dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the full-text XML of publications corresponding to the PMC identifiers were retrieved with corresponding metadata from Europe PMC. Following XML preprocessing, sentences were parsed from the XML tags corresponding to each article section (i. e. introduction, method, results, discussion) and merged into individual section texts. Each text was aligned to the GOLD annotation of the corresponding metagenomic study and TF-IDF (Term Frequency-Inverse Document Frequency) or a vector of precomputed 200-dimensional Doc2Vec embedding  40  was used as features for biome classifiers. To generate Doc2Vec embeddings, publications cited in MGnify records were trained using the document-embedding neural network model Doc2Vec with the following parameters: 15 training iterations, a window size of 5, worker threads of 8 and a dimensional size of 200 (Python genism package 3.
Training random forest biome classifiers on cross-referenced publications. A total of 5 GOLD multiclass hierarchical levels (supplementary Table  1 ) were used to train nearly 50 random forest models (Python Scikit-learn package 0. 22. 1: RandomForestClassifier). For each of the TF-IDF and Doc2Vec approaches, a separate multiclass model was trained using the selected features for every hierarchical level. Each model trained a random selection of balanced datasets that comprised 20-100 texts per class. To validate model performance, datasets were divided into training datasets (80%; 80 publications) and test datasets (20%; 20 publications). The hyperparameter settings of random forest models were determined using a grid search among {50, 100, 150, 200, 250, 300} decision trees in the forest (n_estimators) and {25, 50, 75, 100, 125, 150, None} maximum tree depths (max_depth).
Biome classifiers prediction and implementation. A query was constructed to retrieve all publications associated with all metagenomics studies from the ENA database (determined by the descriptor "library source: METAGENOMIC" via https://www. ebi. ac. uk/ena/browser/home and XREF APIs (https://www. ebi. ac. uk/ena/xref/rest/#!/JSON/getSourcesUsingGET ). This resulted in 4513 publications from 9949 ENA metagenomics studies accessed on 20 February 2020. For each publication, full texts were then retrieved and processed using the same workflow employed in constructing the classifiers training dataset. The highest performing model with the broadest biome coverage was then selected to classify ENA cross-referenced publications. Using the model prediction probabilities, publications with probabilities of P >=0.
Named Entity Recognition (NER) Training dataset. A literature triage comprising 140 ENA cross-referenced publications were categorised into environmental (N=50), engineered (N=44), and host-associated (N=46) publications (supplementary Table  2 ). A total of 16 novel metagenomics entities, covering biome and experimental data, were proposed, and defined for biocuration (Table  1 ). Words pertinent to the aforementioned entities were curated in the publications validated for biocuration by a team of experienced curators, using Hypothes. is (https://web. hypothes. is/) a web-based open-source annotation tool. Every manuscript was assigned to a single curator to annotate the entities under the supervision of another curator, followed by revisions from 2 different curators (see supplementary curation guidelines).
After evaluation, annotations were retrieved via the Hypothesis API (https://h. readthedocs. io/en/latest/api-reference/) and mapped to their corresponding sentences. Those sentences were then tokenized with BERT WordPiece tokenizer  41  , which was modified to retain words that are unknown to the BERT model (e. g. , long primers, kits or chemical states with unknown characters), to be represented and trained by NER models instead of being excluded as unknown tokens (UNK). As entities can be nested within each other (overlapping entities), 16 individual training datasets were constructed to train each entity, separately. Each training dataset consisted of all curated sentences with their entity-related tokens tagged, by the standard NER classification scheme BIO  42  , as the beginning (B), inside (I) or outside (O) of an entity.
NER models prediction and implementation. A total of 16 high-performance NER models (BioModels 43 accessions: MODEL2202160002, MODEL2202170001-MODEL2202170015) were used to annotate the methods sections of 114, 099 metagenomics publications. Since the best model performance metrics were observed when biome classifiers were trained on method texts (see supplementary Table  3 ), method sections were identified as the most representative article section for metagenomics data. The 114, 099 metagenomics publications were identified as follows: 1) cross-referencing metagenomics studies in genome and biosamples databases (ENA, NCBI Taxonomy, BioProject and MGnify) against literature repositories and 2) querying literature repositories (Europe PMC, PMC and PubMed) using metagenomics-and microbiome-related search and MeSH terms (for queries, see supplementary Table  4 ).
