dataesr/scientific-paragraphs-categorization huggingface dataset:
In other words, the column is considered to be pressurized and filled with ambient air instantaneously. During this operation, the solid phase is assumed to be frozen, i. e. , no change in the solid loading. This assumption seems reasonable as the CO 2 kinetics is usually much slower than the pressurization dynamics. In this work, only the CO 2 collected in the desorption/steam desorption steps (DE/SD) is treated as the product. Both TVSA and s-TVSA cycles were simulated using a non-isothermal, non-isobaric boundary conditions  [46]. The partial differential equations (PDEs) governing the mass and energy balances are discretized in the axial direction into 30 volume elements using a finite volume technique, along with a van-leer flux limiter  [46]. The resulting ordinary differential equations (ODEs) system was solved using a stiff ODE solver ode23s/ode23tb in MATLAB.
The GA evaluates the fitness of these individuals based on these values and selects the individuals of the next generation. During this process, operations such as mutation, crossover, etc. , are used to improve the diversity of the population. This process is run over multiple generations until there can be no more improvement in the fitness functions of the individuals. When no more improvement in the fitness functions can be achieved, the system is said to have converged. The multi-objective optimization problems were solved using a nondominated sorting genetic algorithm (NSGA-II) in MATLAB global optimization toolbox  [50]. NSGA-II can be easily coupled with the detailed TVSA/s-TVSA models and parallelized for rapid optimization studies. The genetic algorithm was run for 25 generations in all of our optimizations.
Nevertheless, we emphasize the need to obtain equilibrium and kinetic data to make realistic process decisions. Even though SIFSIX also adsorbs H 2 O, the H 2 O loading at 50% RH on SIFSIX is about 0. 7 mmol/g (10 times lower than the 7 mmol/g of H 2 O that adsorbs on NbOFFIVE). The parasitic energy involved with H 2 O desorption is small enough that it does not guarantee the necessity of high steam flows. To confirm this, Figs. 10(d-e ) shows the CO 2 and H 2 O loading history for SIFSIX at the end of the column for the operating conditions that provide the minimum En with k CO 2 =0. 0002 s -1 and 0. 02 s -1. To appreciate the effect of k CO 2 on En, As shown in Fig. 8 , with a higher regeneration pressure, i. e. , P L =0. optimizations of the TSA cycle, the s-TSA model was used as such, however, by fixing a very low value of steam flow, i.
Sequence analysis. The WGS analysis was performed on the Co-dN cell line. Indels for the gene Nrf2 were detected and can be visualized using IGV software and the deletion has been identi ed (Fig. 2a ). The research of variants between the initial mouse genotype and both cell line exome revealed very few differences (Fig. 2b, c , Table  1 ). The Venn diagram (Fig. 2c ) indicates the number of mutations in genes of interest, which are partly referenced in Table  1. Table  1  shows that, except for Notch1 and Cdk6, no "high" mutations can be detected in the Co cell line, con rming its non-cancerous status. In the Co-dN cell line, the Cdk6 mutation is still detectable con rming that the two cell lines are isogenic. No other "high" mutations were identi ed, indicating that in our experiment, the CRISPR /Cas9 technique did not induce other mutations.
Brie y, DNA was fragmented by sonication, size selection was performed using SPB beads, and adaptors were ligated to be sequenced. Library quality was assessed using an Advanced Analytical Fragment Analyzer and libraries were quanti ed by qPCR using the Kapa Library Quanti cation Kit. DNA-seq experiments have been performed on an Illumina NovaSeq6000 using a paired-end read length of 2x150 pb with the Illumina NovaSeq6000 Reagent Kits. The raw sequences were deposited at SRA under accession PRJNA800346. Bioinformatics analysis. FastQC 51 used to check the quality and no relevant contamination hit was found after the alignment against E. coli, Yeast and PhiX. Reads mapping and variants calling were performed using Next ow v20. 10. 0 and Sarek pipeline v2. 6. 1  52. Genome Analysis Toolkit (GATK) best practices were followed as implemented in the Sarek pipeline.
