dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 7. 1 "bdgcmp" module and transformed to BigWig using Deeptools bedGraphToBigWig. Then, Pearson correlation between our new data and previously published data for H3K9me3 and H3K27me3 was performed using Deeptools plotCorrelation. ChIP-qPCR. Chromatin Immunoprecipitation (ChIP) was conducted as described previously  26. DNA concentrations were quantified with the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). Quantitative PCR experiments were performed using LightCycler DNA Master SYBR Green Mix on the Biorad LightCycler. IP enrichments were assayed with the following protocol: 1 μl of immunoprecipitated DNA samples (IP), input and mock DNA were mixed with 5 μl LightCyclerW DNA Master SYBR Green I 2X, 3 μl forward/reverse primers (1 μM), and 1 μl H2O. For each IP sample, the enrichment of certain histone modification on specific loci was calculated with the following equation: %Enrichment level = 100/2 (Cq[ChIP] -(Cq[Input] -Log2(Input Dilution Factor)).
DNA methylation analysis. McrBC DNA methylation annotation data from Ref. 8  was lifted from the previous assembly to the new 25 chromosomes using Liftoff. Bisulfite sequencing data  20  were processed with Bismark v0. 22. 3  33  and methylated regions having less than 50% methylated reads or less than 5 supporting reads were filtered out. Nanopore DNA methylation data from Ref. 17  was retrieved, converted to a compressed quantitative data format (BigWig) and added to the Jbrowse instance. Pairwise Pearson correlation coefficients of the three DNA methylation data tracks were then calculated and plotted as a heatmap with multiBigwigSummary and plotCorrelation tools from deepTools package v3. 5. 1  29  , using 10 kbp bins size.
Results and discussion Despite being an established model, the genome and annotation of P. tricornutum are not concordant and the existing epigenetic resources generated so far lack a well-defined framework for accurate and user friendly utilization. To address this limitation, we sought to establish a coherent resource rendering the multiple genomic and epigenomic sequencing data sets exploitable from a single platform which we named PhaeoEpiView. Because P. tricornutum recent genome assembly and annotation exhibit inconsistencies, PhaeoEpiView was built using two steps (i) Phatr3 gene annotation lifting onto the new 25 chromosomes assembly (GCA_914521175. 1) and (ii) mapping of the previously published epigenetic marks and transcripts on the new assembly. Next, we proceeded with Phatr3 gene annotation lifting onto the new 25 chromosomes assembly (Phatr4).
In the second step, transposable elements annotation available from Giguere et al. 17  was added to PhaeoEpiView as Phatr4 TEs track. Finally, previously published expression data at two different time points, DNA methylation and PTMs tracks were implemented in the browser, and systematic comparison was made with the previous assembly mapping using unchanged regions as anchors (Supplementary File 1). Our DNA methylation data obtained through bisulfite conversion and MCrBC digest was compared to DNA methylation data from Nanopore sequencing. The results demonstrated an overall good coefficient correlation indicating consistency between the three data sets (Fig. S2 ). For more accuracy and homogeneity of the used data, chromatin immunoprecipitation with deep sequencing was carried out using commercially available monoclonal antibodies to replace two marks, H3K27me3 and H3K9me3 for which polyclonal antibodies were used in the previous study  9  (Fig.
2A-D , Fig. S3A, B ). With few exceptions, H3K27me3 antibody used in this study provided an overall similar pattern of distribution over the genome supported by a strong Pearson's correlation coefficient (0. 80) (Fig. S3A ). We used ChIP-qPCR and validated randomly chosen loci which further supports the accuracy of H3K27me3 mapping (Fig. 2C ). H3K27me3 polyclonal antibody was previously tested in a peptide competition assay and showed very little cross reactivity which is in line with the strong correlation coefficient between the two mapped datasets ( 26 , this study). However, 69 genes and 127 TEs were not recovered using H3K27me3 monoclonal antibody indicating that these regions were false positives in the previous mapping using the polyclonal antibody (Fig. 2A ). Using a monoclonal H3K9me3 antibody, the current study revealed a negative correlation with the previous work that employed a polyclonal antibody, demonstrating a higher specificity of the former (Fig.
