dataesr/scientific-paragraphs-categorization huggingface dataset:
Reassortment analyses To compare reassortment events that occurred among major clades, including clades 2. 3. 4. 4b, 2. 3. 4. 4x, 2. 3. 21 and 2. 2, we first constructed ML trees for each gene using IQ-TREE v. 2. 1. 4. At most 200 isolates with eight segments were subsampled using the Phylogenetic Diversity Analyzer (PDA) tool v1. 0. 3 (www. cibiv. at/software/pda) (76) for each major clade and the minor clade, finally resulting in 909 sequences for each HA, PB2, PB1, PA, NP, MP and NS dataset, and 897 sequences for NA dataset, which excluded minor subtypes of NA genes (N3, N4, N5 and N9). Baltic package (https://github. com/evogytis/baltic) was used to visualize the incongruence between phylogenetic trees of eight genes. The HA tree was rooted using clade 0, and the remaining genes were mid-point rooted.
We combined three independent chains with 5 million MCMC steps for each lineage and sampled every 10,000 states. The first 10% of each run was discarded as burn-in, resulting in 1350 posterior trees with estimates of the ancestral region and host for each internal node. The trunk region/host through time was measured from these posterior phylogenies using PACT v0. 9. 5 (https://github. com/trvrb/PACT), where the trunk consists of all branches ancestral to a virus that was sampled within one year of the most recent sample  (80).
Phylodynamics incorporating geography and host As a complementary to the discrete spatial diffusion analysis to reconstruct a more detailed geographic history, we estimated the HPAIV H5 diffusion in continuous space (latitude and longitude of country level) using a Cauchy relaxed random walk (RRW) model (  81 ) with 0. 001 jitter window size. Moreover, following the Bayesian method proposed by Trovão et al. (50) , a continuous spatial diffusion process and a discrete host transmission process was incorporated in a single Bayesian analysis to quantify host-specific diffusion rates and geographic expansion (wavefront distance) for each lineage (https://github. com/vjlab/episodic-h5). At least two independent MCC chains were performed, sampled every 10,000, and discarded 10% as burn-in to ensure ESS >200 for each parameter.
3. 4. 4b, five sequences for clade 2. 3. 4. 4x, three sequences for clade 2. 3. 2. 1 and six sequences for clade 2. 2 per country per host per season of each year (season 1, January-March; season 2, April-June; season 3, July-September and season 4, October-December). The final subsampled HA datasets contained 715, 617, 579, and 563 sequences for the four clades.
Acknowledgments: The computations were performed using research facilities offered by  Information Technology Services , the  University of Hong Kong. We gratefully acknowledge the staff from the originating laboratories responsible for obtaining the specimens and the submitting laboratories where the genome data were generated and shared via GISAID (Supplementary Data 2). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and writing of the manuscript. Funding: National Institutes of Health contract number  75N93021C00016.
Sampling and extrapolation: While the observation and analysis unit is the domestic group, the unit used for the purposes of random and representative sampling is the household, as described by the ISEE (i. e. all individuals living in the same main house; a domestic group can therefore incorporate several households). The initial sample was drawn at random from the 2009 GPC database of households living in tribes, doubly stratified according to the geographic criteria (10 infra-provincial zones) and household size (7 strata). According to the expertise and practices of the ISEE, a standard survey rate of 14% of the households was applied in each of the 70 strata. The final sample differs slightly from this survey plan, in part drawing on information not in the census lists. Ultimately, 1,786 households were surveyed corresponding to 12.
Survey mechanism: The information was collected by means of a questionnaire conducted on a face-to-face basis with at least one member of the household in order to collect all the data in a single operation. This questionnaire is included with the metadata. Several resource persons were involved in developing it, from formulating the questions to determining the units or measure or the content of the nomenclatures to be used. The questionnaire was validated after several field tests. The team responsible for collecting the data in the field consisted of two coordinators, two field supervisors, ten investigators and three trainees. In order to offer the respondents the best possible welcome and ensure the best possible availability of the people to be interviewed, the study was presented to the traditional authorities and a personal letter was sent to the domestic groups before the investigators arrived.
