dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We thank all past and current members of the  Laboratory of Neuroepigenetics  for their support and discussion throughout this project, in particular,  Paola Arguello  and  Diego Camacho  for their contribution to molecular protocols and analysis; the  Ecole Polytechnique F ed erale de Lausanne (EPFL) Gene Expression Core Facility  for their technical assistance with experiment planning and sequencing; the  EPFL Flow Cytometry Core Facility  for providing the nuclear sorting; and the  EPFL Center of Phenogenomics  for ensuring the welfare of the laboratory animals. The work in the laboratory of J. G. is supported by the  European Research Council  ( ERC-2015-StG 678832 ), the  Swiss National Science Foundation  (SNSF,  31003A_155898 ), the  National Competence Center for Research SYNAPSY  ( 51NF40-185897 ), and the  Floshield and Dragon Blue Foundations.
Data Availability. RNA-seq, snRNA-seq, ChIP-seq, and in-house analysis code data have been deposited in the Gene Expression Omnibus (GEO) database, https://www. ncbi. nlm. nih. gov/geo (accession no. GSE185455; including subseries: GSE185452, GSE185453, and GSE185454)  (92)  and GitHub (https:// github. com/allie-burns/2022_Burns_etal) (93).
First, conceptually speaking, the two concepts relate to two different levels of abstraction. Whereas general meritocracy beliefs entail more diffuse societal beliefs (e. g. , just-world beliefs) that are not attached to specific institutions or to specific agents responsible for safeguarding meritocracy, belief in school meritocracy is a belief that is linked to a specific institution with clear institutional objectives. Second, school meritocracy is a necessary but not sufficient condition for general meritocracy to exist in society; that is a country may have meritocratic schools for instance but not a meritocratic society. Indeed, this is the case in many countries where school meritocracy and violations of general meritocracy (e. g. , high corruption) are both high (e. g. , Maldives, Lesotho, the Philippines, South Africa, and Uzbekistan are among the 10 most educationally mobile countries in the world in spite of their high levels of corruption; see Global Database on Intergenerational Mobility, 2018; Corruption Perception Index; 2021 1 ).
Method Sample. We used the data from the 2009 social inequality module of the International Social Survey Program (ISSP). The ISSP is a series of national surveys based on probability-stratified sampling to ensure cross-national comparability. Given that most of the variables of interest contain questions about income inequality, we excluded participants who did not receive a regular income (i. e. , students, unemployed individuals not looking for employment or individuals who never worked). The analytical sample comprised 51,794 individuals (54. 83% females; M age = 48. 41; SD = 16. 43) from 41 countries (n = 126,327 respondents per country). Variables. All measures used 5-point response scales. Predictor Variable: Belief in School Meritocracy. School meritocracy belief was operationalized following  Garcı´a-Sa´nchez et al.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a doctoral fellowship awarded by the  Swiss National Science Foundation (SNSF)  to the first author ( P1LAP1_164890 ) and a  Vici grant  awarded by the  Netherlands Organization for Scientific Research (NWO)  to  Herman van de Werfhorst  (# 453-14-017 ).
CRONOS is an online survey conducted among respondents of ESS R8 in Great Britain, Slovenia, and Estonia. CRONOS is a panel study re-interviewing respondents in six different waves of the survey from 12/2016 to 02/2018  (Villar et al. 2018). For the purpose of this report, we use information from Wave 3 and Wave 6. Country-level analyses rely on information on country-level unemployment in 2018 sourced from Eurostat, available in the multi-level data set provided by the ESS (Version 1. 0).
Analytical strategy The aim of this report is to provide evidence on the measurement quality of the four items intended to measure agreement with the four distributive principles equity, equality, need, and entitlement. We, first, study the response distribution and then report item non-response as an indicator of measurement quality. Drawing on recent CRONOS data we investigate to what extent the four BSJO items are able to reliably assess individual-level and country-level support for the four principles. We then report associations with related variables spanning socio-demographic characteristics, attitudes on redistribution and income inequality, and the country context. Correlations with related variables are reported for the total population but country patterns are provided in the Appendix.
