dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The strong interaction of electrons with matter favors electron crystallography for solving three-dimensional structures from beam-sensitive sub-micrometre-sized crystals such as protein nanocrystals  (Clabbers & Abrahams, 2018; Henderson, 1995). Using cryo-electron microscopy, protein crystals can be vitrified and studied in their native hydrated states. In contrast to real-space techniques such as single-particle analysis or electron tomography, electron diffraction data are recorded in the diffraction plane. The major advantage of image-based methods over diffraction is that they provide experimental data with crystallographic phases. However, compared with diffraction methods, this advantage comes at a severe price in terms of signal strength, as the electron dose required for a  ISSN 2059-7983  single high-resolution still image is sufficient for a full 3D diffraction rotation data set from the same crystal  (Clabbers & Abrahams, 2018).
Indeed, various protein structures have recently been solved using diffraction data collected with continuous rotation of 3D crystals  (Clabbers et al. , 2017; de la Cruz et al. , 2017; Nannenga et al. , 2014; Xu et al. , 2018; Yonekura et al. , 2015). Early attempts to use electron diffraction in structural biology were promising but were mainly limited by the electron detectors. Coupled with phase information obtained from images, structures were solved to 6-7 A ˚resolution in some early studies  (Subramaniam et al. , 1997; Unwin & Henderson, 1975). Only a few still diffraction patterns could be recorded from any single (2D) protein crystal with the imaging plates or charge-coupled detectors used in these studies. The limitation is due to the significant radiation damage that is imposed on the crystals by the dose that is necessary to obtain sufficient signal strength using these low-sensitivity detectors.
, 2019; van Genderen et al. , 2016). Electron diffraction data collection with fast sensitive electron detectors from continuously rotating crystals has yielded data with a quality and quantity that permits standard X-ray crystallography programs such as MOSFLM  (Leslie, 2006) , XDS  (Kabsch, 2010)  or DIALS  (Clabbers et al. , 2018)  to process the electron diffraction data. Data integration became straightforward using a range of mature public domain software packages, each with a wide user base and supported by longstanding crystallographic software consortia. There is also a range of standard crystallographic software for subsequent structure solution by molecular replacement or direct methods (if permitted by the resolution) and refinement, for example the CCP4 suite  (Winn et al. , 2011)  and SHELX  (Uson & Sheldrick, 1999).
Electron diffraction data collection and data processing The electron diffraction data were collected on Polara and Talos cryo-electron microscopes (Thermo Fisher) both operated at 200 keV and equipped with a Timepix hybrid pixel detector. Electron diffraction crystallographic data were collected from seven crystals of insulin (Supplementary Table  S1 ), two crystals of thermolysin (Supplementary Table  S2 ) and ten crystals of thaumatin (Supplementary Table  S3 ). All data sets were processed with the XDS package  (Kabsch, 2010). The structures of thermolysin and thaumatin were obtained by merging several diffraction data sets from cryocooled submicrometre-sized 3D crystals. Two data sets for the thermolysin structure and ten data sets for the thaumatin structure were merged. The two thermolysin crystals were continuously rotated from 17 through 34 with 0.
186 per frame and 0. 253 per frame in a 2 mm diameter beam. For thaumatin, all ten crystals were continuously rotated starting at 7 and ending at 14. The data sets for thermolysin and thaumatin showed a decrease in the intensity of reflections with time and the last frames were thus discarded. The data were integrated with XDS and merged with XSCALE to 3. 26 A ˚resolution for thermolysin and 2. 76 A ˚resolution for thaumatin, resulting in an overall completeness of 84% and 66%, respectively (Table  1 ). For the insulin data sets, a significant decrease in the intensity of the highest resolution reflections was observed after about 60 s exposure time. Therefore, only the first 50-90 frames were used for data processing. Data sets 3 to 7 were merged with XSCALE, providing a rather complete 3.
