dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGING THE PARTICIPANTS' CONTRIBUTION A lever to ensuring that the participants get involved in a serious, substantial way involves recognising the quality of their work by introducing a form of symbolic reward, and clearly informing them about this when they sign up to the project. It is recommended that it be carefully considered in advance how the participants' work will be acknowledged when communicating the results, including in the form of scientific articles or conferences: citations (named or not) of non-scientific contributors? Acknowledgements? Co-authors? Other? These arrangements should be explicitly communicated to the participants when they sign up to the project. In the case of named citations, their prior consent is obviously necessary. More generally, the returns and benefits of the project should be identified for v2.
• Introduce a system of self-validating the data by the community of users; v2. 9. 21-EN • Ensure a sufficient volume of logged data to apply statistical methods; • Use reasonableness and coherence tests. In the specific case where the participants undertake the data analysis: • Propose the same dataset to several participants to compare the results of the analysis; • Propose a known/calibrated dataset (analysed by an expert) to "test" the quality of the participant's work; • Introduce a system of random verification; • Compare the results from experts with those from participants on a subset; • With transcriptions, compare the results on the same segments of the corpus; • Analyse the reproducibility of the data analysis (similar data analysed by the same participant). In the specific situation where participants undertake data collection: • Cluster the data produced by the participants (mean values, etc.
That was a deliberate choice. " Jean-François Bottollier-Depois, IRSN OpenRadiation Project In the case of built-in smartphone sensors, it is important to study the robustness of the physical quantity measured beforehand because of the high variability in telephone models and thus corresponding sensor models. Additionally, the environmental conditions they are used in should be assessed (low light, the microphone being partially clogged by dust, the impact of parasitic magnetic fields in magnetic surveys, etc. ). For example, a smartphone can be used to collect photos for which most of the useful properties can be obtained in the metadata of the files transmitted. However, measuring sound levels using a microphone or measuring other physical parameters (gyroscope, magnetometer, etc. ) is more complicated.
]  We also gave people the option of answering 'I don't know'. We didn't want to force them to answer if they didn't really know. " v2. 9. 21-EN For example, in the CiTIQUE 8  project, about 6% of the contributions were made on paper. One lever to ensure the diversity of participant profiles is to provide a range of different tools, given that there are disparities in digital literacy -as regards both technical skills and habits. It is also possible to give participants the opportunity to develop their own tools by giving them access to the database through an API. Thus, the project can benefit from their creativity, while taking a bottom-up approach to the needs and desires of the community involved in the project. When designing the project, the tool development should include the associated cost in financial and/or human resources terms if it is developed internally, but also for its maintenance, notably for smartphone applications.
Diversifying the tools used to collect and process the data can be a lever to manage risk: if one tool malfunctions, becomes obsolete or fails to be taken up by the target community, other tools can serve to maintain participation in other ways. "to upload the data, there is the smartphone application via an API: the input format on the database is public, so it is possible to upload the data independently of the application. [. ] The data can even be entered manually. [  The software tool should enable all data to be saved. If a participant makes an error, this should be tracked (in the form of a comment, for example), but no data should ever be deleted or removed. It is important to teach participants about errors and how these are accounted for in the scientific method. As is the case for the upstream tools, the downstream tools should enable every action on a piece of data to be kept in track of and be accessible to the researcher, with an appropriate historization and versioning system.
It should also enable the participants, if appropriate, to answer "I don't know" in cases of doubt -or not answer at all. "there was a clear expectation and there was not enough feedback to the community itself on what the community was doing. " In a project that measures environmental parameters (noise pollution levels for example), when the participants add in a measurement, they should be able to see their contribution immediately on a map available in the application or website, as in the case of the Noise Planet 9  project, for example. Data visualisation (and the tools associated with it) is an important but complex challenge in terms of motivating participants and animating the community. v2.
] [and] a management tool for the facilitator (user management, news publishing, community management, etc. )" Romain Julliard, MNHN Professor Director of the MOSAIC skills centre It is also interesting to let the participants comment on the data where possible and appropriate, as a means of reporting errors and facilitating discussions within the community. The comments in the upstream and downstream tools for data collection and processing then become a way of communicating within the community with the data itself as supporting material. "it is possible to comment on a measurement made by someone else and discuss this with the community. " Jean-François Bottollier-Depois, IRSN OpenRadiation Project.
