dataesr/scientific-paragraphs-categorization huggingface dataset:
| INTRODUC TI ON Scientists have been discussing the impacts of temperature on biodiversity for centuries  (Humboldt & Bonpland, 1805) , but the ongoing global warming crisis has made it a central concern  (Lenoir et al. , 2020). Ecological studies usually rely on gridded temperature maps from large-scale datasets such as WorldClim  (Fick & Hijmans, 2017)  or  ERA5-Land (Muñoz-Sabater et al. , 2021). These 'macroclimate' maps are interpolated from weather stations, systematically located in fully exposed areas, following official recommendations from the World Meteorological Organization  (WMO, 2018) , and are provided at coarse spatial resolutions (1-9 km), hiding important fine-scale (centimetres to metres) microclimate variation  (Lembrechts et al. , 2019). Many species, in fact, experience temperature regimes vastly different from those measured by weather stations and predicted by macroclimate maps  (Potter et al.
We thus added the month as a random intercept term in our 156 LMMs (i. e. one for each sensor and period), 'Month' being a factor with seven and five levels for leaf-on and off, respectively. The slope estimate was extracted from each LMM, and the equilibrium was calculated for each month and for each sensor using the formula: equilibrium = intercept/(1 -slope) (see the Supplement Information for an explanation of how this formula was derived). All steps of data exploration, preparation and visualization were performed in R version 4. 1. 2 (R Core Team, 2021), using the tidyverse and lubridate packages  (Grolemund & Wickham, 2011; Wickham et al. , 2019). We used the lme4 package  (Bates et al. , 2015)  to calculate and extract the slope and equilibrium (see 'Data availability' for the full RMarkdown report).
, 2017). To select the best model, we computed the second-order Akaike information criterion (AICc). Once the best model was selected, we reran it using REML instead of ML, to generate more accurate coefficient estimates. To assess the relative importance of each predictor variable, we standardized each quantitative predictor with gscale from the jtools package (Long, 2020), suitable for situations with quantitative and binary qualitative parameters. We computed the marginal and conditional R 2 using r. squaredGLMM from the mumin package  (Bartoń, 2013) , respectively representing the percentage of variation explained by fixed effects only, and both fixed and random effects. We used tab_model from the sjPlot package  (Lüdecke, 2021)  to print model parameters, and ggpredict from the ggeffects package  (Lüdecke, 2018)  to plot model predictions.
| Slope in forests: The buffering effect Metrics related to forest structure and cover explained a majority of the variance in forest slopes, together with the phenological leaf-on or leaf-off period (Figure  4 ). Here, slopes were considered constant within each phenological period, but with more extensive datasets, slopes could also be computed and modelled at a finer temporal resolution, for instance to investigate the relationship with local soil water status  (Davis, Dobrowski, et al. , 2019; McLaughlin et al. , 2017). Different seasons may involve distinct sets of microclimate predictors  (Greiser et al. , 2018). As postulated in our second hypothesis and consistent with former findings  (von Arx et al. , 2012) , we show that the leaf-off and leaf-on periods were very contrasting for most broadleaf forests, but less so for coniferdominated stands.
These eight fixed effects explain 56% of the slope variance (see Table  S7  for model parameters and estimates). The first six predictors interact with the period (leaf-on in green vs leaf-off in brown), while the last one has an independent effect on slope (predictions are plotted for the leaf-off period). Other factors are here held constant at their median, and considering a broadleaf tree type for (b-g; see Figure  S9  for this figure, with the reference set to conifer). datasets of temperature measurements to confirm the patterns observed, because results could vary from year to year, reflecting weather and not climate  (Lembrechts et al. , 2019). A nonlinear relationship with microclimate temperature offsets has been found for canopy cover  (Ashcroft & Gollan, 2012; Zellweger et al. , 2020)  or height  (Jucker et al.