1 ). It can currently display one track for each of the genes, TEs, transcript levels, McrBC, Bisulfite-seq and Nanopore DNA methylation tracks and five histone PTMs (H3K9/14Ac, H3K4me2, H3K4me3, H3K9me2, H3K9me3, H3K27me3). The browser will be regularly updated with relevant epigenomic data when published in the future, turning PhaeoEpiView into a dynamic platform for a comprehensive genomic and epigenomic resource of the model microalgae P. tricornutum.
Acknowledgements This work was supported by the  Epicycle   ANR  project ( ANR-19-CE20-0028-02 ) and  Région Pays de la Loire  ( ConnecTalent EPIALG  project) to LT. YW was supported by a PhD fellowship from the  Chinese Scholarship Council  ( CSC-201908340073 ). We are grateful to the  Bioinformatics Core Facility of Nantes University (BiRD-Biogenouest)  for its technical support.
899 (JSM), Scomberomorus niphonius; Silver pomfret (SP), Pampus argenteus; and South American pilchard (SAP), Sardinops sagax), 5 demersal (Largehead hairtail (LH), Trichiurus japonicus; Small yellow croaker (SYC), Larimichthys polyactis; Pacific cod (PC), Gadus macrocephalus; Yellow striped flounder (YSF), Pseudopleuronectes herzensteini; and Pacific sandlance (PS), Ammodytes personatus), and 3 invertebrate species (Southern rough shrimp (SRS), Trachysalambria curvirostris; Japanese flying squid (JFS), Todarodes pacificus; and Swimming crab (SC), Portunus trituberculatus) that contribute to a major part of the total biomass (Supplementary Table  S1 ). The OSMOSE-YS model simulates the life cycle of the 13 focus species, from the egg stage to the terminal age, at a time step of 3 months. At the first time-step after the production of eggs, the total number of eggs of each population is split into 120 super individuals (referred to as "schools"), which share the same body length, age, food requirement, and spatial coordinates at a given time step and are distributed spatially according to input distribution maps from geo-referenced data of research surveys.
, 2010; Oliveros-Ramos and Shin, 2016). The unknown parameters for each modelled species were estimated by fitting the simulated species biomass and yield to observed data using maximum likelihood objective functions  (Oliveros-Ramos et al. , 2017). The unknown parameters estimated in the OSMOSE-YS model for each species include larval mortality rates of focus species, availability coefficients of plankton accessibility to the modelled species, fishing mortality for exploited species, and a catchability index (Supplementary Table  S2 ). The catchability index was used to account for the availability of exploited species to fishing gears by scaling the OSMOSE-YS model outputs. Observed yield for the period 1970-2014 in the Yellow Sea was obtained from the Sea Around Us project  (Pauly and Zeller, 2016) , and the observed biomass data were estimated by the optimized catch-only method (OCOM;  Zhou et al.
Assessing fishing scenarios against BH Using the time series of productivity and biomass of the focus species derived from the OSMOSE-YS model outputs, we assessed all the 72 fishing scenarios against all BH forms (i. e. sBH1, sBH2, ssBH1, and ssBH2). We used linear fitting models that were executed with the package "stats" in R (R Core Team, 2022) to determine whether the fishing mortality rate implemented in OSMOSE-YS was proportional to production or productivity. The fitting linear models are expressed as: sBH1 : F i ∼ Production i -1 ( 3 ) sBH2 : F i ∼ Productivity i -1 ( 4 ) ssBH1 : F s i ∼ Production s i -1 ( 5 ) ssBH2 : F s i ∼ Productivity s i -1 ( 6 ) whereF i , Production i , and Productivity i are fishing mortality rate, production and productivity for species i; F s i , Production s i , and Productivity s i are fishing mortality rate, production and productivity for species i and size s.
Funding This work was supported by the  National Natural Science Foundation of China  [grant number:  32073027 ,  41861134037 ]. Y-J. S. and N. B. were supported by the  Biodiversa and Belmont Forum  project  SOMBEE (BiodivScen ERA-Net COFUND  programme,  ANR  contract n •  ANR-18-EBI4-0003-01 ).
Previous research has used ML-IAPs for WDM simulations, but these potentials have often been calibrated for specific temperature and pressure ranges  [37, 39, 40, 46]. In contrast, we train a single ML-IAP that can be used across a range of temperatures using DFT-MD data. To generate the ML-IAP, we use the spectral neighbor analysis potential (SNAP) method  [31] , which represents the local environment of each atom through bispectrum components of the local atomic density projected onto a basis of hyperspherical harmonics in four dimensions. These components serve as descriptors in the SNAP method. We use the DAKOTA optimization software  [47]  to tune the ML-IAP's hyperparameters, such as the cutoff distance for the potential and the weight of specific training data sets. By using this approach, we are able to accurately simulate ambient aluminum up to very high temperatures corresponding to the WDM regime.
