dataesr/scientific-paragraphs-categorization huggingface dataset:
(2007)  analysis of the ice core showed that a palaeoclimatic signal could be found in the top half of the EFZ. Cavitte (2017)  trace what they call "deep scattering zones" in the HiCARS dataset, as well as a single profile flying across Dome C (see their Fig. 5. 17), showing furthermore that their modelled IRH geometries do not match those of the deepest traced IRHs, suggesting a stagnant basal layer. The basal unit observed in the LDC-VHF data of this study and published in  Lilien et al. (2021)  is different because the radar system is sufficiently sensitive at this depth, so the effect is likely to come from physical changes in the ice. The ApRES measurements at LDC show that the ice at the depth of the basal unit has different properties to that above. Moreover, the magnitude of the ApRES-LDC vertical velocity (Fig.
, 2013). Also, it should still cover the MPT and the last part of the 40 kyr world. The MYIC site has very similar characteristics to BELDC. The differences between the two ice cores could help inform future site prospecting, as they may show evidence of processes that are not detectable from the radar data or have not been considered in the model. We also investigated NP as a potential area for finding old ice. According to the model, age density at 1. 5 Ma is twice as good as that of LDC. This is due to the relative lack of stagnant ice predicted by the model at NP, which at LDC causes more thinning in the deepest ice. The reason that NP was not considered further for the Beyond EPICA project was that basal melting at LDC was much less likely, making it the safer option. While the NP was initially flagged by a HiCARS transect passing nearby, the only detailed radar data available at NP are the DELORES transects.
Conclusions In this paper, we presented a 1D numerical model which interpolates and extrapolates the age-depth profile obtained from radar IRHs. The model was applied to three radar datasets collected over the LDC area and was shown to be more relevant when a layer of modelled stagnant ice could be included. Model outputs show similar results for the three radar datasets regarding the stagnant ice thickness and maximum age in the lowest part of the ice. It was shown that the thickness of the modelled stagnant ice is comparable to that of the basal unit observed in the radar, supporting the hypothesis that this basal unit is stagnant. ApRES measurements at LDC also showed that the vertical velocity profile can be best explained when a basal layer of stagnant ice is present. The maximum modelled age at an age density of 20 kyr m -1 for the transect location closest to the BELDC drill site is 1.
Acknowledgements. This publication was generated in the frame of Beyond EPICA. The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 815384  ( Oldest Ice Core ). It is supported by national partners and funding agencies in  Belgium ,  Den- mark,  France ,  Germany ,  Italy ,  Norway ,  Sweden ,  Switzerland ,  The Netherlands  and the  United Kingdom. Logistic support is mainly provided by  ENEA  and  IPEV  through the  Concordia Station  system. This publication was generated in the frame of the  DEEP-ICE  project. The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 955750. AWI acknowledges  Pascal Andreas  and  Sven Lenius  for their contribution to the preprocessing of the data.
Code availability. The code for the 1D numerical model is available publicly from https://github. com/ ailsachung/IsoInv1D (last access: 7 August 2023; DOI: Data availability. The HiCARS IRHs used in this study can be found publicly on the US Antarctic Program Data Center (USAP-DC): https://doi. org/10. 15784/601411  (Cavitte et al. , 2020). The DELORES profiles discussed in this paper will be available from https://data. bas. ac. uk (last access: 7 August 2023). The LDC-VHF radar will be made publicly available in a PANGAEA repository. The LDC-VHF IRHs used in this study are available here, https: //doi. pangaea. de/10. 1594/PANGAEA. 957176 (Chung et al. , 2023), and the DELORES IRHs will be made available in a public PAN-GAEA repository. The BedMachine version 3 bed elevation data come from the work of Morlighem et al.