The following steps were performed: aligning the reads with BWA v0. 7. 17-r1188  53  against the mouse genome assembly C57BL_6NJ_v1 (GCA_001632555. 1), marking duplicate reads (MarkDuplicates), base quality recalibration (BQSR) and calling germline small variants (HaplotypeCaller in GVCF mode) with GATK v4. 1. 7. 0 54 on merged bam by sample. Finally, quality control is performed with MultiQC v1. 8  55. Out of the next ow pipeline the variants were annotated with VEP(5) version 103. 1 with assembly C57BL_6NJ_v1. Variant with at list quality of 30 and depth of 5 were kept to further analyses. Nrf2 invalidation. The CRISPR/Cas9 technique used to generate the Nrf2-depleted cell line used the 3 ''all-in-one plasmids'' containing 3 pre-designed gRNAs (Sigma, Merck Company) that target 3 different sequences at exon 2 of the murine Nrf2 mRNA gene, transcript variant 1, NM_010902.
2. 1 software). Gene expression. Total cellular RNA was extracted with Tri reagent (Molecular Research Center). Total RNA samples (1 µg) were then reverse-transcribed with the iScript™ Reverse Transcription Supermix (Bio-Rad) for real-time quantitative polymerase chain reaction (qPCR) analyses. The primers for Sybr Green assays are presented in Supplementary Table  S1. Ampli cations were performed on a ViiA 7 Real-Time PCR System (Applied Biosystems). The qPCR data were normalized to the level of Hypoxanthine Phosphoribosyl transferase - 1 (Hprt1) messenger RNA and analyzed by LinRegPCR v. 11 software. GSH/GSSG assay. The GSH/GSSG ratio was evaluated with the GSH/GSSG™ Assay (Promega, France) based in a luminescence detection of GSH-dependent conversion of a GSH probe, Luciferin-NT, to luciferin by a glutathione S-transferase enzyme coupled to a re y luciferase reaction.
When indicated, cells were pretreated with curcumin (CCM, 10µM, complete medium, permissive conditions) 24h before HNE treatments. Real-time cellular impedance was measured in each well (cell index values) and the signal was analyzed through integrated software (RTCA Analyzer). When HNE was added, the normalized cell index (NCI) was used, based on the last time point prior to compound addition. Results from 5 independent experiments are expressed as normalized cell index. HNE metabolism was studied using radiolabeled HNE synthesized as previously described  59  (speci c activity 222GBq/mol). Cells were treated by 40 µM [ 3 H]-HNE for 30 min, then cellular supernatants were analyzed by radio-HPLC as described before  13  , except for the HPLC gradient used that has been modi ed to improve peak separation: both mobile phases contained water/acetonitrile/acetic acid with phase A (97.
5/2. 5/0. 1) and B (40/60/0. 1); the elution gradient was 0min 18% B, 2-10min 26% B, 23-37min 41% B, 42min 65% B, 47min 100% B, 60min 100% B, 65min 18% B. Statistical analysis. All data were expressed as mean ± standard error of the mean (SEM) of three (or more) independent experiments. Statistical signi cance was determined by two-ways analysis of variance (ANOVA), followed by Tukey's multiple comparison test when needed, using GraphPad Prism 9 software. Statistical signi cance was indicated by *P< 0. 05, **P< 0. 01, ***P< 0. 005 and ****P< 0. 001 for comparison between treatments, and $, P< 0. 05, $$, P< 0. 01, $$$, P< 0. 005 and P< 0. 001 for comparison between cell lines, except for Fig. 1a  and 1c , where a t-test had been performed. Declarations Figures Figure 1.
Acknowledgements: this project has received funding from  La Ligue Contre le Cancer, Midi-Pyrénées. Authors contribution: CHT and JDU created and characterized the reported cell lines. CBU performed the western blot analyses, EFO performed the RTqPCR experiments, CNO and PMA performed the sequences analyses and comparisons, CHT wrote the manuscript. All authors and specially  FGU  and FPI reviewed the manuscript.
Acknowledgements M. S. -P. and T. S. v. Z. contributed equally to this work. The research leading to these results has received funding from the  European Commission   H2020 Program  under grant agreement  ERC Adv788546  ( NANO-MEMEC), Government of Spain (Severo Ochoa   CEX2019-000910-S ,  State Research Agency (AEI)   PID2020-113068RB-I00 / 10. 13039/501100011033  (to M. F. G. -P. ,  BES-2015-072189  (to M. S. -P. ), grant  RYC-2015-17896  funded by  MCIN/ AEI/10. 13039/501100011033  and " El FSE invierte en tu futuro " (to C. M. ), grants  BFU2017-85693-R  and  PID2021-125386NB-I00  funded by  MCIN/ AEI/10. 13039/501100011033/  and  FEDER "Una manera de hacer Europa"  (to C. M. ),  Fundació CELLEX (Barcelona) ,  Fundació Mir-Puig  and the  Generalitat de Catalunya  through the  CERCA program  and  AGAUR  (Grant No.
