dataesr/scientific-paragraphs-categorization huggingface dataset:
Blots were developed by chemiluminescence using Clarity Western ECL substrate (Bio-Rad) with the ChemidocTM Touch imaging system (Bio-Rad) and analyzed with the Image Lab software (Bio-Rad). RNA isolation for the nEMOTE procedure. Total RNA was isolated from M. smegmatis strains WT/ΔrnJ that were transformed by electroporation with pGMCtoxins and grown to mid-exponential phase. Independent cultures of each strain were launched on different days and with different batches of the medium. When 10 mL cultures reached an OD 600 of 0. 4, anhydrotetracycline was added to a final concentration of 100 ng/mL to induce toxins for 3 or 24 h. After induction, 1 mL of culture was serial diluted on a LB agar plate supplemented with streptomycin and with or without ATc to check the activity of the toxins and the presence of potential suppressor before RNA extraction.
Reverse transcription was performed using DRNA primer and ProtoScript II RT enzyme (NEB). Finally, PCR amplification was performed with barcode primers and Illumina adaptator A and B primer using Q5 Polymerase Hot-Start (NEB). Bioinformatics analyses. Raw sequencing reads were first filtered and trimmed using the emoteStepI method from a Perl program called EMOTE-conv  63. The trimmed reads are mapped against the M. smegmatis genome (NC_008596. 1) with bowtie (version 1. 2)  64  , using the parameters -a --best -strata -v 1. The obtained alignment file is submitted to the emoteStepII step from EMOTE-conv in order to compute an annotated coverage table corresponding to the number of reads per position per EMOTE barcode. The unique molecular identifier that was included in the Rp8 oligo allows us to detect when the cDNA from a single ligated RNA molecule is represented by one or more different illumina reads (this is possible due to the PCR amplification in the library preparations).
Thus, all identical reads with the same unique molecular identifier are only counted as a single RNA cleavage event. Downstream analysis and plots are performed in R (version 3. 4. 4, running under Ubuntu 14. 04. 6). We set a very conservative cut-off for what was considered a true toxin-dependent cleavage event: The genomic positions where the 5' OHends of at least 7 independent RNA molecules were detected for at least one test sample (toxin + PNK) and where no 5'OH-ends were detected in all three negative control samples (toxin-PNK, vector+PNK, and vector-PNK). The putative cut-site motif is plotted with the R package ggseqlogo (version 0. 1). The evolutionary history between M. tuberculosis HigB TAC , HigB2, HigB3, and E. coli RelE was inferred using the maximum likelihood method. Protein sequences were aligned by PROMALS3D and the tree was generated by MEGA11.
The tree with the highest log likelihood (-1228. 63) is presented in Fig. 1b. The tree is drawn to scale, with branch length representing the number of substitutions per site. M. smegmatis gene expression. To generate the violin plot showing M. smegmatis gene expression in transcripts per million the following procedure was applied. The data from M. smegmatis wild-type mRNA sequenced with Illumina 65 consists of 2 replicates: Replicate 1 (112309244 SE reads of 51 bp) and Replicate 2 (126869005 SE reads of 51 bp). After Trimmomatic cleaning, the cleaned reads, replicate 1 (24637386 SE reads), replicate 2 (67623219 SE reads), were remapped to the reference genome of M. smegmatis MC 2 155 (CP000480) with bwa and converted to sorted bam with Samtools. Since no differential analysis was possible with two replicates of the same condition, we decided to calculate the TPM (transcripts per million) for all genes of both replicates and compare them by their mean values.
A3059). Crystallization and crystal structure determination. Purified HigB TAC [K95A] was concentrated to 28. 6 mg/mL in buffer B and crystallized at 12 °C in 30% (w/w) PEG 4,000, 0. 2 M ammonium acetate, 0. 1 M sodium acetate, pH 4. 6. The volume ratio between the purified toxin and crystallization solutions was 200:100 (nL). Crystals were directly flash-frozen in liquid nitrogen. Datasets were collected on beamline MASSIF-3 (ID30-A3) at the European Synchrotron Radiation Facility (ESRF, Grenoble, France). All data were indexed, integrated, and scaled using XDS version Jan 31, 2020, and the CCP4 v7. 0. 078 software suite was used for subsequent crystallographic calculations  67. The structure was solved by molecular replacement using PHASER v2. 8. 3  68  and a structure-based homology model derived from the structure of S.
