dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The database has been treated with two kinds of statistical analysis. The first one concerns the update of the Kd distributions previously published as a function of material type and exchange conditions  (Boyer et al. , 2018). These distributions were assumed to be log-normal, and they were determined by fitting a log-normal distribution to the empirical distribution for each element that had more than ten Kd values. Each fit consisted in determining the geometric mean (GM) and the geometric standard deviation (GSD) and its correctness was checked with the Kolmogorov-Smirnov test. The second statistical analysis aimed to determine conditional statistical distributions of Kd as a function of three selected co-factors: mass/volume ratio, dissolved organic carbon concentration and pH.
For each co-factor, the evolutions of the conditional distributions were calculated by arranging the Kd data in increasing order of the co-factor and by using a sliding window method based on two steps. For a window size defined by a given number of Kd data, the first step is to determine the GM(Kd) and GSD(Kd) of each window, separated from its previous one by shifting one datum to the right (Fig. S. 1). The co-factor value associated with each window is the mean value of its co-factor range. The second step consists of determining the most appropriate window size, defined as the number of Kd data for which GM(Kd) and GSD(Kd) must be calculated. For each Kd dataset, step 1 is applied at different window sizes ranging from 10 to 50 data with an increasing step equal to 5. The best window size is determined by applying step 1 to each case and by considering the size which gives the best correlation between each window's GM(Kd) and the mean tendency of the raw data.
Once the best window size has been selected, a least squares method is used to define the functions describing the evolutions of the GM(Kd) and GSD(Kd) in function of the means co-factor. These relations have been recognized as statistically significant if p-values were lower than 0. 05. The window sliding method was implemented by using MATLAB and Statistics Toolbox Release 2014a The MathWorks, Inc. , Natick, Massachusetts, United States.
Results and discussion Table  S. 2 presents the updated version of the freshwater Kd database for 50 elements. For each element the geometric mean, the geometric standard deviation, the minimum and maximum values, the 5th and 95th percentiles, the number of previous and new data, and finally the type of nuclide (stable or unstable) is given for the different combinations of sediment type (SS and DS) and experimental conditions (in situ, adsorption and desorption).
Evolution of freshwater Kd distributions after updating Since the database has been updated with a significant number of new Kd data, the resulting Kd distributions should be first compared to the previous ones  (Boyer et al. , 2018)  to assess the robustness and representativeness of the datasets. For this purpose, parameters α and β are calculated: = * (2) = * (3) where GM(Kd) and GSD(Kd) relate to the updated database and GM(Kd) * and GSD(Kd) * to the previous one. For suspended sediments and in situ conditions the GM(Kd) values range from 1. 20•10 3 L/kg to 3. 69•10 5 L/kg, and there is a close relationship between the element GM(Kd) ranking and their geochemical properties regarding the periodic table (Fig. 2 ). The alkali and alkali earth metals, which are known to preferentially form outer sphere complex in water and to be less adsorbed by solid surfaces, have GM(Kd) lower than 10 4 L/kg, except Be and Cs.
The low GM(Kd) value for Hg agrees with results from Gormley-Gallagher et al. (  2015 ) who demonstrated that Kd for Hg in sediments from Scottish Lochs was lower than those of Pb, Ni, Co, Cu, Cd, Cr, and Mn. The other maxima and minima values are all in agreement with the previous version of the database  (Boyer et al. , 2018). Another way to assess the effect of this updating is to compare the impact of the material type and exchange conditions on Kd distributions between the two versions of the database, that is to say to compare the GM(Kd) obtained for SS and DS for in situ and adsorption conditions. Whatever the condition, GM(Kd(SS)) is higher than GM(Kd(DS)) as underlined before, except for Be and Po (Fig. 3 ). (The points correspond to GM(Kd) and the upper and lower limits to the 5 th and the 95 th percentiles) There is therefore a recurrent difference between the Kd distributions in function of SS and DS, and this is probably associated with the different properties of the particles rather than to the speciation in the dissolved phase.
Adsorption elements only, and it was decided to mix Kd from stable and unstable nuclides in the dataset in order to define more general trends (Table  S. 2). This choice induced the agglomeration of data corresponding to elements present on exchangeable and non-exchangeable of the solid phase. The exchangeable group is in dynamic equilibrium and it corresponds to the fractions sorbed on the surface of the particles by mechanisms dependent on the outer sphere complex (e. g. , hydrogen links, electrostatic, and van deer Waals forces). The non-exchangeable group concerns the fractions sorbed by mechanisms of surface precipitation, co-precipitation and inner sphere complex which involve strong and very slowly reversible chemical links  (Heling et al. , 1999) , plus the fractions present in the crystalline structure of the minerals.
