dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses The primary outcomes of interest for our analysis were: availability and use of CC prevention services, proportion of girls and women vaccinated, screened, treated for cervical pre-cancer and CC. We used descriptive statistics to report site characteristics. We calculated percentages for reported indicators. We used a changing-denominator, also called target approach, to calculate the CC prevention and care cascade, where all women reaching a given step comprise the denominator for the subsequent step. This approach highlights retention gaps at various points of the CC prevention and care cascade. [21]  We reported outcomes of patientlevel analyses for sites with data disaggregated by HIV status. We reported data for sites that included 10 or more eligible girls or women in care.
Strengths and weaknesses of the study The main strength of our study is that we co-developed the survey with country representatives using internationally standardised tools, which improved the validity for each context. Our focus on WLHIV highlights existing prevention and monitoring efforts for this population and gaps across the continuum of CC prevention and care. Besides the site-level assessment, we also analyzed routinely collected data at patientlevel, which reflects closely the real situation in these sites. However, we acknowledge some limitations. The facilities included in our study are part of the IeDEA consortium receiving some research funding, and may not necessarily reflect the general CC prevention and care environment in sites in these countries. We also selected only 30 sites with fairly advanced CC prevention programmes, further reducing representativeness.
Conclusion Facility-based data have contributed significantly to national and global monitoring of other diseases like HIV. Several efforts have been made by governments and partners to provide CC prevention and care for WLHIV across SSA. However, infrastructure and financial challenges persist, hindering access to vital vaccination, diagnostic and treatment services and proper monitoring mechanisms. Our findings illustrate the need for implementation strategies to improve monitoring of CC prevention among girls and WLHIV. Governments should consider leveraging existing electronic data systems to strengthen CC data collection in the short-to medium term, and improving access to specialised care platforms in the long term, which contribute to the majority of referrals. Data collected and analysed are crucial to assist governments and stakeholders to better plan, target, tailor, and scale-up CC prevention and care interventions and also to track progress towards the WHO 2030 CC elimination targets.
Funding This research was funded by the  Swiss National Science Foundation (SNSF) , under funding scheme: r4d ( Swiss Programme for Research on Global Issues for Development) , grant number  177319. The International Epidemiology Databases to Evaluate AIDS (IeDEA) is supported by the  U. S. National Institutes of Health's National Institute of Allergy and Infectious Diseases , the  Eunice Kennedy Shriver National Institute of Child Health and Human Development , the  National Cancer Institute , the  National Institute of Mental Health , the  National Institute on Drug Abuse , the  National Heart, Lung, and Blood Institute , the  National Institute on Alcohol Abuse and Alcoholism , the  National Institute of Diabetes and Digestive and Kidney Diseases , and the  Fogarty International Center, Central Africa ,  U01AI096299 ;  East Africa ,  U01AI069911 ;  Southern Africa ,.
001° resolution (111 m at the equator), which can be used to derive displacement time series using LiCSBAS software  (Morishita et al. , 2020). Open access datasets and online analytical tools and geographic information 85 services can broaden the user base of satellite data by reducing the requirement for complex processing expertise. This is important when developing products and systems that integrate earth observation data, for example in-country monitoring capabilities for natural hazards to reduce disaster risk, in addition to using the data for disaster response  (Joyce et al. , 2009; Wang et al. , 2012; Poblet et al. , 2014).  Observed signal could be dominated by topographic or other atmospheric effects such as from the ionosphere and be misinterpreted as a deformation signal.  Observed signal is relative to the satellite along a single look direction, without known 3D components.
InSAR tweets We used the Twitter application programming interface (API) and Rtweet package v0. 7. 0  (Kearney, 2019; R Core Team, 2021) 145  to retrieve public tweets (excluding retweets) 18 th August 2020 to 9 th February 2022 mentioning "InSAR". Twitter data are increasingly used in scientific analysis  (Ruan et al. , 2022; Lacassin et al. , 2020; Acar and Muraki, 2011; Sinnenberg et al. , 2016). We processed tweet text to remove stop words, mentions, uniform resource locators, emojis, numbers, and punctuation (e. g. Ruan et al. , 2022). Finally, we removed a list of other words, tweets, and user IDs that were not relevant to our study, including "InSAR" tweets relating to the International Society for Autism Research (Supplementary information). We used 150 word counts from the remaining tweet text to identify the most commonly occurring words or hashtags in the InSAR tweets to identify the context of the InSAR tweets.
