dataesr/scientific-paragraphs-categorization huggingface dataset:
HumanMethlyation450 annotation file was used for annotating the probes and their corresponding genes. Deviation of multiple correlation squared ρ 2 from constant (random model) of G*Power software was used in order to calculate statistical power  [23] , specifically the post hoc power analysis procedure. This procedure is parameter analysis which requires the type of test (tails: one or two), effect size (H1 ρ 2 and H0 ρ 2 ), α error probability, total sample size, and number of predictors. In order to investigate whether the observed association between WC and methylation level was related to genetic variants associated with WC, a linear mixedeffects model with age, sex, BMI, family structure, methylation batch array and white cell counts as fixed effects, and methylation chip as a random effect was carried out for association analysis using the R statistical package nlme  [25].
Results Poor quality probes (n = 764) were excluded from the sample. In total, 77% of probes passed quality control, excluding probes containing SNPs, sex chromosomes, SBE sites, cross-reactive and target polymorphic CpGs, thus leaving 373 626 probes for association analyses (Fig. 1 ). The post hoc analysis of the statistical power of the result was calculated as 100%. One individual was excluded after quality control checks of the methylation array data (outlier of the plotted median of the methylated against unmethylated samples), thus 210 participants were included in the analyses. The results of the analysis showed one novel significant positive association of cg16170243 probe with WC adjusted for sex, age, BMI, family structure, and individual blood cell counts (β = 2. 32, SE = 0. 41; P adj = 0.
Funding Financial support was provided by the  European Union  within the frames of the  Operational Programme FEDER-FSE Lorraine et Massif des Vosges 2014-2020 , by  Agence Nationale de la Recherche ,  programme d'Investissements d'avenir , grant number  ANR-15RHU-0004. Additionally, this work was supported by the regional project  CPER-ITM2P 2015-2020.
Human population data. 2016 census data were obtained at IRIS ("Ilots regroupés pour l'information statistique") level (https://www. insee. fr/fr/statistiques/4228434). Those sub-urban units correspond to the most detailed level of population census available in Reunion (n = 344) (Fig. 1 ). The population was then estimated at the scale of the operational zones mapping used by the local vector control services (n = 1203). To scale down from the IRIS level of the French census, built-up Fig. 1. Location of the study area, Reunion Island. spatial data were downloaded from the French National Geographic Institute (https://data. geocatalogue. fr/id/dataset/a51dafeb-bd2a-4b8 a-929b-ca539a44217e) and the population was reallocated at the prorata of the houses within the operational zones, as compared to the total number of houses in the intersected IRIS zones.
Geographic Information System spatial analysis functions were used (QGIS 3. 4). Only operational zones located in residential areas with an estimated population greater than 10 (i. e. , excluding administrative, business, or sparse habitat) were selected for R 0 computation (n = 1189). Epidemiological data. The weekly confirmed dengue cases recorded in 2018 were aggregated at the IRIS level. The incidence rate (number of confirmed cases in 2018 per 10,000 inhabitants) per IRIS was calculated using 2016 census data. Meteorological data. We obtained daily mean temperature and rainfall recorded from 2017 to 2018 at 32 weather stations (Fig. 1 ) from the national meteorological service 'Meteo France' (https://publitheque. meteo. fr). Indeed, the acquisition of meteorological data covering the year before the period of interest (2018) was necessary for the initialization of the mosquito population dynamics model  [25].
Polynomial regressions adjusted to experimental values  [32]  were used to express the relationship between the temperature and EIP and vector competence (Table  1 ), regarding the dengue virus serotype 2 (DENV-2), the serotype circulating in Reunion Island in 2018  [23]. The impact of dengue infection on the mosquitoes' life cycle, very uncertain  [33] , has been neglected, as well as an eventual variation between symptomatic and asymptomatic hosts regarding incubation period and viremia. Simulations. The R 0 model was implemented in Ocelet language     1 ) and (  2 )). Simulations were run over two years (2017-2018) because the first year was not retained for output computation. Evaluation of the R 0 model. The capacity of the R 0 model to predict the spatial distribution of dengue transmission risk was assessed by comparing the annual statistics (mean and maximum values) of simulated R 0 with the number of confirmed dengue cases reported, at the IRIS level.