The linear regression is carried out using the FitSNAP software package  [65, 66]. To this end, we preprocess the DFT-MD data by extracting the total energies and the forces on all the atoms. The training datasets and parameters are listed in Table S1  [67]. The DFT-MD data is grouped into intervals of 100 K in the temperature range from 300-1000 K, and at intervals of 1000 K from 1000-10000 K. For each temperature, we randomly select approximately 100 snapshots of equilibrated atomic configurations. We include lowtemperature data from 300-900 K in the training because a diverse dataset improves the quality of the trained ML-IAP  [68]. Based on this data, we generate a SNAP ML-IAP over the desired temperature range. Finally, we need to determine the hyperparameters of the ML-IAP. These include the cutoff radius R cut , which defines the length scale of the atomic environment considered, J max , which determines the number of terms in the expansion of the atomic density in terms of bispectrum components, and the weights of the energy and force training sets.
To fix the number of terms in the expansion, we set J max = 3, which defines the number of descriptors, and used the DAKOTA software package  [47]  to optimize the remaining parameters. We used the single-objective genetic algorithm (SOGA), a global optimization scheme, with energy and force objective functions within the DAKOTA optimization package  [69]. The energy and force objective functions are based on the root mean-square error (RMSE) values provided by FitSNAP. To give more flexibility to the genetic algorithm we also allowed DAKOTA to vary the energy and force weights of each DFT training data set. For the SOGA optimization, we adopt a population size of 100 and set the maximum number of iterations to 10000. The cutoff radius was allowed to vary from 4. 8-5. 8 Å, whereas both the energy weights (EW) and force weights (FW) were optimized within the range from 0.
(15) Both the SSF and the DSF are measured in experiments, for example, using neutron scattering or x-ray diffraction. Both are used for the diagnostics of the temperature, density, and ionization state in dense plasmas and WDM  [87]. Rüter and Redmer  [88]  have calculated the ion-ion structure factor for warm dense aluminum using DFT-MD calculations. In this work, we compute the DSF and the SSF by postprocessing our MD data with the DYNASOR code  [89]. First, we illustrate the SSF as a function of wave vector q for temperatures of 0. 5 eV (5802 K) and 5 eV (58022 K) in Fig. 7  and compare our results with those from DFT-MD and OFDFT  [27]. Note the excellent agreement of our prediction with the DFT-MD result at 0. 5 eV (5802 K) and inaccuracies of the OFDFT result in the main peak. This confirms the abilities of our SNAP ML-IAP to interpolate accurately in the range of training data.
IV. CONCLUSIONS Our study has resulted in the training of a single ML-IAP using DFT-MD data and the SNAP methodology to model the properties of aluminum in a wide range of conditions, from ambient to WDM. Previous ML-IAPs have been trained for specific temperature and pressure ranges. However, our findings demonstrate that a single ML-IAP can yield reliable and precise results across a broad temperature range. To evaluate the transferability of our ML-IAP, we computed several material and transport properties, including the ionic part of thermal conductivity, shear viscosity, selfdiffusion coefficient, sound velocity, and structure factor. Our results for the thermal conductivity and shear viscosity are consistent with previous findings from ML-IAPs  [36, 37, 76, 77] , although those ML-IAPs were specifically trained for the tested temperature ranges.
ACKNOWLEDGMENTS This work was partially supported by the  Center for Advanced Systems Understanding (CASUS) , which is financed by  Germany's Federal Ministry of Education and Research (BMBF)  and by the  Saxon state government  out of the State budget approved by the  Saxon State Parliament. M. L. was supported by the  German Federal Ministry of Education and Research (BMBF , No. 01/S18026A-F ) by funding the competence center for  Big Data and AI "ScaDS. AI Dresden/Leipzig. " Computations were performed on a Bull Cluster at the  Center for Information Services and High Performance Computing (ZIH) at Technische Universität Dresden , on the cluster Hemera at  Helmholtz-Zentrum Dresden-Rossendorf (HZDR). This article has been authored by an employee of  National Technology & Engineering Solutions of Sandia, LLC  under Contract No.
