dataesr/scientific-paragraphs-categorization huggingface dataset:
The values reported in the right plot of Fig. 9  are estimated in galaxies at z = 0, using clusters with ages younger than 300 Myr. In general, we observe that the majority of the E-MOSAICS data cluster around a CFE between 1 and 10 %, quite close to the values reported for spiral galaxies like the Milky Way and the Andromeda galaxy. A fraction of the galaxies at z = 0 have also more elevated CFE, tracing the median trend reproduced from the observed data. Due to the choice of simulated galaxies, the simulations do not significantly cover the higher Σ SFR values. Nonetheless, the resulting Γ vs. Σ SFR relation from the E-MOSAICS simulations has a similar normalisation and slope as recovered in the observed data, thereby confirming the dependence of the CFE on the physical parameters used in the model, i.
Gaia now enables the identification of these systems, and galaxy formation simulations including a model for the GC population provide quantitative predictions for the properties and demographics of such streams and their GCs (e. g. ,  Hughes et al. , 2019). In the long run, extremely sensitive, wide-field photometry with LSST will aid the efforts to link the GC population to accretion events, not just in the Milky Way, but also in other galaxies. In summary, the falsification and validation of GC formation models in the context of galaxy formation and evolution are now becoming possible. The combination of revolutionary observational data from Gaia, JWST, LSST, and 30-m class telescopes will provide a comprehensive picture of GC populations in the local Universe and their progenitors at high redshift, for the first time enabling models to be tested simultaneously at the time of GC formation and after nearly a Hubble time of evolution.
Acknowledgements We thank the anonymous referee for a constructive and careful revision of this work. We thank the staff at  ISSI  for their generous hospitality, and the editors to invite us to contribute to this topic collection. We are grateful to  Hui Li  and  Joel Pfeffer  for sharing their data and  Nate Bastian  for providing comments on the manuscript. A. A. thanks  Matteo Messa ,  Katie Hollyhead  and the  LEGUS and HiPEEC teams  for support with the analyses showed in this review. A. A. acknowledges the support of the  Swedish Research Council, Vetenskapsrådet , and the  Swedish National Space Agency (SNSA). P. Z. acknowledges support by the  Forschungsstipendium  (grant number  ZE 1159/1-1 ) of the  German Research Foundation (DFG). J. M. D. K. and M. C. gratefully acknowledge funding from the  German Research Foundation (DFG)  in the form of an  Emmy Noether Research Group  (grant number  KR4801/1-1 ) and a  DFG Sachbeihilfe Grant  (grant number  KR4801/2-1 ).
a. Observational data Monthly SIC based on passive microwave measurements is obtained at 25-km resolution from the National Snow and Ice Data Center (NSIDC) from January 1979 to February 2017, using linear interpolation for December 1987 and January 1988, which have many missing days  (Cavalieri et al. 1996). To reduce the spatial coverage, grid points where the SIC minimum is always. 90% or the SIC maximum ,1% in a given calendar month are omitted. SIC anomalies are obtained by subtracting their long-term monthly means. Monthly anomalies in sea level pressure (SLP), geopotential height at 500 (Z500), 200 (Z200), and 50 hPa (Z50), surface air temperature at 2 m (SAT), 1000-500-hPa thickness (DZ), and zonal wind at 700 (U700), 300 (U300), and 50 hPa (U50) are derived from ERA-Interim interpolated onto a 28 grid  (Dee et al.
Clinical trials of therapeutic interventions With no specific treatments being available for treating COVID-19 patients, the global medical, scientific, pharma and funding communities have rapidly initiated over 500 COVID-19 related trials (https://clinicaltrials. gov/ct2/who_table). These clinical trials have been fast-tracked by ethical committees worldwide and a range of therapeutic interventions registered on clinical trials. gov are taking forward phase 1, 2 and 3 trials of antiviral drug regimens, biologics, repurposed drugs in various combinations, herbal remedies, nutritional supplements, and cellular therapies. A paradigm shift is underway from the current focus of drug treatment combinations targeting the pathogen, to advancing cellular Host-Directed Therapies (HDTs) for tackling the aberrant host immune and inflammatory responses, which underlie the pathogenesis of SARS-CoV-2 and the high COVID-19 death rates.
