dataesr/scientific-paragraphs-categorization huggingface dataset:
The analysis of SERAPHIN and its OL extension reported here includes survival estimates of up to 9 years and thus provides the longest follow-up period to date for PAH patients treated with PAH therapy. Our results suggest that patients in WHO FC I/II who are treated with macitentan have good long-term survival, with a 5-year estimated survival rate of 83. 3%. In line with previous observations  [1, 22] , our data also show that patients in WHO FC I/II at baseline have a better prognosis than those in WHO FC III/IV. Of note, given the long observation period in SERAPHIN OL, the proportion of patients who had their treatment regimen escalated to include a new class of concomitant PAH therapy during the study was low. The main limitation of this analysis is that the data are uncontrolled and descriptive in nature, inherent to OL extension studies.
Written informed consent was obtained from all patients. Data Availability. The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at https://www. janssen. com/clinical-trials/transparency. As noted on this site, requests for access to the study data can be submitted through Yale Open Data Access (YODA) Project site at http://yoda. yale. edu. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
ACKNOWLEDGEMENTS We thank the participants of the study. Funding. The study was funded by  Actelion Pharmaceuticals, Ltd , a  Janssen Pharmaceutical Company of Johnson & Johnson. The study sponsor also funded the journal's  Rapid Service  and  Open Access  fees. Medical Writing and Editorial Assistance. Medical writing support was provided by  Jatta Huotari  and  Ioanna Ntai of eluSCIdate ltd (Meggen, Switzerland ) and was funded by  Actelion Pharmaceuticals, Ltd , a  Janssen Pharmaceutical Company of Johnson & Johnson. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published. Barbarash; The  National Medical Research Center of Cardiology of the Ministry of Health of the Russian Federation , Moscow:  I Chazova ;  Federal State Budget Scientific Institution, Tomsk: A Evtushenko; Regional State Autonomous Healthcare Institution, Tomsk: L Lenskaya; Regional Clinical Hospital, Ekaterinburg: M Arkhipov; Federal State Budgetary Institution, Moscow: AG Chuchalin; State Health Care Institution of Moscow, Moscow :  N Shostak ;  Federal State Budgetary Institution, St Petersburg: OM Moiseeva; St.
Search Strategy The following 4 databases were searched electronically from the earliest available date to June 14, 2021, to identify relevant literature: Web of Science, PubMed, Cochrane, and Ovid PsycINFO. Three key search components were used: eHealth interventions, cancer, and the Netherlands. An overview of the search strategies for each database can be found in Multimedia Appendix 1. Other potentially relevant publications were identified by tracking the reference lists of included articles.
Selection Procedure The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Statement was used to ensure the validity and reliability of the selection procedure  [27]. The PRISMA 2020 checklist can be found in Multimedia Appendix 2  [28]. One investigator (LvD) searched for eligible studies. Subsequently, the reference software program Endnote (Endnote X7; Thomson Reuters) was used to remove duplicates. Two investigators (LvD and LS) independently screened the titles and abstracts of the articles to identify relevant studies. Next, full texts of the potentially relevant articles were assessed. Discrepancies between investigators were mutually resolved through discussion until a consensus was reached. Web-based software Covidence (Veritas Health Innovation)  [29]  was used for the screening process.
• Intended setting to use the intervention: primary care, secondary care, or community • Target population: type of cancer, demographics (gender, age, and nationality), and specific characteristics (eg, smokers) • Support of health care professional: yes or no, with an explanation • Use of theory in the development of the intervention: yes or no, with an explanation • Stakeholder involvement in the development of the intervention: yes or no, with an explanation Information on research methods and outcomes was extracted at the study level for each empirical evaluation study. More specifically, we extracted information on the study design and objective, the number of participants included at baseline, description of the control group (if applicable), data collection period, study measures, and outcomes.