Weather stations as a reference are the golden standard, because although they do not represent well the local climatic conditions close to the ground where most species live  (Potter et al. , 2013) , the parameters they measure can be directly related to all global-scale climatic data currently available. However, it is not always feasible to get data from a neighbouring weather station that can be strictly paired with the microclimate variable at study. Using macroclimate temperature grids at fine temporal resolutions such as ERA5-Land as a reference could be a good substitute, except in the context of regions with marked topographic heterogeneity, for which a different baseline temperature could result in artificial differences with the focal microclimate (Figure  S5 ). Then the use of paired temperature sensors in open conditions as a reference could be recommended.
Electrocardiogram recordings. One-lead electrocardiograms were recorded from freely moving and anaesthetized GMLs. For electrocardiogram recordings in freely moving animals implanted with a transmitter, surgery was conducted in sterile conditions, under veterinary supervision. After administration of diazepam (Valium, 1 mg/100 g, i. m. ) and buprenorphine (0. 005 mg/100 g, i. m. ), anesthesia was induced and maintained by 1-3% isoflurane inhalation. Body temperature was maintained with a heating pad, and the animal's eyes were protected with ocular gel (Ocry -gel; Laboratoire TVM, Lempdes, France). A small transmitter (PhysioTel F20-EET, 3. 9 g, 1. 9 cc; Data Sciences International TM, DSI, St. Paul, United States) connected with 1 pair of electrode wires (silicon elastomer insulated stainless-steel wires, diameter: 0.
3 mm) was inserted inside the peritoneal cavity of the animal. Electrode wires were led subcutaneously from the abdomen to the thorax muscle and were sutured using nonabsorbable polyamide monofilament suture thread, similarly to the procedure described in mice  44. After surgery, nociception was minimized by subcutaneous injection of analgesic and anti-inflammatory drugs (meloxicam, 0. 2 mg/100 g). Electrocardiogram signals were recorded using a telemetry receiver and an analog-to-digital conversion data acquisition system (Data Sciences International TM, DSI, St. Paul, United States). HR and HRV data were analyzed with the dedicated software Labchart 8 (ADInstruments, Dunedin, New Zealand) and ecgAUTO v3. 3. 5. 12 (emka TECHNOLOGIES, Paris, France). For external electrocardiograms, we used solid gel electrodes and the uECG Holter of Ultimate Robotics (Kyiv, Ukraine), to obtain one lead recording of HR in GML males of different ages.
A homemade vest helps to keep the uECG Holter on the GML back, ensuring that the electrode touches the skin of the animals at the level of the shoulders and pelvis. Before that, the animals were shaved in the contact points with the gel electrodes, to facilitate the conduction of the signal through the skin. Electrocardiogram recordings were obtained through dedicated application software provided by Ultimate Robotics and analyzed with the dedicated software Labchart 8 (ADInstruments, Dunedin, New Zealand). 5 min of electrocardiogram recordings obtained after 90 min of adaptation to the ECG vest in the normal environment of GML were used to obtain resting HR. For electrocardiogram recordings under anesthesia, we constantly exposed GML to 1. 5% isoflurane. GML Body temperature was continuously maintained at 36-37 °C using a heated pad connected to a controller that received feedback from a temperature sensor attached to the animal.
Ag/AgCl gel-coated electrocardiogram electrodes (Unomedical; Herlev, Danimarca) were attached to the superior right and to the two inferior limbs of GML. The electrodes were connected to a standard one-lead electrocardiogram amplifier module (EMKA Technologies, Paris, France), which included high-and low-pass filters (set to 0. 05 Hz and 500 Hz, respectively) and a gain selection device (set to 1000-fold). Signals were digitized continuously at 2 kHz and recorded using an IOX data acquisition system (emka TECHNOLOGIES, Paris, France). The recordings were carried out for a 45-min period, and the software ECGAuto (emka TECHNOLOGIES, Paris, France) was used to perform offline analysis of the recorded data. For each GML the mean HR, its standard deviation and the parameters characterizing the electrocardiogram wave, were calculated at 30-min intervals starting 10 min after the beginning of each 45-min recording.