Acknowledgments This work was supported by the  European Regional Development Fund (ERDF)  INTERREG  TROI  project in the framework of the  One Health Indian Ocean network  (www. onehealth-oi. org). M. H. received a grant from  European Research Council  under the European Union's  Horizon 2020 research and innovation program :  ERC Consolidator  grant n  682387  /  REVOLINC  project. APC was funded by  ANISETTE , a project supported by the  French Space Agency, CNES  ( DAR 4800001029 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank  Catherine Cêtre-Sossah ,  CIRAD , for fruitful discussion on the results of this study.
Assessment of the physiological parameters The participants were fitted with a connected suit during the three experimental conditions and during the Intermittent Fitness Test. The connected suit provided an electrocardiogram signal at a frequency of 256 Hz from three electrodes (two on the chest, one on the right supra iliac region). The data that were recorded using the Hexoskin® suit were shown to be reliable and valid under a laboratory condition  (Elliot et al. 2019 ). Then, HR was calculated at 1 Hz, and was analysed during three distinct periods. During the alarm mobilisation phase, the pre-alarm HR corresponded to the average HR of the previous 5 seconds before the alarm signal, the peak HR was the maximal HR reached during the phase and the HR reactivity was calculated as the difference between peak HR and pre-alarm HR.
2017). It has been demonstrated that the HRR60s and T30 indices reflect parasympathetic reactivation immediately after exercise  (Peçanha et al. 2017). HR variability (HRV) derived from the electrocardiogram signal extracted from the Hexoskin® suit was recorded 10 minutes after the simulated rescue interventions. R-R intervals were analysed both on time-and frequency-domains using Kubios software (Kubios HRV Analysis v3. 0; Bio-signal Analysis and Medical Imaging Group at the Department of Applied Physics, University of Kuopio, Kuopio, Finland), and the signal was corrected automatically using software correction with a medium threshold of correction. After a 5-minute period of stabilisation, HRV analysis was performed for a 5-to-10-minute period, post-exercise. In the frequency domain, HRV was analysed using spectral analysis of the intervals from the fast Fourier transform.
Friedman tests were used to analyse the BP differences between time measures (Resting-, Pre-, Post-1 min, post-10 min measures) and the Nemenyis procedure was used when the null hypothesis was rejected. Statistical analysis was performed using XLSTAT software 2013 (Addinsoft SARL, Paris, France). Statistical significance was set at <0. 05. The magnitude of the differences was interpreting using Cohen's d effect-size where <0. 20 is trivial, 0. 20 to 0. 49 is small, 0. 50 to 0. 79 is moderate and >0. 80 is large  (Cohen 1988 ).
rough the use of features that look into the population composition during the search process, we will look into the algorithm's behavior and dynamics and extract some insights. Features are defined in terms of dominance status, membership to the Pareto optimal set, recentness of discovery, and replacement of optimal solutions. Complementing the study with features, we also look at the approximation through the accumulated number of Pareto optimal solutions found and its relationship to a common metric, the hypervolume. To generate the data for analysis, the chosen problem is MNK-landscapes with settings that make it easy to converge, enumerable for instances with 3 to 6 objectives. Studied algorithms were selected from representative multi-and many-objective optimization approaches such as Pareto dominance, relaxation of Pareto dominance, indicator-based, and decomposition.
To this end, we purified endogenous TBP-containing complexes (TFIID, SL1, and TFIIIB) by an anti-TBP immunoprecipitation from HeLa cell nuclear extracts, (Fig. S2 ), cross-linked them with the amine-reactive cross-linker BS3, and analyzed the sample by CXMS. The CXMS analyses of the endogenous TFIID complex identified 65 interlinks (crosslinks between different protein molecules) and 43 intralinks (cross-links involving the same protein) (Tables  S1  and S2 , respectively). We then focused on TAF8 interlinks where TAF8 cross-linked to another TAF peptide and compared our dataset to CXMS data obtained from super core promoter (SCP)-bound hTFIID or SCP-bound hTFIID containing PIC  (12, 14)  (Fig. 1B ). In all of the analyzed endogenous hTFIID complexes, TAF8 cross-linked extensively to TAF2, TAF6, TAF9/9b and less frequently to TAF1, TAF3, TAF5, and TAF12 with cross-linking hotspots in TAF8 at K:20, K:112, K:132, K:178, and K:205 (Fig.