Acknowledgments The authors would like to thank  JWM Plevier  for her assistance in designing the search strategy for the review and  A Suijkerbuijk  and  W Dijkstra  for their comments and suggestions. This study was funded by the  Dutch Ministry of Health, Welfare, and Sport  (for the benefit of the eHealth monitoring project). The funders had no role in the study design, data collection and analysis, data interpretation, writing of the manuscript, or approval for publication.
Introduction Glycosylations are major post-translational modifications (PTMs) observed in plant cell wall proteins (CWPs), and they occur in the secretory pathway. There are three types: N-glycosylation, O-glycosylation, and glypiation (for a review, see  [1] ). N-glycosylation consists of the grafting of an oligosaccharide onto a particular amino acid, asparagine (Asn), in the context of a specific motif, Asn-X-Ser/Thr-X (with X =Pro). This motif is described as PS00001 in the PROSITE database (https://prosite. expasy. org/) (accessed on 22 September 2022)  [2]. Since it is short (only four amino acids), this motif is classified among the motifs with a high probability of occurrence. It means that the chance to find it in protein sequences is high even if the protein does not travel through the secretion pathway where N-glycans are synthesized and grafted.
The fragmentation of a glycopeptide can be performed on the glycan backbone using collision-induced dissociation (CID) or on the peptide backbone using electron transfer dissociation (ETD)  [45]. Different types of proteins are identified in these N-glycoproteomes with regard to their sub-cellular localization. They can be residents in the ER when they harbor an ER retention signal at their C-terminus, which are described as PS00014 in the PROSITE database  [46]  and are mainly Lys/Arg/His-Asp-Glu-Leu in plants. However, they can also be located in the Golgi apparatus, in the vacuole, at the plasma membrane, or in the extracellular space, cell wall or apoplasm. In this review, we consider proteins as CWPs when they have a signal peptide predicted by at least two different bioinformatic programs: no ER retention signal and less than two transmembrane domains  [47].
This latter criterion was found to be necessary to take into account the presence of a peptide signal at the N-terminus of proteins, which is eventually recognized as a trans-membrane domain since it is hydrophobic, as well as the possible presence of a GPI-anchor signal peptide at the C-terminus. In addition, this criterion takes into account plasma membrane receptors, which are located at the frontier between the extracellular space and the cytosol and are critical for signal transduction. In addition, the predicted functional domains and literature data are considered to assign a protein to a CWP category. To easily obtain these predictions, the ProtAnnDB bioinformatics tool can be used (http://www. polebio. lrsv. ups-tlse. fr/ProtAnnDB/) (accessed on 22 September 2022). The main CWP families have been described in previous articles (e.
expasy. org/) (accessed on 22 September 2022). Some of them contain the canonical C-terminal motif for retention in the ER or are predicted to have trans-membrane domains (see Section 2). 1 See Figures  1  and 2  (Section 2). 2 The size corresponds to the total number of identified proteins. The percentages between brackets indicate the proportion of proteins having predicted N-glycosylation sites. 3 The CWPs are predicted as explained in Section 2. The percentages between brackets indicate the proportion of predicted CWPs in the overall N-glycoproteome. 4 The data are presented in WallProtDB-2 (http://www. polebio. lrsv. ups-tlse. fr/ WallProtDB/) (accessed on 22 September 2022). Complementary information can be found at SUBA5, but this database is not specifically devoted to CWPs (https://suba.
Setting of the study and statement We conducted a cross-sectional analysis using data from an online survey that was composed of an experimental investigation on subjective identity, and a general questionnaire including a COVID-19 module. The first author (Fabrice Etile) had started to design the survey prior to the outbreak as part of a research programme on the relationships between subjective identity and economic preferences in young adults. This research program and the experimental section of the survey are not related to the COVID-19 crisis (see the pre-registration of the experimental hypotheses at https://osf. io/mhty3). After the lockdown and confinement policy measures enacted by the French government on March the 16 th of 2020, we decided to add the specific COVID-19 module that was inserted in the questionnaire at a distance from the experimental treatments and measures.