LEGAL ASPECTS The project's data policy should clearly define the ownership of the data produced during the project and be clear as to the role of the participants in the project. The legal considerations in participatory research projects are complex, so it is recommended to refer to relevant legal support services during the project design phase. The specifics of participatory research projects in terms of data rights are also addressed on pages 58 to 62 of reference  [15]. The participants should have a clear understanding of the rules governing ownership of the data and associated databases: • Who owns the data collected/produced? • Who can remove their data from the database? Recommendation 15: To meet with the relevant departments within your home institution to set out ownership arrangements clearly and formally for the data produced during the project.
ACKNOWLEDGEMENTS The authors of this document would like to warmly thank the following people for agreeing to answer their questions:  Jean-François Bottollier-Depois  ( IRSN ),  Fabian Docagne  ( Inserm ),  Jonas Durand  ( Inrae ),  Audrey Dussutour  ( CNRS ),  Pascale Frey-Klett  ( Inrae ),  Emmanuelle Gonzalez  ( MNHN ),  Romain Julliard  ( MNHN ),  Nathalie Lambert  ( CNRS ),  Thomas Lebarbé  ( Université Grenoble Alpes ),  Delphine Mézière  ( Inrae -DipSO ),  Sébastien Payan  ( Sorbonne Université, Data Terra ),  Marta Severo  ( Université Paris Nanterre ),  Delphine Sicard  ( Inrae ). v2. 9. 21-EN.
Data Charter for Citizen Science: A basic set of principles to support open and interoperable citizen science data M. Sterken,  R. Capiau. Scivil (2021) https://www. scivil. be/en/book/data-charter-and-guide-citizen-science Data Management Planning for Citizen Science UK Environmental Observation Framework (2020) https://www. ukeof. org. uk/resources/citizen-science-resources/cswg-data-guidance-bookletweb. pdf/view Briefing Paper on Citizen Science Science Europe (2018) https://doi. org/10. 5281/zenodo. 4925779 Citizen science at universities: Trends, guidelines and recommendations  D. Wyler ,  F. Grey ,  K. Maes ,  J. Fröhlich. League of European Research Universities (2016) https://www. leru. org/publications/citizen-science-at-universities-trends-guidelines-andrecommendations , LIBER Citizen Science Working Group (2021) https://doi.
Study sample We use the panel component of two waves of a nationally representative survey of the adult population conducted in nine countries (Australia, Austria, France, Germany, Italy, New Zealand, Sweden, the UK and the USA) and covering 6379 respondents. These data are part of the Citizens' Attitudes Under COVID-19 Project (CAUCP). 16 17  The CAUCP panel data were collected by established commercial polling companies (CSA Research in Australia and the USA, and IPSOS in all other countries), using the computer-assisted web interview method, in five waves, from the outset of the pandemic (wave 1 in late March 2020), throughout 2020 (wave 2 in April 2020, wave 3 in June 2020, wave 4 in December 2020) and 2021 (wave 5 in June/July 2021). The questionnaires of CAUCP surveys followed existing comparative public opinion studies, but more specific questions were asked to address the consequences of the COVID-19 crisis.
In each country, participants were recruited online and selected with quota sampling based on gender, age, occupation, region of residence, ethnicity (in the USA) and education (in France). The survey companies provided weights that can be applied to ensure that the sociodemographic characteristics of the sample match the characteristics of the population. Our results are based on the raw data, but they are virtually unchanged when using weights. Data collection and analysis were performed blind to the conditions of the experiments. Questions on vaccination intentions were introduced in the December 2020 wave, while questions on actual vaccination behaviour were included in the June/July 2021 wave. On average, respondents took around 20 min to answer the questionnaire in these two waves, which we henceforth refer to as first and second.
32  However, the USA stands Finally, we were limited to only nine countries and it would be good, should the opportunity arise, to replicate this work in more countries, and with larger samples that would make it possible to test additional messages and hypotheses, including the impact of specific national factors. Contributors VG, VP and PP did the conceptualisation of the research question, the data curation, the formal analysis and the writing of the paper. VG, VP, PP, MB, SB and MF participated in the design of the survey questionnaire and of the experiment and in securing funding for the project. MM and DS contributed to the writing of the final version of the paper. VG is responsible for the overall content as guarantor.
