dataesr/scientific-paragraphs-categorization huggingface dataset:
Observation Network In 2016, the IGETS data base was set up at GFZ with GGP data from 32 different stations and has increased its number of stations by 25-42% according to the sta- 30°- 30°- 60°- 90°- 120°-60°60°120°180°0°6 0°9 0°0 °30°4 0°5 0°6 0°7 0°8 0°4 0°5 0°6 0°7 0°8 0°- 30°- 60°- 90°- 10°0°10°20°30°- 120°-60°60°120°180°0°-10°0°10°20°30°-10°0°10°20°30°6 0°9 0°0 °Fig. 1 Map of the 42 stations contributing to the IGETS data base tus from summer 2019 (Fig. 1 ). IGETS is interested in collecting more long time series of geodynamic sensors. Benefits for station operators and data providers are the longterm storage of their data sets, the provision of different processing levels and an optional DOI assignment by the research repository of GFZ Data Services. This guarantees best practice with regard to open data policy.
DataProducts A detailed documentation of the IGETS data products and the data base is provided by  Voigt et al. (2016). Three different products of the SG time series are stored in monthly files: • Raw gravity and local atmospheric pressure records sampled at 1 or 2 s, in addition to the same records decimated at 1-min samples (Level 1 products). These are uploaded by each station operator. • Gravity and atmospheric pressure data at 1-min and 1h sampling corrected for instrumental perturbations, ready for tidal analysis. This product is derived from the previous datasets and is available in two versions computed by UPF and EOST (Level 2 products). • Gravity residuals in 1 min sampling after particular geophysical corrections (including solid Earth tides, polar motion, tidal and non-tidal loading effects).
The corrected gravity signal is then restored by adding back the modeled tidal signal, polar motion and air pressure effects. A major advance of this processing is that corrected offsets and filled gaps are documented in separate channels. Differences between the Level 2 data sets have been detected at some stations, which are dominated by discrepancies in the step corrections, reaching up to 100 nm/s 2. Different strategies for spike reduction and gap filling causing differences of a few tens of nm/s 2  (Hinderer et al. 2002). Also differences in air pressure may reach up to several hPa in sections where gaps were filled. Currently, the processing strategies of UPF and EOST are evaluated, therefore no recommendation for a specific Level 2 data set is given. IGETS strives for standardization of the Level 2 products in the near future.
Scientific Applications of IGETS Data Since its establishment in 2015, IGETS data has been used as the basis for a large number of scientific studies in various disciplines that were only possible by the global integration of the individual stations. In the following we can only give a brief selection. Mikolaj et al. (2019)   Full studies of the only equatorial station, Djougou in Niger (Africa), were performed by  Hinderer et al. (2019 Hinderer et al. ( , 2020)). Antokoletz et al. (2019)  conducted a preliminary study of the South American station of La Plata (Argentina) Conclusions Since its establishment in 2015, IGETS has collected, archived and distributed superconducting gravimeter data from more than 40 stations and 60 different instruments. In addition to the raw 1-s and 1-min gravity and atmospheric pressure data (Level 1), IGETS is producing end-user products, including gravity and pressure data corrected for major instrumental perturbations (Level 2), ready for tidal analysis, and gravity residuals corrected for geophysical contributions (Level 3).
0), which states septic shock as the combination of suspected infection accompanying life-threatening organ dysfunction, requirement of vasopressor therapy to elevate MAP ≥ 65 mmHg and lactate > 2 mmol/L despite adequate fluid resuscitation  [22]. Meanwhile, patients from the preliminary cohort were included under the diagnostic criteria for septic shock stated in the Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock: 2012  [23] , based on the previous 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference  [24] , valid during the period in which the database was constructed. Exclusion criteria for preliminary cohort covered patients < 18-year old, pregnant women, patients with liver failure (protrombin time > 15 s or international normalized ratio ≥ 1.
Cell-Specific Fe Uptake by Bacteria The response of bacteria to size-fractionation (condition) and nutrients amendments (treatment) were overall similar for cellspecific Fe uptake to those presented for bacterial production (Figure  4 ). While no significant differences were found for the control treatment, the uptake of Fe by bacteria was, respectively lowered and enhanced in +Fe and +Fe+C treatments. In +Fe treatment, Fe uptake by bacteria is three times lower in presence of pico-nanoplankton (<20 µm condition) and decreased from 2. 1 ± 0. 5 to 0. 6 ± 0. 1 (×100 amolFe cell -1 d -1 ) compared to the unfiltered condition. This difference was significant (p = 0. 006). Across all datasets, the Fe uptake by bacteria was the highest for the +Fe+C treatment and the <1 µm condition. This result makes precise the sense in which the availability of C together with the removal of potential competitors had the greatest effect on the uptake of Fe by bacteria.