Given the time constraints and the crisis context, the hypotheses developed and tested in the present study were not subjected to a specific preregistration. The questionnaire was developed using Qualtrics online survey services. The survey respondents were recruited by Qualtrics and paid 4. 45 € per completed questionnaire. Qualtrics distributed the survey between April the 10 th and April the 20 th of 2020. The dataset was made available to us on April the 21 st of 2020. All methods were performed in accordance with relevant French and European guidelines and regulations. The whole survey was approved by the Paris School of Economics IRB (Certificate 2020-009). Although the second author (Pierre-Yves Geoffard) is a member of the Paris School of Economics IRB, he did not participate in reviewing the proposal.
The IRB waived the need for informed consent as (i) respondents' anonymity and RGPD compliance were guaranteed by Qualtrics; (ii) the experimental section of the survey used innocuous priming (iii) respondents were free to opt out of the survey at any moment. More details on the overall survey structure and content are presented in (S1 File) document Appendix A in S1 File. The data and codes used for producing the results are available at https://osf. io/gesyt/.
These two variables were selected after a specification search that revealed no effect of other characteristics of lockdown conditions (see B. 2 in S1 File). The data were collected in the context of a larger survey that contained experimental priming manipulations on time and identity. Although these manipulations were unrelated to the COVID-19 (see A. 1 in S1 File), we controlled for potential survey-specific effects via a set of dummy variables identifying the various experimental groups, with deviation contrast coding to ease the interpretation of the effects on the other dummy variables. These fixed effects were never significant in our regressions. We also included dummies for each quintile of the total survey duration as a means of improving the precision of the estimates and controlling for heterogeneity in response quality.
Funding:  FE -AXA Research Award 2015  (https:// axa-research. org/en/project/fabrice-etile)PYG and FE also acknowledge administrative support from the  Paris School Economics  via the  Agence Nationale de la Recherche EUR  grant  ANR-17-EURE-0001  (https://anr. fr/ProjetIA- 17-EURE-0001 ). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All colistin-resistant isolates were screened for the presence of mcr-1, mcr-2, mcr-3, mcr-4, mcr-5, and mcr-8 genes  (59). The positive strains detected by real-time PCR were submitted to conventional PCR to confirm the previous results. Subsequently, products were purified and sequenced using the BigDye terminator chemistry on an ABI 3130XL automated sequencer (Thermo Fisher Scientific, Waltham, MA, USA). The obtained sequences were analyzed using ChromasPro and compared to the ARG-ANNOT database and NCBI GenBank database (https://www. ncbi. nlm. nih. gov/). Phylogenetic analysis. The determination of a genetic relationship among isolates was done by multilocus sequence typing (MLST) using the seven housekeeping genes. Each gene was amplified and sequenced for each E. coli (adk, fumC, gyr, icd, mdh, purA, and recA) and K.
ACKNOWLEDGMENTS This work was supported by the  French Government  under the  Investissements d'Avenir (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  (ANR; fr:  National Agency for Research)  (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI. This work was supported by the research project  PEER 7-349  funded by the  USAID  "Monitoring of antimicrobial resistance of bacteria for a better health of animals in Tunisia. " We are very grateful to  Sihem Chebil  for technical assistance. We also thank  Mariem Saidani ,  Assia Kassah ,  Jouda Kchok ,  Reem Abdallah , and  Rym Lalaoui  for their help during the study. We declare no competing financial interests.
Data Availability Statement The coupled ocean-atmosphere configuration is available here: https://doi. org/10. 5281/zenodo. 6448250 (Gévaudan et al. , 2022). Sea surface salinity data set from ESA Climate Change Initiative project is freely available at https://www. doi. org/10. 5285/4ce685bff631459fb2a30faa699f3fc5 (Boutin et al. , 2020). Chlorophyll data set from GlobColour (009_082) can be obtained at https://www. doi. org/10. 48670/moi-00100.