It has to be pointed out that the Kd coefficient is based on the equilibrium concept, implying that it is only representative of the exchangeable fraction, while analytical measurements of concentrations generally involve both the exchangeable and non-exchangeable fractions, thus leading to overestimation of Kd values. For artificial radionuclides which are expected to be only on the exchangeable fraction, the use of data from stable nuclides may thus lead to an overestimation of their Kd. To assess this possible effect three categories of Kd datasets were distinguished: (i) only unstable nuclides (Am, Be, Cm, I, Po, Pu, Ra, Ru and Th), (ii) mix of stable and unstable nuclides (Ba, Co, Cs, Mn, Pb, Sb, Sr and U; Fig. S. 2) and (iii) only stable nuclides. The representativeness of the Kd datasets for in situ conditions must be considered as a function of their contents for both stable and unstable nuclides.
Kd distributions for in situ conditions as a function of pH Obviously pH is a relevant parameter to consider in order to reduce the variability of Kd distributions, because a large fraction of metals in freshwaters is present as free ions under acidic conditions, whereas they precipitate with oxy-hydroxyde under alkaline conditions  (Stumm and Morgan, 1995). However, the pH variability in freshwater is limited, and the Kd values compiled in the database for SS and in situ conditions correspond to pH ranging mainly from 6. 6 to 8. 4. Moreover, the amount of Kd data for SS is insufficient to determine the evolutions of GM(Kd) and GSD(Kd) as a function of pH, and only a mean tendency of Kd can be investigated for in situ conditions. For most of the elements, the increase of pH leads to an increase of GM(Kd), a trend may be related to the change in adsorption capacities of solid surfaces.
Under acidic conditions, metals form free ionic species, and protons are fixed by the negatively charged surfaces, inducing a reduction of their sorption capacities. Adsorption increases with more natural pH due to the increase of negative surface charges  (Shi et al. , 1998)  that facilitate the sorption of cations  (Veselý et al. , 2001). Therefore, higher Kd values are observed when the pH increases. The elements most sensitive to pH are Co, Zn, Pb and Cr (Fig. 10 ). These results are in agreement with previous publications for Pb, Co and Zn  (Alkhatib et al. , 2016; Alkhatib et al. , 2016b; Tessier et al. , 1989; Veselý et al. , 2001; White and Driscoll, 1987) , but not for Cr  (Peng et al. , 2003). This difference between the results obtained from a specific study and those obtained from a large dataset highlights that these relationships must be applied with caution and that the use of local data remains the best choice.
Conclusions A significant amount (3896) of new Kd data for stable and unstable nuclides (metals and nonmetals) was uploaded to complete and update the freshwater database of Kd values presented in  Boyer et al. (2018). After this update, the freshwater Kd database contains 8564 Kd values for 50 elements: Ag, Al, Am, As, B, Ba, Be, Ca, Cd, Ce, Cm, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Gd, Hf, Hg, Ho, I, K, La, Li, Mg, Mn, Mo, Na, Ni, Pb, Po, Pr, Pu, Ra, Rb, Ru, S, Sb, Se, Si, Sn, Sr, Th, Ti, U, V, Zn. For each element, the Kd values are classified as a function of three exchange conditions (in situ, adsorption, desorption), two environmental components (suspended and deposited sediments) as well as for stable and unstable nuclides. Moreover, each Kd value is associated to several co-factors such as m/V, DOC and pH, when they were available.
The new statistical distributions obtained for the different combinations between elements, exchange conditions and components do not show significant changes with those of the previous version of the database that reinforces the robustness of their representativeness. In the same way, it is again observed, for a large majority of elements, that GM(Kd) for SS under in situ conditions is two orders of magnitude higher than for DS. However, the main objective of this paper was to determine conditional statistical distributions of Kd as a function of m/V, DOC and pH values. These distributions were assumed log-normal and obtained by applying a sliding window method which showed that GM(Kd) and GSD(Kd) change as power laws of m/V, DOC and pH. Although freshwater Kd are characterized by high variability, it was shown that for most elements these relationships between the log-normal Kd distributions and the key parameters m/V, DOC and pH allowed reducing, by several orders of magnitudes, the variability of global Kd distributions for environmental components and sorption conditions.