ACKNOWLEDGMENTS We sincerely thank the reviewers for their help and thorough reviews. We especially thank  SH  and  RB  for taking the time to thoroughly read the manuscript and offer tremendously valuable suggestions. We also wish to thank  Alice Della Penna  ( CSIRO ) who prepared filters for POC measurements,  Diana Davies  ( CSIRO ) who analyzed them, and  Audrey Guéneuguès (LOMIC)  for the preparation of the trace-metal clean stock solution of Glucose. We are also grateful to  Robert Strzepek ,  Sam Eggins , and  Cassie Schwanger  ( IMAS/CSIRO ) for their valuable help at sea and  Christel Hassler  for her suggestions regarding the manuscript. Finally, we would also like to thank  Laurent Besnard  (IMOS) and  Sébastien Moreau  for Figure 1,  Pierre Poisson  (PLUM) for his encouragement in this work, and  Andrew Davidson  for his kindness in facilitating the use of the  Australian Antarctic Division (AAD)  facilities.
FUNDING MF and PB were primarily supported by the  Australian Research Council  through a  Laureate  ( FL160100131 ), and this research was also supported under the  Australian Research Council's Special Research Initiative for Antarctic Gateway Partnership  (Project  ID   SR140300001 ). MB was funded from the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. PIOF-GA-2012-626734  ( IRON-IC  project).
Epidemic Data Extraction We used web sites giving world weather data (like https://www. weather-atlas. com or http://data. un. org/Data. aspx?d=CLINO&f=ElementCode%3A11). Analyses at country level used mean annual temperatures for that country (from https://en. wikipedia. org/wiki/List_of_countries_by_average_ yearly_temperature), and mean daily temperatures for the first half of March in the administrative capital of each French administrative region. Covid-19 spread data exist in databases such as www. who. int/emergencies/diseases/novelcoronavirus-2019, www. cdc. gov/coronavirus/2019-ncov, www. cia. gov/library/publicapublications/ the-world-factbook/fields, www. worldometers. info/coronavirus, www. fr. statista. com/statistiques/ 1101324/morts-coronavirus-monde, www. santepubliquefrance.
fr/recherche/#search=COVID-19%20: %20point%20epidemiologique&sort=date). These repositories are updated daily and offer free access to information concerning new cases, deaths and recovered cases, which is sufficient to build and assess mathematical models. Data for countries are from www. worldometers. info/coronavirus and for French administrative regions from www. statista. com/statistics/1101388/coronavirus-france-confirmed-cases. Data collection was constrained by the date of website accession-third week of March-and the availability of adequate data for specific countries at that date. This implies the inclusion of the first record with at least 100 cases after February 15, which excludes some countries for which the spread of the virus was more advanced at that period. Figure  1  gives an example of data extracted from these databases.
Epidemic Data Extraction We used web sites giving world weather data (like https://www. weather-atlas. com or http://data. un. org/Data. aspx?d=CLINO&f=ElementCode%3A11). Analyses at country level used mean annual temperatures for that country (from https://en. wikipedia. org/wiki/List_of_countries_ by_average_yearly_temperature), and mean daily temperatures for the first half of March in the administrative capital of each French administrative region. Covid-19 spread data exist in databases such as www. who. int/emergencies/diseases/novelcoronavirus-2019, www. cdc. gov/coronavirus/2019-ncov, www. cia. gov/library/publica publications/ the-world-factbook/fields, www. worldometers. info/coronavirus, www. fr. statista. com/statistiques/ 1101324/morts-coronavirus-monde, www. santepubliquefrance.