Cryo-EM data collection. The frozen grids were then transferred to the Structural Biophysical Chemistry Platform of the IECB, where they were imaged using a Talos Arctica cryo-TEM (Thermo Fisher Scientific) operating at 200 kV and equipped with a field-emission gun. SerialEM v3. 8 software was used to automatically record 6381 movies under low-dose conditions on a K2 direct electron detector (Gatan) with a defocus range of 0. 4-2. 0 µm and at a final pixel size of 0. 9291 Å. Image processing. Movies were corrected for the effects of drift and beaminduced motion using MotionCor2 v1. 0. 6 software  71. Contrast transfer function (CTF) parameters were estimated using Gctf v1. 18 software  72. Electron micrographs showing signs of drift or astigmatism were discarded, resulting in a dataset of 5178 movies.
Particles were semi-automatically selected in Cryosparc v2. 12  73  and subjected to two rounds of 2D classification in order to remove defective particles. This resulted in the selection of 284,820 particles. All subsequent data processing was performed using RELION v3. 1. 3  74. An initial three-dimensional (3D) auto-refinement using a large soft circular mask (diameter 380 Å) produced a reconstruction at a resolution of 3. 33 Å. The pixel size was then re-estimated by comparison to the atomic model of the E. coli mature 70S subunit (PDB ID 4YBB), and adjusted to 0. 893 Å/pixel. To improve the homogeneity, the datasets were then sorted into 12 subsets using the 3D-classification function. This resulted in the following grouping: 70S ribosomes (7 classes containing 175,859 particles); ratcheted ribosomes (2 classes and 39,752 particles); the 50S ribosomal subunit (2 classes and 51,812 particles); and poorly resolved particles (1 class of 27,397 particles).
The particles which were clearly homogenous 70S or ratcheted ribosomes were further processed separately using the same protocol. This was followed by a second round of 3D auto-refinement using the same parameters, resulting in reconstructions with resolutions of 3. 4 Å (for the 70S) and 4. 21 Å (for the ratcheted ribosomes). We then subtracted the ribosome signal from the datasets by using a soft mask generated from the previous refinement run (voxel values of 0 inside, 1 outside, extended by 6 pixels, and a soft edge of 6 pixels). The subtracted datasets were then sorted by 3D classification without alignment, using a tight soft spherical mask with a diameter of 200 Å. We tested various combinations in order to split the datasets into as many 3D classes as possible while still keeping the groups homogeneous; however, the HigB TAC [K95A] toxin could not be found in the dataset with the ratcheted ribosomes.
The dataset containing the 70S ribosomes was separated into eight classes, with one containing HigB TAC [K95A]. The 42,261 particles corresponding to that class were then selected and reverted to their original content, before the subtraction of the ribosome signal. A third round of 3D auto-refinement resulted in a 3. 8-Å map of the ribosome containing the CspA mRNA, an fMet-tRNA fMet , and HigB TAC [K95A]. To ensure that a homogeneous class was indeed obtained, 3D classification with signal subtraction was again performed, this time using a soft mask with a diameter of 80 Å to focus on the toxin. This dataset was split into four classes, with one class of 13,877 particles containing the toxin. Once again, we reverted to the original non-subtracted particles and reconstructed a new map at a resolution of 4.
The multibody maps were then fitted and resampled on the consensus map using UCSF-Chimera v1. 13. 1  77. Finally, the four maps were sharpened using Phenix v1. 18. 2  78  , their local resolutions estimated using Resmap v1. 1. 4, and map quality was assessed using Phenix mtriage  79. Model building and refinement. UCSF-Chimera v1. 13. 1  77  was used to rigid-body fit the cryo-EM structure of the E. coli ribosome at a resolution of 2 Å (PDB 7K00) into our maps, with each protein and RNA treated separately. Our crystal structure of M. tuberculosis HigB TAC [K95A] toxin (PDB code 7AWK) was then fitted in the remaining portion of the map. The different molecules were manually adjusted in their respective multi-body maps using COOT v0. 9. 5. Special attention was paid to the toxin, the tRNAfMet, and the CspA mRNA, and their models were compared with the different multibody maps.
