dataesr/scientific-paragraphs-categorization huggingface dataset:
The high R value also suggests that the intermediate, s 2 and minimum, s 3 principal stresses, both deviate considerably from the vertical direction (plunge is 60° for s 2 and 30° for s 2 ) and they have similar amplitudes (Extended Data Fig. 4 ). Therefore, they are virtually indistinguishable and might switch with each other. With s 1 being clearly sub-horizontal (2° plunge), this physically means that under such stress eld the simultaneous activation of both strike-slip and reverse faults is plausible  60. This suggests a nonnegligible reverse movement, related to the ower structures, especially within the ELFS, indicating the existence of active transpressional features along Chain. S-wave model from local Rayleigh and ambient noise tomography. We compare our result the S-wave velocity structure derived from local Rayleigh wave and ambient noise tomography  29.
While such a rheology cannot be precluded here, we also require an additional factor, given that our isotherms are also deeper than predicted by those S-wave model translated into thermal model. We examine two methods for translating seismic S-wave velocity to temperature based on the assumption of a peridotitic mantle. In the main text we use the empirical t to predictions for a pyrolite mantle 30 : where V s is S-wave velocity in km/s, P is pressure in GPa and T is temperature in Kelvin. We also investigate the effects of different composition assumptions on the conversion from S-wave velocity to temperature. We use the Abers and Hacker 61 MATLAB toolbox to calculate the predicted S-wave velocity for three end member compositions. We use a depleted upper mantle (harzburgite), an undepleted mantle (lherzolite) and, for reference, an idealized unmelted mantle composition (pyrolite).
Acknowledgements  K. Leptokaropoulos ,  C. A. Rychert  and  N. Harmon  acknowledge funding from the  Natural Environment Research Council  ( NE/M003507/1 ) and the  European Research Council  ( GA 638665 ). J. -M. Kendall  was funded by the  Natural Environment Research Council  ( NE/M004643/1 ). We thank the captain and crew of the  R/V Marcus G. Langseth  and the  RRS Discovery , and also the scienti c technicians. The active source seismic data were acquired during the ILPAB-SPARC experiment on board the French R/V Pourquoi Pas funded by the  European Research Council Advanced  Grant number 339442_TransAtlanticILAB. Refraction data were analysed by  Christian Filbrandt.
[44]  The group could automatically segment somas and neurites, and measure their cumulated dry mass over the field of view of the microscope for 120 h. First, this work demonstrates the ability of CGM to conduct experiments for arbitrarily long periods of time without affecting the cells (in particular cells as sensitive as neurons), a strong benefit compared with fluorescence microscopy. Second, the authors could provide quantitative data on how mass is distributed throughout the cellular bodies and neurites during their growth. Third, many QPM techniques can hardly measure the dry mass of biological objects as tenuous as neurites, highlighting the high sensitivity of CGM. In 2021, the group of Reed used CGM and dry mass measurements to rapidly identify lymphoma cells that are resistant to a therapeutic agent and cells that are not, overcoming the difficulty of identifying drug-tolerant sub-populations during the early stage of the treatment.
Plasmid construction, algal transformation, and screening for sesquiterpenoid synthase expression Heterologous expression of sesquiterpenoid synthases (STPSs) in C. reinhardtii was achieved through synthetic transgene redesign based on the amino-acid sequences of each STPS, following previously established protocols  34, 49. We selected 21 STPSs from various species encoding isoforms that yield nine different STP skeletons (Table  1 ). Targeted STPSs for aristolochene, δ-guaiene, santalene, valencene, valerianol, zizaene, τ-cadinol, bisabolol, and patchoulol were designed; all accession numbers are listed in Table  1. Their amino-acid sequences were used to generate algal-adapted nucleotide coding sequences using the Intronserter programme  34. This programme back-translates amino-acid sequences to frequently used codons, removes unwanted restriction sites, and systematically integrates the first intron of CrRBCS2i1 at a set distance to enable expression from the C.