X-ray data collection and data processing A 50 mm crystal was taken from the crystal preparation used in electron diffraction experiments, soaked for about 30 s in a cryoprotectant solution containing 25% glycerol and flashcooled in liquid nitrogen. Crystallographic data were collected at 110 K on the BM30A beamline of the European Synchrotron Research Facility (ESRF) using an ADSC Quantum 315r CCD detector. Data were processed with the XDS package  (Kabsch, 2010). A complete 2. 3 A ˚resolution data set was obtained, as shown in Table  1.
Structure refinement The structures were solved by molecular replacement with Phaser from the CCP4 suite of programs  (Winn et al. , 2011)  using the structure of bovine insulin from PDB entry 2a3g  (Smith et al. , 2005) , the structure of T. daniellii thaumatin from PDB entry 6c5y  (Guo et al. , 2018)  and the structure of B. thermoproteolyticus thermolysin from PDB entry 1fj3  (English et al. , 2001)  as search models. The final structures were obtained by several rounds of manual building with the Coot software  (Emsley et al. , 2010)  and maximum-likelihood refinement with REFMAC5 from the CCP4 suite. For refinement against electron diffraction data, the electron atomic form factors provided by the CCP4 library (atomsf_ electron. lib) were used by specifying source EC in the REFMAC5 script.
Overall description of the structures The data-processing and refinement statistics for the three proteins are shown in Table  1. The structure of bovine insulin was determined by both X-ray and electron diffraction using crystals from the same batch, i. e. grown under the exact same conditions. The X-ray structure was refined at 2. 3 A ˚resolution, which was sufficient to yield a reference structure for the electron diffraction structure. The structure is very similar to the structure of T 6 bovine insulin (PDB entry 2a3g; r. m. s. d. of 0. 34 A ˚on C atoms) and belongs to the same space group with similar unit-cell parameters, despite being obtained from crystals grown under different crystallization conditions. The asymmetric unit contains two insulin molecules as well as two zinc ions and two chloride ions located on the crystallographic threefold axis and 23 water molecules.
The final model is very similar to the X-ray crystallographic structure of the same protein at 2. 0 A ˚resolution (PDB entry 1fj3), with an r. m. s. d. of 0. 49 A ˚for 314 C atom pairs. The thaumatin structure was refined against electron diffraction data to 2. 76 A ˚resolution. The fit with the Coulomb potential map is good for all 207 residues. The structure is similar to PDB entry 6c5y, an X-ray crystallographic structure refined at 2. 5 A ˚resolution obtained from microcrystals (r. m. s. d. of 0. 42 A ˚for 202 C atom pairs). No solvent molecules were identified except for one putative chloride ion on a twofold axis coordinated by the guanidinium group of Arg82. Whereas the refinement statistics given in Table  1  are far from ideal compared with X-ray crystallographic standards, they are in line with previous structures refined against electron diffraction data at similar resolution.
Applying the dynamical scattering correction We previously observed an overestimation of the weaker structure-factor amplitudes |F o (h)| compared with calculated amplitudes from the structural model |F c (h)| in the electron diffraction of small molecules  (Clabbers et al. , 2019). We attributed this effect to a dynamical electron scattering term |F e | and described this trend using a hyperbolic function defining the expected value of |F o (h)| as hjF o ðhÞji ¼ ½jF c ðhÞj 2 þ jF e j 2 1=2 : ð1Þ Using this relation, we derived the expected dynamical scattering error term |F e | using least-squares fitting. The dynamical error term |F e | was used to determine a scaling factor k(h) to be applied to both the observed amplitude |F o (h)| and experimental sigmas, kðhÞ ¼ jF c ðhÞj ½jF c ðhÞj 2 þ jF e j 2 1=2 : ð2Þ This method has yielded good results on strongly diffracting crystals of small molecules and a relatively strong lysozyme data set.
Moreover, the noise level has decreased in the difference Coulomb potential maps. For thermolysin, the corrected map revealed the presence of a solvent molecule that could be either a water molecule, a hydroxy ion or a chloride ion bridging the guanidinium group of Arg203 and the zinc ion (Fig. 2 ). For comparison, in PDB entry 1fj3 an acetone molecule is observed at this location, with the acetone O atom forming a similar bridge between the guanidinium group and the zinc ion.