Clinical metadata used for the analysis are provided in Supplementary Table  S1 ; summarized demographic and clinical data are provided in Supplementary Table  S2. Pictures showing normal skin and a typical BP lesion of a patient included in our study are provided in Supplementary Figure  1. Sampling, DNA extraction, and 16S rRNA gene sequencing Samples were collected using Epicentre Illumina collection swabs (Madison, WI, USA) immersed in 600 uL buffer (50 mM Tris, 1 mM EDTA, 0. 5% Tween-20) (Teknova, United States). The swabs were rubbed across the selected body site for 30 s and then placed back into the buffer solution. Immediately after swabbing, swabs were stored at À80 °C until further processing. Skin samples (n = 2,956) were obtained from patients with BP representing different cutaneous microenvironments, including ''perilesional" skin (defined as being within 2 cm of a primary BP lesion, i.
32. 0) [31]  to identify potential contaminant ASVs, according to the prevalence method  [23]. ASVs classified to families Halomonadaceae (n = 1,040) and Shewanellaceae (n = 211) were removed, following recommendations of Weyrich et al. [32]. Summary read data are provided in Supplementary Table  S3. Taxonomic assignment of ASVs was completed in DADA2 with the Bayesian classifier using the NR Silva database training set, version 138  [33]. Representative 16S rRNA gene sequences were queried via the Ribosomal Database Project (RDP; release 11. 6;  [34]  SeqMatch (version 3;  [35] ; Supplementary Table  S4 ).
Acknowledgments We would like to thank  Jan Schubert ,  Katja Cloppenborg-Schmidt , and  Olga Eitel  for their excellent technical assistance. We are grateful to  Sarah Gaugel  and  Stephanie Freyher, Lübeck , for technical assistance with sample storage. We are indebted to  Ana Luiza Lima ,  Kaan Yilmaz , and  Onur Dikmen, Lübeck , for assistance with sample storage and communication with study centers during various phases of the study.
Data availability Datasets related to this article can be found under BioProject accession number PRJNA715468 at https://www. ncbi. nlm. nih. gov/bioproject/, an open-source online data repository hosted at the NCBI SRA BioProject database. Reviewer link: https://dataview. ncbi. nlm. nih. gov/object/PRJNA715468?reviewer= tpob0l00b03qg88ju4lqnp7dou.
21  Fourteen trained panelists evaluated the following attributes: hardness in hand, moldability in hand, stickiness in hand, moistness in hand, crunchiness in mouth, mealiness in mouth, smoothness in mouth, uniform texture in mouth, fracturability in mouth, hardness in mouth, and moistness in mouth. They scored these traits on a scale of 0-10 according to the product profile and sensory lexicon developed for Uganda. 16  The data were cleaned according to the methods documented in earlier studies. 22  High throughput: the spectral method A spectral analysis was performed on a total of 30 potato genotypes harvested from experimental plots located in Kiambu, Kenya (1900 m altitude), and from the local market in Kampala, Uganda. Marketable-size tubers with good visual appearance were sampled.
Spectra were collected from cooked, mashed samples. Calibrations were performed using reference data collected by a sensory panel as described above. Spectra were collected with the NIRS XDS (FOSS, Minnesota, USA ) using ISIscan software (Infrasoft International, State College, PA, USA) in the wavelength range 400 to 2500 nm at 2 nm intervals. Three cooked and mashed tubers per genotype were selected for the spectral collection. Each mashed sample was then scanned in a cuvette, producing three spectra per genotype. The spectrum was expressed as log (1/R). Details of sample preparation and spectra collection are published in a standard operating procedure. 23.
Statistical analyses Analysis of variance was used to determine significant differences between means using JMP Pro 15 software (SAS Institute Inc. ,  North Carolina, USA) and the means were separated by a Tukey test. Principal component analysis (PCA), bivariate correlations, discriminant analysis, and hierarchical clustering were used to determine clustering and association between variables. 22  To visualize relationships between the spectral data and the potato genotypes, PCA was generated using mean scores for each replicate with the prcomp package, using the covariance option in R. 24  For spectral data, individual prediction models, using partial least squares regression (PLSR) in R,  25  were developed for each trait. The calibrations were performed using full cross-validation due to the limited number of samples.