No adverse events or toxicity was observed at any of the doses tested. High-dose MSCs improved daily SOFA score compared to lower doses. In the Phase 2 START trial, a randomized placebo controlled evaluating a single intravenous infusion of allogeneic bone marrow derived MSCs (107 cells/PBW) compared with placebo (2:1 ratio), with primary outcome of safety and secondary clinical outcomes including all-cause mortality at day 28 and day 60, no adverse respiratory or haemodynamic events were observed  (Matthay et al. , 2019). The 'MUST-ARDS' clinical trial (https://clinicaltrials. gov/ct2/ show/NCT02611609) tested safety of MultiStem (multi-potent adult progenitor cells, related to MSCs) cells in patients with ARDS. The results have been published in abstract form  (Bellingan et al. , 2020)  and results of 1 year followup are awaited.
The use of MSCs for treatment of COVID-19 patients MSCs are now being used as a potential therapy for treating COVID-19 patients in order to reduce mortality. Although the use of MSCs has been found to be safe when used for treatment of other diseases, it is important to evaluate whether they are safe to use specifically in COVID-19 patients. There have been reports of early Phase studies in COVID-19 patients from China (NCT04252118 and NCT04288102). Leng et al. (  2020 ) investigated the use of MSCs in hospitalized patients with COVID 19 who were not improving despite standard therapy. Seven patients were administered intravenous MSCs at a dose of 1 Â106 cells per kg. MSC infusion was well tolerated in all patients with no acute infusion related reactions. A study published in Aging and Disease claimed the effectiveness of MSCs therapy was safe and attributed the recovery of all 7 patients who were administered MSCs.
Ongoing trials of Mesenchymal stromal (stem) Cell Therapies for COVID-19 The excessive host response seen in patients with COVID-19 appears to have induced a paradigm shift in longstanding focus of drug treatment interventions targeting the pathogen (SARS-CoV-2 in this case) to targeting the host response. Currently, ClinicalTrials. gov and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) report a combined 28 trials exploring the potential of MSCs and their products for treatment or prevention of COVID-19. Table  1  lists clinical trials of MSCs or their products which have been registered on clinicaltrials. gov. Not all of the registered trials will be pursued and in recent weeks, five trials registered on the Chinese Clinical Trial Register ("ChiCTR") and one trial registered on ClinicalTrials.
Acknowledgements Sir Zumla  and  Prof Ippolito  are members of the  Pan-African Network on Emerging and Re-Emerging Infections (PANDORA-ID-NEThttps ://www. pandora-id. net/)funded by the  European and Developing Countries Clinical Trials Partnership  the  EU   Horizon 2020 Framework Programme for Research and Innovation. Sir Zumla  is in receipt of a National Institutes of Health Research senior investigator award. Professor Ippolito  and  Dr Vairo  are supported by the  Italian Ministry of Health  (Ricerca Corrente Linea 1). Prof Maeurer  is funded by the  Champalimaud Foundation  and member of the innate immunity advisory group of the  Bill and Melinda Gates Foundation. We are indebted to  Dr. Joana Lerias ,  Fundacao Champalimaud  for creating and updating the clinical trial information for MSCs.
, Ltd. pneumonia kg/dose 3 (days 1, 3 and 7) glass shadow in the lungs (day 14) NCT04339660 Puren Hospital Affiliated to COVID 19 Umbilical cord derived 1 Â 10 6 cells/ IV Immune function (TNF-α, IL-1β, Wuhan University of Science pneumonia MSCs kg/dose 1/2 (Second IL-6, TGF-β, IL-8, PCT, CRP) and Technology infusion after 1 (within 4 weeks) week optional) Blood oxygen saturation (within 4 weeks) NCT04252118 Beijing 302 Hospital COVID 19 Human MSCs 3 Â 10 7 cells/ IV Size of lesion area by chest pneumonia dose 3 (Days 0, 3 and 6). radiograph or CT (day 3, 6, 10, 14, 21 and 28) Side effects in the MSCs treatment group (indicated by treatment related adverse events) (day 3, 6, 10, 14, 21, 28, 90 and 180) NCT04333368 Assistance Publique - COVID 19 -ARDS Umbilical Cord-Wharton's 1 Â 10 6 cells/ IV PaO 2 /FiO 2 ratio STROMA- Hôpitaux de Paris Jelly derived MSCs kg/dose 3 (Days 1, 3 and 5) (baseline to day 7) CoV2 NCT04273646 Wuhan Union Hospital, COVID 19 Umbilical Cord-derived 0.