Acknowledgements We are very grateful for the support and advice of  Igor Nederlof ,  Martin Weik  and  Andrew Leslie. We thank  Franck Borel  and  Jean-Luc Ferrer  from the BM30A beamline for their help with synchrotron data collection at the  European Synchrotron Research Facility (ESRF), Grenoble, France  and  Emmanuelle Neumann  for her work at the IBS electron microscopy platform. IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ). This work used the platforms of the  Grenoble Instruct-ERIC Centre (ISBG ;  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The IBS electron microscope facility is supported by the  Auvergne-Rhone-Alpes Region , the  Fonds FEDER , the  Fondation Recherche Medicale (FRM)  and the  GIS-Infrastructures en Biologie Santeet Agronomie (IBISA).
All the analyzed phases were examined under SEM to discard those data that falls on visible cracks, pits or regions that were not part of this study. The instrumental mass fractionation in olivine grains was corrected using San Carlos olivine, and for I-type and some G-type spherules that were dominated by magnetite we have used terrestrial magnetite standard. Further, detailed technical description of the SEM, electron microprobe and ion microprobe is given elsewhere  (Rudraswami et al. , 2019). 1  and 2 ). The back-scattered electron images of nine S-type spherules are shown in Fig. 1 , while the bulk chemical compositions of all the spherules are summarized in Appendix A. Some of the spherules analysed have FeNi metal beadthese include one porphyritic, three barred, two cryptocrystalline, fourteen glass and one G-type (Fig.
, (2005) , but have a similar minimum δ 18 O. An increasing trend of δ 18 O with size was observed except for few outlier datasets such as AAS-38-167-1-P115 and AAS-38-167-1-P116 (circled in Fig. 6 ). The oxygen isotope data values did not correlate with the total abundance of magnetite in I-type spherules, which is likely to be a proxy for total oxidation, nevertheless, δ 18 O strongly correlated with the thickness of the magnetite rim (Fig. 6  and Table  2 ). The thickness of the rim is large in spherules dominated by magnetite unlike those that are wustite-dominated. This undoubtedly relates to increased mass fractionation as a result of larger degrees of evaporation from larger particleslargely a consequence of their higher peak temperatures  (Genge, 2016). The increase in the range and maximum value of δ 18 O with particle size is consistent with the variation of their peak temperatures with entry parameters as modelled numerically by  Genge (2016).
These show the genetic relationships with precursor materials very well and allow some quantitative estimates of precursor affinities. The progressive heating of different S-type spherules with bead and non-bead do have some correlation with oxygen isotope enrichment of δ 18 O. and~0. 94, respectively  (Clayton et al. , 1977; Clayton and Mayeda, 1999). All error bars shown are smaller than the size of symbol. The oxygen isotope compositions of least altered and most altered cosmic spherules, scoriaceous and CAT spherules analyzed using ion microprobe. The oxygen isotope data are given in Appendix A. The solid lines represents TF and CCAM line with slope~0. 52 and~0. 94, respectively  (Clayton et al. , 1977; Clayton and Mayeda, 1999). All error bars are small than the size of the symbol. (Clayton et al.
Acknowledgements The project has been supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  712721  (NanOQTech),  Ville de Paris Emergence Program , the  Région Ile de France DIM C'nano and SIRTEQ , the  LABEX Cluster of Excellence FIRST-TF  ( ANR-10-LABX-48-01 ) within the  Program "Investissements d'Avenir " operated by the  French National Research Agency (ANR) , and the EM-PIR 15SIB03 OC18 program co-financed by the Participating States and from the  European Union 's  Horizon 2020 research and innovation program.
Because of a small overlap in observations and a ∼ 6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from 3 years of GMI brightness temperatures are first calculated and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data records of microwave brightness temperatures over continental surfaces.
, 2017) ). We will use the CM SAF FCDR for SMMR data set as the starting point of our developments. In Sect. 2, the satellite observations used in this study are briefly described, along with their preprocessing for the analysis. The result of the inter-comparison is presented in Sect. 3, along with the proposed inter-calibration procedure and its evaluation. Section 4 concludes this study. 2 Data and method.