Results The presence of strong signatures of positive selection during primate evolution is a frequent characteristic of lentiviral restriction factors  (6). When MORC2 was identified as a protein essential for HUSH repression, we wondered whether MORC2 has been evolving under positive selection in primates, in contrast to HUSH components(2). To address this possibility, we analysed MORC2 orthologous sequences from 25 species of primates, reconstructing its evolutionary history on 60-80 million years of divergence. The topology of the phylogenetic tree reconstructed from the primate MORC2 coding sequences was concordant with the primate species tree (Fig. 1A ). To determine if MORC2 has evolved under positive selection, we performed several positive selection analyses available in the Detection of Genetic INNovation (DGINN) pipeline and from the HYPHY package (see Methods)  (23).
Sequence and evolutionary analyses The sequences of primate orthologous genes of MORC2 were retrieved from publicly available databases using NCBI Blastn, UCSC Blat or Ensembl, with the human sequence as the query. For phylogenetic and positive selection analyses, we retrieved one coding sequence per primate species for a total of 25 species. For each gene, two datasets were independently analyzed: one included the entire primate evolutionary history (spanning approximately 87 million years of primate evolution) and one was restricted to the simian primate species (spanning approximately 40 million years; with 21 simian primate species included). Orthologous sequences were codon-aligned using Muscle v3. 8  (74)  and the phylogenetic tree was built using PhyML v3  (75)  with a HKY+I+G model with 1,000-bootstrap replicates for statistical support.
To comprehensively determine the evolution of MORC2 in primates, we performed positive selection analyses. Maximum-likelihood tests to assay for positive selection were performed using three packages: HYPHY v2. 3  (76) , PAML v4(77) and Bio++ v2. 2. 0 , all available within the DGINN pipeline  (23, 78). In HYPHY, we used the branch-site unrestricted statistical test for episodic diversification (BUSTED) method, which detects gene-wide evidence of episodic positive selection within a codon alignment. In PAML, we used the Codeml program with the corresponding gene tree inferred from PhyML as input. Parameters were checked using the M0 one ratio model and the sequence alignments were then fit to models that disallow (M1 and M7) or allow (M2 and M8) positive selection. Likelihood ratio tests (LRTs) were performed to compare model M1 versus M2 and M7 versus M8, using a χ2 test to derive P values.
We finally used the Bio++ package to similarly test for evidence of positive selection (M1 NS versus M2 NS and M7 NS versus M8 NS , as implemented in Bio++; NS for non-stationary models). To look more specifically for site-specific positive selection, we used the fast, unconstrained Bayesian approximation for inferring selection (FUBAR) method from HYPHY/Datamonkey. In Codeml and Bio++, we used Bayesian empirical Bayes (BEB) analyses and Bayesian posterior probabilities, respectively, from the M2 and M8 models to determine if any codon was under significant positive selection. Protein structure representations were conducted using UCSF Chimera (The Regents) software. Protein structure predictions are from AlphaFold Protein Structure Database  (79). All plasmids are transfected in cells by the calcium-phosphate co-precipitation method.
Luciferase activity assays Cells were washed twice with Dulbecco's PBS 1x then lysed with the lysis buffer cell culture Lysis Reagent 1X from Promega and centrifugated 10 min at 15000g. Luciferase activity was measured using a luciferase assay system from Promega and a TECAN multimode reader Infinite F200 Pro. Luciferase activity data was normalized on protein concentration with the use of Pierce BCA Protein Assay Kit (232255, Thermo-Fisher).
Flow cytometric analysis J-Lat A1 and Jurkat cells were collected and washed with PBS and resuspended in PBS-EDTA 0,5 mM. Data was collected and analyzed with a BD Accuri C6 cytometer and software CFlow Plus. At least 10,000 events in P1 were collected, the GFP-positive population was determined using a GFP-negative population when possible (for Jurkat cells) or arbitrary (as for J-Lat cells) and the same gate was maintained for all conditions. Analysis was performed on the whole GFP-positive population. Regarding reactivation assay, Jurkat cells are reactivated with 1 ng/mL of TNF-a.