32 ns that is also observed in acetonitrile and thus could be tentatively attributed to enol emission, less sensitive to solvent polarity. Altogether these data suggest that the open-enol form is the ground state precursor for the high energy emission (405 nm) and the closed-enol form, the one for low energy emission (513 nm) resulting from the ESIPT process. In order to provide evidences of the presence of ESIPT in PyOHTr-2, emission spectra were recorded for PyOMeTr-7, analogue of PyOHTr-2 in which ESIPT is excluded due to methyl ether group. A unique emission band located at 410-420 nm was observed in CH 3 CN, DCM, and EtOH as solvents (Figure  S22 , Supporting Information). Emission wavelengths and Stokes shift of PyOMeTr-7 were almost identical to that of PyOHTr-2 for the enol band, which indicates a similar geometry (open enol form) for these two compounds in the excited state.
Acknowledgements The authors thank  Sorbonne Université and CNRS  for funding, the  Fédération de Recherche de Chimie Moléculaire de Paris Centre  ( FR2769 ) for providing technical access to analytical instruments. The authors thank  Interface pour le vivant doctoral program  from  Sorbonne Université  for a PhD grant to A. N. -D. The authors gratefully acknowledge  Arnaud Brosseau  (PPSM) for his help with the TCSPC measurements.
The CoMix survey has been collecting multiple waves of a representative sample of data on social contact behavior since March 2020, in order to gain insights into behavioral changes. This longitudinal study gathers information on attitudes, awareness, and behavior in response to COVID-19 over time in Europe  [16]. Nevertheless, as a long-term longitudinal survey, the CoMix survey could be subjected to fatigue effects that might appear when the survey's participant becomes bored or less interested in the survey  [22]. Such a respondent's fatigue effect is a well-known phenomenon, leading to deteriorating data quality  [23] [24] [25]. For example, participants may skip questions, spend minimal time answering questions (which will lead to increased variability and/or bias, e. g. , because of under-reporting).
CoMix study CoMix is a longitudinal multi-country behavioral survey that was designed to monitor public awareness and behavior during the COVID-19 pandemic. Survey data were collected in the United Kingdom (UK), Belgium, and the Netherlands from March 2020 onward  [16]. In Belgium  [10] , the sample was selected bi-weekly from existing panels of people who frequently partake in online surveys and was chosen to be representative of the Belgian population based on age, gender, and region of residence. Participants' age, education level and occupation were recorded, together with information regarding their socio-economic status, health status, whether they experienced symptoms, and attitude towards and adherence to NPIs (limited to the first data collection period). In each wave, participants were asked to report all contacts made on a specific day between 5am on the day preceding the survey day and 5am of the survey day.
A contact was defined as either an in-person conversation of three or more words in the physical presence of another person with (such as kiss or handshake) or without skin-to-skin contact  [30]. Methodological details have been reported elsewhere  [10, 12, 16]. The CoMix survey in Belgium spanned two data collection periods. The first period consisted of eight waves of data collection (April 2020 to August 2020) in a representative panel of adults (18 years and above) in terms of age, gender, and geographic area to have the same people participate in each wave. Children were omitted in the first period in order to get ethical clearance as quickly as possible. These 8 waves corresponded to different NPI regimes (Fig. S1 ). As a result of respondents dropping out in subsequent waves of data collection, the panel was supplemented with new members to meet the target quota.
The second survey period (November 2020 to March 2022) included 35 data collection waves (numbered from 9 to 43) and covers periods of both high and low SARS-COV-2 circulation and corresponding burden of disease in Belgium (for details, see Tables  S1-S3 ). In the second period, a change in design was implemented to allow children's data to amass. The questionnaires for children were completed by a parent within their household as a proxy. Given that the second period of the survey involved children, we focused on this part of the longitudinal survey (wave 9 -wave 43) to investigate how fatigue affects survey responses throughout the population. Therefore, we assume that wave 9 is the first wave of participation. Although participants may have taken part in the previous data collection, this ended 3 months earlier, and we assume that this lag was sufficient to eliminate possible fatigue bias from the previous data collection.