M-matrices We estimated M-matrices separately for each founder genotype (N2 and PB306). The 6 transition rates q k were fitted as a multivariate response variable y in the model: y = µ + β T * T + β H * H + β D * D + ζ lineID + η block +. ( 3 ) where µ k is the general mean (intercept) of the q k traits and β i are fixed environmental effects of temperature (T), log density (D) and relative humidity (H) at the time of phenotyping for 8 trait q k. ζ, η and as defined above for mutational bias. We then estimated a matrix of genetic (co)variance as half the line covariance matrix (ζ lineID ). Models were fit with the R package MCMCglmm  (Hadfield, 2010). We defined priors as the phenotypic variances for each trait. Model convergence was verified by visual inspection of the posterior distributions and by ensuring that the autocorrelation remained below 0.
We thank  H. Gendrot  and  V. Pereira  for help with worm handling, and  M. A. Félix ,  L. Noble ,  P. C. Phillips  and  J. Picão-Os  ório for discussion. This work was partially supported by the  Agence Nationale pour la Recherche  ( ANR-14-ACHN-0032-01 ,  ANR-17-CE02-0017-01 ) to HT, and the  National Institutes of Health  ( R01GM107227 ) to CB, and a  Marie Curie fellowship  ( H2020-MSCA-IF-2017-798083 ) to LN.
Data for Model Development Data from the China Kadoorie Biobank (CKB), a large-scale nationwide prospective cohort of 512,725 participants aged 30-79 years, were used for model development. The baseline survey was performed between 2004 and 2008 in 10 geographically defined regions (5 urban and 5 rural). The details of the study design and survey methods have been reported previously  [16]. Incident cases of EC and all-cause mortality were identified through linkage with the mortality and disease registries and national health insurance claims database, supplemented with local residential records and annual active confirmation. The International Classification of Diseases, 10th revision was used to code all EC (C15) by trained staff who were blinded to the baseline information. The adjudication of the incident cancer cases is ongoing, with medical records of 1283 EC cases having been retrieved, in which 1246 (97.
Data for External Validation An independent prospective cohort from Changzhou of the Jiangsu province, a low EC-risk rural area in China, was used for external validation. In brief, 20,803 participants aged 30 years and older were recruited from 23 villages in 2004-2005. Incident EC cases and all-cause mortality were identified through active follow-up in  2008-2009, 2012-2013, and 2018-2019 , and through linkage with the disease and mortality registries. Trained staff who were blinded to baseline information further confirmed suspected cases of nonfatal cancer by reviewing local medical records or visiting village doctors.
The criteria for defining high-risk areas are described in Multimedia Appendix 1  [19, [21] [22] [23] [24] [25] [26]. The details of baseline prevalence and incidence of EC by study region are shown in Multimedia Appendix 2. Because data on the family history of EC and hot food consumption were not recorded at baseline in the CKB, we used family history of cancer and hot tea consumption as surrogates for the above-established risk factors. The details of the assessment of predictors are shown in Multimedia Appendix 1.
Statistical Methods In the CKB cohort, participants who were previously diagnosed with cancer (n=2578) or had missing data on BMI (n=2) were excluded, leaving 510,145 participants for development. In the Changzhou cohort, participants who were previously diagnosed with cancer (n=239), out of the age range of 30-79 years (n=1902), had a recorded implausible censoring date for loss to follow-up (n=5), or had missing data on candidate predictors (n=216) were excluded, leaving 18,441 participants for external validation. Participants were considered at risk from enrollment to the first date of diagnosis of EC, death, loss to follow-up, or end of follow-up (CKB: December 31, 2017; Changzhou cohort: January 31, 2019).