Then, methods sections were extracted from full-text XML via parsing the sentences wrapped within methods XML tags. Because metagenomics publications sometimes lack scientific methods sections, a multiclass random forest model was trained to classify full-text into abstract-, introduction-, methods-, results-and discussion-related texts. This section classifier (see supplementary Table  5  and Figure  1 ) was then used to extract methods-related text from publications lacking methods sections. Training NER models on predicted datasets. To further demonstrate the accuracy of predicted annotations, a random subset of new metagenomics publications (1500 articles) was retrieved with their NER-models-predicted annotations to train new 16 NER BioBERT models from scratch. To construct this subset, trained biome classifiers were used to classify new metagenomics publications into 3 broad biome environments.
Then, a random subset of 500 articles, with predicted probabilities of P >=0. 5, was selected per biome class (i. e. , engineered, environmental and host-associated classes), followed by the retrieval of their NER-models-predicted annotations. In this approach, those new publications with their NERpredicted annotations were considered as the training dataset, whereas the curated dataset was considered as the test dataset. This approach is like the student self-distillation process used in improving ImageNet classification  44  and protein structure prediction (AlphaFold)  45  models, where student models were trained on datasets predicted by teacher models (models trained on curated dataset). Though in our approach, it was hypothesised that teacher NER models -models trained on curated datasets -can generalise to novel biome publications, if students NER models -models trained on datasets predicted by teacher models -predicted curated datasets with token-wise macro-average F1-score >=70%.
Database enrichment Europe PMC enrichment. Metagenomics annotations of 114, 099 publications in Europe PMC were represented in the annotations data model provided by the Europe PMC annotations submission system (https://europepmc. org/AnnotationsSubmission). To make it easier to categorise and search publications with metagenomics annotations, specific metagenomics ontologies were required to standardise the predicted annotations. However, with the surge in different environments being sampled, it became clear that metagenomics ontologies, including GOLD and ENVO, are insufficient for standardising most of the predicted annotations. Thus, ZOOMA (https://www. ebi. ac. uk/spot/zooma/ ), a semantic ontology mapping tool was used to map each of the predicted non-standardized annotations to a wide range of ontologies in the Ontology Lookup Service (OLS; https://www.
Then, the entities of metagenomics annotations and a subset of ENA fields used in more than 50 studies were mapped to a corresponding subset of the minimum information about any (x) sequence (MIxS)  33  checklist (see supplementary Table  7 ). For each MIxS term, data were pooled from mapped ENA fields (author-submitted metadata) and metagenomics entities (metagenomics annotations) and compared per study. Studies with any identical or non identical metadata in both sources, or having metadata from one source only were then counted per each MIxS term, with the results from the twenty most common MIxS fields shown in Figure  4.
Acknowledgement and Funding The authors would like to thank the curators from  Molecular Connections  (https://www. molecularconnections. com/)for their biocuration efforts,  Zhan Huang  and  Lynne Faulk  ( EMBL-EBI ) for user interface design and implementation,  Audrey Hamelers  ( EMBL-EBI ) for proofreading the manuscript, and  Awais Athar  and  Santosh Tirunagari  ( EMBL-EBI ) for testing and validating the  Gitlab repository. Francesco Talo ' and  Zunaira Shafique  are funded by the  Europe PMC  grant, provided by 33 funders of life science research (https://europepmc. org/Funders/)under  Wellcome Trust  Grant  221523. Maaly Nassar  and  Santiago Sanchez  were funded by  Biotechnology and Biological Sciences Research Council  ( BB/S009043/1 ).
