dataesr/scientific-paragraphs-categorization huggingface dataset:
In Fig. 5  we show the full methane record on the eventual age scale,  shown in purple (continuous is a line, discrete data as dots). In black is a spline of Antarctic data 309  (Köhler et al. , 2017). WAIS Divide is shown in yellow  (Buizert et al. , 2015; Mitchell et al. , 2013). Ages 310 shown here are AICC2012. Gas age tie points are shown along the bottom of the figure. The grey 311 shaded area represents the ice (605-617m) with unreliable ages due to flow disturbance (see section 312 9). 313 6. 2. 10 Be across the Laschamp Event 314 In Fig. 6  we show the Skytrain 10 Be concentration from 509-520 m, aligned with the reference 315 dataset. The common shape across the wider event as well as the presence of individual peaks and 316 troughs is clear. We chose 5 tie points in the range 39.
9 ) one can see how the data clearly differentiate samples of different ages -this is particularly true in the section from about 120-150 ka (section that goes clockwise in increasing age from mid-blue to green). While the δ 18 Oatm data were used mainly in combination with CH4 to assess the ages of ice around the LIG, δ 18 Oatm was also measured in two Skytrain ice core samples from the Holocene and two from the last glacial maximum: these were not used to construct the age scale but the values were entirely consistent with the modelled ages. Three samples were also measured between 435 and 456 m. These three values of δ 18 Oatm, along with the less precise CH4 data obtained from the continuous measurements, were used to assign ages (Table  S1 ) more precisely between 11 and 15 ka in a section in which δ 18 Oatm is increasing rapidly with age (Fig.
Acknowledgments The authors thank  Shaun Miller ,  Charlie Durman ,  Amy King ,  Emily Ludlow ,  Liz Thomas  and  Victoria Alcock  for help with cutting, processing and analysing the ice core. This project has received funding from the  European Research Council  under the  Horizon 2020 research and innovation programme  (grant agreement No  742224 ,  WACSWAIN ). TS and LS acknowledge funding from the  Swiss National Science Foundation  (# 172745  and # 2000492 ), and all authors from the  University of Bern  gratefully https://doi. org/10. 5194/cp-2022-84 Preprint. Discussion started: 7 November 2022 c Author(s) 2022. CC  BY 4. 0 License. acknowledge the long-term support of ice core science by the  Swiss National Science Foundation. This material reflects only the authors' views and the Commission is not liable for any use that may be made of the information contained therein.
Data availability The continuous methane and nssMg used in this paper (and shown in Figs  5  and 7 ) have been submitted to Pangaea. The discrete CH4, δ 18 Oatm and 10 Be data used in this paper are attached as supplementary data (Tables  S1  and S2 ). The air and ice tie points used in Paleochrono are attached as supplementary data (Tables  S3  and S4 ). All reference data used in this paper are already published and available online. The final derived age model ST22 is attached as supplementary table  S5 , and has been submitted to Pangaea.
Study design and population This population-based cohort study was a retrospective analysis of a prospective cohort using data from the French National Uniform Hospital Discharge Database (Programme de Médicalisation du Système d'Information, [PMSI]), which systematically collects administrative and medical information, including obstetrical and infant characteristics. The PMSI database is based on diagnosis-related groups, using the International Classi cation of Diseases, Tenth Revision (ICD-10)  (21). All data are fully anonymized and informed written patients consent are not required. In France all of the Regional Health Agencies are granted access to this database (Agence Technique d'Information sur l'Hospitalisation https://www. atih. sante. fr/), in compliance with French law after a deliberation of the French Commission for Data Protection and Liberties (CNIL) with the methodological reference MR005.
Primary Outcome and sociodemographic data The primary outcome of interest was the hospital admission rate after postpartum discharge for up to 28 days after birth. The causes of hospitalization were determined according to ICD codes and classi ed as infectious disease, metabolic disorders (i. e. mainly jaundice P57-P59 and R17), digestive and nutrition/growth disorders (i. e. dehydration P74. 1 and E86, feeding intolerance/insu cient feeding/, growth insu ciency, P92, P98 and E40-E46), neurologic abnormalities, non-infectious respiratory events, surgery and trauma and other causes (including social problems Z59, Z63, Z81) (Supplementary table). Maternal and obstetric data were obtained from the maternal hospital discharge database. Information on maternal age, obstetrical pathology, mode of delivery (i.