These relationships are thus very useful for decreasing the uncertainties in the prediction of the elements mobility in freshwater systems when the Kd approach is applied and in situ data not available. The new database of freshwater Kd compiled for this work contains 8564 Kd values for 50 elements. For each element, the Kd values were classified as a function of three exchange conditions (in situ, adsorption, desorption), two environmental components (suspended and deposited sediments) and separated between stable and unstable nuclides. Moreover, each Kd value was associated to several co-factors such as m/V, DOC and pH when available. For a large majority of elements, GM(Kd) for suspended sediments under in situ conditions is two orders of magnitude higher than for deposited sediments. Statistical distributions of Kd were defined as a function of m/V, DOC and pH values.
has presented a freshwater Kd database for 52 elements: Ag, Al, Am, As, B, Ba, Be, Ca, Cd, Ce, Cm, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Gd, Hf, Ho, I, K, La, Li, Mg, Mn, Mo, Na, Ni, Np, Pb, Pm, Po, Pr, Pu, Ra, Rb, Ru, S, Sb, Se, Si, Sn, Sr, Th, Ti, U, V, Zn, Zr. In the present paper, Kd values of 22 elements (Ag, Al, As, Ba, Be, Ca, Cd, Ce, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, Pb, Po, Si, Sr, U, Zn) were updated using values reported in 49 recent articles (Table S. 1). In particular, Kd data for Hg, previously unavailable, have been added. 3896 new values were added to the initial 4668 values. Among them, 76. 5% correspond to suspended sediments and 23. 5% to deposited sediments. 80. 5% have been obtained by in situ measurements, 19. 1% through adsorption studies and only about 0. 4% by desorption studies.
The highest number of new Kd values are for the following elements: Hg (547 values vs 0 initially), Cu (458 vs 131), Pb (419 vs 99), Zn (392 vs 81) and Fe (308 vs 88). The freshwater Kd database has been classified using 50 elements; two material types: suspended (65. 7%) and deposited sediments (34. 3%); and three exchange conditions: in situ (64. 1%), adsorption (31. 6%) and desorption (4. 3%). A significant part (63%) of the Kd values correspond to stable nuclides (i. e. metals), because studies focusing on Kd for radionuclides in freshwaters are limited and mainly related to Am, Be, Cm, Cs, I, Po, Pu, Ra, Ru and Th.
Market exchanges Exchanges in the model first occur with the day-ahead energy market clearing the day before delivery. Typically, the gate closure for submitting bids occurs at midday and results are provided 30 min later. In SiSTEM, the clearing of the day-ahead market is formulated as mixed-integer linear program aiming at maximising the market surplus. This optimization problem is a primal-dual formulation able to simultaneously include constraints on the volumes and prices in the formulation. After the clearing of the day-ahead market, e. g. at 15:00, the intraday market opens for the next day. In most European countries, the real intraday market is a continuous market where offers are updated continuously by the market participants. In our model, the intraday market is implemented as a series of auctions taking place at every simulation time step, i.
MethODs study design and contributing programmes This retrospective cohort study is based on data from 15 birth defect surveillance programmes (table 1) that are members of the ICBDSR. The ICBDSR is an international network of birth defects surveillance and research programmes, whose mission is collaborative surveillance of birth defects and research into their causes and outcomes ( www. icbdsr. org). The 15 programmes represent 12 countries from Europe, North America, South America and Asia. Participating programmes had to be able to provide case-level data with specific diagnoses for CHD and extracardiac malformations for at least two birth years. Most contributing programmes are population based, while the remainder are hospital based. The programme from India is hospital based and a solely prenatal programme, meaning that only cases that are prenatally diagnosed For cases with more than one CCHD diagnoses, one clinical geneticist with specific expertise in paediatric cardiology (LDB) developed a structured hierarchical process to assign a single main CCHD diagnosis (for details, see the appendix in the online supplementary material).
Timing of detection of the CCHD (prenatal vs postnatal) was examined by programme, by CCHD type and by clinical presentation (isolated, MCA and genetic/syndromic). The proportion prenatally diagnosed over time was also examined for trends (χ 2 test for trend). Analyses were performed in Excel (Microsoft Office Professional plus 2010) and IBM SPSS Statistics for Windows, V. 23. 0. Each programme has local approved procedures for ethics approval, and because this study was done using deidentified data, no additional ethics committee approval was required.
