dataesr/scientific-paragraphs-categorization huggingface dataset:
The yellow stars in subclade A3 indicate two sequences that lack one or two of the catalytic histidines, which are replaced by glutamines (see Supplemental Fig. S3 ). All sequences are of bacterial origin with the exception of four viral (cluster with chitin-active enzymes from Proteobacteria) and one of plant origin (subclade A2). LPMO10A from Tectaria macrodonta (Tma12; PDB ID: 6IF7  38  ) as the starting model (Table  1 ). The structure shows the typical LPMO fold with a central β-sandwich built up by two distorted β-sheets that are connected by several loops and helices (Fig. 3 ). The first β-sheet is formed by three antiparallel β-strands (S1, S4 and S7) and the second β-sheet is built up by four antiparallel strands (S5, S6, S8 and S9). Two small β-strands (S2 and S3) are found before and after the long "L2 loop" that holds most of the α-helices (H1, H2, H3 and H5), in which one is a 3 10 -helix (H5).
A structural model (AlphaFold) of MaLP-MO10A shows an unusual active site that has not been seen in other LPMOs (Fig. 4E  and Supplementary Fig. S2 ) where the residues corresponding to Arg198 and Glu203 in ScLPMO10D are replaced by Asn189 and Asp194, Table  1. Crystal data, diffraction data and refinement statistics for the ScLPMO10D CD structure (PDB code 7ZJB). a Values for outer shell in paranthesis. b R sym = |I -�I�|/ I c R cryst = (|F obs |-|F calc |)/ |F obs | d R free is the R cryst value calculated on the 5% reflections excluded for refinement. respectively. Two residues that help shaping the copper site and that are commonly alanine and phenylalanine in AA10s (see Fig. 4 ), are replaced by an isoleucine and a tyrosine (Ile123 and Tyr196 in MaLPMO10A), in which the latter residue is fully conserved in sequences belonging to subclades A3 and B.
to recovered product formation for both enzymes (Fig. 6B ), indicating that the stagnation visible in the progress curves of Fig. 5A  is primarily due to inactivation of the LPMO. The detrimental effect of removing the CBMs on the stability of CjLPMO10A under turnover conditions has been correlated to the low affinity of the catalytic domain for chitin, which is weak in comparison to CBMs and natural single domain LPMOs such as SmLPMO10A  44, 45. ScLPMO10D does not have a CBM and binding to both chitin and cellulose has been shown for a C-terminally polyHis-tagged variant of this protein  33. Comparative   31  and includes seven chitin-oxidizing LPMO10s, SmLPMO10A (PDB ID: 2BEM; also known as CBP21), JdLPMO10A (PDB ID: 5AA7), BaLPMO10A (PDB ID: 2YOY), EfLPMO10A (PDB ID: 4ALC), CjLPMO10A (PDB ID: 5FJQ), ScLPMO10D (PDB ID: 7ZJB) and PaLPMO10A (also known as CbpD, PDB ID: 7SQX), and four cellulose-active LPMO10s, namely ScLPMO10C (PDB ID: 4OY7; also known as CelS2), TfLPMO10B (also known as E8; no crystal structure available), ScLPMO10B (PDB ID: 4OY6) and MaLPMO10B (PDB ID: 5OPF).
Methods Bioinformatics analysis. The amino acid sequences of ScLPMO10D, MaLPMO10A and the six other S. coelicolor LPMO10s were analyzed using CAZy (http:// www. cazy. org) and InterPro (http:// www. ebi. ac. uk/ inter pro) for information on domains and functional sites as well as for identifying potential motifs for sortasemediated cell wall-anchoring. Prediction of transmembrane helices was carried out using the TMHMM server v. 2. 0 (http:// www. cbs. dtu. dk/ servi ces/ TMHMM). To construct a phylogenetic tree, 150 AA10 sequences (catalytic domains only) were aligned using the MUS-CLE online tool  70  provided by EMNL-EBI. To construct the pool of sequences containing representatives from all clades of AA10 LPMOs, as previously defined by Book et al. 31  , the sequences of 45 previously characterized LPMOs (Supplementary Table  S1 ) were used and extended by using sequences of experimentally characterized LPMOs from each clade as queries for protein-protein BLAST (blastp) searches against the non-redundant NCBI database.