39  Future studies should consider evaluating sensory and texture profiles for different cooking methods since preparation can differentially affect the profiles of the genotypes. 40  The texture traits are normally assessed in the later stages of a breeding program after selection for more easily quantifiable traits, due to the cost of most conventional methods. Several nondestructive methods such as NIRS are available to enable selection during the early stages of breeding and these have also been described for characterizing different traits of potatoes as well as other crops, such as sweet potato. 23, 35  In the current study, while NIRS shows the potential to predict color, most predictions still need improvement. One of the most important criteria for developing a robust NIRS calibration is that the natural variation of the parameter values of interest must be accounted for within the sample populations of the calibration and validation datasets.
ACKNOWLEDGEMENTS The authors are grateful to the following organization for financial support:  USAID Feed   the Future Crops to End Hunger  (award numbers  DIS-B-AID-BFS-IO-17-00005  and ID:  OPP1178942 );  RTBfoods ; the  French Agricultural Research Center for International Development (CIRAD), Montpellier, France , and the  Bill & Melinda Gates Foundation (BMGF). The views expressed in this article do not necessarily reflect those of BMGF and  CIP (International Potato Center). The team would also like to acknowledge the  National Agricultural Research Organization (NARO), Uganda , for its contribution to the study by providing potato tubers and facilities. The editorial comments by  Hernán Ceballos  and  Dominique Dufour , as well as the proofreading of the manuscripts by  Clair Hershey  greatly improved the quality of this manuscript.
These latter are used to build our language models. 4. Language model training -The final step was to train age-based language models on the preprocessed and tokenized corpora. Our experiment was conducted on a stochastic N-gram prediction model, Predict4all (subsection 3. 1). The fundamental premise of this approach is to use probabilistic analysis to determine the co-occurrence of words in a sequence of n tokens. This acquired knowledge is then applied to generate a ranking of likely following words for any sequence of n-1 tokens. This procedure can be followed to any machine learning techniques. We produced one model for adolescents Adolescents all and three models for children Children stories, Children info and Children all. Adolescents all was trained with texts extracted from news sites for adolescents and Vikidia, resulting in a 4-gram model based on a vocabulary of 67 190 unique words.
PREDICT4ALL The experiments reported in this paper were conducted on the Predict4All prediction module. Predict4All is an open-source library  5  that can be integrated into any AAC system (and actually any other application). In particular, the LifeCompanion 6  AAC system developped in the AAC4All 7  project of the French ANR research agency integrates the Predict4All prediction. As explained before, Predict4All is based on a standard 4-gram prediction model. It incorporates a general prediction language model combined with a dynamic user model trained on-the-fly on the messages entered by the user 8. The general prediction model has been trained on more than 20 millions words from Wikipedia and subtitles (of films and series) corpus. It has a vocabulary of 111 880 unique words. Pre-dict4All offers also the possibility of a semantic adaptation of the prediction  [7]  according to the current of communication.
Procedure The present study capitalizes on data from the International Sex Survey (ISS)  [26, 27]. The ISS is a cross-sectional survey conducted in 42 countries 3  and 26 languages using self-report and online questionnaires. The survey was preregistered (general methodology of the study: https://osf. io/xcgzf and validation papers' methodology: https://osf. io/qg8c4) and had a pre-defined protocol with the use of standardized translation, data collection and recruitment, and data cleaning procedures across the participating countries. Participants were recruited through convenience sampling (except the Czech Republic where online panel was used) between October 2021 and May 2022. The ISS collaborators contacted news sites with broad coverage in each participating country in a standardized way to advertise the opportunity to participate in the research.
Participants The total sample of the ISS included 82,243 participants. For sociodemographic characteristics of the entire sample by country, see https://osf. io/cj658. However, in the present study, only data from participants who had consumed alcohol in the past 12 months were used to avoid the conditional dependence on responses to the AUDIT items. Specifically, respondents with consistent responses who reported on the first item of the AUDIT that they had not consumed alcohol in the past 12 months must have had responses on items 2-8 of the AUDIT in the first response category. Which could have biased the findings of the analyses (e. g. , violation of the assumption of local independence, it is not possible to differentiate between abstinent and low-risk alcohol users). Consequently, the final sample included data from 62,943 individuals.
Based on the Monte Carlo simulation performed during pre-registration, at least 485 individuals in a subgroup had to be included in the analysis (see: https://osf. io/qg8c4). As a result, nine countries and five language subgroups were excluded from the final analyses. In addition, CFA and measurement invariance testing with ordered categorical variables also required to have valid data for each response option of all the items and/ or not to have empty cells in the bivariate cross-tables between the AUDIT items in each subgroup separately. For this reason, 11 countries and six language subgroups were not included in these analyses. In total, 21 countries and 14 language subgroups were included in the final analyses. For all three gender subgroups (i. e. , men, women, gender diverse individuals) and all four sexual orientation subgroups (i.