Socioeconomic status was determined at the individual/family level based on health care insurance coverage and at the geographical level using a socioeconomic disadvantage index (SDI). Complementary health care insurance coverage, which provides universal health care coverage, is dedicated to the most disadvantaged families based on home income. SDI was calculated using ve key socioeconomic and demographic variables extracted from the 2015 census data linked to each local residential municipality (French National Institute of Statistics and Economic Studies [Institut National de la Statistique et des Etudes Economiques]): median household income, unemployment rate, percentage of people aged ≥ 15 years who failed to graduate from high school, percentage of single-parent families, and percentage of families in rented accommodations.
Data analysis Univariate parametric and nonparametric analyses were performed using the χ 2 test or Fisher's exact test for categorical variables, and the Student t t-test or Mann-Whitney U test was performed for quantitative variables, as appropriate. A stepwise multivariable regression analysis was performed to estimate the LGA and SGA factors for postpartum hospital admission for up to 28 days of delivery. Available variables were included in the nal regression model. A two-sided P-value < 0. 05 was considered statistically signi cant. Odds ratios (ORs) with 95% con dence intervals (95% CIs) were calculated. Statistical analysis was performed using SAS 9•4 (SAS Institute).
Strengths and limitations The French National Health Database System does not include some relevant individual factors, such as ethnicity, maternal BMI, breastfeeding, home environment (parental smoking, air pollution), siblings, educational status, and quality of parenting. Some comorbidities and clinical characteristics derived from ICD codes, such as maternal smoking or obesity, could be underreported. Thus, we were unable to identify how these factors and their severity mediate the associations of LGA or SGA on hospital admission after postpartum discharge. However, some of these factors are linked to socioeconomic status which was considered in the analysis. The French National Health Database System accurately registers data on hospitalized newborn and maternal health. All consecutive deliveries in private or public hospitals providing different levels of healthcare are collected in this registered database system, strengthening the ndings of the study.
5A-B ), and not identical to the ear bars used later during implantation surgeries. Therefore, we used the opportunity to exchange the tips on those ear bars. We exchanged the tips with custom-built tips made of aluminum. The aluminum gives excellent Xray contrast (Fig. 5A  and 5C ), and at the same time minimizes stray artifacts, and it was easy to produce tip diameters optimized for monkey ear canals and identical to those used during implantation surgeries. The   use of identical tip diameters during CT scanning and surgery improves the alignment between the two head fixations. From the CT based 3D volume, the skull was segmented using either Brain Voyager QX  (Goebel et al. , 2006)  or 3D Slicer  (Fedorov et al. , 2012). Table  2  summarizes the software packages used per monkey for implant planning.
Shaping of the baseplate with CNC-milling. Three monkeys were Monkey C and Monkey K were implanted with a central CNC-milled baseplate version (Fig. 3B ) whose position and shape were planned in a different way. First, the coordinates of the brain areas of experimental interest were estimated in the skull segmentation, and then the baseplate legs were designed to allow later access to those areas. In all cases, the skull was segmented from the CT volume using 3D Slicer  (Fedorov et al. , 2012). The resulting skull model was imported into the Geomagic Design X software (https://www. oqton. com/geo magic-designx/) for further processing that facilitated the overall planning procedure. An area of interest on the skull was chosen and isolated (Lasso selection). Then, the isolated surface was fitted with a mesh using the function "Mesh Fit".
An alternative to the Geomagic Design X software might be the FreeCAD software, an open-source parametric modeler (https://www. freecad. org/). It has not been used in the current experiments, but offers similar functionality. FreeCAD can be used for repairing and smoothing mesh data (with the "Mesh Workbench"), fitting a parametric surface to the skull model (with the "Surface Workbench"), and creating parametric models of solid parts (with the "Part Design Workbench"). Note that FreeCAD can also be an alternative for the SOLIDWORKS software. In the case of Monkey C and Monkey K, two surfaces were created: a) a detailed one that closely follows the geometry of the skull (resolution by allowable deviation: 0. 1 mm) and, b) a smoother version of it (allowable deviation: 1 mm). These two reconstructed surfaces were exported as parasolid surfaces which were then imported into SOLID-WORKS and used for the next planning steps.