Data quality: The data were cleaned, with any input errors and inconsistencies corrected, extreme values and incomplete data checked and duplicated data identified  (Berti-Equille, 2006). The raw data were processed to produce files for analysis. The corrections made by the survey teamwhich sometimes even involved making a telephone call or a return to the field -resulted in a complete dataset deemed to be error-free. The quality of the dataset was substantiated by the fact that it was in line with the results of other survey mechanisms  (ISEE, 2010; DAVAR, 2011; ISEE, 2015). The dataset we present here is a selected excerpt of the original set. Il contains both raw data drawn directly from the collection procedure and the result of the data processing (aggregation of secondary units in the domestic group and certain calculations such as the RAP).
The original data comprise more than 70 inter-related tables. Using them requires an in-depth involvement in the rationale underpinning the study. Access can nevertheless be envisaged through agreements to be established with the IAC. The data are made available in accordance with the terms of Creative Commons licence CC-BY-NC-SA-4. 0: Attribution-NonCommercial-ShareAlike 4. 0 International. They may in no event be used for commercial purposes and can only be reshared in the same conditions as the original. Whenever the data are used, the sources must be indicated. They should be cited as follows: Apithy Leïla; Bouard Séverine, Guyard Stéphane; Passouant Michel; Sourisseau Jean-Michel; Bélières Jean-François, 2017, "Agriculture des tribus de Nouvelle-Calédonie" [database], IAC, retrieved from CIRAD Dataverse: https://dataverse.
Controls assay laccase fluorescence in the buffer in the absence of aflatoxins, and AFB 1 and AFG 2 fluorescence in the absence of laccase. To convert the fluorescence readout to the corresponding toxin concentration, we employ a calibration curve based on measurements of a set of known toxin concentrations (Supplementary Fig. S1 ). The reaction kinetics are simulated in Matlab. The method of least squares (lsqnonlin in Matlab) is employed to fit the Michaelis-Menten kinetics to the experimental data. Detoxification efficiency is estimated by using the data from the first 100 min for each experiment, and finding the best linear fit (no intercept) as a function of the initial toxin concentration.
Identification of degradation products of laccase activity on aflatoxins via LC/MS. 25 U/mL laccase from Trametes versicolor (Sigma-Aldrich CAS80498) was added to 10 μg/mL of toxin, AFB 1 or AFG 2 (both from Cayman Chemicals) separately, in acetate buffer (100 mM, pH 6. 5) and left at 28 °C for 24 h. Degradation products were assayed under the following conditions: Column: Kinetex 2. 6 μm EVO C18; 100 × 2. 1 mm; Mobile phase A: Water 5 mM Ammonium Acetate, 0. 5% Acetic Acid; Mobile phase B: Methanol 5 mM Ammonium Acetate, 0. 5% Acetic Acid; Flow rate: 350 μL/min; UV Wavelength: 354, 360 nm. The following gradient method was used in all runs. The eluent from the column was directed into the electrospray source of an Agilent 6220 TOF mass spectrometer operated in positive ionization mode. Data was converted into the mzML file format and analyzed using the MZMine software.
Supplementary Figs. S2  and S3  (2) d[T] dt = -k L [L] [T] K m + [T] , (3) d[T] dt ≈ - k L K m [L][T]. show the resulting traces for AFB 1 and AFG 2 , respectively. AFB 1 and AFG 2 detoxification byproducts are shown in the supplements. Density functional theory (DFT) calculations. QM calculations are performed within the framework of Kohn-Sham Density Functional Theory (KS-DFT)  37  , employing the Perdew-Burke-Ernzerhof (PBE)  38  exchange and correlation level of theory. The numerical results are extracted with the BigDFT code  39  , which uses Daubechies wavelets to express the KS orbitals. Hartwigsen-Goedecker-Hutter (HGH) pseudopotentials  40  are used to remove the orbitals in the core electrons. The use of a wavelet basis sets enables systemic control of the resulting accuracy. Isolated boundary conditions are explicitly included in the calculations, without supercell aliasing effects, using the Poisson Solver of the code  41.