Crystals were obtained in 2. 0 M ammonium sulfate and 0. 1 M sodium cacodylate pH 6. 5 at a protein concentration of 7. 7 g/L. Protein crystals were soaked in a cryo-solution containing mother-liquor with 35% glucose (w/v) before they were flash frozen in liquid nitrogen. Diffraction data were collected at the ID23-1 beamline at ESRF, Grenoble, France. Datasets were processed with XDS  78  and scaled with POINTLESS  79. The CCP4i2 package was used to solve the structure by molecular replacement (Phaser)  80  and the structure was refined using REFMAC  81. Model manipulations were carried out using Coot  82  and molecular graphics were generated using PyMOL2 (The PyMOL Molecular Graphics System, Version 1. 8, Schrödinger, LLC. ). The 3D model of MaLPMO10A (UniProt ID: D9TC53, residues 27-298) without the signal peptide was built using AlphaFold 2.
The bands corresponding to the ribosomal proteins bS1, uS2, uS3, uS4, uS5, and uS7 are also indicated. Using the A260nm absorbance data of the various fractions, we estimated the 21S ribosomal particles to be about 1% of the crude extract total rRNA. We next purified 200 ± 50 pmoles of 21S from a total volume of 30 L of cultures after heat shock. The same protocol was applied to E6006 cells grown at 30 °C (without heat shock), but no 21S particles were detected. The 21S particles were also absent in cells left to recover at 30 °C for 1 h after heat shock. These results are in accordance with the previous observation  [22]  Figure  3. Purification of 21S particles. (A) Escherichia coli E6006 cells were grown in M63 medium and subjected to heat shock for 1 h at 45 • C. The clarified supernatants were loaded onto a 10-40% (w/v) linear sucrose gradient, and the sucrose density gradient profile is shown here.
The fractions corresponding to 21S are outlined in red. (B) These fractions ("a") were pooled, while 30S subunits were retained on NiTa resin and eluted with imidazole ("b"). The flow-through was applied to streptactin resin and eluted with desthiobiotin (21S), with the non-retained fraction ("c") and the molecular weight (MW) markers shown. The bands corresponding to the ribosomal proteins bS1, uS2, uS3, uS4, uS5, and uS7 are also indicated. Using the A260nm absorbance data of the various fractions, we estimated the 21S ribosomal particles to be about 1% of the crude extract total rRNA. We next purified 200 ± 50 pmoles of 21S from a total volume of 30 L of cultures after heat shock. The same protocol was applied to E6006 cells grown at 30 • C (without heat shock), but no 21S particles were detected.
The chromosomal structure of mutants was confirmed by PCR (Supplementary Figure  S1 ). KanR colonies were then transformed with pCP20 plasmid. The resulting AmpR transformants were selected at 30 • C, incubated at 42 • C, and tested for antibiotic resistance. A PCR was also used to confirm the mutations of rpsB and rpsT (Supplementary Figure  S1 ). The rpsB:his and rpsT:strep strains were named 'E6001' and 'E6004', respectively. The rpsB-KanR mutation was transduced into E6004. Transductants were selected as KanR colonies, and then pCP20 was introduced by transformation. The KanR-AmpR colonies were isolated and their chromosomal structures were confirmed by PCR. Finally, KanR was deleted after induction of Flp recombinase, resulting in an 'E6006' strain containing both rpsT:his and rpsB:strep.
The following parameters were used: the repeat count was set to 1; the exclusion list was limited to 500; singly-charged precursors were rejected; and the maximum injection time was set to 500 milliseconds for full-scan MS, or 300 milliseconds for MS/MS. The fragment ion spectra were recorded in the LTQ mass spectrometer in parallel with the Orbitrap full-scan detection. The Orbitrap was externally calibrated before each injection series to ensure an overall error rate under 5 ppm for the detected peptides. MS data were saved in the RAW file format using Xcalibur 2. 0. 7 and Tune 2. 4 (Thermo Fisher Scientific, Bremen, Germany). A data analysis was performed using Proline 1. 4 software, Mascot Distiller, and Mascot Server 2. 5. 1 (Matrix Science, Boston, MA, USA). The identification of peptides and proteins was done using the Mascot search engine via its automatic decoy database search, calculating a false discovery rate of 1% at the peptide level.
Cryo-Electron Microscopy After diluting 150 nM ribosomes in buffer III (5 mM HEPES-KOH pH 7. 5, 9 mM MgOAc, 50 mM KCl, 10 mM NH 4 Cl, and 1 mM DTT), 2 µL of each complex was applied to glow-discharged holey carbon grids (QUANTIFOIL R2/2), then flash-frozen in liquid ethane using a Vitrobot Mark III (FEI). The grids were transferred to a Cs-corrected Titan Krios electron microscope (FEI) operating at 300 kV. Using FEI's EPU software 1. 4. 0, images were automatically recorded on their Falcon II direct electron detector set to a nominal magnification of 59,000× (corresponding to a calibrated pixel size of 1. 16 Å), and a defocus range of -1. 5 to -3. 0 µm. The images were acquired in movie mode during exposures of 1 s (corresponding to 7 frames), under low-dose conditions (50 e -/Å 2 ). Semiautomatic particle picking and reference-free 2D classification for discarding defective particles were both done using CryoSPARC 2.