, 2014). The important role of anomalies in soil moisture in the coupling between land and atmosphere has been shown in several studies, using numerical climate models  (Zhang et al. , 2011)  and observation datasets  (Zhang et al. , 2008a; Dirmeyer et al. , 2006). For instance, over East Asia,  Zhang et al. (2011)  showed that soil moisture is found to have a much stronger impact on daily maximum temperature variability than on daily mean temperature variability, but it generally has small effects on daily minimum temperature, except in the eastern Tibetan Plateau. They also showed that soil moisture has a prominent contribution to precipitation variability in many parts of western China. West Africa is known to exhibit strong coupling between soil moisture and precipitation  (Koster et al.
Model description and observed datasets The fourth generation of the International Centre for Theoretical Physics (ICTP) regional climate model (  RegCM4 ) is used in this study. Since its release, its physical representations have been continuously developed and implemented. The version used in the present study is RegCM4. 7. The MM5 (Mesoscale Model version 5;  Grell et al. , 1994)  non-hydrostatic dynamical core has been ported to RegCM without removing the existing hydrostatic core. RegCM4 is a limited-area model using a sigma pressure vertical grid and the finite differencing algorithm of the Arakawa B-grid  (Giorgi et al. , 2012). The radiation scheme used in this version of RegCM4. 7 is derived from the National Center for Atmospheric Research (NCAR) Community Climate Model version 3 (CCM3)  (Kiehl et al.
The topography data are taken from the United States Geological Survey (USGS) Global Multi-resolution Terrain Elevation Data (GMTED;  Danielson and Gesch, 2011 ) at a 30 arcsec spatial resolution, which is an update to the Global Land Cover Characterization (GTOPO;  Loveland et al. , 2000)  dataset. Our analysis focuses on precipitation and the 2 m air temperature over the West African domain during the June-July-August-September (JJAS) period. We validate the simulated precipitation with the high-resolution 0. 05 • Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset, developed at the University of California at Santa Barbara, for the period from 1981 to 2020  (Funk et al. , 2015). The validation of the simulated 2 m temperature relies on the CRU (Climate Research Unit) datasets, version 3.
Acknowledgements. The research leading to this publication is cofunded by the  NERC/DFID  " Future Climate for Africa" program  under the  AMMA-2050  project (grant no. NE/M019969/1 ) and by the  Institut de Recherche pour le Développement (IRD) ,  France (UMR IGE Imputation , grant no. 252RA5 ). Financial support. This research has been supported by the  NERC/DFID  " Future Climate for Africa" program  (grant no. NE/M019969/1 ) and the  Institut de Recherche pour le Développement (IRD) ,  France (UMR IGE Imputation , grant no. 252RA5 ).
Code availability. The RegCM4. 7 model is freely available for public use at https://github. com/ICTP/RegCM/releases/tag/4. 7. 1  (Giuliani, 2021). The NCL language was used for the computations and the plots, and the source can be found at https://www. ncl. ucar. edu/ (NCAR, 2019). Data availability. The validation of simulated precipitation was done using the high-resolution 0. 05 • Climate Hazards Group In-fraRed Precipitation with Station (CHIRPS) dataset, developed at the University of California at Santa Barbara, for the period from 1981 to 2020  (Funk et al. , 2015). The CHIRPS dataset is available from https://data. chc. ucsb. edu/products/CHIRPS-2. 0/global_ daily/netcdf/p05/  (Funk et al. , 2014). The validation of simulated 2 m temperature relies on the CRU (Climate Research Unit) datasets, version 3.
Using a decade of acoustic data, a previous study showed that the distributions and movement patterns of six baleen whale species were shifting in the western Atlantic  (Davis et al. , 2020). Moreover, previous studies have shown the relationships between baleen whales phenology and environmental factors. For example,  Derville et al. (2019)  assessed humpback whales' habitat shifting under the impact of ocean warming in Oceania breeding grounds using 19 years of survey data. Charif et al. (2020)  used a 6-year dataset to analyse the phenological changes of North Atlantic right whales as environmental conditions change. An eight-year humpback whale acoustic dataset was used to study the whale presence and climate oscillations  (Schall et al. , 2021). The Indian Ocean is an ideal location to identify how environmental changes affect whale behaviour because of its high biodiversity.