Docking. The initial 3D crystallographic structure of the Trametes versicolor beta isoform, based on a structure from the Protein DataBank (accession code 1KYA)  42  , is taken from previous work. The protein model is cleaned up using the pdb4amber script, part of the Amber 2020 software suite. Once the structure passes inspection by Amber's LEaP program, it is protonated using the H++ webserver (version 3. 2)  [43] [44] [45]  for a target pH of 6. 5 to reflect conditions feasible in application in the context of the food industry production process. Because H++ does not account for metals, the resultant protonated structure is manually cleaned to flip histidine residues in order to maintain proper ligation of the embedded copper atoms. No explicit solvent molecules are included in the final structures.
Acknowledgements The authors thank  Laura Ratcliff ,  Claudia Mondelli , and  Nicolas Coquelle  for useful discussions. BM and MZ were supported by a start-up fund and an  Ignite internal Grant  from  Boston College , by an  Award for Excellence in Biomedical Research  from the  Smith Family Foundation , and by an  NSF-Env. Engineering award (NSF# 2103545 ). We thank the  Boston College Mass Spectroscopy Core  for infrastructure and support. This work was supported by the  Next-Generation Supercomputer  project (the K computer) and the  FLAGSHIP2020  project  (Supercomputer Fugaku) within the priority study 5 (Development of new fundamental technologies  for high-efficiency energy creation, conversion/storage and use) from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.
Structures of catalysts can be represented either as full structures or only by the modulable substituents depicted as Ar and R; the latter would reduce noise from the common scaffold and enable the application of different descriptors suitable for each substituent. Physico-chemical parameters of solvents are also included as descriptors in the model. Other reaction conditions, namely temperatures and concentrations, are separately considered; all data points are calibrated to an identical condition before constructing a predictive model, and the predicted free energy differences are calibrated back to each experimental condition, providing the predicted enantiomeric ratio in the given conditions (see Supporting Information for details). Thus, this workflow represents a generalized approach to handling catalysts, reaction schemes, and reaction conditions simultaneously.
Code and data availability Data used in the study (structures and enantioselectivities of catalysts, experimental conditions, calibration curves, prediction results) is available in the Supporting Information. Python libraries developed in the study are open to public and are available on GitHub: https://github. com/POSidorov/ ChemInfoTools (CircuS fragment descriptors), https://github. com/POSidorov/HPLCAnalysis (analysis of experimental HPLC data).
Acknowledgements We thank the generous support from the  Max Planck Society , the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ,  Leibniz Award  to B. L. ) and under  Germany's Excellence Strategy  ( EXC 2033-390677874-RESOLV ), the  European Research Council (ERC, European Union 's  Horizon 2020 research and innovation program  " CÀ H Acids for Organic Synthesis ,  CHAOS" Advanced Grant  Agreement No. 694228 ). This work was also financially supported by the  Institute for Chemical Reaction Design and Discovery (ICReDD) , which was established by the  World Premier International Research Initiative (WPI) ,  MEXT, Japan , and by  JSPS KAKENHI  Grants  21H01925 ,  22K14672 ,  21H01924  and  JST-ERATO JPMJER1903. The authors thank  Dr. Thomas Buyck  and  Dr. Jennifer L. Kennemur  for screening data and preliminary studies.
Saturated transposition in Planctopirus limnophila genome A TraDIS method was applied to P. limnophila in order to obtain a transposon mutant library  10, 35. A complex consisting of a mini-Tn5 transposon bearing a kanamycin resistance cassette together with a transposase was electroporated into competent cells and grown on a selective medium. Individual colonies were collected to construct the library, estimated to be approximately 1. 1 million mutants. The pooled library was sequenced before storage or outgrowth to map the location of the insertions. Sequencing data were obtained from extracts of the transposon library, resulting in 8,132,446 sequence reads. After the removal of short reads, poor-quality data, and fragments that did not contain the transposon sequence, 2,863,686 reads were mapped to the P.
To contrast these numbers, we compared the P. limnophila results with the ones available for E. coli 10 , the workhorse model of microorganisms. As the E. coli data exclusively contain CDS, we considered only the 4258 P. limnophila CDS, of which 711 are essential, 3404 are nonessential, and 143 are unclear. We did not include the 'domain essential' genes in the list, as this method was not applied to the E. coli dataset.