9 software  [50] , with 174,052 particles extracted from 922 micrographs. All subsequent steps used RELION 3. 0. 6 [51]. The original image stacks were corrected for drift and beam-induced motion, and summed using that software's MotionCor2-like approach. For each micrograph, the contrast transfer function parameters were determined using CTFFIND 4. 1. 13 [52]. The initial 3D reconstruction was done using the 30S-portion of the crystal structure of the E. coli ribosome (PDB 4V4Q)  [53]  as the initial reference. This reference model was first low-pass filtered to a resolution of 40 Å to prevent bias toward high-frequency components in the map. The particles were then split into eight classes. As previously described  [54, 55] , these classes represent several stages of 30S biogenesis, with the small subunit (SSU) body almost completely assembled, but the head at different levels of assembly.
The incident laser beam was focused onto the sample through a microscope with a 100 × objective (NA = 0. 9, Olympus) with acquisition data recorded between 200 and 2000 cm -1. Scattered light was dispersed by 1200 grooves/mm holographic grating system. Raman spectra have been corrected by the Bose-Einstein factor. All normalization has been done to the integrated area of the full Raman response. Micro-luminescence was realized with a LABRAM 800-HR spectrophotometer (Horiba Jobin-Yvon) and a 50 × microscope objective (NA 0. 75) using a 405 nm excitation wavelength. Micro-luminescence spectra were recorded thanks to a thermoelectric cooled CCD Camera (Synapse Model 354308). We conducted the measurements twice, first measurements using the 150 grooves/mm grating which is blazed at 500 nm and second time using the 300 grooves/mm grating which is blazed at 1000 nm.
Super-resolution microscopy Bacteria were imaged using single-molecule localization microscopy and stochastic optical reconstruction microscopy (SMLM-STORM), using a previously described method  (70). Overnight cultures were fixed with PFA 4%, permeabilized with Triton 0,05%, and labeled with either GFP monoclonal FluoTag®-Q -Sulfo-Cyanine 5 (Cy5), or RFP monoclonal FluoTag®-Q -Cy5, which are single-domain antibodies (sdAb) conjugated to Cy5. Labeling was performed at 1:250 (concentration), and washing steps were carried out three times using Abbelight's SMART kit buffer. For imaging, Abbelight's imaging system was used with NEO software. Abbelight's module was added to an Olympus IX83 with 100x TIRF objective, N. A. 1. 49. We used Hamamatsu's sCMOS Flash 4 camera and a 647nm 500mW Oxxius laser, with an astigmatic lens, to allow for 3D imaging of the sample  (71).
ACKNOWLEDGMENTS We thank  Philippe Delepelaire  for insightful comments and material support. We thank  Uwe Sauer  and  Philip Warmer  for initial assessment of lipid composition of some of the strains used in this study. We are grateful to  Eva Wolrab  and  Sven van Teeffelen  for their initial interest in the project and for providing the strains for msfGFP and mCherry constructions. This work was supported by  EU Horizon   2020 Rafts4Biotech  grant  720776  (to JMG, DL, AKW, YY, AR, UV and MS), the  French government 's  Investissement d'Avenir Program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  n°ANR-10-LABX-62-IBEID ) and the  Fondation pour la Recherche Médicale  (grant no. DEQ20180339185 ). This work benefited from the facilities and expertise  of Add Photonic BioImaging platform (UTechS PBI, Institut Pasteur).
Experimental Section Sample Preparation: The sample was a SrVO 3 single crystal grown with the floating zone technique. Microparticles were obtained by crushing small pieces of the single crystal. Slabs and nanostructures were obtained by Ga-FIB polishing from the single crystal. Slabs were obtained in a similar approach as conventional TEM lamella preparation but preparing them with different controlled thicknesses. The final polishing of the slab was done with a 2 keV Ga beam to reduce the damaged surface layers. Nanostructures (half-rods, slits) were then obtained by FIB nano-machining of the slabs with a Ga beam of 30 keV. STEM-EELS: STEM-EELS measurements were done using a monochromated Nion microscope operated at 100 keV. EELS data were collected using a CMOS detector optically coupled to a scintillator (Figures  1 2 3 4 ) or a direct electron detector (MerlinEM from QuantumDetectors, Figure  5 ).