Optical raw data were analysed using dedicated software from the camera developer, BV workbench (Brainvision; Tokyo, Japan), in combination with ClampFit (ver. 10. 0. 7, Molecular Devices, LLC; San Jose, California). Isolation of SAN myocytes. The GML SAN tissue was isolated as described above (see paragraphs above) and cut in tissue strips immediately after transport to the laboratory for experimentation. Strips were then transferred into a low-Ca 2+ , low-Mg 2+ solution containing (in mM): 140. 0 NaCl, 5. 4 KCl, 0. 5 MgCl 2 , 0. 2 CaCl 2 , 1. 2 KH 2 PO 4 , 50. 0 taurine, 5. 5 d-glucose and 1. 0 mg/ml BSA, plus 5. 0 mM HEPES-NaOH (adjusted to pH 6. 9 with NaOH). The tissue was enzymatically digested by adding 229 U/ml collagenase type II (Worthington Biochemical Corporation), 1. 9 U/ml elastase (Boehringer Mannheim), 0.
Then these samples were permeabilized for 30 s in -20 °C acetone, preincubated for 30 min with 0. 5% BSA in PBS. SAN tissues were then incubated for 1 h at 37 °C with primary antibodies against HCN4, (1:100, Alomone), sarcomeric alpha-actinin, clone EA-53 (1:100, Sigma-Aldrich). Specific staining was revealed using anti-mouse or anti-rabbit Alexa Fluor 488 and Alexa Fluor 555-conjugated secondary antibodies (InVitrogen) with Hoechst33358 nuclear stain (0. 1 µg/ml) for 30 min at 37 °C. Statistical analysis. HR, maximal life expectancy, body size and the resulting number of heartbeats per lifetime were obtained from data available in the literature for different mammals including 10 species of primates, 12 species of rodents and 9 species of domestic mammals from different orders. In particular we took in consideration the species GML, common marmoset (Callithrix jacchus)  80, 81  , squirrel monkey (Saimiri sciureus)  82, 83  , capuchin Monkeys (Cebus apella)  66  , rhesus macaque (Macaca mulatta)  84, 85  , chimpanzee (Pan troglodytes schweinfurthii)  86  , Babouin hamadryas (Papio hamadryas)  87  , orangutan (Pongo pygmaeus pygmaeus)  88  and gorilla (Gorilla gorilla gorilla)  89  , in comparison to humans  55  for the primate order; mouse  45  , hamster  64, 90  , rat  47  , Mongolian gerbil (Meriones unguiculatus)  91, 92  guinea pig  93  , red North American red squirrel (Tamiasciurus hudsonicus)  2  , muskrat (Ondatra zibethicus)  94  , marmot (Marmota monax)  95  , capybara (Hydrochoerus hydrochaeris)  96, 97  , agouti (Dasyprocta primnolopha)  98  , North American Porcupine (Erethizon dorsatum)  95  , north american beaver (Castor canadensis)  94  for rodents and rabbit  99  , dog  99, 100  , sheep  101  , cat  102  , pig  103  , goat  104, 105  , donkey  106  , horse  107  and camel  108  for the domestic mammals.
HR were reported from results obtained in unanesthetized and mainly freely moving animals under resting conditions. Significance was evaluated through paired or unpaired Student's T test, one-way-and two-way ANOVA and comparison between regression law as specified in figure legends. When testing statistical differences, results were considered significant with p < 0. 05. Data analysis was performed with GraphPad Prism 9. 0 and IBM SPSS Statistics 28. 0. 0. 0.