We also extracted all user IDs that followed the top ten accounts in (1) and (2). These followers represent the users that would receive tweets on their timeline from the top ten 160 InSAR tweeters in (1) and (2). The profile description of these users were classified using key word search strings from  Côté et al. (2018)  into 'scientist' and 'non-scientific' groups using the R package 'stringr'  (Wickham, 2017)  to determine different applications of InSAR and whether InSAR communications were predominantly amongst scientists or outreach. The keyword strings we used are shown in the Supplementary information. Examples of the 'scientist' class included: 'professor', 'phd', 'post doc', 'doctoral', and 'research scientist' and could be within academia (universities) or applied industry applications.
LiCSAR Earthquake InSAR Data Provider (EIDP) LiCSAR data are produced following specific earthquake events and consolidated through an 'Earthquakes' tab on the LiCSAR Portal. First, earthquake events that meet a threshold magnitude and depth, trigger the activation of LiCSAR frames for data 195 processing  (Lazecký et al. , 2020). These events are plotted on an interactive JavaScript Leaflet map along with key metadata from the United States Geological Survey (USGS). The map interfaces with a data table that allows users to search and download data for each event from an online event page. These event pages are automatically generated for each earthquake event and are regularly updated in the following weeks to include new coseismic and post-seismic data. Details of the earthquake events with a link to the event page are automatically tweeted by @COMET_database, in addition to the first 200 processed interferogram overlapping the earthquake epicentre.
InSAR Twitter We collected a total of 16,621 tweets from 18 th August 2020 to 9 th February 2022 mentioning "InSAR", which was reduced to 255 5,349 after removing retweets, and down to 4,312 after cleaning the dataset (Section 2. 1). These cleaned tweets originated from 1,935 unique Twitter users. We observed a mean of 8±7 tweets per day and spikes related to notable events including scientific conferences, news media reports, earthquakes, and popular Twitter threads (Figure  3 ). A data gap was present in the timeseries (29/10/2020-06/12/2020); however, the source was unknown and could be related to an issue with the collection of tweets. Specific earthquake events related to increased activity included those in Croatia (USGS ID: us6000d3zh. Date: 2020-260.
Non-scientist paired words were more diverse with lower occurrence counts, including 'civil engineer' and 'animal lover' (n=21), and 'gis specialist' (n=18). Commonalities between both communities included 'remote sensing', which was most frequent in both lists and suggests the non-scientist group included a large community of satellite data users, likely 315 including industry Earth Observation experts applying InSAR analyses. Of the ten most retweeted InSAR tweets (Figure  6 ), two were plain text (a, e); one contained a video of volcanic deformation (b); one contained a photograph and description of subsidence (c); three contained wrapped interferograms of volcanic deformation of Nyiragongo, Democratic Republic of the Congo (d), Iceland (g), and Galapagos (h); one contained new open-320 source SAR code (f); and two contained line of sight displacement measurements, one of volcanic deformation in Iceland (i) and one of an earthquake in Haiti (j).
Earthquake InSAR Data Provider (EIDP) 370 The EIDP component of the LiCSAR Portal included 430 earthquake event activations between April 2019 and February 2022, which were plotted on the interactive event map (Figure  9a ). The earthquake event pages and first wrapped interferogram generated over the epicentre (Figure  9b ) were automatically tweeted by @COMET_database, featured in the top ten InSAR tweeting accounts (Figure  5a ) and accounts for the high instance of 'LiCSAR' word occurrence (n=190) in the InSAR tweets (Figure  4a, d ). The five most retweeted EIDP interferograms all displayed a clear earthquake signal, shown by the 375 colour fringe cycles and black arrow (Figure  10a ). Three of these interferograms corresponded to earthquakes that were associated with peaks in tweet activity identified in Figure  3.