EMDB and PDB under the following accession codes, respectively: 7AWK for the crystal structure of the M. Tuberculosis HigB TAC [K95A] toxin alone; and EMD-12261 and 7NBU for the toxin and its target mRNA on the translating E. coli ribosome. The nEMOTE sequencing data have been deposited in Zenodo [https://doi. org/10. 5281/ zenodo. 6397033]. Source data are provided with this paper.
Acknowledgements We thank  Pauline Texier  et  Michele Coddeville  for plasmid gifts and for their assistance during the course of this study, and  Patrick Viollier  for useful advice. We also thank  Israel Mares-Mejía  for his help with toxin purification. This work has benefited from the facilities of the  Microscopy Rennes Imaging Center (Mric)  and from the facilities and expertise of the  Structural Biophysico-Chemistry platform (BPCS)  at the  IECB  ( CNRS UMS3033 ,  Inserm US001, Bordeaux University. We thank the scientific staff of the  European Synchrotron Radiation Facility (Grenoble, France). We thank the staff of beamline MASSIF-3 ( ID30-A3 ) at the  European Synchrotron Radiation Facility  where the crystallographic experiments were conducted. The crystallization and macromolecular crystallography equipment used in this study are part of the  Integrated Screening Platform of Toulouse (PICT, IPBS, IBiSA ).
We also thank the iGE3 genomics platform at the  University of Geneva. This work was supported by  Agence Nationale de la Recherche  ( ANR-19-CE12-0026 ) to P. G. , R. G. , and L. M,  Programme d'Investissements d'Avenir  ( ANR-20-PAMR-0005 ) to P. G. , by the  National Natural Science Foundation of China  ( 32000021 ) to X. X. , by  Fondation pour la Recherche Médicale  ( FDT201805005796 ) to M. M. , and by the  Swiss National Science Foundation  ( CRSII3_160703 ) to P. G. and L. F.
Due to the large number of separate steps, data types and organisations involved in WRAs, it is challenging to accurately and robustly evaluate the accuracy of different tools or workflows for the entire process. A key obstacle is the lack of availability and suitability of relevant validation data. The only previous studies examining the steps required for full WRAs are the set of 'CREYAP' exercises  Mortensen. 2015 and a US-based study on Annual Energy Prodution (AEP) errors  (Lee and Fields, 2021). CREYAP stands for "Comparison of Resource and Energy Yield Assessment Procedures" and is carried out by Ørsted and the Technical University of Denmark. In the 2021 version, participants estimate the net energy yield for the Walney Extension wind farm, accounting for wind speed variations over time and across the site, and for turbine interaction losses 1.
Underlying wind models The workflows applied were associated with different wind models as follows: -WF-1: WindPro: The industry-leading software suite for design and planning of wind farm projects 3. It allows calibration data to be entered directly at the mast location and splits the flow into a total of 12 wind direction sectors. The turbulence intensity is calculated directly from the input met mast data standard deviations and mean values. -WF-2: WindSim: An industry software using a Computational Fluid Dynamics (CFD) model based on the PHOENICS code, a 3D Reynolds Averaged Navier Stokes (RANS) solver from the company CHAM. It allows calibration data to be entered directly at the mast location and splits the flow into a total of 12 wind direction sectors. The turbulence intensity is calculated directly from the input met mast data standard deviations and mean values.
In this work, the simulations were conducted using the standard k-ϵ turbulence model  (Rodi and Spalding, 1983). The boundary condition at the top was fixed pressure. -WF-3: ANSYS CFX: An all-purpose CFD software. In this work, ANSYS CFX (version 19. 2) was applied using an Unsteady RANS approach in combination with the k-ϵ model  (Richards and Hoxey, 1993 ). The equation system was extended to an anelastic formulation whereby the density is influenced by buoyancy forces using the Boussinesq approximation  (Montavon, 1998). The Earth's rotation was considered with additional terms in the momentum equation to describe the Coriolis force at a given angular velocity. Forested areas were considered through a canopy model  (Liu et al. , 1996). Individual roughness lengths of the canopy were attributed to each land use and are chosen according to the work of Wieringa  (Wiernga, 1993).