5 Â 10 6 cells/ IV Pneumonia severity index China pneumonia MSCs kg/dose 4 (Days 1, 3, 5 and (baseline to week 12) 7) Oxygenation index (PaO 2 /FiO 2 ) (baseline to week 12) NCT04341610 Rigshospitalet, Denmark COVID 19 Adipose derived MSCs 100 Â 10 6 IV Changes in clinical critical ASC COVID-19 pneumonia cells/dose 1 treatment index (at day 7).
Finite element model The experimentally measured pillar geometry was used to model the microcompression experiment. The model used the Abaqus 2018 software coupled with Zset using the Zmat module. The pillar geometry and mesh is represented in Fig. 3. Substrate dimensions were chosen to prevent any effects of the boundary conditions on the load displacement curve (see Appendix A). The mesh was composed of linear reduced hexahedric elements (C3D8R) to prevent locking effects arising from high plastic incompressibilty  [9]. Hourglass was corrected by adding an artificial stiffness to the elements. Only a small correction was necessary (15 MPa) as little hourglass was observed. The mesh size varied from 0. 14 μm in the pillar to 1. 4 μm in the substrate and was determined after a convergence study (see Appendix B).
Acknowledgement The authors would like to thank  G. Guillonneau  and  D. Frey  for their advices on dynamical effects during high dynamic microcompression tests and the use of the Alemnis high dynamics apparatus,  D. Piot  for useful discussions on size effects plasticity mechanisms,  M. Mondon  and  G. Blanc  for their help regarding the sample preparation. This work was supported by the  Natural Sciences and Engineering Research Council (NSERC)  discovery grants  Canada Research Chairs  programs (grant number,  RGPIN-06412-2016 ), the  LABEX MANUTECH-SISE  ( ANR-10-LABX-0075 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  and by l' EQUIPEX MANUTECH-USD  ( ANR-10-EQPX-36-01 ). Finally, the authors would like to thank the  Region Auvergne-Rhône-Alpes  for their financial support within the  SCUSI  project.
Background: the International Cohort Consortium of Infectious Disease (RESPOND) is a collaboration dedicated to research on HIV and other infectious diseases. Methods: RESPOND is a flexible organization, with several independent substudies operating under one shared governance. HIV-related variables, including full antiretroviral therapy (ART) history, are collected annually for all participants and merged with substudy specific data into a shared data pool. Incident clinical events are reported using standardized forms. Prospective follow-up started 1/10/17 (enrolment) with retrospective data collected back to 01/01/12. Results: Overall, 17 cohorts from Europe and Australia provided data on 26,258 people living with HIV (PLWH). The majority (43. 3%) of the population were white, with men-sex-with-men accounting for 43.
Introduction The International Cohort Consortium of Infectious Disease (RESPOND) was formed in 2017 as a prospective, multi-cohort collaboration for the study of infectious diseases, with a special focus on people living with HIV (PLWH). RESPOND was founded upon the groundwork laid by outstanding European HIV cohort collaborations such as EuroSIDA, the Collaboration of Observational HIV Epidemiological Research Europe (COHERE), and Data Collection on Adverse events of Anti-HIV Drugs (D:A:D) studies, and utilizes a similar, well-established infrastructure  [1] [2] [3] [4]. RESPOND offers a research framework with a flexible organization, applying a common data model across different substudies, utilizing one shared data pool. Additionally, all involved in RESPOND can contribute to the ongoing scientific agendas.
Together, these dynamic features facilitate responses to a broad range of unmet research needs. The large size of RESPOND allows scientific questions to be addressed that individual cohorts do not have the size to investigate themselves. Furthermore, the heterogeneous study population with participants from across the whole of Europe and Australia and a high degree of data quality-including the central validation of standardized clinical event definitions-ensure that the results are reliable and applicable to a broader population of PLWH. RESPOND will address clinically relevant research questions, such as the risk and outcomes of non-AIDS comorbidities and the possible relationship to long-term ART exposure; to the outcomes and treatment of viral hepatitis B (HBV), hepatitis C (HCV), and tuberculosis (TB) co-infections; and to support public health initiatives.
Cohort Participation The cohorts that constitute RESPOND are largely collaborators from the EuroSIDA-, COHERE-, and D:A:D multi-cohort studies. However, the consortium is an open network that welcomes additional cohorts to join if predefined criteria regarding the inclusion of a minimum number of participants, the need for a designated data manager, and data quantity-and quality are met. The first (and current) version of these criteria can be found in full at https://chip. dk/Studies/RESPOND/Study-documents [5].