Acknowledgements The authors would like to thank  M. Kociak ,  O. Stéphan ,  X. Li. , and  A. Zobelli  from  University Paris-Saclay , for fruitful discussions. The authors acknowledge funding from  TEMPOS-CHROMATEM -NANOMAX  No. ANR-10-EQPX-50 , and from the  Horizon 2020 program  under grant agreement No  823717  ( ESTEEM3 ). Computational time was granted by  GENCI  (Project No. 544 ). C. -P. S. acknowledges the  Taiwan Paris-Saclay doctoral scholarship , which is cost-shared by the  Ministry of Education, Taiwan , and the  Université Paris-Saclay, France.
However, this approach may introduce a sampling bias since only large visible pyrite grains are selected for analysis. Moreover, very few data in pyrite older than 3 Ga are available from bulk measurement, leaving most of the Archean period uncovered. In situ analyses of pyrite at the micrometer scale allow to study a larger variability of samples, even in rocks with limited amount of pyrite grains. Microscale δ 56 Fe values previously measured in Precambrian pyrite show a wide range, including positive values throughout the Archean (e. g. ,  Yoshiya et al. , 2012; Agangi et al. , 2015; Yoshiya et al. , 2015b; Marin-Carbonne et al. , 2014 , 2020) , potentially controlled by local environmental variations (e. g. Decraene et al. , 2021b). In situ δ 56 Fe database might bring new perspective on the Fe isotope variations in Archean and Paleoproterozoic pyrite.
In the present contribution, we report the first compilation of in situ Fe isotope compositions in pyrite, throughout Archean and Paleoproterozoic. Our compilation includes data from previous studies (Table  S1 ), as well as new data from the Francevillian Group (2. 2-2. 06 Ga, Gabon), Turee Creek Formation (2. 43 Ga, Australia), Tumbiana Formation (2. 7 Ga, Australia), Mapepe Formation (3,2 Ga) and Buck Reef Formation (3. 4 Ga, South Africa) (see supplementary materials). From this dataset, we evaluate the effect of metamorphic and lithological variations and revisit the evolution of Fe isotope composition through the Precambrian.
Data compilation Both published and new in situ Fe isotope compositions of iron sulfides from Archean to Proterozoic were compiled (see the supplementary materials). The measurements were performed by secondary ion mass spectrometer (SIMS), or laser ablation multicollector inductively coupled plasma mass spectrometer (LA-MC-ICP-MS). New data were measured on samples from two IPGP drillcores (TCDP1 and PPDP2), from the BARB3 drillcore  (ICDP, 2008)  and the Francevillian Group. All samples are described in the supplementary materials. The compilation primarily includes the age, size and morphology of pyrite together with the lithology of the host rock (Table  S1 ). The metamorphic grade and organic matter content were also compiled when available.
Statistical analysis The database presented in this study comprises isotope compositions reported in 12 scientific publications, in addition to our new data (see supplementary material). All data are considered, without being filtered, although the contributions of each publication are unequal among the studies and/or geological formations (from 16 to more than 200 reported δ 56 Fe values). Iron isotope compositions are displayed as distributions, in the form of histogram and often probability density. The former allows to appreciate the amount of data per bin, while the latter is necessary as it reflects both measurement values and errors, which are different for SIMS and LA-MC-ICP-MS. Gaussian function was used for probability density calculation (Equation (  2 ), where μ is the expected value and σ the standard deviation).
For the amphibolite facies, most data were obtained from a small number of samples from the Isua Greenstone Belt (3. 8 Ga, Greenland) and the Talvivaara Formation (1. 95 Ga, Finland), which may not be widely representative. Interestingly, both modes are present in the δ 56 Fe distribution of pyrite from the Isua Greenstone Belt (Fig. 6a, b ). This bimodality can hardly be explained by a unique process, such as metamor- phism. Therefore, we conclude that primary pyrite δ 56 Fe signatures were likely preserved in Archean and Proterozoic rocks, even in those which experienced amphibolite-facies metamorphism.
xlsx in supplementary materials). Similarly, the chert distribution includes data measured in pyrite hosted in (black or white) chert, chert with barite, amphibole-rich cherts. The detrital sediment distribution includes data measured in pyrite hosted in (green, grey, black, calcareous or tuffaceous) mudstone, black shale, diamictite, (siliceous, calcareous or tuffaceous) sandstone, quartz-pyrite pebble, carbonaceous shale, carbonate laminated siltstone, quartz-rich clastic sedimentary rock and conglomerate.