385 Figure  10 : Examples of wrapped interferograms tweeted for earthquake events (magnitude, date, and USGS ID are annotated) by @COMET_database, including the five most retweeted interferograms (a) with the earthquake signal annotated with the arrow, and five interferograms without an easily-identifiable earthquake signal (b). Note that the 100 km scale bar is applicable to all panels except the Mongolia earthquake (us6000d7ix).
390 We evaluated the engagement with EIDP using page views logged through Google Analytics for events that occurred during our Twitter data collection (18 th August 2020 to 9 th February 2022). Users accessed EIDP webpages directly (n=1,232), through social media (n=736), through referrals (linked from another website) (n=263), and organic searches (n=76). Twitter accounted for 98% of social media access to EIDP with the remainder from Facebook. The page view time series for the ten most viewed events revealed a peak in page views associated with Sentinel-1 InSAR data tweets from @COMET_database for events with 395 a deformation signal (n=6) (Figure  11 ). Three events did not have an associated data tweet and one had no deformation signal. The mean time between an earthquake and subsequent tweeting of InSAR data was 4.
In Ecuador, where InSAR data are already integrated into volcano monitoring practice, the portal 415 was considered most useful as (1) an independent check on observatory results and (2) for interrogating timeseries, applying quick atmospheric corrections and comparing to topographic profiles. It was emphasised that it was critical for processing to be up to date for active volcanoes, in order for the portal to have a significant impact on monitoring. While Ecuadorian colleagues emphasised the importance of transparent explanations of the functionality of the different portal tools, Ethiopian colleagues considered general education about the potential of satellite radar in volcanology to be most important. This 420 feedback is being integrated into plans for the development of the portal. Other developments of the volcano portal will focus on mitigation of 'fading signal' in long term time series, establishing near real-time processing that functions globally, and providing comprehensive guidance for interpreting LiCSAR data in multiple languages.
LiCSAR data dissemination 460 The availability of open access Sentinel-1 data has led to the development of semi-or fully-automated processing systems to produce InSAR information from the vast amount of data acquired each day. Scientific systems are largely focused on geohazards (e. g. Lazecký et al. , 2020; Kennedy et al. , 2021; Galve et al. , 2017; Piter et al. , 2021) , whereas commercial systems cover a diverse range of applications including monitoring subsidence, mining activities, engineering projects, and geohazards. Two commercial systems using Sentinel-1 and other data sources (@TRE_ALTAMIRA and @SatSenseLtd) were identified 465 in the top tweeters InSAR network (Figure  4a ). Open access LiCSAR data disseminated through an online portal and @COMET_database was also a top ten tweeter of InSAR data, specifically through automatic tweets of coseismic earthquake interferograms (Figure  10 ).
Demonstrating the applicability and reliability of automatically produced InSAR data for volcano monitoring would benefit from comparisons with bespoke processing and ground truthing, both of which also require collaborations with local volcano observatories and researchers. These are also aided by engagement with international initiatives such as the CEOS Volcano Pilot/Demonstrator  (Pritchard et al. , 2018)  or the Global Volcano Monitoring 510 Infrastructure Database (GVMID). Continual automated processing that is integrated into existing monitoring networks therefore offers opportunities to inform broader goals of preparedness and disaster risk reduction.
, 2010; Acar and Muraki, 2011; Earle et al. , 520 2011). We found that peaks in InSAR tweets corresponded earthquakes (Figure  3 ) and ongoing volcanic events (Figure  4c ), where InSAR products were used to reveal the spatial patterns of deformation. For the automated @COMET_database tweets, data were acquired, processed, and tweeted within 4. 7±2. 8 days of the earthquake event, which may limit applicability of the data in the immediate response effort. It would therefore be useful to investigate the links between data dissemination, knowledge building over Twitter, and local event response to determine the specific applications and benefits to disaster 525 management (Figure  12 ). This is particularly relevant when considering the coupling between open access data, derived products, related uncertainties, and quality interpretation, which are all necessary for effective communication.