Gas chromatography data analysis The analysis of terpenoid extracts followed a procedure similar to previously reported methods  26, 38, 39  , and qualitative analyses primarily relied on the retention index and match factor. The GC-MS/FID data were processed using the MassHunter Workstation software version B. 08. 00 (Agilent Technologies, USA). The identification of compounds was assisted by the NIST Mass Spectral Library Version 2. 3 (National Institute of Standards and Technology, Gaithersburg, MD, USA). The mass-spectral data, derived from the GCxGC-TOF/MS analysis, were evaluated with GC Image TM Version 2. 9 software (Lincoln, NE, USA) and referenced against the NIST2020/EPA/NIH EI Mass Spectral Library. The spectral data were cross-referenced with library spectra to identify potential chemical structures, facilitated by calculating the match factor and probability, thereby generating a list of probable compound matches.
Acknowledgements This work received support from the competitive research grant ( 4715  to K. J. L. ) and baseline research funding from  King Abdullah University of Science and Technology (KAUST)  to G. S. and K. J. L. K. J. L would like to express his gratitude to  Ahmed Alfahad  of  King Abdulaziz City for Science and Technology (KACST ) who found and suggested several of the terpene synthases used in this work and whose discussions inspired the conception of this work.
Plant material The data consisted of 2,456 sixth-generation (F 6 ) winter wheat lines (T. aestivum L. ) and 21,894 plot observations developed by Nordic Seed A/S breeding company. The breeding lines originated from nine breeding cycles (BC) tested in the years 2013 to 2021 (cycle 1:  2013-2014, cycle 2: 2014-2015, cycle 3: 2015-2016, cycle 4: 2016-2017, cycle 5: 2017, cycle 6: 2018, cycle 7: 2019, cycle 8: 2020, cycle 9: 2021)  in three locations in Denmark (DK): Skive (north-west DK), Odder (central DK), and Holeby (south DK). In total, 26-yearlocation subsets were successfully assessed, and one failed  (Odder, 2018)  due to operational issues. The number of lines, plot observations, and average line replications per BC and for the whole population are presented together with descriptive statistics for grain yield in Table  1.
Corrected phenotypes As a first step, a linear mixed model including all relevant effects for the population was utilized to analyze raw data. Such a model has been previously used in  Raffo et al. (2022b)  with a similar dataset from the same breeding program, and in brief, it was defined as: y = Xb + Z 1 l + Z 2 g + Z 3 f + o 9 i=1 Z i+3 s + e, where Xb , Z 1 l , Z 2 g and Z 3 f were defined as below in the LMM-HET (eq. 1), o 9 i=1 Z i+3 s is the spatial effect where s is the vector of spatial effect with s e N(0, Is 2 s ) and s 2 s is the spatial effect variance, and e is a vector of random residuals with e ˜ N(0, Is 2 e ). The spatial effect contains the X and Y coordinate of the plot of the observation (i. e. , target plot) and the eight surrounding plots (n = 9), and thus, it is the sum of the effects centered on those plots.
Assessment of models and cross-validation The predictive performances of the LMM-HET and the DHGLM were evaluated using the leave-one-line-out (LOO) cross-validation (CV) analysis. The LOO CV was performed by randomly masking the phenotypes of all replicates of one line and using the remaining lines to predict the additive genetic values. This process was repeated t -times (t = no. of lines = 2,456) until all lines were predicted. Three estimates were computed to evaluate the predictive performance of the LMM-HET and the DHGLM models: i) Predictive ability (PA) calculated as the Pearson correlation (r) between the average value of lines after correcting for fixed effects and the vector of predicted additive genetic effects in EGY [ r(y c , ĝ ) and DGY [r(y d , ĝd ). The y c and y d were obtained by subtracting the fixed effects b and b d estimated with the full dataset from each corresponding plot observation for EGY and DGY, respectively, and then averaging the resulting values by line.