Ethical Considerations Studies performed within the RESPOND consortium are conducted according to the Declaration of Helsinki  [7]  and the requirements of Good Clinical Practice (GCP), as defined in the European Union's (EU) GCP Directive  [8]. All the data supplied to RESPOND follow local or national guidelines as appropriate, and enrolled participants are pseudonymized by the assignment of a unique identifier by the participating cohort before data transfer. As data controller, the Coordinating Centre (CC) located within the Danish Capital Region of Copenhagen, Denmark, stores, shares, and protects data in accordance with current legislation and under approval by The Danish Data Protection Agency (j. nr. : RH-2018-15, 26/1/2018), currently under the EU's General Data Protection Regulation (EU) 2016/679.
Consortium Organization and Governance: The consortium is overseen by an Executive Committee (EC), which is composed of scientific stakeholders, representatives from the three largest participating cohorts, and the chairs from Scientific Steering Committee (SSC) (Figure  2 ). The role of the EC is to ensure the financial and structural integrity of RESPOND and to safeguard that study objectives are being met in a timely manner. However, the EC has no direct involvement in formulating specific scientific agendas, which is carried out under the supervision of the SSC  [6]. The principal investigators from all the The scientific agendas that drive the consortium's scientific progression are formed under several substudies termed scientific interest groups (SIGs; Figure  2 ). These substudies utilize the common RESPOND data pool and have the option to collect additional substudy specific data.
This flexible and open structure enables new substudies to easy be added to the consortium's on-going operations if imminent research questions arise. Participation in the SIGs is open for all individuals associated with RESPOND cohorts, community, funders, and external experts. The RESPOND SIGs are formed and managed by two scientific moderators that are responsible for organizing all scientific activities within the SIG and for ensuring a contemporary research agenda. Relevant variables for SIG analyses may or may not already be collected as part of the standard RESPOND data collection. If a collection of new variables is needed, funds for the collection of these and following analysis must first be obtained, and this relies on the SIG participants and moderators. Therefore, the time from conceptualizing to a functioning SIG largely depends on the SIG's scientific focus and the availability of data.
More information on the initiation of a SIG in RESPOND can be obtained via the RESPOND webpage (https://chip. dk/Studies/RESPOND) or by contacting the RESPOND secretariat (respond. rigshospitalet@regionh. dk). Secretariat functions and data management are handled by the Coordinating Centre (CC). The statistical centers located at University College London, England, and Kirby Institute Sydney, Australia, manage data cleaning, statistical analyses, and support for cohort-led specific analyses. RESPOND can be obtained via the RESPOND webpage (https://chip. dk/Studies/RESPOND) or by contacting the RESPOND secretariat (respond. rigshospitalet@regionh. dk). Secretariat functions and data management are handled by the Coordinating Centre (CC). The statistical centers located at University College London, England, and Kirby Institute Sydney, Australia, manage data cleaning, statistical analyses, and support for cohort-led specific analyses.
Data Collection and Quality Assurance The first data merger for RESPOND took place in 2017, collecting data for the period 01/01/12-01/10/2017. Since then, prospectively collected data from cohorts are annually submitted to the RESPOND CC and merged with the RESPOND data pool. The clinical and demographic variables collected by RESPOND are summarized in Table  1. All the variables are categorized as either RESPOND core variables-which all participating cohorts are required to supply for all enrolled participants-or study-specific variables, which are only required for participants in specific substudies. Both types of variable are merged into the common data pool and can be included in all RESPOND substudies (Figure  2 ). Data can only be utilized upon approval by the individual cohorts that supplied the data, and participation in specific substudies can be retracted at any time, following the RESPOND governance  [6].