Acknowledgements We are thankful to the personnel of the animal facility of the lemur colony at Brunoy, to the animal facility at  RAM-CECEMA , the  MRI platform of imaging  and the statistic platform Statabio. We thank  Fu Lingjian ,  Dr. Yuquan Wang  and  Emma Zub  for helping with software installation and exploitation. M-L DiFrancesco was recipient of a postdoctoral fellowship by the  Laboratory of Excellence Ion Channel Science and Therapeutics  supported by a grant from  Agence Nationale de la Recherche  ( ANR-11-LABX-0015 ). This work was supported by  ANR  grants ( 2010-BLAN-1128-01  and  ANR-15-CE14-0004-01  to M. E. M. ) and the  Fondation Leducq (TNE FANTASY   19CVD03  to  Matteo E. Mangoni  and  Peter J. Mohler ).
Results Distribution of the Mur Ligase Chimeras. In order to understand the extent of the presence the chimeras throughout different bacterial phyla, we selected a representative sequence from each bacterial species that harbored annotated chimeras, generated sequence similarity networks (SSN) using EFI-EST  (36)  and analyzed the resulting networks using Cytoscape  (37). In these analyses, we observed that different Mur chimera types are concentrated in specific phyla (Fig. 2 ): MurE-MurF are highly present in Proteobacteria; MurG-MurC in Actinobacteria; MurC-MurB in Verrucomicrobia; MurC-Ddl in Chlamydiae and Firmicutes; and MurD-FtsW in Proteobacteria and Actinobacteria. Further analyses of the protein sequences in the Uniprot database revealed that out of the five major types of Mur chimeras, MurE-MurF is the most abundant one, representing 64% of all chimeras, followed by MurC-Ddl (11%), MurC-MurB (11%), MurG-MurC (9%), and MurD-FtsW (3%) (Fig.
Materials and Methods In Silico Analyses. Sequences of Mur chimeras were obtained from Uniprot (www. uniprot. org/) by focusing on proteins from the Mur family with more than 600 amino acids (resulting in a total of 5,914 sequences in our initial dataset). The composition of Mur chimeras was confirmed by homology analysis using Interpro (www. ebi. ac. uk/interpro/). Mur chimeras from unidentified species were excluded from the dataset; a representative sequence for each identified species was selected for further analysis. Regarding the study of the distribution of major types of Mur chimeras in bacteria, SSN were generated for each type of Mur chimera using EFI-EST web [efi. igb. illinois. edu/efi-est/;  (36) ] and were further analyzed using Cytoscape  (37). MurE-MurF sequences were selected from the dataset and aligned with ClustalX 2.
1  (58). A phylogenetic tree was constructed with iTOL v. 6. 5. 2  (59). Taxonomy information was obtained from Uniprot, and protein lengths were based on Interpro homologies. Regarding hydrophobic surface conservation analyses, MurE-MurF chimeras from the dataset were divided into two groups according to the presence or absence of the predicted linker region. We also analyzed the conservation of the hydrophobic patch in Bordetella spp, Achromobacter spp and other β-proteobacteria that have MurE-MurF chimeras. To analyze residue conservation in separated MurEs and MurFs, we selected MurE and MurF variants from fully annotated and identified species. Each group was aligned with the sequence of the B. pertussis MurE-MurF FL chimera using local MSA (Multiple Sequence Alignment) with Muscle v.
688 keV. Data were indexed and integrated with XDS  (63). Due to diffraction anisotropy in the high-resolution shells, the dataset was anisotropy-corrected using STARANISO (Global Phasing Ltd. ). The anisotropic-corrected amplitudes were used for molecular replacement with Phaser  (50). Several automatic molecular replacement programs, including MrBump  (64) , Balbes  (65)  and MoRDa (66) were initially tried but did not produce satisfactory results. The top-scoring PDBs from these programs, both truncated and nontruncated, were also selected and tested with Phaser and/or Molrep, but these methods failed to yield acceptable outcomes for refinement. Hence, a model of B. pertussis MurE-MurF generated by AlphaFold2 (49) was tested. The search model was split into three fragments (residues 1 to 338, 339 to 565, and 566 to 803) and a three-component search yielded the strongest MR solution.