For example, if the daily R0's tend to be the same (without peak), the entropy is maximum, equal to the logarithm of the number of contagious days. The decrease in the duration of the contagiousness period is considered favorable and corresponds to the "mitigation" of the contagion. The calculation of the entropy needs a precise estimation of the ARIMA coefficients a(i)'s in the development, N(t) = Σ i=1,s a(i) N(i) + W(t), which is difficult during the transient start of the spread, due to the weak number of cases. The two independent datasets, analysed at the level of French metropolitan administrative regions and of countries, show that temperature affects the increase in the number of cases at relatively local, as well as global, levels. The mechanisms by which temperature decreases the rates of detected cases are unknown.
Versions 3. 1 (patient 6), 3. 2 (patients 1, 5, and 7), and 3. 7 (patients 2, 3, and 4) of VCP were used. Sanger sequencing was performed to confirm the TLR3 variations, primer information and sequences are available upon a request. The analysis included only exonic and splicing variants. Synonymous variants, variants with minor allele frequency (MAF) > 0. 01 in Genome Aggregation Database (gnomAD; Cambridge, MA, USA; https://gnomad. broadinstitute. org/) and REVEL pathogenicity score < 0. 3 were filtered out  [22]. The analysis was targeted to known Primary Immune Deficiency Disease (PIDD) genes (in-house designed customized list of 513 genes) and to disease genes causative for encephalitis in Human Gene Mutation Database (HGMD; phenotype search "encephalitis," list of 42 genes, July 2019).
Genetic Analysis Exome sequencing identified patients 1 and 7 to carry the same heterozygous TLR3 variation (rs147431766; c h r 4 : 1 8 7 0 0 5 0 6 4 C > T E N S G 0 0 0 0 0 1 6 4 3 4 2 : E N S T 00000296795:exon4:c. C2224T:p. L742F) which is enriched in the Finnish population (allele frequency (AF) 0. 01621 in Finnish population) compared to more heterogenous European population (AF 0. 0007 in European non-Finnish, Genome Aggregation Database; gnomAD; https://gnomad. broadinstitute. org/), and has not been described in any human patients. Regardless, the p. L742F variation is significantly enriched in our patient cohort (29%, p = 0. 0195) compared to general Finnish population. Other two patients, patients 2 and 3, are heterozygous for a common TLR3 p. L412F variation (rs3775291, AF 0. 324487 in Finnish population), but this variant showed no enrichment in our patients.
Discussion Inborn errors in TLR3-mediated immune response can explain at least severe HSV1 and influenza complications in some patients  [18, 26]. Our current study suggests that TLR3 deficiency may predispose also to hantavirus encephalitis. In previously described HSV1 and influenza cases, poor TLR3-mediated in vitro interferon production has been documented. In agreement with these previous results, heterozygosity of the TLR3 p. L742F mutation was related to significantly compromised responses to TLR3 stimulation in patient fibroblasts, in terms of interferon and IL-6 induction. The TLR3 activities were examined in widely used cell culture conditions including a TLR3 deficient cell line and SV40 immortalized patient skin fibroblasts. Our results are also supported by the previously published data on ability of hantaviruses to activate TLR3-dependent immune responses in vitro  [34, 35].
Acknowledgments We are very grateful to  Cumrun Vafa  for discussions about instabilities in AdS 4 vacua. KP would like to thank  Bruno de Luca  for a discussion and correspondence. The work of IB is supported by the  ANR  grant  Black-dS-String   ANR-16-CE31-0004-01  and by the  John Templeton Foundation  grant  61149. The work of KP and NW was supported in part by the  DOE  grant  DE-SC0011687  and IB and NW were also supported by the  ERC  Grant  787320 -QBH Structure. KP is grateful to the  IPhT Saclay  for hospitality during the initial stage of this project.
τ denotes the maturity of each disbursement and D i,j r,t-τ is the outstanding principal balance remaining at time t on a disbursement e ected at time t -τ , and i i,j t-τ D i,j r,t-τ the corresponding interest payments at time t. e internal rate of return irr i,j is the value that sets the sequence of net transfers to zero. We compile data on disbursements, repayments, and interest payments for the eight borrowing countries and ve o cial international lenders described above. We make two key assumptions. First we assume that the most current speci cation of future repayments and interest rates coincides with future realized outcome. is assumes that there will be no more debt renegotiations in the future. Any changes to the current agreement, such as delaying interest payments or extending the overall maturity, could only make the terms more favorable for the borrower and would result in a larger transfer than we estimate.