Acknowledgements We thank the technical service team of the  Ernst Strüngmann Institute  and especially  David Konietzny ,  Georg Haas ,  Thomas Bischoff ,  Joscha Schmiedt  and  Kai Rönnburg  for their exceptional technical support and valuable contribution to all technical advances described in this work. We also thank  Marianne Hartmann ,  Julia Hoffmann ,  Sabrina Wallrath ,  Olga Arne , and  Johanna Klon-Lipok  for their assistance in surgical procedures and animal care. We are grateful to  Dr. Christa Tandi ,  Dr. Alf Theisen  and  Dr. Christiane Kiefert  who provided excellent veterinary care and advice. We thank  Alina Peter ,  Georgios Spyropoulos ,  Jarrod Dowdall  and  Tommaso Tosato  who were involved in some of the experiments with the monkeys on whom this paper reports.
Funding This work was supported by  DFG  ( SPP 1665 FR2557/1-1 ,  FOR 1847 FR2557/2-1 ,  FR2557/5-1-CORNET ,  FR2557/7-1-DualStreams  to P. F. ),  EU  ( HEALTH-F2-2008-200728-BrainSynch ,  FP7-604102-HBP  to P. F. ), a  European Young Investigator Award  to P. F. , the  LOEWE program  (NeFF to P. F. ).
D ATA A N D C O D E AVA I L A B I L I T Y The facilities of IRIS Data Services ( www. iris. edu ), and specifically the IRIS Data Management Center, were used for access to waveforms and related metadata. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience (SAGE) Award of the National Science Foundation under cooperative support agreement EAR-1851048. Data from the TA network were made freely available as part of the EarthScope USArray facility, operated by Incorporated Research Institutions for Seismology (IRIS) and supported by the National Science Foundation, under cooperative agreements EAR-1261681. Earthquake parameters were.
Statistical analysis All statistical analyses were carried out using the R software, v3. 6. 1 (https://www. r-project. org/). Assessment of Ae. albopictus dispersion from the village towards the wild compartment of the park was based on its presence at collection sites. To assess the level of colonization of the forest ecosystem, we used count data from the transect experiment. We used the Chi-squared test to compare the percentages of mosquitoes that developed from the egg to the adult stage. As the number of emerged adult mosquitoes was not equal to the number of collected eggs in the field due to egg viability, we used the weighted number of Ae. albopictus eggs (WNAE) as the main variable of interest. The WNAE is an estimation of the number of Ae. albopictus eggs collected relative to the percentage of Ae.
albopictus adults after emergence. We calculated the WNAE as the frequency of Ae. albopictus adults multiplied by the total number of eggs collected in the field for each collection site. Then, we tested various generalized linear models to fit the penetration into the forest by Ae. albopictus using the "WNAE" as the response variable, and the "Distance" of the trap to the forest edge and the "Habitat" as fixed and random effects, respectively. We did not include data from Station 2 in the models because of insufficient number of positive traps. We checked the WNAE distribution normality visually and using the Shapiro-Wilk normality test. We tested different models with a log link function and two alternative error structures: Poisson or negative binomial, using the stats, glmmADMB, lme4, and MASS packages  [34] [35] [36].
Model of penetration into the forest ecosystem We evaluated Ae. albopictus colonization potential of the forest ecosystem by analyzing its oviposition activity at increasing distances from the forest fringe. The Shapiro-Wilk normality test (S2 Fig)  showed  that the WNAE values did not follow a normal distribution (W = 0. 75, p < 0. 001). We tested four different generalized linear (mixed) models with the residuals following the Poisson or the negative binomial distribution, and the "Habitat" variable included as a random effect, or otherwise without random effects. After model testing, we discarded the Poisson models because they showed data over-dispersion, and we focused on the negative binomial ones. We selected the best model using Likelihood Ratio tests (LR). The LR test indicated that random effects (p = 0.
