removed with cutadapt  (84) , and then reads aligned to rRNA were removed by alignment with hisat2  (87). Cleaned non-rRNA reads were aligned to (spliced) transcripts with hisat2 (87), sorted and indexed with samtools  (88) , and then quantified on annotated ORFs with bedtools (89), followed by calculation of transcripts per million (TPM) and quality control with R (90) scripts included in RiboViz. The cleaned non-rRNA reads were also aligned to the genome with hisat2, and processed analogously, then used to generate figures of genome alignments using ggplot2  (91)  in R  (90).
Data analysis and visualization Data analysis and visualization were scripted in R (  90 ), making extensive use of dplyr  (92) , ggplot2  (91) , and cowplot  (93). Sequence logos were prepared in ggseqlogo  (94). Analysis of differential expression for upf1Δ data was performed in DeSeq2  (95). Figures were assembled and annotated in Inkscape v0. 92 (https://inkscape. org). Protein sequences were aligned using muscle  (96) , with default parameters for protein sequences and 100 iterations. Phylogenetic trees were constructed using ClustalW2 tool v2. 1 (97) by using the neighbor-joining method with 1000 bootstrap trial replications. Structural figures were prepared in PyMOL (Schrodinger).
External datasets N. crassa (strain OR74A) ribosome profiling data from ((  14 ), GEO:GSE97717) was used to generate highly-translated genes, and ribosome profiling and RNA-seq data from ((98), GEO: GSE71032) used to estimated TE. In both cases, we estimated TPMs using the RiboViz pipeline as above, using the NC12 genome annotation downloaded from EnsemblGenomes  (99). TL sequences were also obtained from NC12. S. pombe (strain 972h) ribosome profiling and RNA-seq data are from  (100) , and the authors provided us with a table of RPKMs for all replicates as described. Genome sequence and annotation ASM294v2, including TL annotation, were downloaded from EnsemblGenomes  (99). C. albicans (strain SC5314) ribosome profiling and RNA-seq data are from  (101) , GEO:GSE52236), processed with the RiboViz pipeline as above using the assembly 22 of the strain SC5414 genome annotation from CGD  (102).
S. cerevisiae (strain S288C/BY4741) highly-translated genes use the RPKM table from ((103), GEO:GSE59573), and highly-expressed genes use  (104). For TE estimates, we used matched ribosome profiling and RNA-seq estimates from (  105 ), although we did not use this for the list of highly translated genes because near-duplicate paralogous ribosomal protein genes were not present in the dataset, which thus omits a substantial fraction of highly-translated genes. TL sequences were downloaded from SGD  (106)  Protein homolog lists were assembled with OrthoDB  (47)  and PANTHERdb  (48) , with reference to FungiDB  (107). The list of cytoplasmic ribosomal proteins was assembled in S. cerevisiae based on  (108)  with help from SGD  (106) , extended to other fungi with PANTHERdb (47), and manually curated.
ribosome profiling. Work in the Madhani lab is supported by grants from the US National Institutes of Health. H. D. M. is an Investigator of the Chan-Zuckerberg Biohub. E. W. J. W. is a Sir Henry Dale Fellow, supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 208779/Z/17/Z). L. T. is supported by a Wellcome-University of Edinburgh ISSF3 award.
Statistical analyses Statistical analyses were performed using Stata software (v14. 2, StataCorp, USA) and Prism software (v7, GraphPad, USA). Continuous variables were compared between groups by using Wilcoxon tests and qualitative variables with Fisher's exact tests. Spearman's correlation coefficient was employed to quantify the correlations between continuous variables. Cox models were used to determine the predictive values of HIV DNA levels for clinical AIDS onset. Mixed-effect linear models (MELMs) were employed to estimate changes in total HIV DNA and integrated HIV DNA over time, considering the value at enrolment as time t = 0 for each patient. Logarithm 10 transformation of HIV RNA and HIV DNA levels was performed to fulfil the model assumptions. Integrated HIV DNA levels were set to half of the threshold when undetectable.
Acknowledgements This work is dedicated to the memory of  Jean-Florian Mettetal ,  Denis Bucquet , and  Christiane Deveau. We thank all the patients and clinicians who participated in this study,  Laurent Tran  and  Anne Persoz  for data management and the laboratory technicians ( EA 7327 ,  Université Paris Descartes)  for their technical assistance. We thank  Michaela Müller-Trutwin  and  Asier Sáez-Cirión  for helpful discussions.