Bayesian inversion In this study, an inversion algorithm is used to perform retrievals of frozen hydrometeors. This inversion method is taken as the same Bayesian algorithm which is used to assimilate microwave cloudy and rainy observations operationally within the ARPÈGE model using retrievals of relative humidity profiles  (Guerbette et al. , 2016; Duruisseau et al. , 2019; Barreyat et al. , 2021). Within this framework, each observation is collocated with a First Guess and a surrounding neighborhood (210 km in diameter). From this neighborhood, atmospheric profiles are taken to create an inversion database. A weight is computed for each member of the database. It is calculated from the difference of brightness temperatures BT between OBS and a given FG member, and taking into account observation errors: norm neighbor = channels BTobs-BTneighbor obs_error 2 nchannels (1) weight = exp(- 1 2 norm neighbor ) (2) for each neighbor profile.
A retrieved profile, hereafter named RET, is then defined as a weighted mean of the inversion database. The corresponding brightness temperature BT RET is also derived from the weighted mean of the inversion database. This method allows to select channels both in the IR and MW, either separately or together to constrain the inversions. As a first approach, only vertically polarised channels are used for the microwave instruments. have been made: FCI will refer to the selection of each of its infrared channels; ICI will refer to the selection of each of its vertically polarised channels; MWI will also refer to the selection of each of its vertically polarised channels; COMB (combined) will refer to the selection of all vertically polarised channels of ICI and MWI plus the FCI selection. To determine the observation error that will be used in the Bayesian inversion, a posteriori diagnostics  (Desroziers et al.
Introduction of differences in radiative transfer. Parameters in RTTOV v13 for the frozen hydrometers are modified in this experiment named mRT (modified RT). Differences in parameterization and scheme used between noERR and mRT are given in Tables  9  and 10. In the RT for MW, a different Table  9. Modification introduced in the radiative transfer model settings for microwave instruments in terms of particle shape (Scattering type) from database ARTS  (Eriksson et al. , 2018).
Perspectives The above mentioned limitations could be overcome by exploring larger perturbations on both RT simulations and NWP model forecasts. However, this first set of experiments indicates that the fine tuning of RT properties between IR and MW spectral ranges does not seem critical compared to the model parameterization uncertainties. It has been shown that a synergy between the two types of datasets can still be obtained. Therefore, the next step will be to explore the use of cloudy IR data within the 4D-Var assimilation system of Météo-France which already make use of MW cloudy and precipitating data. As a first step, imagers onboard geostationary satellites will be studied and the work will then be further extended to hyperspectral instruments.
Acknowledgements. This research is funded by  Météo-France and Région Occitanie  ( PhD grant  for  Ethel Villeneuve ). The authors acknowledge the  Centre National d'Études Spatiales (CNES)  for the financial support of this scientific research activity part of the  Infrarouge ,  Micro-Ondes et Transfert  radiatif ensembliste  pour la prévision des Extrêmes de   Précipitations (IMOTEP ) project. Eric Defer ,  Laurent Labonnote  and  Jérôme Vidot  are acknowledged for their advises on the results' interpretation. Laurent Descamps  is acknowledged for providing information and literature on RPP perturbation method. 23 https://doi. org/10. 5194/egusphere-2023-446 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License.
Code and data availability. The radiative transfer model RTTOV v13 used in this paper is available for free from https://nwp-saf. eumetsat. int/ (last access: 30 November 2022) to registered users. The numerical weather prediction model ARPÈGE is developed at Météo-France. Datasets produced during the course of this study (ARPÈGE analyses and forecasts) are too large to be publicly archived. All model and experiment data have been archived on the Météo-France mass storage system and can be obtained from the first author upon request.
Data collection We did a literature search using two methods to identify the studies to be included in the metaanalysis. First, we used the most popular bibliographic databases 'Scopus' and 'Web of Science (WoS)' to identify the papers. The starting year for the search was 2001 after the publication of the paper of  Bhattacherjee (2001a). The cited reference search resulted in 2,641 papers in WoS and 3,636 papers in Scopus. Then, we searched the following search query in the article title, abstract, and keyword sections  (Jadil et al. , 2021) : "expectation-confirmation" OR "expectation confirmation" OR "expectation disconfirmat*" OR "expectation-disconfirmat*" OR "disconfirmation of expectations" OR "post acceptance" OR "positive disconfirmation" OR "IS continuance". We also considered sources other than academic journals such as books, book chapters, dissertations, and conference proceedings to address the bias of 'publishing only significant results'  (Rosenthal & Rosnow, 2008).