Appendices A Construction of the Dataset A detailed description of the data construction is available on a not-for-publication online appendix to the paper. • IMF Data for Cyprus, Greece, Ireland, and Portugal comes from the IMF website (h ps://www. imf. org/), which reports actual and projected disbursements, repayments of principal, and interest payments. Spain did not receive IMF assistance. (2012). For future interest payments, we include a term premium calculated using euribor OIS rates relative to the last observed euribor 3-month rate. We use the Bloomberg Tickers as in Du, Im and  Schreger (2018). • Data for the Greek Lending Facility (GLF) for Greece comes from the European Commission website and various reports therein. To calculate interest rates, we add the reported margins to the end of period 3-month Euribor rate.
F Construction of the Dataset Depending on the o cial lender (e. g. , IMF, ESM, etc. ), the level of detail regarding the timing of disbursements, the repayment projections, and the estimated interest payments varies. Here, we report broadly how we construct disbursements, repayments, and interest payments by lender, and relegate speci c details to Section G. IMF Data for Cyprus, Greece, Ireland, Portugal, Hungary, Latvia, and Romania comes from the IMF website, which reports actual disbursements, actual and projected repayments of principal, and actual and projected interest payments. We use the listed SDR amounts for disbursements, repayments, and interest payments and convert these amounts to Euros by using the average realized EUR/SDR exchange rate prevailing during the month of the disbursement.
For forecasted payments, we use the last observed date for the SDR/EUR exchange rate of October 2018. 69  Realized ows come from the "Flows" database. Disbursements are those listed as "GRA Purchases, " repayments are those listed as "GRA Repurchases, " and interest payments are those listed as "GRA Charges. " ese include both GRA Charges and GRA Burden Sharing Charges. 70  Projections come from the "Projections" database. Projected repayments are those listed as "GRA Repurchase. " Interest payments are those listed as "GRA Charges". 71  EFSF and ESM Data for Cyprus, Greece, Ireland, and Portugal are available from the ESM website (the EFSF data is kept updated on this website as well). Actual disbursements and actual and projected repayment schedules are typically reported. In our baseline speci cation, we use the actual numbers and projections as of December 2019 to calculate the size of the transfer.
For interest payments, we apply the historical blended rates as of December 2019, one each for EFSF and ESM loans, to the outstanding level of debt. We go into more detail on this interest rate in the Historical Overview section. EFSM Data for disbursements and repayments, both realized and projected, for Ireland and Portugal come from the European Commission website. 72  For interest payments, we apply the average cost of funding as of 2012. Realized interest payments are calculated by applying lending rates from European Commission (2012). For future interest payments, we include a term premium calculated using euribor OIS rates relative to the last observed euribor 3-month rate. We use the Bloomberg Tickers as in Du, Im and Schreger (2018). GLF Data for Greece comes from the European Commission website and various reports therein.
G Historical Overview e European Sovereign Debt Crisis dates back to at least March 2010, when Greece formally requested assistance from the rest of Europe. However, nancial assistance to countries within Europe occurred earlier, during the Global Financial Crisis, at least since 2008. In this section, we follow the main text and discuss the details of the the lenders and borrowers during this time period, with an emphasis on the construction of our dataset. We begin by documenting broadly how the major lenders structured nancial assistance during the European debt crisis. We then give a broad overview of the lending programmes for the Eurozone borrowers (Cyprus, Greece, Ireland, Portugal, and Spain). 73  Finally, we discuss the lending programmes for the Non-Eurozone borrowers.
Study design A prospective case-control study was performed at the University Hospital of Besançon, (Besançon, France), Department of Obstetrics and Gynecology from April to November 2016, in strict accordance with the ethical guidelines of the Declaration of Helsinki. The « ELASTOMAP study » was approved by human ethical research committee (Comité de Protection des Personnes EST II, process number 15/494) and the French National Agency for Medicines and Health Products Safety (process number 2015-A01575-44)  [27]. The study was registered on clinical trial. gov with the following number: NCT02870608. All participants provided written informed consent. Details of the rationale and design of the trial have been published previously  [27]. Fetal lungs and liver were studied by 2D-SWE between 24 and 34 weeks of gestation (WG) in two groups: women with an uncomplicated pregnancy composed the control group (group 1), and women with a threatened preterm labor requiring administration of corticosteroids were enrolled as cases (group 2).