For example,   1 ). Chennai, India programme is not included in prevalence estimates because for this exclusively prenatal programme the denominator data (total births and total live births) are unavailable. Open access programmes reporting the lowest prevalence rates (Slovak Republic and Argentina) have a short postnatal ascertainment period (at birth/hospital discharge). Also, with few exceptions, programmes with low prevalence rates tend to report few syndromic CCHD cases (table 2). A further factor is a programme's ability to ascertain and record terminations of affected pregnancies (table  2 ). In countries where terminations of pregnancy are illegal, no terminations are recorded. However, in countries where terminations are legal, a reliable surveillance system may not be able to include these events, and they will be under-reported in these data.
35 e examined the patterns of prenatal diagnosis in relation to TOPFA proportions. In programmes where such terminations are legal, TOPFA occurred in less than 1%-35% of CCHD cases. Two patterns seemed to emerge. In some programmes such as USA-Arkansas and Slovak Republic, low TOPFA proportions co-occur with a low proportion of prenatal diagnosis, and second, clinically complex cases (eg, associated with other extracardiac anomalies or syndromic cases) seemed to be prenatally diagnosed more often (figure  3 ), though the relation between clinical complexity and TOPFA was less clear. Pregnancy outcome is not a direct function of prenatal diagnosis. For example, factors that can influence the TOPFA proportion after prenatal diagnosis may be social or cultural (for instance acceptance of TOPFA) and include the legal gestational age limit for pregnancy termination and the extent to which TOPFA are reported to or captured in the healthcare databases.
Acknowledgements We would like to thank each  International Clearinghouse  for Birth Defect Surveillance and Research member programme's staff for providing and processing the information on the cases with critical congenital heart defects and on the characteristics of their program. Contributors LDB conceived the study. MKB and LDB developed the protocol and supervised the study. MKB, LDB and SK conducted the data analysis. LDB and JEHB reviewed the clinical classification of cases. MKB and LDB wrote the first draft of the article. EA, GC, JC, HEKdW, MG, BG, SL, WNN, AP, AR, SC, AS, ES, GT, DT and PM curated and submitted the case data from their programs. All coauthors made substantial contributions to the conduct of the study, interpretation of results and critical revisions of the manuscript.
Analysis and tting Magnetic data were corrected for diamagnetic contributions using Pascal's constants. 81 EPR and magnetic simulations and ts were performed using the easy spin tool 82 in MATLAB. The crystal eld analysis was performed using the f-shell program. 83 orque ts were performed using a home-made FORTRAN 90 program. Ab initio calculations were carried out on the full molecule by means of the CASSCF/RASSI-SO/SINGLE_ANISO routine using the MOLCAS 7. 8 soware package. 84 One of the two Dy 3+ ions was replaced with a diamagnetic Lu 3+ ion. Basis sets were taken from the MOLCAS ANO-RCC library (Dy. ANO-RCC. 8s7p5d4f2g1h). Complete active space self-consistent eld (CASSCF) calculations include 9 electrons in the 7 4f orbitals. Spin-orbit mixing within the restricted active space state interaction (RASSI-SO) procedure included all sextet ( 6 H,  6  F, and 6 P), 128 quartet ( 4 I,  4  F,  4  M, 4 G, 4 K,  4  L, 4 D,  4  H, 4 P, 4 G,  4  F and 4 I) and 130 doublet terms ( 2 L, 2 K, 2 P, 2 N, 2 F, 2 M, 2 H, 2 D, 2 G and 2 O).
Acknowledgements We thank the  Deutsche Forschungsgemeinscha  ( SPP1601 ,  SL104/5-1, INST 41/863-1, INST 41/864-1, INST 41/887-1 ), the  Alexander von Humboldt-Foundation (PZ)  and  COST CM1006 Eufen (MG)  for funding. We thank  Prof. Roberta Sessoli  ( Florence ) for access to the torque magnetometry setup and  Prof. Martin Dressel  ( Stuttgart ) for access to the SQUID magnetometer. Prof. Stoll  and  Prof. Rauhut  (Stuttgart) are kindly acknowledged for scientic discussion on the ab initio calculations.