Limitations and prospects The presented work can be seen as a proof of concept for studying micro-environmental sensitivity in crop breeding programs. Breeding program datasets could be reanalyzed in order to identify and select more resilient lines exhibiting lower micro-environmental sensitivity. Selecting for reduced micro-environmental sensitivity is a way to deal with/handle some specific elements of G×E. The variability at macro- environmental scale is another important source of G×E in crops. Future studies extending the proposed DHGLM to account for macroenvironmental sensitivity could represent a comprehensive approach to reducing sensitivity at micro-and macro-environmental levels. Furthermore, this may provide a better understanding of the relationship between micro-and macro-environmental sensitivity, and hence more research in the area is justified.
Acknowledgments We thank  Aarhus University  for financial support for authors MR, and JJ, and the  National Institute for Agricultural, Food and Environmental Research (INRAE) of France  for financial support of  BC , RR, LM, and TM-H. We also thank  Nordic Seed  A/S breeding company for PS funding, and  Jeppe R. Andersen ,  J. Orabi  and  A. Jahoor  from  Nordic Seed A/S  for providing phenotypic and genotypic data. Financial support from the  National Research and Innovation Agency (ANII) of Uruguay  for the PhD scholarship of author MR (code  POS_EXT_2018_1_154284 ) is greatly acknowledged.
This different behavior is explained by the lower E 1/2 1 value of 2 À in comparison to 1 2À , and the subsequent isolation of the first complex already under its monoanionic form. We additionally performed electrochemical measurements in methanol, which is the solvent used for the crystallization of the complexes (Supporting Information). The same trend in potentials was observed between the complexes, whereby the nickel complex is 0. 2 V easier to oxidize than the copper one. Interestingly, the potentials are anodically shifted by about 0. 2 V when compared to acetonitrile (Table  2 ), which also explains why the copper complex did not undergo significant oxidation during the crystallization process. Altogether, these data confirm that 2 À could be isolated as an anion at the solid state (in air), whereas the copper complex crystallized under the form of a dianion under similar conditions.
APCI-MS: m/z = 582. 3 [M + H] +. Crystal structure analysis. A single crystal was coated with a paraffin mixture, picked up with nylon loops and mounted in the nitrogen cold stream of a Nonius 4 circles diffractometer at 200 K. The MoÀ Kα radiation (λ = 0. 71073 Å) from an Incoatec micro Motarget X-ray source equipped with Montel optics was used. The data were collected with a Bruker APEXII detector. Final cell parameters were obtained from refinements using the whole data. Intensities were corrected for Lorentz and polarisation effects using EVAL 14 and then for absorption using a multiscan method implemented with the program SADABS. The structures were solved and refined by charge flipping methods and subsequent difference Fourier techniques. SHELX implemented by the Olex2 software was used for the refinement.
[33] All non-hydrogen atoms were anisotropically refined and hydrogen atoms were placed at calculated positions and refined as riding atoms with isotropic displacement parameters. Deposition Numbers 2180498 (for 1), 2180497 (for 2), 2181720 (for 3) contain the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service. DFT Calculations: Full geometric optimizations were performed with the Gaussian 16. 0 program. [34] The TPSSh  [35]  functional was used together with the Def2svp basis set  [28]  for the C,H,N,O atoms and the central metal ion. Solvent effects were included in all calculations via an implicit model of solvent (PCM) accounting for CH 2 Cl 2.
[28] Frequency calculations were systematically performed on the optimized structures in order to ensure that they correspond to a real energy minimum and not a saddle point. The ORCA software (release 3. 0. 1)  [36] was used to calculate the EPR parameters (gfactors) on the above optimized structures. For this purpose the B3LYP functional was used, in combination with the RIJCOSX approximation. [37] An increased grid, as well as tight SCF and slow convergence criteria were used. The Def2-TZVP basis set  [28a,38]  was used for the atoms of the ligand, with a partially contracted coreproperty basis set CP(PPP)  [39]  for the nickel and copper ions. This basis set is based on the TurboMole DZ basis developed by Ahlrichs and coworkers and obtained from the basis set library under ftp. chemie.
Acknowledgements The authors thank the  French National Research Agency  in the framework of the " ,  Labex ARCANE  and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) for financial supports. The authors are grateful to the  Centre de Calcul Intensif en Chimie de Grenoble (CECIC)  for providing the computational resources. The  ICMG Platform  ( FR 2607 ) is acknowledged for the analytical support.