Model Development Based on the whole CKB data set, we separately fitted a model for EC and a model for all-cause mortality. For the EC model, a flexible parametric model on the cumulative hazard scale was used to estimate the baseline hazards and hazard ratios of the predictors for EC, with age as the time scale  [27]. Age was modeled using restricted cubic splines with boundary knots at 30 and 90 and internal knots at 60 and 70. The established risk factors of EC (age, sex, smoking, and alcohol use) and regional risk level (high-risk/low-risk areas) entered the model directly. Two strategies were employed for the selection of the other predictors. First, other candidate predictors were included in the full model and kept if P<. 05. Second, the predictor selection was repeated using stepwise backward elimination.
Model Validation The methods for model validation are detailed in Multimedia Appendix 1. In brief, we externally validated the age-only, simple, and intermediate models, but not the full model, because data on physical activity in metabolic equivalent of task-hours and hot tea consumption were unavailable in the Changzhou cohort. We also conducted an internal validation in the CKB by using data splitting and 500-sample bootstrapping. Cancer-free participants whose retention in the cohorts was less than 10 years were included to test calibration but were excluded from other validation measures, since it was unknown whether they could have experienced an EC if they had been followed up to 10 years. Discrimination was quantified by calculating the AUC. Calibration was assessed by plotting the observed risk obtained using Kaplan-Meier analyses against the predicted risk by decile.
Because of the large geographical variation in the incidence of EC in China, we recalibrated the models by using the method proposed by the World Health Organization Cardiovascular Disease Risk Chart Working Group with a slight modification  [21]. Further, continuous Net Reclassification Improvement and Integrated Discrimination Improvement were used to evaluate the added predictive ability of additional predictors  [28, 29]. In the internal validation using data splitting, calibration and discrimination were also assessed in subgroups defined by regional risk level, residence area, sex, age group, and special population aged 65 years and older or with diabetes or hypertension who are of particular concern to the Basic Public Health Service. To offer a reference for primary care practices, we estimated a range of performance indices corresponding to a series of cutoffs.
To the best of our knowledge, ours is the first study to develop and externally validate EC models by using 2 independent prospective Chinese cohorts. Last but not the least, we developed and validated an abbreviated version of the risk prediction model that could be easily embedded within the EHR system and enable an efficient and automatic population risk stratification. To facilitate the usage of our models, we provide an easy-to-use Stata code and example in Multimedia Appendix 16 (Stata calculator, which is a modified version of the code shared by Dr Muller)  [22]. Some limitations of our study merit consideration. First, some EC cases in the CKB had only clinical diagnoses but no pathological diagnoses for various reasons. Therefore, we could not exclude cases of esophageal adenocarcinoma.
Acknowledgments We thank the participants in this study and the members of the survey teams in each of the 10 regional centers as well as the project development and management teams based at Beijing, Oxford, and the 10 regional centers. This work was supported by the  National Natural Science Foundation of China  ( 82192904 ,  82192901 ,  82192900 ,  82125033 ,  81922061 ). Y Han  is supported by the  China Postdoctoral Science Foundation  ( BX20220018 ,  2022M720007 ). The  China Kadoorie Biobank baseline survey  and.
Annotations of Arabidopsis pri-miRNAs Before studying the conservation of miPEPs between species, the first step consisted in the identification of A. thaliana miPEPs. The study (and prediction) of miPEPs first required the identification of primary transcripts of miRNAs. Whereas the pre-miRNAs are well referenced (mirbase. org), the pri-miRNAs of A. thaliana are far from being fully characterized. To achieve the annotation of conserved A. thaliana pri-miRNAs, we crossed the data from EST sequences (www. ncbi. nlm. nih. gov/genbank/), Illumina RNA-seq data  (Wang et al. , 2019) , rapid amplification of cDNA ends (RACE) PCR data  (Xie et al. , 2005) , PacBio Iso-Seq data (this study), and results from our 3 0 RACE PCR experiments. Corresponding sequences have been deposited (GenBank: MW_775349-MW_775424).