Overall degree distribution While generalized linear mixed models (GLMM) and generalized additive mixed models (GAMM) are constrained to the exponential family of distributions, GAMLSS are an extension of GAMM that allow for a broader range of conditional distributions of the response  [31]. In GAMLSS, all four parameters (mean, variance, skewness, and kurtosis) can be modeled, either using linear parametric, non-linear parametric, or non-parametric (smooth) functions of the predictors, as well as normal or non-parametric random effects  [32]. Negative Binomial  [6]  regression (R package 'gamlss'  [31] ) was used to model the reported number of contacts as a function of covariates and additive terms. However, as an extension of the GAMM model, GAMLSS does not provide parametric marginal regression functions by default, despite the fact that such functions are required for population-averaged inferences.
Given that marginal and random-effects models have completely distinct interpretations  [27] , the marginal models are preferable when inferences about the population-average are the focus  [33]. Marginal interpretation of negative binomial (NBI)  [34]  GAMLSS model can be derived by building a connection between the aforementioned model with the combined model for count data (See Additional file 2)  [35, 36]. Thus, with appropriate precautions, all parameters in the NBI GAMLSS model, except the intercept, will have a marginal interpretation. We defined 3 age groups (0-17 years (Children), 18-69 years (Adults), and 70 years and over (Elderly)) to build age-specific comparisons on the average reported contacts in a day. We selected three age groups because they reflect the various social contact patterns found in society.
Additionally, participant vaccination status was excluded from the model as it caused convergence problems. Using social contact matrices created with the 'socialmixr' package  [43] , we compare age-group-specific social contact rates in the study period. This comparison can be used to examine the impact of under-reporting due to fatigue on estimating the reported number of contacts between age groups. The World Population Prospects of the United Nations  [44]  was used as the population reference, and weights were limited to a maximum of three to limit the influence of single participants, as used by Willem et al. [45]. To account for variability in the model prediction, contact matrices were produced based on 500 Negative Binomial simulations from the NBI GAMLSS model results (both with and without correction for under-reporting due to fatigue) for each predicted value of each participant.
By substituting the value of X 1t with 1, we reflect participants are participating for the first time, hence correcting for under-reporting due to fatigue. A first-order auto-regressive logistic regression model for the dropout process  [27, 48]  was developed by modelling the outcome as a function of previous outcomes for the same subject. This analysis was performed to see if current and previous values, as well as other covariates, had an effect on participants dropping out. A data set containing participants dropping out with dropout indicator as a response was constructed. The dropout indicator was assumed to follow a binomial distribution. Socio-demographic features such as gender, area of residency, and socio-economic status were used as predictors. By utilizing 'gam' package  [49] , random intercepts for each participant and cubic spline on the standardized days were used as smoothing parameters.
Moreover, there are no statistically significant differences in the number of reported contacts between the Elderly and Adults based on symptomatic and vaccination Fig. 8  Effects on correcting for under-reporting due to fatigue in R 0. A Relative changes in R 0 with and without correcting for under-reporting due to fatigue and B The impact of correcting for under-reporting due to fatigue on the hospitalizations reproduction number estimated from heterogeneous CoMix data status. Although these effects are not significant, these parameters could be confounders as both of them could influence the mixing patterns. We also observed a high inter-generational age-mixing between adults and children, with most contacts reported in the Children-Adults bracket. This aligns with previous studies and can be explained by contact networks of families and workingage groups.
Conclusion This study highlights the importance of acknowledging the presence of under-reporting due to fatigue in longitudinal contact surveys in Belgium, which provided valuable information on human behavior in the transmission of airborne diseases. As the CoMix study can describe how people mix in the population, during the second data collection period, we discovered that there are, on average 20% under-reported contacts due to survey fatigue. This effect is more pronounced in waves with a small number of new participants than in waves with many new participants and vice versa. However, the effect is consistent throughout the study and can be well understood. Therefore, upon making reasonable assumptions in future analyses, the survey fatigue effect can be managed and will not unduly affect the epidemiological conclusions drawn from the survey.