METHODS To measure period fertility by education for 24 EU and 4 non-EU countries in Europe, we use data from the European Union's Survey of Income and Living Conditions, EU-SILC (Eurostat 2020). A semi-retrospective approach is used to observe the parityspecific fertility behavior of cohorts that are of childbearing age, while at the same time recording the educational level correctly. Bayesian statistics allow us to obtain credible intervals for the age-, education-, and parity-specific birth probabilities for each country. These birth probabilities are then combined into a multi-state life table in order to obtain parity-specific and total birth intensities by education. A post-stratification of birth probabilities allows consistency with national fertility estimates, enabling international comparisons of specific groups (e.
Introduction Although fertility increased in many European countries between 1990 and 2010, since 2010 fertility has been stagnating or decreasing in countries where previously fertility was close to 2 children per woman. The COVID-19 crisis certainly accelerated the trend of declining fertility, but did not trigger it  (Sobotka et al. 2022). Fertility levels and trends in Europe -and thereby long-term population-aging trends -have important impacts on both economic and social outcomes, and need to be monitored in detail. While European cross-country differences in total fertility rates (TFR) are rather well-documented today (see for example the OECD Family Database: https://www. oecd. org/els/family/ database. htm; or the Human Fertility Database https://www. humanfertility. org/cgibin/main.
By combining several demographic tools (semi-retrospective approach, Bayesian statistics, post-stratification) and by using a dataset that has not yet been used for this kind of demographic analysis (European socioeconomic survey data: EU-SILC), we quantify the educational gradient of fertility for the cohorts that are currently of childbearing age (ages 15 to 49) for the large majority of European countries. Our country-by-country analysis allows a timely insight into the educational composition of fertility levels in Europe and their components by birth order. Our measures of the educational gradient of period fertility reveal whether fertility differentials are important for those who are currently of childbearing age, within each European country. This is particularly relevant in a context where total fertility rates are below replacement level in all European countries, and are even re-decreasing in some: The EU average increased from 1.
48 children per woman in 2005 to 1. 60 in 2010, but then decreased to 1. 50 in 2020 (Human Fertility Database 2023). 3  Fertility increased after 2010 in a few countries (e. g. , Germany and Hungary), but since 2017 total fertility rates have been decreasing in most European countries. Measuring the educational gradient of period fertility allows for a better understanding of which educational group has the lowest fertility, and whether the contrasts by education are larger for the entry into motherhood or for the progression from one birth to the next. Moreover, consistent measures for each European country allow for comprehensive international comparisons. By delivering measures of the educational gradient of fertility for women who are of childbearing age rather than for women who have already completed their reproductive years, our research enables a timely analysis of the within-country differentials of period fertility behavior.
Limited availability of internationally comparable data The focus on specific parities and single countries that prevails in demographic research analyzing period fertility behavior in Europe is due to a lack of harmonized international data that combines high quality demographics with comparable socioeconomic measures. Some European countries deliver survey data that combines demographic and socioeconomic measures, be it cross-sectional or longitudinal -for example, Germany (SOEP) and the United Kingdom (BHPS). However, as not all available panel surveys are harmonized (socioeconomic variables such as education and labor market participation are not always coded in the same way), comparative analysis combining several countries is difficult, and the small number of panel surveys makes it impossible to carry out multi-level analyses.
4 One possible reason is that filling out an additional census form for a very young child does not seem useful to some parents, as not much information can be given other than purely demographic information. A harmonized demographic survey is the Gender and Generation Survey (GGS). In the first round of data collection (GGS-I) from 2004-2012, data was collected from 20 countries. Currently, the GGS is engaged in the second round of data collection (GGS-II). Thus, it still has relatively limited country-and time-coverage. More socioeconomically oriented surveys with larger country-and time-coverage are possible alternatives. The European Union's Labour Force Survey (EU-LFS) could be used as a harmonized large-scale dataset to estimate fertility (see, for example,  Bordone, Billari, and Dalla Zuanna 2009) , but it comes with several technical shortcomings.