All data in the table are based on the responses of individuals with past-year alcohol use. Bolded subgroups were included in the analyses. 1 The subgroup was not used in the analyses because the final subgroup sample size (N AU ) did not reach the minimum required sample size of N = 485. 2 Although the sample size of the subgroup exceeded the minimum sample size required, the subgroup was not included in the analyses because for some of the items of the AUDIT there were no valid responses for some of the response options/categories and/or due to one or more zero cells in bivariate cross-tables between the items of the AUDIT. 3 Chile was not included in the analyses as it joined as a collaborating country after publishing the study protocol. are recommended to detect an adequate fit when testing for invariance between a large number of groups (i.
Acknowledgments The authors would like to thank  Anastasia Lucic  and  Natasha Zippan  for their help with project administration and data collection, and  Abu Bakkar Siddique ,  Anne-Marie Menard ,  Clara Marincowitz ,  Club Sexu ,  Critica , Digital Ethics  Center  ( Skaitmenines etikos centras ),  Día  a Día,  Ed Carty ,  El _ Siglo ,  Jakia Akter ,  Jayma Jannat Juma ,  Kamrun Nahar Momo ,  Kevin Zavaleta ,  Laraine Murray ,  L' Avenir de l'Artois, La Estrella de Panamá, La Voix du Nord, Le Parisien ,  Lithuanian National Radio and Television  (Lietuvos nacionalinis radijas ir televizija),  Mahfuzul Islam ,  Marjia Khan Trisha ,  Md. Rabiul Islam ,  Md. Shahariar Emon ,  Miriam Goodridge ,  Most. Mariam Jamila ,  Nahida Bintee Mostofa ,  Nargees Akter ,  Niamh Connolly ,  Rafael Goyoneche ,  Raiyaan Tabassum Imita ,  Raquel Savage ,  Ricardo Mendoza ,  Saima Fariha ,  SOS Orienta  and  Colegio de Psicólogos   del Perú ,  Stephanie Kewley ,  Sumaiya Hassan ,  Susanne Bründl ,  Tamim Ikram ,  Telex.
g. ,  Cole et al. , 2009; Lien & Gregg, 2001; Pichon et al. , 2013). Here, we report on an observed internal tide beam generated by an abyssal hill in the rift valley of the MAR. The beam is sampled by a Shipboard Acoustic Doppler Current Profiler (SADCP) that measures the horizontal velocity field through a section that crosses the main axis of an abyssal hill. We use a linear theory for internal wave propagation that allows us to reconstruct the observed pattern of the beam. The paper is organized as follows. Section 2 presents the context of the data collection and the datasets used in the study. Section 3 introduces the methods and Section 4 presents the results. These are discussed in Section 5 and summarized in Section 6.
The MoMARSAT 2022 Cruise Data were collected by R/V Pourquoi Pas ? during the MoMARSAT cruise on 6-27 June 2022  (Sarradin & Matabos, 2022). The MoMARSAT cruise series provides annual maintenance for the European Multidisciplinary Seafloor and water column Observatory (EMSO) Azores observatory on the Lucky Strike hydrothermal field  (Cannat & Sarradin, 2010; Matabos et al. , 2022). This seafloor observatory has been operated since 2010 and aims to acquire long time series on hydrothermal and geophysical processes and ecosystems of an active hydrothermal field on the MAR (Figures  1a  and 1b ). Most of the ship time was dedicated to servicing the seafloor observatory but during the crew's off-duty time slots (6-8 hr long) we sailed on the section shown in Figure  1b  to measure the fine-scale variability of horizontal currents with the SADCP.
Hydrographic Data We use temperature and salinity profiles from a Conductivity-Temperature-Depth (CTD) probe mounted on a rosette to compute N 2 from the TEOS-10 toolbox  (McDougall & Barker, 2011). Overall, 22 casts were performed in the vicinity of Lucky Strike, approximately 10 km east of the easternmost point of the section. N 2 from those profiles is shown in Figure  2a.
Barotropic Tidal Currents We compute the barotropic tidal currents in the eight major components (M 2 , S 2 , K 2 , N 2 , K 1 , O 1 , P 1 , Q 1 ) at the Lucky Strike site for the time of the cruise using the Oregon State University Tidal Prediction Software (https://www. tpxo. net/otps) with TPXO9-atlas  (Egbert & Erofeeva, 2002). Currents are shown in Figures  1c  and 1d.