To go further, we performed an analysis by 5 0 and 3 0 RACE PCR of nine pri-miRNAs presenting short transcripts in Iso-Seq data. Thus, we found that some of them (miR160a, miR172a) potentially possessed two TSS, confirming a previous study  (Xie et al. , 2005) , while others had only one TSS. In addition, we identified several transcripts that did not contain pre-miRNA sequence or with truncated pre-miRNA (Figure  2B ). These data suggest that pri-miRNAs are transcribed either as long full-length sequences containing all the information (miRNA, miPEP sequence) or shorter or AS transcripts containing only the miPEP sequence, but without a stem loop. We can hypothesize that these different populations of transcripts have different fates: short (and possibly AS) transcripts without complete pre-miRNA could translocate to cytoplasm, be loaded by ribosomes and translated into miPEPs, whereas long transcripts, containing stem-loop structure of the pre-miRNA could be processed by DCL1-containing complex in the nucleus to finally give the mature miRNA.
We first focused on the conservation of miPEPs in two related species: A. thaliana and Arabidopsis lyrata. To achieve this, we annotated A. lyrata pri-miRNAs from available databases and from sequence similarities with A. thaliana pri-miRNAs. We then compared the conservation of nucleic acid sequences between the two species for different types of sequences: coding sequences, intergenic sequences (before and after the pri-miRNA on the genome), pri-and pre-miRNAs, and miPEPencoding sequences (miORFs) (Figure  4B ). While coding and pre-miRNA sequences were strongly conserved between the two species, intergenic regions revealed a weak conservation of about 50% between the two species. Interestingly, the miORF sequences showed a strong variability between the two species, with a median conservation comparable with that of intergenic regions, suggesting a neutral selection pressure on these sequences (Figure  4B ).
Data analysis Orthologous proteins between A. thaliana and A. lyrata were found using Orthomcl software  (Rice et al. , 2000). Nucleic acid sequences of orthologous genes, intergenic sequences, miORFs, and miRNAs were aligned using Needle  (Rice et al. , 2000). Alignments of miPEPs were performed using Multalin  (Corpet, 1988).
ACKNOWLEDGMENTS This work was funded by the  French ANR  project  BiomiPEP  ( ANR-16-CE12-0018-01 ). It was carried out in the  Laboratoire de Recherche en Sciences Ve´ge´tales , which belongs to the Laboratoire d'Excellence entitled  TULIP  ( ANR-10-LABX-41 ). We thank the  « Plateforme GENTYANE INRAE Clermont » (Clermont-Ferrand, France ) for PacBio Iso-seq sequencing. We thank  PK Trivedi  ( CSIR, India ) for sending miPEP858 constructs.
The diurnal pattern of particle numbers is strongly reflective of a road traffic influence upon concentrations, especially in autumn and winter. There is a clear influence of diesel traffic at nighttime when it is permitted to enter the city, and also indications in the size distribution data of a mode <15 nm, probably attributable to CNG/LPG vehicles. New particle formation appears to be infrequent, and in this dataset is limited to one day in the summer campaign. Our results reveal that the very high emissions of airborne particles in Delhi, particularly from traffic, determine the variation of particle number size distributions.
Data and Quality Management Data from SMPS and GRIMM were measured with 1-min resolution. In this study, data sets were used by taking their hourly averages. Simultaneous measurement data from the SMPS and GRIMM were used. In the literature, PNCs measured below 1 µm are frequently split into three ranges: nucleation, Aitken and accumulation  (Gani et al. , 2020). Size range of modes can be highly variable according to the description of the nucleation size range and maximum measured size. Nucleation size ranges have been described as below 30 nm  (Masiol et al. 2016)  or below 25 nm  (Gani et al. , 2020)  or below 20 nm  (Wu and Boor, 2021). Despite this, there are also limited studies on wide range PSD, some evaluating wide range PSDs split into 4 ranges (nucleation, Aitken, accumulation and coarse)  (Masiol et al.