Allergen-specific CD8 1 Trm cells persist for months within the epidermis and dermis of healed CHS lesions In this study we used mice with DNFB allergy that were sensitized (day 25) on the abdomen and challenged 5 days later (day 0) on the left ear with DNFB, leading to a CHS reaction peaking at day 2 and resolving by day 15 (Fig  1, A ). Mice with DNFB allergy were then analyzed at different time points after the resolution of CHS (day 30 and months 3, 6, and 12) for the presence of Trm cells, as well as for their functional properties. In this model the left ear, which received DNFB at day 0 and had a CHS response at day 2, is called the ''healed skin,'' whereas the right ear, which received the vehicle and did not have CHS is called the ''naive skin. '' At day 30 and thereafter, naive and healed skin look identical, without any clinical or histologic signs of inflammation (data not shown).
7] [18]  Although the epidermis and dermis of naive skin were nearly exclusively seeded with CD4 1 T cells, increased numbers of CD8  Skin CD8 1 and CD4 1 T-cell subsets were subsequently collected at 30 days, FACS sorted, and reactivated ex vivo with allergen-pulsed bone marrow-derived dendritic cells (BMDCs) to determine the proportion of allergen-specific Trm cells among CD8  1  Trm cells present in the healed lesions of mice with DNFB allergy. The following day, the number of spot-forming cells producing IFN-g, IL-17A, or IL-4 was determined by using an ELI-Spot assay. Although approximately 1% of epidermal and 0. 2% of dermal CD8 1 T cells of healed CHS lesions were antigen specific and produced IFN-g (Fig 1  , E ), IL-17A and IL-4 were not detected (data not shown). Comparatively, the frequency of DNFB-specific cells among total CD8 1 T cells from draining lymph nodes (dLNs), which comprise a mix of effector memory T (Tem) cells and central memory T cells and dLN Trm cells, was less than 0.
CD8 1 Trm cells are hyporesponsive to low DNFB doses We next examined flare-up reactions induced in allergic animals by low DNFB doses (ie, less than the standard dose of 0. 13%). As expected, repeated applications of low DNFB doses on healed skin induced an intense flare-up reaction. It required 3 consecutive administrations of 0. 05% DNFB (on days 0, 2, and 6) to generate flares a of similar magnitude to those provoked by a single optimal dose (Fig  3, A ). By contrast, 6 consecutive administrations (on days 0, 2, 6, 8, 13, and 15) of 0. 01% DNFB did not induce a flare-up reaction. On naive skin, 6 consecutive administrations of 0. 05% DNFB (on days 0, 2, 6, 8, 13, and 15) did not induce significant flares (data not shown). These data indicate that although specific Trm cells are seeded in the skin at significant density, they are actively restrained to prevent excessive immunoreactivity in response to DNFB application of less than a certain threshold (Fig  3, A ).
Flow cytometric analysis Flow cytometric experiments were conducted on dLN, spleen, or skin cells harvested from sensitized animals. Isolated single-cell suspensions were stained with fluorescently labeled mAbs, and fixable viability staining was performed to exclude dead cells from the analysis. Stained cells were analyzed on a LSRFortessa flow cytometer (BD Biosciences), and data were analyzed by using FlowJo software (TreeStar, Ashland, Ore). Total cell numbers were calculated based on numbers of viable cells determined by means of trypan blue exclusion during the isolation process. In some experiments the numbers of skin Trm cell subsets present in the epidermis and dermis of allergic animals were estimated by using AccuCheck Counting Beads (Invitrogen, Cergy Pontoise, France), according to the manufacturer's protocol.
Patients We consecutively selected for this retrospective study all GCA patients diagnosed between September 2010 and October 2018, in 2 university hospitals (Saint Antoine and Tenon Hospitals). Patients were included in the GCA-related stroke group if they had: (1) a diagnosis of GCA with the fulfillment of at least three criteria according to the American College of Rheumatology (ACR); (2) the occurrence of a transitory or constituted ischemic cerebrovascular event; (3) a delay between GCA diagnosis and stroke inferior to 12 months; (4) no other etiology of stroke, notably the absence of atrial fibrillation at the time of stroke. Data regarding age, gender, time from the GCA diagnosis, clinical manifestations, computed tomography angiography data, acute-phase reactants (erythrocyte sedimentation rate and Creactive protein levels), treatments and outcome were retrospectively collected at the time of GCA diagnosis, stroke and last visit.