Wind speed long-term extrapolation For each site, the long-term corrected measured wind speed was obtained at both the calibration and validation locations by firstly averaging the wind speeds over a time period for which valid data was available for both the calibration and validation measurements. Secondly, these average wind speeds were extrapolated for a 20-year period using the Measure-Correlate-Predict method with long-term reference data. This data was either obtained from a nearby met mast or from MERRA-2 data 5  , as discussed in each individual section.
AEP calculation The wind industry tools applied in this work, WindPro and WindSIM, as well as E-Wind  (Alletto et al. , 2018) , offer a user interface containing all the steps required to get from wind measurements to AEP predictions. However, the other CFD tools, Fluent, CFX and PALM needed to be extended in order to calculate the AEP. In previous work  (Barber et al. , 2020b) , automated workflows for carrying out wind flow modelling in complex terrain were developed for the commercial CFD tools ANSYS Fluent and ANSYS CFX. These processes included a pre-processing tool, a meshing script, a simulation script and a preprocessing tool. It was shown that the total simulation costs, including model set-up and post-processing effort costs, could be reduced by a factor of 12 for Fluent and seven for CFX through this automation.
For PALM, a slightly different method was applied in order to reduce the computational time in a pragmatic way. This involved firstly searching for a time period in the met mast data for which the wind speed remained relatively stable at the average wind speed for each wind direction sector. For this time period, COSMO-D2 data 6  was taken for the PALM boundary conditions by reading the data from a dynamic input file (offline nesting). Finally, this time period is simulated with PALM and the average wind speed is extracted for the calibration and validation positions at the reference height. In the present work, these workflows were extended to include AEP estimations. The AEP was calculated at the validation location(s) as follows: -Speed-up factors between the validation location(s) and the calibration location were calculated from the simulation results for each wind direction sector (in this case, 12 sectors).
Site 1 Site 1 is a complex terrain, partly-forested site close to Stoetten in southern Germany, whose central feature is a steep incline above 30% and a main wind direction of 270°-300°. Wind speed and direction data was available from a met mast located about 1 km away from the incline, as well as a lidar, as described in  (Schulz et al. , 2014). In this paper, the met mast data from four cup anemometers with an accuracy of 1% and three 3D sonic anemometers with an accuracy of 1. 5% were used for calibration, and the wind speed data recorded using the SWE-Scanner, a fast pulsed lidar wind scanner based on a Leosphere  ). An examination of the time series of the measurement data showed that the sensors at 47 m, 48 m and 82 m contained some errors, and therefore the sensors at these heights were not used.
STIG2 at 83 m is taken as the calibration mast, with coordinates of (4086674. 443, 3958009. 528) (coordinate system EPSG3035). For the long-term extrapolation, MERRA-2 data was. The details of the applied workflows are shown in Table  3. WF-3 and WF-7 were not applied to this site. to met masts and wind turbines. The two main wind directions are E and NNW, as can be seen on the wind rose in Fig. 1. In this project, data from two met masts 'EAST' and 'WEST' were used. For both of these masts, measurement data was provided from Thies First Class Cup Anemometers at a range of heights up to 50 m from September 2009 to September 2010. Due to the high amount of overlapping data points (48,941), they can be used for the calibration and validation data reliably. The MERRA-2 reanalysis dataset was used for the long-term adjustment of both met mast data sets.
From the MERRA-2 data, a factor of the mean wind speed during 10 years to the mean wind speed over the measurement period was determined. For the long-term adjusted data sets at both locations, the measurement series were extrapolated with this factor. The WEST mast was used for calibration and the EAST mast for validation. The coordinates of the calibration location are (442929. 200, 4741536. 600) (coordinate system EPSG3035). The details of the applied workflows are shown in Table  4. All seven workflows were applied to this site.
The typical procedure of SPA comprises (i) image acquisition, (ii) particle detection and segmentation, (iii) selection, (iv) alignment, (v) averaging and (vi) model generation. An example pipeline showcasing a dSTORM dataset containing the nuclear envelope of a Xenopus oocyte labelled for the nuclear pore complex (NPC) protein gp210  [23]  (data available at https:// doi. org/10. 5281/zenodo. 5068525) is shown in figure  2.