Occurrence of sylvatic populations of Aedes albopictus To the best of our knowledge, we report here for the first time a situation in continental Africa where Ae. albopictus occurs in a wild environment without established human presence. Indeed, our data highlight the multiple occurrences of this species at diverse locations throughout the LNP, up to 15km from the village, and its persistence over several years at sites distant from human habitations. These findings indicate that these Ae. albopictus populations might persist as sylvatic populations in areas where the human footprint anthropogenic influence varies from moderate (mainly limited to ecotourism and associated activities) to absent. In the northern part of the LNP, we recorded Ae. albopictus occurrence at many sites located mostly in gallery forests across the savanna that interconnects the village to the primary forest at multiple locations southwards.
Acknowledgments We thank all study participants. We are grateful to  La LopeNational Park  and all staff members, especially  Mr. JosueEdzang-Mve  for his precious assistance in the field experiment implementation. Special gratitude to the staff of the  laboratory Écologie des Systèmes Vectoriels (ESV)  of the  Interdisciplinary Center for Medical Research of Franceville (CIRMF, Gabon ):  Dr Boris Makanga ,  Dr Martha Nigg ,  Dr Neil Michel Longo-Pendy ,  Mr Patrick Yangari  and  Mr Ousmane Akone-Ella.
This study received financial support by the  Agence Nationale de la Recherche (ANR PRC TIGERBRIDGE , grant  n˚16-CE35-0010-01  to CP), the  CNRS  (grant  n˚PEPS 2014 , " ECOLOGIE DE LA SANTE-INEE " to CP), and was co-funded by the  Interdisciplinary Center for Medical Research of Franceville  (grant  CIRMF  to JON), the  Africa Research Excellence Fund  (Grant  n˚AREF-312-OBAM-F-C0894  to JON), and the  European Union  (Grant  n˚ARISE-PP-FA-72  to JON), through the  African Research Initiative for Scientific Excellence (ARISE) ,  pilot program. ARISE is implemented by the  African Academy of Sciences  with support from the  European Commission  and the  African Union Commission. The contents of this document are the sole responsibility of the authors and can under no circumstances be regarded as a reflecting position of the  European Union , the  African Academy of Sciences , and the  African Union Commission.
opok, Ae. metallicus Zika African monkeys Ae. albopictus, Aedes africanus, Ae. opok YaoundeRodents Ae. aegypti West Nile Birds Aedes from africanus group Saboya Rodents Aedes from africanus group Bunyavirus Bunyamwera Unknown Aedes from africanus group, Ae. opok Bozo Unknown Aedes from africanus group, Ae. opok Ar B 28215 Unknown Aedes from africanus group Orbivirus Orungo Unknown Aedes from africanus group, Ae. opok Rhabdovirus Kamese Unknown Aedes from africanus group Other Mengo Rodents Aedes africanus, Ae. opok Source: Adam & Digoutte, 2005. Pasteur Institute and IRD CRORA (http://www. pasteur. fr/recherche/banques/CRORAdatabase, Last access 2015-04-15) [24]. In gray: viruses isolated in Central Africa: Cameroon, Central African Republic, Republic of the Congo. Bouboui, yellow fever, and Zika viruses were recovered from African monkey species or closely related species (same genus) present in the LNP (genus Cercopithecus, Colobus, Erythrocebus and Galago) https://doi.
Our study had several strengths. First, a urine test was done to confirm the use of drugs which is not often done in studies in this field. Declarative addictological data are often criticized, but here we see that users declared consumptions that are consistent with what was found in their urine samples. These results are important and will make it possible in the future to interpret with less difficulty the drugs declared by users. Second, our findings, which include PWUD not enrolled in addiction care or harm-reduction centers, and the use of RDSS have major public health implications as they challenge the current methods of measurement and surveillance of drug use in France and Europe. Furthermore, contrary to popular belief, even if care is free of charge, coverage for medication-assisted treatment is not large.
Acknowledgements We would like to thank all participants for their contribution, the  Montpellier University Hospital  for their assistance, the peers and the staff of the  different NGOs (GROUPE SOS Solidarité, AMT CSAPA Arc en Ciel, CAARUD Réduire les Risques-La Boutik, SOS Hépatites and L' Avitarelle)  for their collaboration, and Apothicom for their donation of harm-reduction equipment. This work was supported by the  Agence Nationale de Recherches sur le Sida et les Hépatites Virales-Maladies Infectieuses Emergentes  (France national research agency on HIV, hepatitis and infectious emerging diseases) [grant number  95050 ]. The funding agency had no role in study design, data collection, analysis and interpretation, nor in writing of the manuscript or decision to publish.