The winter and autumn diurnal profiles had two peaks for below 1 µm particle size in the morning and evening Small midday PN peaks were observed during the summer in the nucleation, Aitken and coarse modes. Another study conducted in Delhi reported the same midday peaks in the warm season and the highest levels in the cold season  (Gani et al. 2020 ), which may be related to bus and LCV emissions at midday. Figure  3  shows the differences in diurnal variations of total PN levels between the weekday and weekend. These are based upon a small dataset, and hence the rather small differences within a season may not be meaningful. In winter the PN levels on Saturday and Sunday are higher than on the weekdays during the night (from 8 pm to 10 am the next day). However, after the morning rush hour peaks, during the daytime the PN levels are the same for all days.
In this study, the identification of NPF events was conducted manually using the criteria set by Dal Maso et al. (  2005 ) and used by  Bousiotis et al (2019; 2021). The data were analysed visually on a day-to-day basis: each 24-hour period, from midnight to midnight. According to these criteria, a NPF event is considered when: a distinctly new mode of particles appears in the nucleation mode size range, prevails for some hours, and shows signs of growth. These are the initial criteria used in identifying the events. Following that, as the dataset starts from a rather large size (15 nm), to be more confident about the events and not to confuse them with pollution events, high time resolution data for NOx as well as the fluctuations of the condensation sink were also used to identify pollution events affecting particle concentrations which were not considered.
In the morning and afternoon there are two small peaks at 60 nm and 40 nm for the Aitken fraction and 170 and 130 nm for the accumulation fraction in autumn. During the day in summer, there are two peaks at approximately 30 nm from 10 am to 6 pm. This may be associated with summer nucleation events and NPF on 3rd June 2018 (Figure 4). Furthermore it may be related to the growth of particles from 10 am to 2 pm in autumn and summer. The full reasons for these changing PSDs are not clear, and it would be unwise to attempt a detailed interpretationof a very small dataset. Figure  8  shows the average geometric mean diameter (GMD) change with hour of the day. Two overall periods of GMD increase are observed. One of them is in nighttime in all seasons with GMD growing at between 4. 6 nm/hour in summer and 6.
There is little evidence for a contribution of new particle formation (although the summer season dataset is small), consistent with earlier work by  Gani et al. (2020). Another notable feature is the apparent complexity and seasonal variability of sources of NO, NO2 and BC, pollutants which can often be used to identify or locate sources of emissions. This is reflected in the various particle fractions, which generally correlate poorly with the other pollutants and with other than proximate size fractions. The diurnal variation of all particle fractions is strongly suggestive of a road traffic influence, especially in the winter campaign. This appears strongly influenced by the emissions of heavy duty diesel traffic which is only able to access central Delhi at night. A size mode of <15 nm may well be attributable to vehicles using LPG/CNG fuels.
The paper analyzes the national DK-model hydrological information and prediction (HIP) system and HIP portal viewed as a 'digital twin' and how the introduction of real-time dynamic updating of the DK-model HIP simulations can make room for plug-in submodels with real-time boundary conditions made available from an HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges were addressed as part of the Joint Governmental Digitalization Strategy 2016-2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction.
This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terabytes of hydrological model data to the portal, with robust calibration methods and hybrid machine learning (ML) being key parts of the deliverables. This paper discusses the challenges and potentials of further developing the HIP digital twin with 'plug-in digital twins' for local river basins, including feedback to the national level.
HIP DT delivers augmented credibility by making it possible to examine and compare model results with online monitoring data in the present and into the near future under high, as well as modest, climate change emission scenarios. For UN-Water, the focus was on tackling the global water crisis and realizing Sustainable Development Goal (SDG) 6: water and sanitation for all by 2030, and as highlighted by UN-Water: "Groundwater is invisible, but its impact is visible everywhere. Out of sight, under our feet, groundwater is a hidden treasure that enriches our lives. In the driest parts of the world, it may be the only water people have. Almost all liquid freshwater in the world is groundwater, supporting drinking water supplies, sanitation systems, farming, industry and ecosystems". However, as also argued in the campaign, groundwater will play a critical role in adapting to climate change because human activities overuse and pollute groundwater in many places.