Conservation To further characterize the novel biology of Planctomycetes, we contrasted the essentiality and conservation of the P. limnophila proteins in the Planctomycetes phylum, the PVC superphylum, and the prokaryotic domains. A total of 4655 organisms collected in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  43  were considered, of which 4365 bacteria (including 79 PVC: 45 Planctomycetes, 17 Verrucomicrobia, and 17 Chlamydiae) and 290 archaea (Table  1 ). Among the universal proteins conserved within prokaryotes, we found 49 ribosomal proteins, 21 aminoacyl-tRNA synthetases, MreB (Plim_2620, annotated as "cell shape determining protein, MreB/Mrl family"), and FtsK (Plim_2063) (Sup. Data 6). Some regions of the P. limnophila genome contain genes that are both conserved in many organisms and essential.
a EggNOG orthologs group (NOG) heatmap according to the EggNOG database annotations for the P. limnophila essential gene set. Each row represents one of the fifty selected classes. Each column represents the essential genes discovered in this study with a NOG. b Selection of proteins of interest (in green, blue, pink and orange boxes) from the NOG heatmap based on EggNOG-mapper annotations for the E. coli essential protein set. Each row represents the essential genes reported by Goodall et al. 10  with a NOG. GenBank ID of the gene encoding this protein and the gene name, followed by the NOG with its functional category and function (right). Each column represents one of the fifty selected classes. The heatmap color reflects the degree of distribution of a particular NOG in each class, with red representing complete distribution in all selected organisms of that class and light yellow representing absence in all selected organisms of that class.
Sequencing data mapping Raw data were collected and analyzed using a series of custom scripts. In order to map the sequence data to the genome (GenBank codes for genome and plasmid, respectively: CP001744. 1 and CP001745. 1), reads were filtered for the ones containing the exact sequence of the transposon termini with the program cutadapt cutadapt 62 (v4. 4) and the following parameters: -g XNNNNNGTTCGAAATGAGATGTGTATAA-GAGACAG -e 0 -O 34. The adaptor sequence was trimmed from the reads passing the filter with the program cutadapt and the following parameters: -m 15 -a ATGGAATTCTCGGGTGCCAAGG. The resulting reads were mapped to the genome with bowtie2 63 (v2. 5. 1). The subsequent steps of conversion from SAM (sequence alignment/map) files to BAM (binary version of SAM) files, and the requisite sorting and indexing, were done using SAMtools 64 (v1.
The ratio of both likelihoods was used to calculate a log-likelihood score. Genes were assigned as essential by TraDIS if the log of the likelihood ratio is higher than log 2 (12), as non-essential if this is less than log 2 (-12), and unclear if this is between these values. A Python (v3. 10. 12) script was used to interrogate the TraDIS unclear genes. A window of 300 bp started the insertion count at the beginning of the gene and slid every 150 bp. A window was labeled as essential when no inserts were found inside the window.
P. limnophila conservation A protein BLAST was carried out for each P. limnophila protein (accession no. CP001744. 1 and CP001745. 1) against a custom BLAST protein database created from the KEGG database [https://www. genome. jp/kegg/] (v2105)  [68] [69] [70]  using only prokaryotic organisms (Sup. Data 8). Subsequently, subject coverage was calculated for each of the results and those with a query/subject coverage greater than or equal to 70% and with an E-value less than or equal to 1 × 10 -5 were selected. Hits were grouped according to species to minimize differences at the strain level.
Phyletic distribution The orthology groups of the genes of P. limnophila and E. coli were retrieved from EggNOG Database 5  45  [http://eggnog5. embl. de/#/app/ home]. For each of these ortholog groups, the frequency of occurrence per class was calculated for over 5, 454 organisms collected in the KEGG database [https://www. genome. jp/kegg/]. Classes with less than five organisms were not considered, reducing the number to 5277 organisms in a total of 50 classes (Sup. Data 9). For those groups that appeared more than once in the same organism, only one occurrence was considered to denote their absence or presence. For the assignment of ortholog groups to these selected organisms, EggNOG-mapper  45, 46  (v2. 1. 12) was used with default values. With the frequency data of orthologous groups in the different phylogenetic classes, a heatmap was made using package Pheatmap (v1.