Interpretation of δ 56 Fe distributions in the other time intervals is uncertain and not discussed herein, as the small number of pyrite localities may induce a lack of representativity. In summary, microscale δ 56 Fe distributions in pyrite point to Fe (oxyhydr)oxide precursor as early as 3. 6 Ga ago. In addition, an influence of continental weathering might be recorded in Fe isotope signatures from 3. 2 Ga. Further investigations of the δ 56 Fe evolution through time would increase the present database and possibly strengthen the tentative conclusions drawn here.
Acknowledgements We are thankful to  Pascal Phillipot ,  Frantz Ossa Ossa  and  Axel Hofmann  for providing the TCDP, Gabon, and Barberton samples, respectively. V. B. thanks the  Institut Universitaire de France  for funding (IUF# 2017(IUF# -2021 )). This research was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (STROMATA, grant agreement  759289 ). We thank  Anne-Sophie Bouvier ,  Florent Plane  and  Thomas Bovay  for their SIMS expertise. The SwissSIMS is hosted by the  Center for Advanced Surface Analyse, CASA , a joint facility between  UNIL  and  EPFL. Virgil Pasquier ,  Itay Halevy  and  Claire Rollion-Bard  are thanked for fruitful discussions and comments on an earlier version of this manuscript. This manuscript.
The differences observed in the two compilations can therefore arise from sample bias. Pyrite δ 56 Fe is tied to lithology (Fig. 5), as illustrated by the offset between δ 56 Fe datasets measured in pyrite originating from different lithologies. An offset of 2. 4 is observed between median δ 56 Fe values measured by bulk and in situ tech- niques in black shales versus sandstone and mudstone, respectively, in Jeerinah Formation (2. 7 Ga, Western Australia, Fig. 2a). Further- more, comparing millimeter pyrite of roughly the same age (∼2. 7 Ga) reveals that bulk and in situ analyses of black shale-hosted pyrite are consistent, contrasting with δ 56 Fe values measured in quartz pebble-hosted pyrite (Fig. 2b). Various pyrite grain size and morphology can record different formation processes within the same lithologies, with millimeter grains mostly recrystallized (Marin- Carbonne et al.
Future research should aim to explore cross-cultural differences among dyadic homework-helping practices. Because this study was correlational and the data were collected at a single time point, inferences cannot be made about the causal direction of the observed relations. In future research, a longitudinal design may provide more information about whether children's math anxiety develops because of the negative quality of the homework-helping interaction or if, rather, higher math-anxious children's engagement with their parents affects how the quality of the interaction unfolds. It is also possible that there are reciprocal relations between children's math anxiety and their experiences in homework interactions. For example, Else-Quest et al. [56]  found that mothers were more likely to show negative emotions (i.
The second member of that couple is the desired committor function. The loss function used to train the FFNN is the crossentropy loss. It is well suited to assess a distance between the true committor and the data-based estimation. Moreover, it is closely related to the measures we are using to evaluate the performance of the different methods (see Sect. 2. 7. 1). Unlike the training of AMC, training an FFNN involves randomness (e. g. , shuffling of the train dataset). To ensure robustness of the results, we use k-fold cross validation during the training process. It consists in randomly splitting the whole training set into k subsets and then training the FFNN k times. Each time, validation is performed using a different subset and the remaining k -1 are used for training. This method allows making statistics on the performance of the network for a given setup.
(  18 )-(  20 ) are defined. The key constraint is that each basis function should obey the homogeneous boundary conditions. It ensures that the projection of g onto this subspace also obeys the boundary conditions. Calling L the projection of L onto this subspace and ḡ that of g, each basis function must then obey φ i , L ḡ = -φ i , L1 B. (21) Furthermore, let two function u and v from the state space to R. Following  Thiede et al. (2019) , given a sampling measure µ, the inner product of the Hilbert space can be defined as u, Lv = u(x)v(x)µ(dx). (22) If we now have a dataset (e. g. , a trajectory) {X n , n ∈ [1, N ]}, this product can be approximated by the following sum: u, v = 1 N N-1 i=1 u(X n )v(X n ). ( 23 ) Given a time step t, the definition of the generator L yields u, Lv = 1 N N -1 n=1 u(X n ) v(X n+1 ) -v(X n ) t.
Train and test dataset To be able to compare different methods for the committor estimation, we need to train them and to test them with different trajectories. We thus need to create a training and a test dataset. For simplicity and consistency, we set a standard length for all test trajectories of both models: 5000 time steps (corresponding to 500 years of simulation, similar to the trajectory and its associated committor function plotted in Fig. 2 ). The total test set consists of 100 independent such trajectories. The logarithm and difference scores are averaged over these trajectories. When it comes to training methods for the committor estimation problem, what really matters is to have reactive trajectories; that is, trajectories going through both on and off states. Consequently, it makes sense to count the length of trajectories in terms of the number of transitions N T.