MAP1LC3A is known as LC3A protein and is required for autophagosome formation  [25]. To get further insight on which cell type in blastocysts was most affected by Zc3h11a inactivation, we explored the expression profile of the DE genes in Zc3h11a -/-embryos in embryonic lineages (ICM and epiblast) and trophectoderm (TE). Using previously published datasets of mouse gene expression (GSE76505  [26] ), the ICM/TE ratio of expression was computed for genes down-regulated and up-regulated in the KO embryos (FDR ≤0. 05, fold change ≥2). We also explored the expression of DE genes at earlier stages using gene expression dataset (E-MTAB-2950)  [27]. This showed that down-regulated genes in Zc3h11a -/-embryos are primarily expressed in the ICM/early epiblast rather than trophectoderm (Figure  5C ), while the expression of the up-regulated genes is nearly equally present in ICM/early epiblast and TE (Figure  S2 ).
genome. ucsc. edu). The abundance of gene expression was calculated as count-per-million (CPM) reads. Genes with less than one CPM in at least three samples were filtered out. The filtered libraries were normalized using the trimmed mean of M-values (TMM) normalization method  [56]. P-values were corrected for multiple testing using the False Discovery Rate (FDR) approach. Gene set enrichment analyses (GSEA) were performed using the fgsea R package  [57]. Genes were ranked based on the fold-change and the datasets were downloaded from the GSEA website (https://www. gsea-msigdb. org/gsea/).
Mass spectrometric data analysis Data analysis of raw files was performed using MaxQuant software (version 1. 6. 4) and the Andromeda search engine  [59, 60] , with the following parameters: cysteine methyl methanethiosulfonate (MMTS) as a static modification and methionine oxidation and protein Nterminal acetylation as variable modifications. First search peptide MS1 Orbitrap tolerance was set to 20 ppm, and iontrap MS/MS tolerance was set to 0. 5 Da. Peak lists were searched against the UniProtKB/Swiss-Prot Mus musculus proteome database (UP000000589, version 2019-04-01) with a maximum of two trypsin miscleavages per peptide. The MaxQuant contaminants database was also utilized. A decoy search was made against the reversed database, with the peptide and protein false discovery rates both set to 1%.
Only proteins identified with at least two peptides of at least 7 amino acids in length were considered reliable. The peptide output from MaxQuant was filtered by removing reverse database hits, potential contaminants, and proteins only identified by site (PTMs). Intensity values were used to determine the protein abundance. First, proteins with missing values in more than one replicate in at least one group were filtered out. Thereafter, the filtered intensities were normalized using the variance stabilizing normalization (vsn) method  [61]  and followed by the imputation of missing values with the deterministic minimal value approach (MinDet)  [62]  to replace the missing values in the normalized intensities. The normalized intensities were fitted to a linear model and the empirical Bayes moderated t-statistics and their associated P-values were used to calculate the significance of differential enriched proteins  [63, 64].
Crosslinking immunoprecipitation sequencing (CLIP-seq) Cultured mESCs were cross-linked using a 254 nM UV crosslinker with an energy setting of 400 mJ/cm 2. The cross-linked cells were collected in ice-cold PBS with a cell scraper and aliquoted in 1. 5 ml tubes (25 million cells/ml). The CLIP-seq library preparation was performed as indicated  [66]. Briefly, the total lysate was digested with RNase-I and immunoprecipitated with 10 µg of the following antibodies: anti-ZC3H11A (HPA028526 and HPA028490, Atlas Antibodies), anti-ALYREF (ab202894, Abcam) or anti-IgG (ab37415, Abcam). The IP-RNA complexes were loaded on a 4-12% Bis-Tris (Bio-Rad) gel, transferred to a nitrocellulose membrane, and the bands above 75 kDa for each lane were cut. Extracted RNA molecules from the membrane were used for Illumina library construction as indicated  [66].
CLIP-seq reads were trimmed out using trim_galore with the criteria to remove reads with low quality and shorter than 15 bp. The trimmed reads were aligned to the mouse reference genome mm10 using STAR aligner with end-to-end options --alignEndsType EndToEnd. The CLAM workflow were used for peak calling and counting the fold enrichment of IP vs IgG control  [67]. The identified peaks with adjusted Pvalue <0. 01 were annotated to the mouse mm10 genome using the peak_annotator function from CLAM. HOMER software was used for motif finding using the findMotifsGenome. pl script with default parameters for RNA motifs  [68].