The model was then improved by iterative manual building into 2F o -F c and F o -F c electron density maps using Coot  (67). Refinement was carried out using Lorestr  (68). Model quality was checked using MolProbity  (69)  and the wwPDB validation tool (https://validate-rcsb-2. wwpdb. org/). The structure was deposited in the Protein Data Bank under code 8F5D. All molecular figures were prepared using Chimera 1. 15 and data collection and refinement statistics are summarized in SI Appendix, Table  S1. Data, Materials, and Software Availability. Data (PDB coordinates) have been deposited in Protein Data Bank (8F5D)  (70).
ACKNOWLEDGMENTS. We acknowledge support to A. D. from the  Laboratoire International Associé BACWALL (CNRS) , and grants from the  São Paulo Research Foundation (FAPESP )  2011/52067-6  and  2017/12436-9. K. T. S. was supported by fellowships  2018/16346-7  and  2020/01286-9  from  FAPESP , the  CNPq , and  CAPES. This work made use of the  MANACÁ  beamline of the  Brazilian Synchrotron.
The cryo-EM map does not resolve the full 3'-end of the acceptor arm, indicating that it is not engaged in the aminoacylation active site (Extended Figure  3d ). Therefore, in addition to the insights gained into the METTL6-tRNA Ser -SerRS interaction, the complex structure reveals novel details of the binding of eukaryotic SerRS to tRNA Ser. The m 3 C-specific tRNA binding domain of METTL6 is conserved to the m 3 C RNA methyltransferase family METTL6 shares the Rossmann-like fold of class I methyltransferases  40, 41. The family of m 3 C-specific RNA methyltransferases (Interpro: IPR026113) have divergent N-terminal sequences and a conserved insertion within the Rossman fold, both of undetermined function (Extended Figure  4 ). Our structure of the METTL6-SerRS-tRNA Ser complex pinpoints that three m 3 C methyltransferase-specific elements form a tripartite domain comprising the N-terminal region, the internal insertion, and the extended hairpin of the central ß-sheet.
Overall, our METTL6-SerRS-tRNA Ser complex structure identifies the m 3 Cmethyltransferase-specific RNA binding domain, which differs from so-far characterised DNA or RNA methyltransferases. The beta-elongation and alpha-helical part of the insertion embrace the anticodon loop from the opposite side and coordinate the i 6 A37 RNA modification. e Superposition of free tRNA (PDB:1ehz, gray) and the tRNA in the SerRS-METTL6 complex (black, rmsd = 1. 124 Å). In all panels, the methyltransferase core is coloured in light orange and the m 3 C-RBD in dark orange; the RNA and SAH in shades of grey; stick models are coloured by heteroatom.
The mutation of tyrosine Y190 to alanine (Y190A), in proximity of m 3 C32, could still bind tRNA Ser UGA but its catalytic activity was attenuated. All mutants were impaired in complex formation on size exclusion chromatography (Extended Figure  7d ). As a side note, mutants of aspartate D189 to alanine or asparagine (D189A or D189N) were not soluble, suggesting a structural scaffolding role of this residue. We conclude that single-point mutations in the active site impair the interactions of METTL6 with tRNA Ser , the methyl-donor and SerRS, leading to attenuated methylation activity. 7a ) SAH binding by Thermofluor (Extended Figure  7c ) and tRNA binding by FP (Extended Figure  7d ). Methyltransferase control experiments in Extended Figure  7b. Data is represented as the blank-subtracted mean and standard deviation of independent replicates (n=3).