Both REST and REDCap users will be met with multiple automated quality assurance checks when uploading or entering data. These built-in checks enable sites and cohorts to correct errors before data submission. Within a week of submission, raw datasets from individual cohorts are assessed for completeness and correctness by staff at the RESPOND CC. The cohorts are queried and asked to clarify and immediately resubmit corrected data as appropriate. After data cleaning, the statistical center produces comprehensive summaries of the data quality and completeness and provides individual feedback to cohorts, with the intent of improving data prior to the next data submission. In brief, assessments are made for all specific core variable since the last data submission. Correctness is assessed by a wide range of checks, aimed at identifying unintuitive reporting or systematic import errors (i.
e. , codes indicating a participant is receiving ≥2 INSTIs simultaneously, a participant who has no available HIV-RNA measurement available within the proximity of a change in ART, or a participant with no new serum creatinin measure). Information on clinical events is collected using non-identifiable data on study-specific Case Report Forms (CRFs) for the following events: myocardial infarction (MI), stroke, invasive cardiovascular procedure (ICP; coronary angioplasties/stenting, coronary by-pass surgery, and carotid endarterectomy), end-stage liver and renal disease (ESLD and ESRD), and AIDS-and non-AIDS defining malignancy (ADM and NADM; excluding non-melanoma skin cancers and pre-cancers). Opposite from the routine follow-up data, CRFs are collected in real time from the period the 1st of December to the 1st of July.
Baseline Demographics and Clinical Caracteristics By 1/10/17, 17 cohorts had agreed to engage in the consortium; 15 cohorts contributed to the first data merger, with a total of 26,258 individuals (Table  2 ). Two additional cohorts subsequently joined the second data merger in 2018. After the third data merger in 2019, RESPOND followed more than 34,000 PLWH. At the study enrollment, the median age was 48 years (interquartile range, IQR, 40-56); 24. 9% were younger than 40 years and 14. 6% were older than 60 years. Women (25. 7%) and persons of non-white ethnicity (27. 6%) made up a substantial proportion of the study population. The RESPOND participants originated mainly from Western-(51. 2%) and Southern Europe (26. 6%), with 12. 5% from Central Eastern-or Eastern Europe and 9. 7% from Northern Europe (including Australia).
Clinical Events At time of the first data merger, the CC had received and centrally validated >90% of all expected event forms, with a total of 716 events. The predominant events were malignancies (n = 351 (50. 4%), of which 35. 9% were NADM and 14. 5% were ADM; Figure  3b ), followed by CVD (168 (23. 5%), of which 9. 4% were ICP, 7. 4% were MI, and 6. 7% stroke), fractures (131, 18. 0%), ESLD (45, 6. 0%), and ESRD (11, 2. 0%). In addition, there were 942 deaths recorded in the period between 1/1/12 and 1/10/17.
RESPOND will systematically assess the safety of newer ARVs, with a particular focus on INSTIs and other ARVs that may still be used in only a subset of PLWH and that therefore require a large dataset to reliably assess the longer term outcomes. Furthermore, the large number of persons under follow-up will allow RESPOND to focus on well-defined clinical endpoints, where individual cohorts may have too limited statistical power to conduct reliable analyses. In addition, the large and diverse composition of the consortium population provides the possibility of subgroup analyses stratified by clinical characteristics such as sex, smoking status, and age, in addition to stratifications based on geographical-and economic regions. Thus, the results will be broadly generalizable to the population of PLWH seen in routine clinical care in across Europe and Australia.
Going forward, RESPOND will help generate pivotal information necessary for tailoring ART to meet the individual needs of an aging population of PLWH and to gain insights into the development and outcomes of comorbidities. The RESPOND data pool already holds a significant amount of data on ARV exposures, comorbidities, and mortality, with the most common clinical events being CVD and malignancies. Therefore, RESPOND will focus on these clinical events in the shorter term. More specifically, analysis investigating the possible associations between cumulative INSTI exposure and the risk of CVD and malignancies are ongoing. Other analyses will investigate weight gain and dyslipidemia in PLWH using contemporary ART and causes of death in PLWH within RESPOND. Additional follow-up time will allow the analysis of other less frequently occurring comorbidities and the possible relation to the use and discontinuation of modern ART.
Hepatitis and Other Infectious Diseases While HIV is the main focus area of RESPOND for the immediate future, there is a significant amount of research activity revolving around other co-infections within the consortium. Founded in the EuroSIDA cohort study network, the Hepatitis SIG has now been incorporated into the consortium and has recently added HCV treatment data and HBV-DNA to the already collected information on HCV-RNA and hepatitis serology. Among the ongoing research areas are the long-term outcomes of direct acting antivirals, HCV reinfection rates, and HBV/HDV co-infections. In addition, plans are being made to identify biomarkers predicting the development of hepatocellular carcinoma and investigations on the impact of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in PLWH.