The initial template is built based on sequential registration in the order of the sum LL rank. The final fused particle is used to register all sites in the 318-site dataset. This procedure yields an updated fused particle, which is used to register the dataset again. This process is iterated until convergence. The final average (model) was calculated from 318 particles without any assumption on the underlying geometry or symmetry in a tilted view (mode, top left). The STORM localizations of an individual structure are fitted to an ellipse, which is then deformed to a circle. The circularized structure is normalized to a ring with a fixed diameter calculated by averaging the diameter of 31 original structures. Image (ii) shows the resulting averaged structure after alignment. Scale bar represents 100 nm.
Particles detected in a dataset can be included or excluded from the analysis with respect to how well they match the chosen template. Additionally, particle averaging involves iterative refinement of the shape of an initial template. The template chosen is typically arbitrary, often consisting of a simplified shape recapitulating a priori knowledge on the structures of interest. For example, a simple spherical shape can be used as a template if the structure of interest is a mature HIV-1 or HSV-1 particle. Another option is to select a representative particle from the dataset, which can be a single particle, a subpopulation or even a class average (e. g. VirusMapper  [40] ). The problem shared among these approaches is that the final reconstructed structures are biased towards the template used.
A quality control step is performed to remove outliers and registration errors by inferring the combinations of relative parameters from the absolute parameters obtained previously. These retrodicted parameters are then compared to those found in the all-to-all registration, and pairs deviating from their registered counterparts above a specified threshold are discarded from the next steps. These procedures generate a data-driven template that is further refined by particle averaging. The algorithm's performance was first evaluated using a dataset containing two-dimensional DNA origami particles imaged with DNA-PAINT and generated image reconstructions with approximately 3 nm resolution. In addition, the algorithm was able to perform using particles with labelling densities as low as 30%. The algorithm was also tested using the NPC dataset in  [32]  and successfully retrieved the NPC's eightfold symmetry without requiring prior knowledge.
More recently, this framework was extended to enable three-dimensional SPA  [106]. Another example is Blundell et al. [107] , where DL was used to predict each particle's pose. Here, a neural network fits a three-dimensional model against a library of particles viewed in two-dimensional. The framework was able to discern the centriole's expected toroidal structure from the STORM dataset acquired in  [72]. It is important to note that, although 'template bias' exists when using arbitrary models as an initial reference for SPA, completely discarding prior knowledge on the structures of interest might not be the most sensible action. Indeed, the 'template-free' algorithms showcased so far produce reconstructions that can be further improved by taking prior knowledge into account. On this note, Wu et al.
The framework was evaluated using a twodimensional DNA origami test dataset imaged with DNA-PAINT  [16, 112] , containing particles belonging to several different classes. Under these conditions, the algorithm correctly classified greater than 95% of the analysed particles. The averaged reconstructions achieved resolutions between 3. 7 and 5. 7 nm. Most importantly, the algorithm correctly classified an NPC with ninefold symmetry in a simulated dataset where this class was highly under-represented (2% of the total number of particles). Furthermore, the algorithm detected classes corresponding to elliptical NPCs in the Xenopus oocyte NPC dataset mentioned previously  [32]. Although the presence of these elliptical structures in the data was deemed by the authors to be an artefact generated during sample preparation without any biological significance, their successful detection showcases once again the high sensitivity of the classification framework and its ability to detect unknown structures.
The remaining image sections are aligned, and an averaged view of the object's architecture is generated, which is then used to infer a model of the supramolecular structure. (NPC and HIV-1 CA structures were created using Mol* [4] ; PDB IDs: 7N9F (NPC), 2M8N (CA monomer), 6OBH (CA hexamer) and 3J3Y (HIV-1 capsid)).
Sea Level and Climate Dataset In this study, four stations were selected along the north French coast (Figure  1  Sea level data were provided by the French national hydrographic service (Shom), and data are available on data. shom. fr. These stations provide time series of hourly observations qualified as "valid" and spanning over at least 30 years. The observations are referenced to the zero tide gauge, which corresponds to the Chart datum (hydrographic datum). Nevertheless, the time series used present some gaps, and the average data availability is ~89% (from the first year of measurement to 2018) for Brest station, ~58% for Cherbourg station, ~59% for Le Havre station, and ~81% for Dunkirk station (Figure  2 ). Sea level data were provided by the French national hydrographic service (Shom), and data are available on data.