0. 12) (Kolde, Pheatmap: Pretty Heatmaps. R package version 1. 0. 12 (2019). A hierarchical clustering was made with the complete linkage method and Euclidean distance by this library. The number of clusters was designated manually based on tree observations. Cell division-related proteins in P. limnophila Hidden Markov Models (HMM) of all division-related genes were created using HMMER (v3. 3. 2) hmmbuild function from the multisequence alignment of the protein reported by Pierre S. Garcia et al. 71. Then, hmmsearch was used to find orthologs of division proteins in P. limnophila, using the model mentioned above. These models were run on the models themselves and the mean and standard deviation were calculated. These results were filtered by the bit-score values, considering the results with a bit-score value higher than 50% of the mean minus one standard deviation, followed by manual inspections.
Image analysis The image analysis workflow runs as follows under FIJI software (v2. 9. 0): Image Acquisition → Subtract Background → Gaussian Blur → Invert → Enhance Contrast → Unsharp Mask → SFC H Watershed Thresholding using seeds → Convert to mask → Binary Watershed → Analyze Particles. We set a filter of size using the upper and lower limits of 0. 3 to 2 µm 2 , since we consider that any particle out of this limit was not a cell. The presence of this filter does not affect the distribution of the results as visible in the plots (Fig. 6b ).
Acknowledgements Thanks to  Ildefonso Cases  ( CABD computational unit ) for bioinformatic services and  Alejandro Campoy  ( CABD Advanced Light Microscopy and Imaging Facility ). E. R. M. is supported by  Fondo Europeo de Desarrollo Regional (FEDER ,  Confunding percentaje FEDER   80% ) and  Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía)  ( UPO1381049 ); D. M. P. by  Junta de Andalucía Predoctoral  Grant ( Pre-doc_00913 ); V. H. by  Fundação para a Ciência e a Tecnologia, Portugal  ( 2022. 11400. BD) and D. P. D. by  Mineco  (Grant No. PID2020-119733GB-I00 ) and the " Moore-Simons Project on the Origin of the Eukaryotic Cell"  (Grant No. 9733/  https: //doi. org/10. 37807/GBMF9733 ).
This aspect will be measured by manual analysis of samples of generated texts; 3. an application perspective: we want to create an artificial clinical corpus that can be used to train NER models to be applied to a real clinical corpus for clinical information extraction. This aspect will be measured through the extrinsic evaluation of Section 4. 2. Figure  1  presents the global setup for evaluating generated data for NER. The specific corpora (MERLOT and E3C) named in the figure will be described in Section 3. 2. Briefly, we leverage a corpus of clinical documents in French containing high-quality entity annotations (MERLOT). This corpus represents the target real data we aim to analyze. It is used in part to train clinical NER models and in part for evaluating clinical NER models. We then use a clinical case corpus (E3C) for fine-tuning generation models, with the option of generating tagged text or plain text that can then be tagged with the Gold NER model.
Generation Pre-trained models such as GPT-2 have been proven to allow for generating complementary textual data in a data augmentation context. In this work, we explore the ability of auto regressive neural models (viz. GPT2 and BLOOM) trained partially or exclusively on French to adapt to the medical domain and generate relevant clinical case descriptions. We use two existing GPT models. The first model is a one-billion-parameter model pre-trained on generic French texts by  Simoulin and Crabbé (2021). The corpus used to pre-train the model was extracted from Wikipedia, Project Gutenberg 3  , OpenSubtitle  (Lison and Tiedemann, 2016) , and CommonCrawl. In this paper, this model will be referred to as LLF. The second model is a smaller version of the Big-Science BLOOM model 4. BLOOM was trained on 46 different languages, including French 5.
Fine-tuning the models We used the freely available E3C corpus for finetuning our model on medical texts so that the generation process can be replicated 8. We added special tokens for marking the beginning and end of each document to make the model generate whole documents. Two versions of the corpus were used: one with the annotations described in 3. 3 so that the models directly learn to generate annotations, and one without annotations. The corpus was split into training and validation sets, with 90% of the corpus for training and 10% for validation. The model was trained with a maximum of 10 epochs, but training stopped early if the perplexity on the validation set did not decrease significantly during two consecutive epochs. We selected a learning rate of 2e-5, negative likelihood loss with the AdamW optimizer from the torch package 9.