Study Area Underwater acoustic data was obtained from the international data system of the Comprehensive Nuclear Test-Ban Treaty Organisation (CTBTO). Two hydrophone arrays were located 220 km apart on either side of Diego Garcia Island: one on the northwest side (Diego Garcia North -DGN), and the other on the southeast side (Diego Garcia South -DGS) (Figure  1 ). Within each array, a set of three hydrophones was moored in the sound fixing and ranging (SOFAR) channel, where sound achieves the maximum speed  (Hanson, 2001). The CTBTO hydroacoustic stations continuously monitor underwater sound waves with a sampling frequency of 250 Hz. For the DGN site, the data recorded between January 2002 and February 2014 by the hydrophone number H08N1 (6. 34°S, 71. 01°E) were used, and for DGS the data recorded between January 2001 and December 2019 by the instrument number H08S1 (7.
Whale Acoustic Presence Data Hourly presence or absence of Chagos whale songs was inferred from automated detections of the signal as described in  Leroy et al. (2021). All data from the DGN site and data prior to February 2018 from the DGS site were from  Leroy et al. , 2021 , however, data after February 2018  to the end of 2019 from the DGS site were obtained and tested for this study using the same approach as described in  Leroy et al. (2021). The individual detections were logged into Matlab matrices along with the information related to each individual song, such as the exact date-time of the detected event. The data from each site were divided into eight-day blocks (192 hours), consistent with the local environmental data (see 2. 3 Environmental Data, below). Whale acoustic presence is defined as the proportion of hours in the eight-day block in which whale songs were detected.
In cases where acoustic recording had been interrupted, and if the recorded hours were less than 50% of the time block, these blocks were considered to have insufficient data and were excluded from the analysis. In 13 years of acoustic data, 82. 3% (498 eight-day blocks) of DGN recordings and in 18 years 91. 7% (760 eight-day blocks) of DGS data had sufficiently complete data to be included in the analysis. Acoustic presence at both sites was pooled to analyse seasonal variability (Similar method to aggregate presence data for right whale see (Similar method to aggregate presence data for right whale see  Charif et al. , 2020). Pooled data was divided into months for comparison to oceanic data (see Environmental Data, below). We used the eight-day grouped whale acoustic presence data to analyse the peak and extended peak seasons.
Environmental Data Two local drivers and two oceanic drivers were considered in this study. Data for the two local drivers, sea surface temperature (SST) and chlorophyll-a (as an indicator of phytoplankton abundance), were downloaded from the NOAA ocean watch website (https://oceanwatch. pifsc. noaa. gov/doc. html#currents). The seasonal oceanic data including Dipole Mode Index (DMI) representing the event of Indian Ocean Dipole (IOD) and Southern Oscillation Index (SOI) representing El-Nino Southern Oscillation (ENSO) were extracted from the Australian Bureau of Meteorology (BOM) (http://www. bom. gov. au/climate/enso/soi/) and the NOAA Physical Science Laboratory of Global Climate Observing System (https://psl. noaa. gov/gcos_wgsp/Timeseries/DMI/). SST was downloaded through the CoralTemp dataset of the NOAA website.
The analysed data were continuously recorded (24 hours per day) throughout the study period. The mean SST was computed (In total four data spots with two units at each side, spatial resolution for one unit is approximately 5 km) in the approximately 100 km2 encompassing the hydrophone stations. Then the mean SST was calculated for the corresponding eightday period. Mean SST and minimum SST were calculated for each year and for two seasons: December to May and June to November. Chlorophyll-a was used as a proxy of phytoplankton biomass. Blue whales almost feed exclusively on krill (Euphausiid spp. ). Krill information in the Indian Ocean is limited. We will therefore use the concentration of chlorophyll-a (as an indication of phytoplankton biomass) as blue whale food approximation  (Allen, 1971).