38 is worth noting that pretraining BioBERT models on new PMC publications would have enabled the recognition of those unknown tokens during fine-tuning. But, due to the computationally expensive generation of contextual word representations from millions of publications, neither BioBERT models or analogous neural architecture models, such as XLNet 36 , RoBERTa37or ELECTRA38, were pre-trained or compared. Overall, these training approaches and modifications have been shown to contribute to high prediction accuracy; hence, the implementation of metagenomics NER models in enriching the metadata of diverse microbiome studies in ENA and MGnify from Europe PMC publications. Another limitation of the training dataset is that the metadata of a small subset of entities (e. g. , primer, place and date) can be listed in publications tables, where rows and cells were merged and parsed as one sentence that might exceed the maximum sequence length (128) of model input.
This resulted in the exclusion of tokens beyond maximum input length from being annotated by the model and subsequent low recall. On the other hand, training 16 individual NER models (one model per entity) on all curated sentences provided extensive negative dataset and considerable advantage for training overlapping entities, where each entity model has shown to require specific learning rate and training epochs to predict test datasets with higher precision. Alternatively, training one multiclass NER model on all entities was deemed to perform poorly due to the unbalanced BIO class distribution of single-(non-overlapping) versus multiclass (overlapping) entities, high number of trained entities(16)and small training dataset. Moreover, modifying BERT tokenizer to keep and unsegment words with long lengths (e.
g. , disease) Treatment Any treatment performed on the host (e. g. , medicine) or the environment (e. g. , fertiliser) from which the sample was collected Kit DNA extraction kit Primer PCR primers Gene Microbiome target genes, e. g. , rRNA subunit and amplified region(s) LS Library source or library strategy (e. g. , amplicon, whole genome) LCM Library construction method or layout (e. g. , paired-end, single-end) Sequencing Sequencing platform To validate the performance of each model, each dataset was divided into a training dataset (90%) and test dataset (10%). Fine-tuning was performed with a batch size of 32, maximum sequence length of 128 and fixed random state for reproducibility. A grid search was performed over 5 learning rates {1e-5, 2e-5, 3e-5, 4e-5} and 7 epochs {10,30,50,70,90,110,130} to select the highest performance hyperparameters for each model; thus, different hyperparameters for each training model.
Method Participants The study involved nine sighted (5 females; age: M ± SD = 30. 11 ± 6. 89 years, range = 23-44 years; ethnicity: white) and nine congenitally blind individuals (7 females; age: M ± SD = 31. 11 ± 8. 08 years, range = 20-46 years; ethnicity: white). All sighted participants reported no visual impairment and had a visual acuity higher than 9/10. All participants in both groups had normal hearing and cognitive abilities. Clinical details are shown in Table  1. Experiments were approved by the ethics committee of the local health service (Comitato Etico, ASL 3, Genoa, Italy) and performed in keeping with the Declaration of Helsinki. Informed written consent for the study was obtained from all participants. Both blind and sighted participants were recruited from the Italian Institute of Technology (IIT) database.
Virtual Reality System To develop the system, we started with the 3D Tune-In Toolkit, an acoustic simulation tool  (Cuevas-Rodríguez et al. , 2019; Levtov et al. , 2016)  based on binaural spatialisation, which allows the creation of three-dimensional soundscapes using headphones as a playback device. The audio-Corsi test was designed through communication between MATLAB and the Toolkit (MATLAB R2017, The MathWorks; see Supplemental Materials). To create the task, we designed an acoustic virtual environment composed of six virtual sources (Figure  1 ). We designed an Arduino Uno-based keyboard to register participant responses during experimental sessions. The keyboard replicates the virtual environment's spatial features to allow participants to respond by pressing the button corresponding to the perceived sound source position (Figure  2 ).
Data Analysis We calculated the percentage of sounds correctly recalled after the training session, to verify its effectiveness. Testing performances were quantified using two metrics: (a) memory span and (b) the number of sequences correctly recalled. Memory span was calculated based on a previously validated procedure  (Lépine et al. , 2005; Pearson et al. , 2001; Röser et al. , 2016; Setti et al. , 2018) , and calculated as X 9 i≡1 #corr:subtrialsðiÞ × i=3. (1) In Equation (1), i is the sequence length. This measure is more sensitive than the classical span measure of the length of the longest sequence recalled  (Smyth & Scholey, 1992). The number of sequences correctly recalled for all sequence lengths was summed and divided by 3 (the number of replicates performed per sequence length).