The satellite data Table  2  summarizes the major characteristics of the SMMR and GMI instruments for the Ku and Ka channels. SSM/I on board F08 is also included, as it is the only instrument with an overlap period with SMMR (albeit of only 28 d), and it will be used for evaluation. GMI, with its non-Sun-synchronous orbit, observes the full diurnal cycle, including the SMMR and SSM/I overpassing times. SMMR was launched in 1978 on the Nimbus-7 satellite and operated until August 1987. It is the first multichannel microwave imager, designed mainly for oceanic applications to estimate the surface wind speed and sea surface temperature  (Gloersen and Barath, 1977). Due to power limitation on board, measurements were performed only every other day. The EIA decreases from 50. 2 to 49. 3 • starting in 1986.
This correction is computed only over cloud-free water surfaces. It is described in detail in  Fennig et al. (2020). In this study, we will only use SMMR data showing the best quality (data with sun intrusion, field-of-view, or scan error are removed). GMI is a recent microwave imager launched in 2015. It observes between 10 and 190 GHz to measure precipitation across the globe. It will be used here as a reference standard for calibration. The instrument characteristics are summarized in Table  2 , listing only the channels relevant to this study. The satellite has a 65 • inclination, allowing non-Sunsynchronous observations of the Earth, from the tropics to the high latitudes. The antenna has an EIA of 52. 8 • and a swath of ∼ 900 km. This swath width associated with the inclination means that polar regions are not fully covered.
, 2012). A land mask is used to filter out water pixels at 25 km resolution. GMI data are collected for the months of January, February, July, and August from 2015 to 2017. These 4 months per year include the boreal summer and winter T b diurnal cycles, and with data sampled from the whole Earth most of the possible surface conditions are represented in the analyzed data set. As GMI has a higher spatial resolution than the target grid, all observations falling within a grid cell of ∼ 12 km radius are averaged to derive one value per grid cell. Regarding the temporal dimension, the values are averaged with a 15 min window for each grid cell to suppress small-scale variability in the signal. If no observation in a given grid cell is available in a particular 15 min interval, a linear interpolation with the closest existing measurements is performed.
Finally, to get a smooth diurnal cycle, a moving average is applied over each grid cell with a 75 min window (two data points before and after the target one). For SMMR, T bs are collected from 1981, 1982, and 1987 for the same months as GMI. The observations are available twice a day (for the ascending and descending overpasses). Given the comparable resolutions of SMMR observations and the target grid, a nearest-neighbor technique is used to project the T bs onto the EASE grid. Using several years of data for each instrument can alleviate possible effects of strong inter-annual variability in the signals (related to El Niño or La Niña events for instance). The resulting data sets contain almost 2 million data points spanning all the continents and two contrasted seasons (summer and winter).
Acknowledgements. The authors are grateful to  Karsten Fennig  and  Marc Schröder  for their valuable help on using the CM SAF FCDR data as well as the discussion regarding the SMMR instrument and the work performed in this paper. We would also like to acknowledge  Wesley Berg  and  Christian Kummerow  from  Colorado State University  for the data and information they provided us. Review statement. This paper was edited by  S. Joseph Munchak  and reviewed by  Linwood Jones  and two anonymous referees.
Data availability. The Satellite Application Facility on Climate Monitoring provides access to the Fundamental Climate Data Record of Microwave Imager Radiances (https://doi. org/10. 5676/EUM_SAF_CM/FCDR_MWI/V003; Fennig et al. , 2017 Fennig et al. ,  , 2020)). The Global Precipitation Measurement Microwave Imager GMI_R Common Calibrated Brightness Temperatures Collocated L1C 1. 5 h 13 km V05 (GPM_1CGPMGMI_R, https://doi. org/10. 5067/GPM/GMI/R/1C/05; Berg, 2016; Berg et al. , 2016)  is provided by NASA.
ACKNOWLEDGMENT This research was supported by the  European Research Council  ( ERC AdG 692775 ). D. M. holds the  Israel Matz Professorial Chair of Organic Chemistry. A. K. is thankful to the  Planning and Budgeting Committee of Israel and Feinberg Graduate School  for a (senior) postdoctoral fellowship. Y. -Q. Z. acknowledges the  Sustainability and Energy Research Initiative (SAERI)  foundation for a research fellowship. Computations were performed using HPC resources from  GENCI-CINES  (Grant  2019 AP010811227 ).