1B ). The K:20 hotspot of TAF8 cross-links to the TAF9/TAF6 HFD, to the TAF6 middle (TAF6M) and TAF6C domains, containing five HEAT repeats  (29) , and to one position at the C-terminus of TAF5. These cross-links agree with the potential position of the N-terminal end of TAF8 in the TFIID structure (Fig. 1 , A and B); however, K20 is absent from the published human TFIID structure (PDB: 6MZM, (  14 )), suggesting that the N-terminal end of TAF8 may be very flexible. K:112 at the C-terminus of the HFD of TAF8 cross-links to a region downstream of the HFD in TAF9/9b and to TAF3. K:132 in the ID of TAF8 cross-links to TAF3, TAF6, and TAF12b. K:178 at the C-terminus of the PRD cross-links to the nonconserved TAF6 linker between TAF6M and TAF6C, to the third HEAT repeat of TAF6, to TAF2 and to the N-terminus region of TAF1.
K:205 in T2R1 of TAF8 cross-links in all the three CXMS analyses to K:875 in TAF2. Surprisingly, between amino acids 205 and 310 of TAF8 (situated in T2R2 and T2R3 regions), no cross-linked peptides were detected in any of the three TFIID preparations (Fig. 1B ), suggesting that either the C-terminal end of TAF8 does not produce peptides that are amenable to MS analysis, it is buried in a non-cross-linkable structure or positioned away from the TFIID surface. Note that the visible path of hTAF8 in the cryo-EM structure of the human TFIID is from amino acids 28 to 229 (PDB: 6MZM, (  14 )) encompassing the HFD, ID, PRD, and T2R1 regions of TAF8 (Fig. 1A ). Nevertheless, these crosslinking results in agreement with cryo-EM structures further indicate that TAF8 plays a triple role in TFIID: (i) participates in the assembly of the six HFD-containing 7TAF complex in lobe B via its N-terminus and HFD regions; (ii) connects lobes B and C by interacting with the HEAT repeat regions of two TAF6 subunits, the TAF1 region between amino acid (aa) 378 and 427, and TAF2 via its PRD; and (iii) interacts with TAF2 in lobe C via its C-terminal part.
A 90 min gradient from 5% ACN to 40% ACN was used. The RAW files were converted to mzXML files by Rawconverter  (47). For cross-linked peptide searches, we used two different cross-link database searching algorithms: pLink  (48)  and an in-house designed Nexus. Cross-linking data were analyzed using pLink  (48)  with default settings (precursor monoisotopic mass tolerance: ±10 ppm; fragment mass tolerance: ±20 ppm; up to four isotopic peaks; max evalue 1; static modification on Cysteines; 57. 0215 Da; differential oxidation modification on Methionines; 15. 9949 Da) against a database containing TFIID, TFIIIB, and SL1/TIF-IB protein sequences. The Nexus program can be directly downloaded from https://www. dropbox. com/sh/o7z1h12sf3nu89f/AAD5 tR_iEXaf8IUDcYZjSj3ja?dl=0. For Nexus searches, the same databases were used with the following parameter settings: A 5% of FDR cutoff was used for both pLink and Nexus.
Data analysis Proteins were identified by database searching using SequestHT (Thermo Fisher Scientific) with Proteome Discoverer 2. 4 software (PD2. 4, Thermo Fisher Scientific) on Mus musculus database (Swissprot, nonreviewed, release 2019_08_07, 55,121 entries). Precursor and fragment mass tolerances were set at 7 ppm and 0. 6 Da, respectively. Trypsin [KR/P specificity] was set as enzyme and up to two missed cleavages were allowed. Oxidation was set as variable modification, and carbamidomethylation as fixed modification. Peptides were filtered with a false discovery rate (FDR; which was calculated based on decoy-reverse database used by the Proteome Discoverer 2. 4 software) at 1%, rank 1, and proteins were identified with 5% FDR and one unique peptide. For the Label-Free Quantification, the protein abundancies were calculated from the average of the peptide abundancies using the TOP N (where N = 3, the three most intense peptides for each protein), and only the unique peptide was used for the quantification.
Acknowledgments-We are grateful to the IGBMC cell culture and flow cytometry services for assistance. We thank  D. Schmit  for starting the initial experiments on this project,  F. El-Saafin  and the  Tora lab members  for helpful discussions,  N. Jung  and  B. Reina San Martin  for help with the CRISPR/Cas9 experiments, and  L. Negroni  with MS experiments and data submission. Author contributions-E. S. , K. G. , J. R. , and L. T. conceptualization; E. S. , J. L. , A. B. , F. R. , and J. R. data curation; E. S. , J. L. , and F. R. formal analysis; J. R. funding acquisition; E. S. , J. L. , F. R. , J. -M. G. , and I. B. investigation; E. S. , J. L. , F. R. , J. -M. G. , I. K. -C. , K. G. , I. B. , and J. R. methodology; J. R. and L. T. resources; J. R. software; K. G. , I. B. , J. R. , and L.