Statistical Analyses Statistical analyses were carried out in MATLAB (MATLAB R2017, MathWorks) and R  (RStudio 4. 3. 2 version; Martin, 2021). The normality of the data was checked with the Jarque-Brera test (jbtest()MATLAB function). The analysis of the percentage of sounds correctly recalled after the training was performed in R. If the data did not follow a normal distribution, permutation tests were carried out. In detail, we used a mixed measures two-way (2 × 2) analysis of variance (ANOVA) based on permutations (aovp() function, lmPerm package;  Wheeler, 2010)  with Vision Group (blind or sighted) as a between-subject factor, and Condition (semantic and non-semantic) as a within-subject factor. In reporting the results of the permutation analyses, we indicated the number of repetitions (n) employed to run the analyses.
Testing performance analyses were carried out separately for memory span and number of sequences correctly recalled. We used a mixed measures three-way (2 × 2 × 2) ANOVA based on permutations (aovp() function, lmPerm package;  Wheeler & Torchiano, 2016) , with Vision Group (blind or sighted) as a between-subject factor, and Condition (semantic or non-semantic) and Recall Direction (forward or backward) as within-subject factors. Post-hoc analyses were carried out with two-tailed Student's t-tests, both paired and unpaired (ttest() and ttest2() MATLAB functions). A Bonferroni correction was used to correct for multiple comparisons, and p <. 05 was considered significant. Cohen's coefficient was evaluated to quantify effect size (Cohen's d).
We would like to thank all the people who took part in both the pilot and experimental sessions. The research is partially supported by the  MYSpace  project ( PI Monica Gori ), which has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 948349 ). Walter Setti  played a lead role in conceptualisation. Luigi F. Cuturi  played a lead role in writing of review and editing, supporting role in supervision and writing of original draft, and equal role in conceptualisation and methodology. Isaac Engel played an equal role in methodology, software, and validation. Lorenzo Picinali  played an equal role in formal analysis, methodology, supervision, visualisation, and writing of review and editing.
For example, a previous study identified the modifications in brain connectivity involved in the cognitive control of eating in the context of obesity  (19) , but data are lacking to document these modifications in healthy normalweight subjects with contrasted eating habits. In this study, we aimed to investigate whether we could characterize an ongoing trajectory toward maladapted eating behavior as determined by chronic hedonic food consumption in relation to the components of eating behavior and brain functions in a healthy normal-weight young population, with no ED or any other pathology. We specifically focused on the cortico-striatal pathway as a sentinel of cognitive control efficacy related to food choices. We deliberately selected a population of young women who are known to be more compliant with experimental protocols and for whom the probability of risky eating habits and EDs is higher than that in men.
Functional Magnetic Resonance Imaging Wanting Task Liking (i. e. , Hedonic) Evaluation of the Food Pictures Participants completed a hedonic evaluation  (17)  answering the question "Indicate how much you like this starter/main course/dessert using the numeric scale from 1 to 10 at the bottom of the screen, " ranging from 1: "I really don't like it" to 10: "I really like it, " for each of the 160 food pictures from our database. The responses were used to determine dyads of pictures for the fMRI wanting task.
Functional Magnetic Resonance Imaging Analysis Image analysis was performed using Statistical Parametric Toolbox 12 (Wellcome Department of Imaging Neuroscience, London, United Kingdom) in MATLAB (Mathworks, Inc. , Sherborn, MA, United States). Functional images were realigned to the mean to correct for motion during the acquisition and coregistered on 3D T1 anatomical images. The anatomical and functional images were then normalized to the Montréal Neurological Institute (MNI) space. Spatial smoothing of functional images was performed with a 6-mm full width at half maximum kernel (FWHM). Vector onsets were compiled for the SL, DL, HE, and LE conditions using MATLAB. A general linear model analysis was performed on brain masked data, using the canonical hemodynamic response function (HRF) without time and dispersion derivatives.