Postnatal variables Two pediatricians examined all the infants at birth and were blinded to the prenatal exposure to 2D-SWE. The following data were collected: term of birth, weight, neonatal transfer, Apgar score and evaluation of respiratory distress by Silverman score. In the two days after birth, automated evaluation of auditory evoked potentials were recorded in case of premature delivery before 37 WG to despite cochlear damage in the exposed fetus. After 37 WG, auditory evoked potentials were performed if otoacoustic emissions were absent. At three months after birth, the women received a phone call to assess infant well-being and to collect the following data: medical history, health problems, respiratory diseases or symptoms, liver disease or symptoms and number of hospitalizations since birth.
Statistical analysis Given the paucity of data regarding 2D-SWE on human fetal lung, the sample size calculation was based on measurements of fetal lungs in pregnant baboons. All calculation hypothesis were detailed in the published protocol  [27]. Forty-two patients per group were needed to have 90% power to statistically demonstrate such a difference (0. 2 versus 0. 057) assuming an α risk of 0. 05 and standard deviation of 0. 2. We used descriptive analysis to examine the population in the study. Qualitative variables were expressed as raw numbers, proportions and percentages and quantitative variables were expressed as means, SDs and ranges. Feasibility was evaluated by assessing the number of examinations performed and the number of examinations with interpretable results (invalid data).
Results During the study period, 103 patients were included: 55 women with an uncomplicated pregnancy composed group 1, and 48 women with a threatened preterm labor requiring administration of corticosteroids were enrolled as cases in group 2 (Fig. 1 ). Maternal characteristics are summarized in Table  1. A total of 540 measurements were performed on each ROI. For the proximal lung, 8. 6% of measurements were considered invalid (21. 1% for A1, 2. 8% for A2, 2% for A3), and 6. 9% were considered invalid for the liver (14. 4% for IV, 3. 3% for V, 2. 9% for VI). The number of invalid measurements did not differ between the control group and cases for each ROI. These data were excluded for statistical analysis. For the 3 operators, measurements on each ROI were repeatable except for region IV for both operator 1 (mean difference = 0.
1; 95 % CI: 0. 002 -0. 206; SD = 0. 65) (P= 0. 04) and operator 2 (mean difference = 0. 13, 95 % CI: 0. 006-0. 26; SD = 0. 54) (P = 0. 04] (Table  2 ). Interobserver ICC values for stiffness values (average of duplicates) in the different ROI among the three operators are presented in Table  3. Considering invalid data for ROI A1 and IV, absence of repeatability for region IV for two operators, and low ICC between operator 1 and 3 for ROI A2 (ICC = 0. 28; 95% CI: -0. 72 -0. 69), these ROIs were excluded for fetal LLE ratio modeling. Only ROI A3 for the lung and ROI V and VI for the liver were considered eligible for statistical analysis. The stiffness value for each region was calculated from the average of duplicates performed by the operator. Three ratios were modeled and compared. There were: Fetal LLE ratio 1: A3/ [(V + VI)/2]; Fetal LLE ratio 2: A3/ V, and Fetal LEE ratio 3: A3/ VI.
To date, no health agency has published recommendations about widespread use of 2D-SWE in obstetrics due to lack of data concerning impact of acoustic radiation force on particular displacement in fetal tissues  [33]. However, observational and reassuring data are reported in the literature about use of 2D-SWE after 24 WG in preterm infants  [21, 34, 35]. The "Elastomap study" is the first study to provide a collection of postnatal variables in all infants exposed in utero to 2D-SWE  [36]. Indeed, vigilance data were collected at birth and 4 months later to ensure the absence of clinical effects. We did not collect severe adverse outcomes in the whole cohort, and the unique case of stillbirth is secondary to uterine rupture. The cochlea may be a particularly sensitive structure as 2D-SWE provides a mechanical pulsatile vibration  [37].
Collected data Maternal weight was recorded at each prenatal care visit. Total gestational weight gain (GWG) was calculated by Fig. 1  Flow chart subtracting pre-pregnancy weight from the last recorded weight before delivery. Social and demographic characteristics, pregnancy characteristics, labor, maternal and neonatal outcomes were collected from medical records. The entire medical file was computerized and was completed prospectively at each visit, at delivery, and during the post-partum period by health professional including midwives and obstetrician in charge of the patient. All data were verified at the daily obstetrical staff meeting. During the study period, all women were managed regardless of their BMI regarding the choice of the mode of delivery, induction of labor, and labor management.