The double mutant of the SerRS methionines to alanines (MM416/417AA) disrupted the complex and suppressed methylation activity (Figure  4e  and Extended Data 8e, f). It is important to note that the METTL6 SerRSinterface residues phenylalanine F32 and arginine R51 are exclusive to m 3 C-methyltransferases that target cytosolic tRNA Ser and that the SerRS contact loops are conserved as well (Figure  4f , Extended Figure  8g, h ). Thus SerRS is the substrate recognition factor for METTL and the conserved direct contacts between METTL6 and SerRS are indispensable for the coordinated binding of the variable arm of tRNA Ser. 8e ). Methyltransferase assay control experiments in Extended Figure  8f. Data is represented as the blank-subtracted mean and standard deviation of independent replicates (n=3).
ebi. ac. uk/) are rather similar to the tRNA-bound structure of METTL6 (Extended Table  3 ). However, they are distinct in their divergent N-terminal sequences. Our data show that this N-terminal region of METTL6 has a dual role in mediating co-factor-specific interactions with SerRS and in providing substrate-specific interactions to the long variable arm of tRNA Ser. Analogously, METTL2 and METTL8 might carry specificity signals in their Ntermini, e. g. for recruitment of mitochondrial SerRS2 to METTL8 for mt-tRNA Ser selection, or for recruitment of DALRD3 to METTL2B for tRNA Arg modification. Notably, DALRD3 is predicted to carry an anticodon binding domain similar to class Ia aminoacyl-tRNA synthetases. Our discovery of the tripartite configuration of the m 3 C-RBD shows that the three elements of the METTL6 m 3 C-RBD act collectively for SerRS binding and tRNA Ser recognition.
This observation clarifies why previous attempts to swap N-termini between METTL6 and METTL2A in chimeric enzymes did not change their substrate specificities  20. Although the genes for METTL2, METTL6 and METTL8 are non-essential, all three enzymes have been linked to disease. METTL6 acts as an oncogene in luminal breast tumours and hepatocarcinoma 2,3,24-28 , and METTL2A also seems to play a role in breast cancer  51. Similarly, the mitochondrial m 3 C methyltransferase METTL8 has been linked to severe pancreatic adenocarcinoma and colon cancer and is involved in neurogenesis  22, [52] [53] [54]. Our data reveal how m 3 C methyltransferases interact with RNA. Hence we establish a foundation for future detailed investigations on the enzymes of this family but also reveal promising prospects for potential therapeutic applications.
Cryo-EM data acquisition, processing, and model-building Data was acquired on a Titan Krios transmission electron microscope (FEI) equipped with a 300 kV accelerating voltage field emission gun electron source, a Quantum energy filter (Gatan) and a Gatan K3 camera, at 130K magnification with a pixel size of 0. 645 Å/pixel. The microscope was operated using the software SerialEM. Movies were acquired in counting mode with an electron dose of 63. 27 e -/Å 2 in 40 frames with a defocus range from -0. 8 µm to -1. 8 µm. Motion correction and CTF estimation on the collected micrographs was performed using RELION 3. 1  55. Particles were picked with the BoxNet_20220403_191253 model in Warp  56  , and extracted in RELION in a 550 pixel box. A total of 1,625,946 particles were used as input for data processing using cryoSPARC  57.
2 M ammonium sulfate and 25% PEG3350 at 20˚C. The crystals were harvested using the automated Crystal Direct harvesting system of the HTX platform  59  and flash-frozen without cryoprotectant. X-ray diffraction data sets of full-length METTL6 were collected on the MASSIF-1 beamline at the ESRF with an x-ray energy of 12. 65 keV (= 0. 9801 Å). For solving the phase problem for METTL6 by single-wavelength anomalous dispersion (SAD) with the anomalous signal from sulfur, a total of 35 x-ray diffraction datasets with an x-ray energy of 6 keV (= 2. 066 Å) were collected on beamline P13 at the PETRA-III storage ring at DESY. X-ray diffraction data sets of METTL6Δ were also obtained on this beamline with an x-ray energy of 12. 3 keV (= 1. 008 Å). All diffraction images were processed using XDS  60.