Resting-State Functional Magnetic Resonance Imaging Analysis Functional connectivity (FC) was performed using CONN Toolbox version 20. b on MATLAB 2017b environment (The MathWorks, Inc. , Natick, MA, United States). After all preprocessing steps including normalization to MNI atlas space, spatial smoothing with an 8-mm FWHM, motion artifact detection, denoising, and first-level analysis, a seed-to-voxel second-level analysis were performed with the same covariates as for the task fMRI analysis (see above). The right nucleus accumbens was chosen as seed, given its detected activation during food choice, depending on the PCA1 value. The right insula was chosen as seed, given its detected activation during food choice, depending on the PCA2 value. Statistical significance was set upon Gaussian Random Field Theory parametric statistics, cluster threshold pFDR < 0.
ACKNOWLEDGMENTS We gratefully thank  Christian Barillot ,  Jean-Christophe Ferré ,  Isabelle Corouge , and  Quentin Duché , as well as the technical and clinical staff of the  Neurinfo imaging platform of Rennes University-Hospital , where all the imaging sessions were performed. This includes all the MRI operators including  Marithé Desclos ,  Catherine Guillemot ,  Vanessa Even ,  Gwenola Morrisse ,  Lucille Patier , and  Fabien Vigouroux. We thank  Fabrice Lainé  from the clinical investigation unit (UIC) of  Rennes Hospital , as well as the  UIC  nurses and especially Cécile Réminiac who was in charge of programming the volunteers' appointments. We acknowledge  Restauration Collective Casino  ( R2C ) for its partnership, and especially the cooks  Matthieu Chevallier  and  Marina Briand , as well as their kitchen assistants  Marie-Yvonne Vauleone  and  Yvette Vilette , for their welcome and collaboration in preparing all the dishes that were either used to constitute the food picture database or offered to the volunteers.
FUNDING This study was funded by  INRA  in the context of the  Hed-O-Shift Priority Action  coordinated by DV-L. YG benefited from a Ph. D. grant from  INRA  and  Région Bretagne , in the context of the  GIS Nutrition Alimentation Métabolisme Santé. MRI data acquisition was partly supported by the  Neurinfo   MRI research facility  from the  University of Rennes 1. Neurinfo was.
, 2012 (Schepers et al. , , 2014;; Boorsma et al. , 2018). A more detailed summary of dedicated wind tunnel experiments has recently been published online  (Boorsma, 2021). Although these wind tunnels have been used successfully to validate rotor aerodynamic models, translating these results to 'real life' flexible turbines in turbulent inflow conditions remains a challenge. Therefore the comparison rounds of IEA Task 29 Phase IV  (Schepers et al. , 2021)  have focussed on newly released field measurements on a 2MW turbine from the DanAero experiment  (Bak et al. , 2010; Madsen et al. , 2010b). The unique data from this experiment, including a description of the turbine, were made available to the participants of IEA Task 29 Phase IV so that it could form a basis for a thorough analysis. This paper presents the progress of this task, where many participants from different countries simulated the same experiment.
DanAero field measurement campaign Detailed aerodynamic measurements on MW-scale wind turbines are scarce and open publications about them even more so. An exception lies in the DanAero experiment  (Bak et al. , 2010; Madsen et al. , 2010b) , which was carried out in atmospheric field conditions on a NM80 2. 3MW turbine in a Danish project by Danish Technical University DTU and four industrial partners (LM Glassfiber, Siemens WindPower, Vestas and Dong Energy) in two periods from 2007 until 2010 and from 2010 until 2013. At the initiation of IEA Task 29 Phase IV in 2018, the DanAero partners agreed that the measurements as well as the model data for aerodynamic and aeroelastic modelling of the NM80 turbine could be shared with the partners participating in this task. The pitch controlled turbine features three LM38.
Acknowledging the earlier observed variations in axial induced velocities, this discrepancy seems to be in contradiction with momentum theory, where a higher loading is accompanied with more induction. However this could also be attributed to uncertainties in the engineering extensions used for the turbulent wake state in which the turbine is operating for this case. Grouping the results by code type, as will be shown in section 3. 3, allows to better observe the loading differences. The comparison with measured values (obtained from the integration of the measured pressure distributions) shows the normal force to be consistently overpredicted by approximately 10% for all radial positions. It could then be expected that  this overprediction is reflected in the measured flatwise moments from Figure  3c  as well.