T. supervision; E. S. validation; A. B. and L. T. visualization; I. B. , J. R. , and L. T. writing-original draft; L. T. writing-review and editing. Funding and additional information-This study was supported by grants from  Agence Nationale de la Recherche  ( ANR ):  ANR-19-CE11-0003-02 ,  ANR-PRCI-19-CE12-0029-01 ,  ANR-20-CE12-0017-03 ,  NIH   R01 GM131626  and  NSF  (Award Number: 1933344) grants (to L. T. );  NIH   R01 GM110064  and  GM136974  (to J. R. ) and supported by funds from  CNRS, INSERM, Strasbourg University , and Investissements d' Avenir grants  ( ANR-10-IDEX-0002-02  and  ANR-10-LABX-0030-INRT ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other granting agencies.
34, 35 urrent research has demonstrated that DRF increased during the pandemic, and these increases were related to several demographic factors. However, there is still much we do not yet know about why dream recall increased, or its effects on mental health. To assess the effects of the pandemic on psychological health and sleep habits, ICOSS -the International COVID-19 Sleep Study was initiated in March 2020. 7 This worldwide project over four continents with 14 participating countries and over 19,000 participants provides the opportunity to examine this important question across countries and cultures. Gathering data from different countries allows us to take the varying level of restrictions and number of infections with COVID-19 into account. Based on previous research, we hypothesized that DRF was elevated during the COVID-19 pandemic as compared to retrospective pre-pandemic levels.
Methods This paper is part of the International COVID-19 Sleep Study (ICOSS). The research protocol and the final standardized survey questionnaire used in the project were previously published by Partinen and colleagues. 7 The project was carried out in the form of a cross-sectional survey in 14 countries including Austria, Brazil, Canada, Hong Kong, Province Jilin (China), Finland, France, Italy, Japan, Norway, Sweden, Poland, the UK, and the USA. Data used for the analysis were obtained from May to July 2020 using an online survey, which was distributed via media platforms, newsletters at universities and hospitals and through websites to different sleep societies. Web survey platforms were used, including REDcap and Qualtrics. All investigators obtained ethical approval or exemptions from their local ethics committee.
Before taking part in the survey, all participants provided consent. Participants aged 18 years and older were eligible to complete the survey. The survey was anonymous, and participants did not have to provide identification information except for general sociodemographic variables. General data protection regulations were applied to ensure privacy and confidentiality. The survey was translated into the national language of each country. The survey included questions taken from existing and validated questionnaires, as well as questions that were developed for this study. The survey included sociodemographic variables (age, gender, marital status, number of people living in the same household, residential area, ethnicity, education, work) and COVID-19 related data (infection, severity of disease, treatment, confinement, number of people infected among family and friends, worsening of financial status).
Statistical Analysis The statistical procedures were carried out using Stata/SE 16. 1. 46 The original data included 25,484 participants. We excluded data from the USA from the analyses, as the data collection method differed from the other countries (eg paid mTurk nationwide vs convenience sample, which overemphasized the US with stratification). Additionally, data from Sweden were excluded from the analysis as there was no lockdown in Sweden at the time of data collection. After excluding data from USA and Sweden, the sample size was n= 23 tests. The results were described as proportions (percentages). We compared differences in sociodemographic data of participants between low and high DRF using independent sample t-test or chi-square test. The participants' characteristics were described using mean ± standard deviation or frequencies (percentages).
Associations with Pandemic Dream Recall Frequency Logistic regressions were conducted to examine associations between independent variables and high DRF during the pandemic. We excluded all missing values within the independent variables from the analysis, and data from Poland were excluded completely due to a large number of missing values within the independent variables. In total, we included 15,899 participants with complete data in the regression. The results of the logistic regression are shown as OR in Table  4  and as adjusted OR (aOR) in Figure  2. Female gender was associated with high DRF (aOR=1. 25, 95% CI 1. 10-1. 41), compared to males. Age group 55-64 years (aOR=0. 69, 95% CI 0. 58-0. 83) reported less high DRF than younger participants. Other characteristics such as marital status and living alone were not related to high DRF after adjusting for other independent variables.