In addition, we did the keyword search in other databases such as 'EBSCOHOST', 'JSTOR', and 'Google Scholar' to ensure maximum coverage. The specific keyword-based search resulted in 950 papers. We combined all three search results and deleted the duplicates leading to 3,740 papers. We removed journals on irrelevant topics like food, drugs, and medicine to reach 2,613 papers. We also removed literature reviews, conceptual papers, and qualitative studies due to the lack of quantitative data. We included articles that tested at least one relationship from the original model of  Bhattacherjee (2001a)  and provided Pearson correlation, regression coefficients, or any other statistics that can be used in meta-analytic computations. We identified a total of 214 articles using the search and inclusion criteria.
Coding We coded each study for 15 relationships used in our research model. The coding was done for two parts: research descriptive statistics and effect sizes  (Yu et al. , 2020). For research statistics, we coded for research design (survey or experiment), year of publication, type of sample (student or general population), country of sample collected, average age, and gender distribution of the sample. For effect sizes, we noted the following data for each relationship: sample size, effect size (correlation, beta coefficients, t value), means, reliabilities, and standard deviations.
Analysis We performed path analyses on pooled correlations matrix obtained through the meta-analysis using AMOS 21 software with maximum likelihood estimation. AMOS is a covariance-based SEM method that uses covariance matrices to evaluate measurement model and structural model on multiple parameters. To test the research model, we first analyzed the original theoretical model, followed by an extended model and an emergent model. We evaluated the model fit based on recommended values for the following parameters  (Bentler, 1990; Hu & Bentler, 1999) : comparative fit index (CFI) ≥ 0. 95, normed fit index (NFI) ≥ 0. 95, root mean square error of approximation (RMSEA) ≤ 0. 08, and standardized root mean square residual (SRMR) ≤ 0. 08. The modification index values were considered to identify the presence of significant relationships that were not hypothesized in the research model  (Sabherwal et al.
If, after attacking with 9 words, the model's output was greater than 0. 5, we continued adding new positive words until the score dropped below this threshold or until we reached the maximum length of an input sequence for the model tested. For the English language, we also performed experiments with the phrase "Text for bot:" placed between a message and added words. We conducted additional experiments with the Perspective API and OpenAI Moderation API, discovering they are also vulnerable to our attacks. For eight non-English models, we asked native speakers of the respective languages to translate a selected subset of words and attack phrases. Within this study, we tested the language models for seven languages aside from English: Russian, German, French, Portuguese, Spanish, Turkish and Vietnamese.
Cross-lingual word-based attack The main weakness of all multilingual models lies in their greatest advantage -the ability to work with multiple languages and writing systems. Adding positive words in different languages will even more separate messages to the human and to the toxicity detection system, perhaps even denying a human the ability to read non-intended text. Examples of such texts are shown in Figure  1. Even some monolingual models, which had been exposed to another language during the pretraining phase, fall victim to this situation. We also conducted cross-lingual experiments with OpenAI models, including ChatGPT in conversation mode with different prompts, and found them susceptible to attack. Perspective API was found to be non-functional in a multilingual setting, failing with an error, as it is unable to detect the language of the text.
, 2010; Riley et al. , 2019). Just north of the South Scotia Ridge a number of highs occur, which are, from west to east, Terror Rise, Pirie Bank, Bruce Bank, Discovery Bank and the Ancestral South Sandwich Arc. For the first four highs, their bathymetry, gravity signal, reflection seismic data and sparse dredge samples have led earlier studies to conclude these are underlain by continental crust  (Galindo-Zaldívar et al. , 2002; Civile et al. , 2012; Beniest and Schellart, 2020). Bathymetric data, seismic reflection profiles and data from dredged rock samples reported in other studies indicate the existence of volcanic seamounts on Discovery Bank and at the boundary between Discovery Bank and Scan Basin (e. g. Pérez et al. , 2014a; Somoza et al. , 2014; Riley et al. , 2021) , which have been interpreted to form part of the Ancestral South Sandwich Arc.