Discussion started: 14 December 2022 c Author(s) 2022. CC BY 4. 0 License. natural hazards (UNISDR, 2015; UN, 2020). Increased uptake and dissemination of InSAR data has followed the breakdown of barriers to data accessibility, including open access data and processing chains; however, improved metadata detailing processing methods and uncertainties are still required to maximise applications of InSAR data and minimise misinterpretations due to radar data complexities (Table  1 ). For example, evaluating apparent deformation signals alongside weather models is often essential at volcanoes to avoid incorrectly inferring a deformation signal (e. g. Figure  S2 ). Integration 550 of InSAR datasets into cloud-based analysis platforms and portal such as Google Earth Engine or the European Space Agency's Geohazard Exploitation Platform could further facilitate analysis with other data sources, and offers cloud computing capabilities for users lacking local resources  (Gorelick et al.
Where feasible, data should be provided at each level of processing (e. g. pre-and postatmospheric correction) when corrections are applied. Corresponding difference maps would also reveal the spatially variable nature of the applied corrections. Metadata is essential for consistent and reproducible analysis and to allow third-party users or organisations to derive higher level products from InSAR datasets such as LiCSAR. 590 Recommendation (2): Development of new and existing products should be accompanied by clear communication of the uncertainties associated with each stage of processing and guidance for interpretation. This is important for higher-level products such as time series displacement data, where unresolved uncertainties and biases may be present. Recommendation (3): Develop a protocol for generating priority LiCSAR data in response to volcanic activity, and making 595 sure that COMET Volcano Portal outputs are accessible to volcano observatories.
Conclusion In this study, we evaluated the dissemination of ground deformation measurements from InSAR through Twitter in relation to sources of open access data. Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were tweeted. For earthquake events in the LiCSAR EIDP system, data were acquired, processed, and tweeted within 4. 7±2. 8 days (shortest was one day). 605 Applications of open access Sentinel-1 data prevailed in the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Twitter InSAR dissemination was primarily outreach to a non-scientific audience (62%), including industry Earth Observation experts. LiCSAR data were widely accessed through the online portal and disseminated following major earthquake events to reveal ground deformation.
Acknowledgements COMET is the  NERC Centre  for the  Observation and Modelling of Earthquakes, Volcanoes and Tectonics , a partnership between  UK Universities  and the  British Geological Survey. LiCSAR uses JASMIN, the UK's collaborative data analysis environment (http://jasmin. ac. uk). This study contains modified Copernicus Sentinel data (2015-2022). We thank  Elias Lewi Teklemariam ,  Patricia Mothes ,  Pedro Espín Bedón ,  Marco Yépez ,  Marco Córdova  and  Santiago Aguaiza  for feedback during 640 the development of the COMET Volcano Deformation Portal. https://doi. org/10. 5194/gc-2022-15 Preprint. Discussion started: 14 December 2022 c Author(s) 2022. CC BY 4. 0 License.
dissemination of InSAR data into longer-term disaster risk reduction strategies and broader UN Sustainable Development 610 https://doi. org/10. 5194/gc-2022-15 Preprint. Discussion started: 14 December 2022 c Author(s) 2022. CC BY 4. 0 License. Goals to develop sustainable cities, in addition to the prevailing emergency response applications. This requires tailoring the online analysis tools that are founded on open access data to meet a diverse range of end user needs, including long-term monitoring of tectonic and volcanic activity that can better inform risk reduction strategies in the context of an urbanising global population. Effectively communicating uncertainties is also critical given the often complex interpretability of InSAR products. 615.
| Study design We performed a supplementary analysis of a prospective randomized observer-blind crossover trial that had received institutional ethics committee approval (Comité de Protection des Personnes Sud-Ouest et Outre-Mer II, N 2019-A01333-54, Chairperson Professor Bettina Couderc, Toulouse, France) and had been registered in the public registry ClinicalTrials. gov No. NCT04097236. 8 The study was conducted between November 27, 2019 and September 6, 2021. Written informed consent had been obtained from each volunteer. This secondary analysis was planned in the protocol submitted to the ethics committee and was registered in ClinicalTrials. gov. The present report follows the Standards for Reporting Diagnostic Accuracy (STARD) statement. 20.