Combining different data sources (national censuses and surveys, GGS, etc. ) might enable researchers to cover several countries, but would result in many variables not being harmonized. A harmonized, large-scale dataset, which observes not only households but also individuals and includes exact age, is the European Union's Statistics of Income and Living Conditions (EU-SILC). EU-SILC now covers 32 European countries: 28 EU countries and 4 non-EU countries (United Kingdom, Iceland, Norway, Switzerland). Years are covered from 2004 on, for most countries. Besides annual crosssectional waves (which we use in this article), EU-SILC comes with a short rotational panel (not used in this analysis) which follows up individuals and households for a period of four years. However, EU-SILC does not provide straight fertility measures.
Like the EU-LFS, it has no explicit question on the number of children in and outside the household. This makes the use of EU-SILC rather inconvenient for the analysis of socioeconomic differentials of period fertility behavior, albeit not impossible, as children have their own register file in EU-SILC (see section 3 for more detailed information about the method applied to enable the observation of period fertility in EU-SILC). The number and age of household members, including children, are used to produce statistics on living standards, income distribution, and poverty, based on the "equivalized household size" and "equivalized disposable income" (Eurostat 2023). Greulich and Dasré (2017)  evaluate positively the accuracy of period fertility measures derived from the cross-sectional database in EU-SILC when taking certain precautionary measures (see section 3 for more details).
To avoid any distortions due to differences between groups in the timing of birth and the variation of socioeconomic characteristics over age, it is common in demographic research to calculate socioeconomic differentials in fertility by focusing on education, as education evolves uniformly over age, and by only taking into account cohorts that have already completed their childbearing years. For the latter reason, most fertility measures focus on women only, as their reproductive years are more restricted than those of men. Tomas Sobotka has recently provided completed cohort fertility rates for a large set of developed countries, differentiated by level of female education (www. cfedatabase. org). Completed fertility rates (CFR) have a negative educational gradient in most European countries (see  Sobotka, Beaujouan, and Van Bavel 2017  for an overview), suggesting that 10 to 20 years ago (at the time of their fertility decisions) it was mainly higher-educated women who were limiting their fertility.
For example, to reduce attrition bias, we apply the own-children method to the cross-sectional database rather than the rotational panel and use a two-year delay between the year of observed childbirth and the survey year, as suggested by  Greulich and Dasré (2017). To obtain sufficient sample size, we group together several waves. 2) We combine a longitudinal and a cross-sectional approach from EU-SILC crosssectional data (semi-retrospective approach), rather than focusing on completed cohort fertility (retrospective approach only). This serves to observe the fertility behavior of cohorts which are of childbearing age (15 to 49), while at the same time recording the educational level correctly. 3) Bayesian statistics allow us to obtain credible intervals for the age-, education-, and parity-specific birth probabilities for each country.
Measuring period fertility We use the European Union Statistics of Income and Living Conditions (EU-SILC) to obtain period fertility measures. The EU-SILC is a European survey provided by Eurostat. It was created in 2003 to replace the European Community Household Panel (ECHP) and now includes 32 European countries. The survey provides information on individual and household characteristics, with a particular focus on harmonized and comparable measures of education, labor market participation, income, and living conditions. We use the cross-sectional database of the EU-SILC, which provides nationally representative probability samples. The cross-sectional data in EU-SILC is available from 2004 on for most countries, which enables covering almost 20 years when computing period fertility levels.
However, they also find that there are no socioeconomic differentials in attrition in the longitudinal sample. In addition, the country ranking in terms of fertility levels based on their EU-SILC measure is approximately the same as unbiased fertility measures coming from the Human Fertility Database or the World Bank World Development Indicators. They also find that the downward bias in period fertility disappears when allowing for a certain time delay between the childbirth year and the survey year, as families who have recently moved due to childbirth re-enter the probability sample. For the analysis proposed in this study, a time delay of 2 years emerges as the best compromise to meet two different goals when measuring period fertility using EU-SILC: reducing the attrition-linked measurement bias in period fertility while at the same maintaining information that is as up to date as possible in a cross-sectional setting.