Acknowledgements and funding sources We thank all the members of the  Retrovirus, Infection and Latency group ,  Stéphane Emiliani  and  Sarah Gallois-Montbrun  at  Institut Cochin  for fruitful discussions. We also thank the members of the  LP2L team at CIRI, Lyon , for support. We acknowledge the  CYBIO  and GENOM'IC platforms of the  Institut Cochin. This work was supported by grants from  SIDACTION , the  "Agence Nationale de la Recherche sur le SIDA et les hépatites virales" (ANRS)  and  Fondation pour la Recherche Médicale  (FRM,  PROJECT EQU202203014684  atttibuted to F. MG. ). R. M. was supported by  SIDACTION  and FRM ; M. M. M. by SIDACTION ; A. L. , S. M. and P. L. by  Université de Paris Cité  ; V. V. by  ANRS. This work in the laboratory of L. E. is supported by grants from the  French Research Agency on HIV and Emerging Infectious Diseases ANRS/MIE  (# ECTZ118944  to LE) and the  Sidaction  (n°21 -1-AEQ-12972-2  to LE and FMG).
Data and Reagent availability. Data for sequence analyses (codon alignments, and phylogenetic tree) are available at https://doi. org/10. 6084/m9. figshare. 22337539 , https://doi. org/10. 6084/m9. figshare. 22337542 , and https://doi. org/10. 6084/m9. figshare. 22337545. All other data and reagents are available from the corresponding authors.
Acknowledgements L. M. T acknowledges funding from the  Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund  ( 204804/Z/16/Z ). L. M. C. acknowledges  UGent  for a position as postdoctoral assistant and funding from  FWO/ Embassy of France in Belgium-Campus France  (joint exchange/mobility project Tournesol:  VS01022N ). N. D. B. acknowledges funds from the  Wellcome Trust  ( 221647/Z/20/ Z ). A. B. was supported by an  MRC   Clinical Research Training Fellowship  ( MR/W000881/1 ). C. A. and A. M. acknowledge funding from the  UGent Special Research Fund (BOF)  through the MemCLiP concerted research action. D. J. H. was supported by an  MRC   Project Grant  ( MR/S025618/1 ), a  UKRI Frontier Research Guarantee Grant  ( EP/X026833/1 ), an  ERC Starting Grant  ( 715884 ) and the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre.
Bioinformatic analyses/GO term analysis Sequencing was performed on each library to generate 101-bp paired-end reads on Illumina HiSeq2500 Genome Analyzer platform. Read quality was checked using FastQC and low quality reads were trimmed using the Trimmomatic version 0. 32 (http:// www. usadellab. org/cms/?page=trimmomatic) with the following parameters: Minimum length of 36 bp; Mean Phred quality score higher than 30; Leading and trailing bases removal with base quality below 3; Sliding window of 4:15. After pre-processing the Illumina reads, the transcript structures were reconstructed using a series of programs, namely, TopHat (ver. 2. 1. 1; http://tophat. cbcb. umd. edu/) for aligning with the genome, and Cufflinks (ver. 2. 2. 1; http:// cufflinks. cbcb. umd. edu/) for gene structure predictions.
Crosslinked chromatin was sonicated by the use of a water bath Bioruptor UCD-200 (Diagenode; 15-s on/15-s off pulses, 15 times). The complexes were immunoprecipitated with antibodies overnight at 4°C with gentle shaking and incubated for 1 h at 4°C with 50 mL of Dynabeads Protein A (Invitrogen). Immunoprecipitated DNA was then recovered by the use of the IPure kit (Diagenode) and analyzed by qRT-PCR as described previously by  (Volz et al. , 2018). An aliquot of untreated sonicated chromatin was processed in parallel and used as the total input DNA control. The transgenic lines carrying the IDD4:GFP construct under the control of the UBI10 (At4g05320) promoter were used  (Völz et al. , 2019b). The retrieved binding patterns were normalized by comparison with data obtained from pUBI10::GFP expressing plants.