Acknowledgments This project was funded by the  Swedish Research Council  ( 2017-02907 ) and the  Knut and Alice Wallenberg Foundation  ( KAW 2017. 0071 ), as well as by the  French REVIVE Labex  ( Investissement d'Avenir ,  ANR-10-LABX-73 ). Sequencing was performed by the  SNP&SEQ Technology Platform in Uppsala. The Swedish facility is part of the  National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory. The SNP&SEQ Platform is also supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation. Animal experiments performed in France were done in INRAE Infectiology of Fishes and Rodents Facility (IERP-UE907,  Jouy-en-Josas Research Center) , which belongs to the  National Distributed Research Infrastructure for the Control of Animal and Zoonotic Emerging Infectious Diseases through In Vivo Investigation (EMERG 'IN, doi: 10.
Procedure Data were collected at all study sites roughly simultaneously and in-person using a paperpencil method or by completion of the questionnaire on the computer with the assistance of a researcher. Participants first gave informed consent. They then filled out a set of questionnaires, including the TLS-45  (Sorokowski et al. , 2021) , marital satisfaction  (Kowal et al. , 2021; Sorokowski et al. , 2019) , mate preferences  (Walter et al. , 2020) , and social media  (Kowal et al. , 2020). For more details on sampling and procedure, see  Sorokowski et al. (2021).
Participants We recruited 307 Polish participants, 150 men (49%) and 157 women (51%). Ages ranged from 19 to 41 years (M = 31. 19, SD = 5. 56, Mdn = 31). There were 17 dating individuals (5. 5%), 125 individuals in a committed relationship (40. 7%), and 165 married individuals (53. 7%). Furthermore, three participants (1%) indicated completing only up to primary school, 116 (37. 8%) up to high school, 178 (56%) attained up to a bachelor's or master's degree, five individuals (1. 6%) attained a Ph. D. degree, and five participants (1. 6%) indicated 'other' level of education. All participants passed the attention check, implemented to control for the quality of engagement and enable the exclusion of respondents who did not provide meaningful responses, and thus all participant data were included in analyses.
Participants A total of 118,320 participants from 175 countries responded to the recruitment call and completed the survey in one of the 43 available languages. Of those, data from 60,311 adult individuals across 156 countries were processed for further analysis including 41,447 (69%) female participants, 18,169 (30%) male participants, 449 (0. 7%) non-binary and 246 (0. 4%) who preferred not to indicate their sex, aged between 18 and 90+ years (M = 32. 41, SD = 12. 89, Mdn = 28). Inclusion in the analyses was based on the following criteria: participants reported that they were romantically involved, they passed the attention check providing meaningful answers, and there were at least 100 responses per language version of the survey. Six languages (i. e. , Bengali, Swahili, Urdu, Chinese-simplified, Mongolian, and Thai) did not pass this latter inclusion criterion, while the following 37 languages were included in our study: Arabic, Bosnian, Brazilian Portuguese, Bulgarian, Chinese Traditional, Croatian, Czech, Dutch, English, Estonian, Farsi, Finnish, French, Georgian, German, Greek, Hebrew, Hungarian, Italian, Japanese, Korean, Lithuanian, Macedonian, Malaysian, Norwegian, Polish, European Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Spanish (Latin), Swedish, Turkish, and Ukrainian (see Table  S22  in the SM).
Procedure We forward-and back-translated  (Brislin, 1970 (Brislin, , 1983 ) the survey into 43 languages (see SM for detailed instructions for all translating teams). Data collection spanned five months (from April to August 2021). Data were collected online via the Qualtrics website in all but four countries. One Russian collaborator collected data using the Toloka website (a crowdsourcing platform popular in Russia). Individuals from Algeria and Morocco could not access the Qualtrics website. Thus, data were collected in person, using a paper-and-pencil method. Moreover, Iranian participants also had difficulties accessing the Qualtrics website, so the survey was re-created, and data were collected using Google Forms. Collaborators invited participants from as diverse sample pools as possible (men and women, older and younger, from rural and urban areas, from the community and university samples, and so forth).