The comparison between the sites was performed on the period common to all sites (1 July 2016-13 February 2018). The datasets are calibrated on the reference World Meteorological Organization scales for CO 2 and CO with high precision and accuracy levels. At all sites, the mean annual CO 2 growth rate is found to be quite similar to the Mauna Loa (Hawaii) reference site one, but mean annual CO 2 concentrations are higher of several ppm at both urban sites than at both background sites. The diurnal cycle shows a higher amplitude at the urban sites (14. 5 ppm at SME; 18. 8 ppm at CAV) than at the background sites (5. 3 ppm at OHP; 0. 5 ppm at ERSA), as in other urban studies. While the urban stations are influenced by large urban anthropogenic emissions (mostly from traffic and heating, especially in winter), both background sites are mainly influenced by natural fluxes.
At ERSA, the CO 2 diurnal cycle is found to be primarily controlled by the small air-sea CO 2 fluxes. At OHP, the diurnal variability of CO 2 is mainly driven by the activity of vegetation (photosynthesis and respiration) and ABLH dynamics. For similar reasons, atmospheric CO 2 concentrations are also characterized by larger seasonal variations in the city (29. 2 ppm at CAV and 20. 3 ppm at SME, respectively) than at OHP (13. 1 ppm) and at SME (13. 9 ppm). The influence of local, regional and remote anthropogenic emissions is assessed through a classification of the datasets by wind conditions. Similarly to other urban studies, a dome of several tens of ppm of CO 2 gets formed over the city at low wind speed (less than 4 m s -1 ). For higher wind speeds (4-10 m s -1 ), the influence of regional and remote emissions on atmospheric CO 2 is function of wind direction, varying from a few ppm at the background sites to a plume of more than 10 ppm at the urban ones.
, 2015) , a regional atmospheric network of 4 in-situ CO 2 observing stations was developed along a rural, urban, coastal and marine gradient in and around the Aix-Marseille area (the urban and coastal sites being in Marseille city) to collect data during at least 1. 5 year at each site between April 2013 and February 2018. Based on these datasets, this work presents an analysis of the spatio-temporal variability of atmospheric CO 2 at the hourly to the seasonal scales in the south-east of France and north-west Mediterranean basin, which has never been studied earlier. The Aix-Marseille-Provence metropolis is settled on a rough landscape made of valleys and hills, which height varies between 300 and 800 m  (François et al. , 2005; Mestayer et al. , 2005; Puygrenier et al. , 2005). This complex topography, combined to the proximity of the Mediterranean Sea, generates local to regional sea/land breeze regimes and a strong regional wind called Mistral  (Mestayer et al.
, 2005; Puygrenier et al. , 2005). To our knowledge, there has been only a few atmospheric field studies in coastal cities which assessed the role of breezes on modulating atmospheric CO 2 concentration (e. g. Verhulst et al. , 2017)  and usually on relatively short time scales (e. g. Pérez-Landa et al. , 2007; Mahesh et al. , 2014). This work presents for the first time an analysis of atmospheric CO 2 in a coastal city of the Mediterranean Basin and in France on a time scale larger than 1. 5 years. In the following, Section 2 introduces the observation sites, the instrumentation and the datasets that were developed and used to conduct this work. Section 3 presents the analysis of atmospheric CO 2 at different timescales on the area of interest from June 2016 to February 2018 and a discussion on the results.
Observing sites Within the AMC (Aix-Marseille Carbon Pilot Study) project funded by the Aix-Marseille LABEX OT-MED, continuous measurements of atmospheric CO 2 were carried out at four sites in the SUD-PACA region and north-west Mediterranean Sea on a rural, urban, coastal and marine gradient (Fig. 1  and Table  1 ). The environment of each site is illustrated in Fig. 2. The urban site (CAV) was installed in the center of Marseille, which is the most populated area of the Aix-Marseille-Provence metropolis. Another site (SME) was installed at the coastal edge of Marseille and of the Mediterranean Sea to study the impact of the landsea breeze process on atmospheric CO 2 variability. Two ICOS-France sites (OHP and ERSA) were used as regional background sites to infer the urban CO 2 plume of Marseille city from the CAV and SME datasets.