66 to 3. 11 of positively regulated genes and from -2. 11 to -0. 63 of negatively regulated genes, among which 174 genes are up-and 74 genes are down-regulated. Hierarchical clustering of significantly deregulated genes (p<0. 05) in idd4 before and after NaCl treatment, by using normalized FPKM values, revealed distinct differences in gene expression patterns in idd4 after NaCl perception (Figure  6A ; Supplementary Data 2). To categorize DEGs into functional modules, gene ontology (GO) enrichments were determined using AgriGO platform  (Tian et al. , 2017 ) (TAIR10). The oppositely regulated genes between WT and idd4 without salt-stress and after salt-perception are shown in Cluster I (Figure  6B ; Supplementary Data 2). Cluster I was further sub- B A FIGURE 5 Phenotype of WT, mpk6-2 and mpk6/idd4 double mutants under salt-stress.
Genome-wide expression data of IDD4 overexpressor lines  (Völz et al. , 2019b)  unveiled higher transcript levels of these factors (Figure  7E ) compared to WT, suggesting that IDD4 acts as a transcriptional activator. Furthermore, we also found IDD4 to be B A FIGURE 6 Differential expression of Arabidopsis idd4 mutant plants in response to salt-stress (A) Hierarchical clustering of WT and idd4 transcriptome with and without salt treatment. The original FPKM values were adjusted by normalized genes/rows and subsequently processed by hierarchical clustering using average linkage method using MeV4. 0. Blue and yellow color indicates relatively low and high expression levels, respectively. For the complete gene list see Supplementary Data 1. (B) Centroid graph (red) and individual expression graphs of differentially regulated genes dedicated to clusters 1, 3, 8, and 14.
Besides the graph chats, gene ontology annotations are presented for cluster 1, 3 and 8. GO terms were determined by using the agriGo database (TAIR10). associated with the promoter regions of several well-known saltstress regulators, such as two members of the salt-overly-sensitive (SOS) pathway, which participate in ion-homeostasis regulation. SOS3 and SOS3-INTERACTING PROTEIN 2 (SIP2) encode a sensor of cytosolic Ca 2+ that is essential for K + nutrition, K + /Na + selectivity, and salt tolerance  (Ishitani et al. , 2000; Li et al. , 2013; Van Oosten et al. , 2013)  (Supplementary Figures  4B, C ). IDD4 was also found to be associated with the promoter regions of the genes for the protein phosphatase 2C ABA-insensitive 1 (ABI1) (Supplementary Figure  4E ), which is a key component of the ABA core signaling complex, and the transcription factor WRKY33 (Supplementary Figure  4D ), which is also involved in ABA signal transduction of salinity-stress  (Jiang and Deyholos, 2009).
Background The risk of mortality in intensive care units (ICUs) is currently addressed by the implementation of scores using admission data. Their performances are satisfactory when complications occur early after admission; however, they may become irrelevant in the case of long hospital stays. In this study, we developed predictive models of short-term mortality in the ICU from longitudinal data. Methods Using data collected throughout patients' stays of at least 48 h from the MIMIC-III database, several statistical learning approaches were compared, including deep neural networks and penalized regression. Missing data were handled using complete-case analysis or multiple imputation.
These databases enable the collection and centralization of detailed data throughout inpatients' stays via demographic characteristics, physiological measures, diagnoses, laboratory analyses, medical imaging, medical notes, etc. ICUs are highly monitored environments and important data sources for these warehouses. Repeated collection of data allows us to study the evolution of patient characteristics and to identify factors associated with the occurrence of worsening conditions, possibly leading to complications or death. Appropriate machine learning algorithms are required to address the massive amount of data available in these warehouses. Deep learning methods have been extensively studied in recent years for their abilities to manage large amounts of data, and specific architectures of deep learning networks, such as convolutional and recurrent neural networks, have been developed to handle longitudinal data  [9, 11].
Such predictive modeling approaches may, however, present limited interest when their use relies on a large number of predictors, several of which may be unavailable in some patients. Considering recent advances in predictive modeling from longitudinal data using neural networks  [12, 13] , we aimed to develop and validate models predicting ICU mortality for higher lengths of stay than those well evaluated by the existing scores. These models were built from ICU hospitalizations lasting more than 48 h, using longitudinal health care data with missing values from electronic health records available in the freely accessible Medical Information Mart for Intensive Care (MIMIC-III) critical care database  [14, 15]. Different architectures of deep learning neural networks were evaluated in the context of missing values for some predictors and compared with predictive models based on penalized regression.