| Statistical analysis The sample size had been calculated to show that qualitative ultrasound assessment in the semirecumbent position allowed the detection of gastric fluid volume >1. 5 mL. kg À1 with a sensitivity ≥90%. 22, 23 e inclusion of 20 volunteers, in whom three ultrasound assessments were made in each position, had been planned. 8 It was predetermined that if a volunteer withdrew prematurely from the study or if ultrasound evaluation of the gastric contents was inconclusive, the data collected would be excluded from the analysis. In such cases, a new volunteer would be recruited to replace the participant who left prematurely or whose ultrasound evaluation was inconclusive. Statistical analyses were performed using MedCalc ® version 12. 1. 4. 0 for Windows (MedCalc Software, Ostend, Belgium).
23  By methylating and stabilizing KLF4 and KLF5, PRMT5 sustains stemness in TNBC, and its inhibition reduces tumor growth in xenograft models derived from TNBC cell lines. 35, 38  These data suggest that PRMT5 could be an attractive therapeutic target for TNBC. 23, 38  In this study, we used a more potent PRMT5 inhibitor, EPZ015938, optimized from EPZ015666, to improve pharmacokinetic properties, yielding a more drug-like molecule that has been evaluated in a clinical trial. 37, 39  We assessed its antiproliferative effects on several breast cancer cell lines and non-cancerous breast cells. We evaluated the therapeutic benefit of combining it with different chemotherapies currently used in the clinic to treat TNBC patients. 14  We also combined EPZ015938 with different FDA-approved inhibitors targeting the HER family members: erlotinib,  40, 41  neratinib (EGFR, HER2, and HER4 inhibitor),  42  and tucatinib (HER2 inhibitor).
Cell proliferation was measured after four doubling times by MTT or CellTiterGlo assays (Table  S1 ). Inhibitors were used at a maximal concentration of ~2xIC 50 (or 5 µM if the cell line was resistant to the inhibitor) (Tables  S2  and S3 ), followed by two-fold serial dilutions. Drug interactions were assessed using the Loewe model and calculated on the Combenefit software. 44  The combination index (CI) was calculated using the formula CI ¼ Cax ICax þ Cbx ICbx , where Cax and Cbx are the concentrations of drugs A and B that produce an effect "x" (such as 50% cell death), and ICax and ICbx are the concentrations of drugs A and B that yield the same effect "x" when used alone. 45.
Transcriptomic Microarray Total RNA was purified with miRNeasy kit from Qiagen (Qiagen, Courtaboeuf, France) according to the supplier recommendations. A quality control of total RNA was carried out with a Nanodrop ND1000 spectrophotometer (Thermo Fisher) to monitor the concentration and purity of samples, and integrity of total RNA was controlled using RNA6000 Lab-on-a-chip with a Bioanalyzer (Agilent Technologies) as described. 48  Samples were then hybridized with the GeneChip Human Exon 1. 0 ST arrays (Affymetrix) following the manufacturer's instructions. Data quality control was performed using Affymetrix Expression Console. The data were analyzed as described elsewhere. 48.
Acknowledgments We thank  Dylan Payet  ( BCBG lab, Institut Curie ) for performing some experiments and  Dr. David Gentien  ( Genomics platform, Institut Curie ) for performing the microarrays on the cell lines. We are grateful to  Virginie Maire  ( BCBG ,  Institut Curie ) and  Dr. Ramón García-Areas  ( BCBG ,  Institut Curie ) for critical reading of the manuscript. We also thank  Dr. Lisa McPherson  ( Division of Oncology, Stanford University School of Medicine, USA ) for verifying the English language of the manuscript.
Funding This work was supported by the  Institut Curie. S. S. was funded by the  European Union 's  Horizon 2020 Research and Innovation Program  ( Marie Skłodowska-Curie grant  agreement No  666003 ). R. D. was financed by the  French Embassy of Lebanon  and the  Lebanese University (Safar Volet 1). https://doi. org /10. 2147/BCTT. S430513.