ACKNOWLEDGMENTS We thank the  LABEX ECOFECT  for building an "ecofectian" community. This work was supported by the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). A. -S. G. and M. -H. L. were also supported by  Programme Jeune Chercheur  from  VetAgro Sup. A. L. and M. H. were supported by the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES).
The rise in these long-term research programs in mammalian populations  [6]  and the development of powerful statistical methods and softwares (E-SURGE  [7] ; Bayesian Survival Trajectory Analysis (BaSTA)  [8] ) allowed increasing evidence of actuarial senescence in the wild (see  [2]  for a review). Nowadays, the prevailing view is that the presence of actuarial senescence constitutes the rule rather than the exception in mammals  [9]. In addition, while environmental conditions modulate age-specific mortality patterns (e. g. ,  [10]  in elk, Cervus elaphus canadensis), actuarial senescence has been reported under a wide range of environmental conditions (e. g. , zoological gardens  [11] ; semi-captive populations  [12] ). Therefore, the current challenge in evolutionary biology of aging is not to assess the universality of senescence across species but to assess and understand the diversity of age-specific mortality patterns that have been identified across species  [13].
Dataset Demographic data on captive mammals were retrieved from previous studies  [11]  using data from the Species360 non-governmental organization (https://www. species360. org/). This organization collates and standardizes information from approximately 6. 5 million individuals belonging to >21,000 species (mostly vertebrates) across >1000 zoological and aquaria institutions in over 90 countries. For each species, we got access to the age at death of individuals. Following previous works using the same dataset  [11, 25] , we restricted our analyses to extinct cohorts where the age at death was known for each zoo-housed individual. In this study, we only performed the analysis on females that survived to the age at first reproduction. We included species with at least 50 individuals still alive at the age at first reproduction to have a large enough sample size to ensure reliable estimates of mortality at old ages.
Age-Dependent Models of Mortality Changes during the Adult Stage Three types of models have been widely used in the literature to analyze the increase of mortality with age associated to actuarial senescence. (1) The Gompertz model  [1]  is a two-parameter model in which the hazard of mortality follows an exponential increase with age (see Equation  (1)  and Figure  1a, d ). However, it is known that the increase of mortality associated with senescence does not always begin right after the age at first reproduction  [14, 25]  and thus could lead to inadequate fitting of the different models. The package BaSTA  [8]  in R version 4. 0. 1  [28]  proposes 3 different structures of Bayesian models to account for this issue: the 'simple' models corresponding to the 3 basal models (i. e. , Gompertz, Weibull, logistic) described above, the 'Makeham' model in which a positive constant c is added to each of the three basal models, and the 'bathtub' model in which both a constant c (to represent potential age-independence during the prime-age stage) and a declining mortality following a Gompertz model (to represent potential decreasing mortality early in life) is added to the 3 models described above (see Equation (  4 )).
µ(x) = e b 0 +b 1 x 1 + b 2 e b 0 b 1 (e b 1 x -1) (3) where µ(x) is the hazard of mortality for the logistic model with b 0 and b 1 corresponding to the parameters a and b of the Gompertz model, respectively, and b 2 describing the rate of deceleration of the speed of mortality increase. However, it is known that the increase of mortality associated with senescence does not always begin right after the age at first reproduction  [14, 25]  and thus could lead to inadequate fitting of the different models. The package BaSTA  [8]  in R version 4. 0. 1  [28]  proposes 3 different structures of Bayesian models to account for this issue: the 'simple' models corresponding to the 3 basal models (i. e. , Gompertz, Weibull, logistic) described above, the 'Makeham' model in which a positive constant c is added to each of the three basal models, and the 'bathtub' model in which both a constant c (to represent potential age-independence during the prime-age stage) and a declining mortality following a Gompertz model (to represent potential decreasing mortality early in life) is added to the 3 models described above (see Equation (  4 )).