Limitations There are methodological limitations to be addressed. The data collection via online survey might have introduced a bias in the final sample, since participation was limited to individuals with internet access and self-selected for accepting to participate, which could lead to that people who were more interested in the topic of dreams and sleep were more likely to take part in the survey. DRF has been linked to attitudes toward dreams, so people who are more interested in the topic of sleep and dreams might be more likely to overestimate their dream recall. 82 Additionally, we had an uneven distribution of characteristics, such as gender and ethnicity within the sample, which limits the generalizability of the results. There were large variations concerning the sample sizes from each country.
However, this issue was addressed by weighting the data accordingly in the logistic regression. There were local differences concerning the level of restrictions applied to contain the spread of COVID-19 in each country at the time of the survey, which could have influenced the data collected. Another limitation of our study concerns the self-reported data. Self-reported retrospective data in general are prone to inaccuracy and can be distorted by memory bias compared to prospective sleep and dreams diaries. 83 It is therefore important to emphasize that our study evaluated the subjective perception of dream recall frequency before and during the pandemic. Lastly, our measures of depression, anxiety and PTSD were very short versions of well-validated measures and correlated highly with each other.
thank  Mrs Violette  for helpful discussions, and  R. A. A. for providing excellent temporary work conditions. This research was funded by " Investissements d'Avenir" program  managed by the  ANR  under reference  ANR-11-IDEX-0004-02 , and called " Bio-Psy Labex ". E. M. benefited from a PhD fellowship from this institution. funders had no role in the design of the study; in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
Cell cycle analysis. A total of 125,000 cells were incubated with 150 µl of solution A (trypsin 30 µg/ml; Sigma-Aldrich; Merck KGaA) at room temperature in the dark for 10 min. 125 µl of solution B (trypsin inhibitor 0. 5 mg/ml, RNAse A 0. 1 mg/ml; Sigma-Aldrich; Merck KGaA) was then added in the dark for 10 min. Finally, cells were incubated with 125 µl of solution C (propidium iodide 0. 6 mM, spermine tetrahydrochloride 3. 3 mM; Sigma-Aldrich; Merck KGaA) at 4˚C for 10 min. All the solutions were prepared in a storage buffer pH 7. 6 containing 3. 4 mM sodium citrate 2H 2 0 (Sigma-Aldrich; Merck KGaA), 0. 1% Igepal CA-630 (Sigma-Aldrich; Merck KGaA), 3 mM spermine tetrahydrochloride and 1 mM tris-aminomethane  (32). Patient samples. The research protocol was approved by the Institut de Cancerologie de l'Ouest Paul Papin (ICO, Angers, France) and written informed consent was obtained from all participating patients (approval number NCT02653105).
A total of 74 samples were included in this protocol with patients' age range from 50 to 74 (recruitment date: March 2016). Tumor specimens were embedded in paraffin as normally performed for routine clinical analysis. Following histopathological diagnosis, the FFPE (Formalin-Fixed Paraffin-Embedded) tissues were sectioned at 20 µm, mounted on glass slides and compared with hematoxylin and eosin-stained (10 sec hematoxylin, 3 sec eosin, at room temperature) slides from the same block to identify tumor-rich tissue regions. Sera from female patients with breast cancer were collected at the ICO in Angers between 2014 and 2017. All sera were collected following written informed consent. The study protocol was approved by UNICANCER (approval no. NCT00630032) and 197 patients >18 years old were included in this protocol.
As the peptides eluted, they were directly injected into a hybrid quadrupole-TOF mass spectrometer Triple TOF 5600+ (Sciex) operated with a 'top 30' data-dependent acquisition system using positive ion mode (pressure at the curtain plate: 60 psi without heating; flow rate 5 µl/min). The acquisition mode consisted of a 250 msec survey MS scan from 400 to 1,250 m/z, followed by an MS/MS scan from 200 to 1,500 m/z (75 msec acquisition time, 350 mDa mass tolerance, rolling collision energy) of the top 30 precursor ions from the survey scan. The peptide and protein identifications were performed using Protein Pilot software (version 5. 0; Sciex) with a human Swiss-Prot/TrEMBL concatenated target-reverse decoy database (https://www. uniprot. org/, downloaded in March 2016) containing 142,441 human protein sequences, specifying MMTS as Cys alkylation.