dataesr/scientific-paragraphs-categorization huggingface dataset:
After classifying the overall level of evidence (from unlikely to very likely), our health impact estimation will then consider associations classified as "likely" or "very likely," weighting each impact estimate by the corresponding level of evidence (e. g. ,  105  ). Exposureresponse functions will be taken from the existing evidence, prioritizing meta-analyses done in children, if available. This will lead to an estimation of the impact of several components of the exposome, including urban exposures (particulate matter, noise, green space) and chemicals (lead, mercury, organophosphate pesticides, polybrominated flame retardants, and, depending on the estimated level of evidence, PFASs, bisphenol A, phthalates, triclosan). Health impacts will be calculated based on biomonitoring data collected in ATHLETE and on representative national consumption and chemical concentration surveys whenever available.
Dissemination and exploitation towards policy intervention (WP9) Efforts to translate evidence into practice often fail because researchers have not understood, nor taken into account, complex contextual factors, because they are lacking capacities to engage relevant stakeholders, or because effect estimates (relative risks) remain an abstract notion without direct public health meaning. Rapid translation of evidence into practice will require engaging communities, regulators and decision-makers across many components of the exposome from the earliest stages of the project, and effective tailoring of dissemination strategies and key messages for different audiences in multiple languages. ATHLETE contains a WP dedicated to dissemination, including engagement channels and activities tailored to the specificities of stakeholders, policymakers, or the general public.
ATHLETE online toolbox All parts of the work described above will provide input to the ATHLETE online toolbox that will ensure that exposome data and tools are not only developed and used within the project but will be available to researchers and policymakers long after the project has finished. This toolbox will include the FAIR data infrastructure, searchable results catalogs, approved analysis pipelines and protocols for different research areas, the EXPOapp3, HIA and intervention toolkits, e-learning tutorials, and policy recommendations. Data sharing and access procedures will be developed during the project and will form part of the toolbox. The online toolbox will be implemented in compliance with the General Data Protection Regulation (EU 2016/679).
Strengths and limitations ATHLETE incorporates existing exposome data resources, the existing European network of birth cohort studies and harmonized data platform, and pilot work in exposome methodology (e. g. , statistical methods,  36, 37  exposome variability  31, 32, 106  ), which provide the project with a base of data, knowledge, and solid collaborations. By focusing on the early part of the life course and on the early signs of health damage before the onset of disease, the project is of high relevance for prevention. Also, ATHLETE focuses on pollutants and risk factors that are widespread in the general population and of regulatory relevance: air pollution, noise, lack of green space, heavy metals, pesticides, endocrine disruptors. This broad range of environmental stressors, together with information on living and social environments and on personal habits and behaviors, offers unique data to study the complexities of the exposome.
The main advances that ATHLETE will make to the application of the exposome concept can be summarized as follows: -The assembly of a large, harmonized, prospective exposome cohort and FAIR data infrastructure for the early part of the life course will be a major step forward compared with the relatively small and scattered current data sets and provide a sustainable platform for future early life exposome research. Of specific relevance here is our expansion of the rich HELIX subcohort exposome database with a new follow-up data point and new measurements of exposures, omics, and health outcomes. -The combination of targeted and nontargeted biomonitoring techniques for the measurement of chemical pollutants in the same subjects will be a powerful approach to evaluate their relevance for exposome research.
-Within-subject biospecimen pooling will provide greater accuracy in the estimates of long-term exposure to nonpersistent chemicals than has previously been achieved. -The deployment of personal monitoring approaches in entire cohorts will improve the accuracy of external, urban exposome assessments; this was previously limited to small validation and panel studies. -The focus on new biological pathways and approaches, such as the microbiome, placental epigenetics, composite measures for aging and stress pathways and cross-omics risk prediction, in longitudinal datasets, will push the integration of omics data into environmental health studies beyond the state-of-the-art, which is currently largely limited to single exposures and single omics layers, often in small studies. -New statistical and bioinformatics strategies will tackle the next set of analytical exposome challenges, including the evaluation of longitudinal exposome-health associations, the estimation of combined effects of exposures, causal structure models, and integration of cross-omics data.
Furthermore, the translation of exposome tools and findings to communities, stakeholders, and decision-makers is challenging due to the many complexities of the exposome; ATHLETE will make specific efforts to explain the unique features of exposome research and how they may contribute to a better understanding of the impact of environment on disease risk, and to the development of better prevention strategies. Lastly, ensuring the long-term sustainability of tools and data beyond the project's 5-year duration is an important challenge. Construction of the FAIR data infrastructure and online toolbox are aimed at ensuring such long-term sustainability, but future funding of these resources will need to be secured. Ultimately, combining early life exposome data as gathered in this project, with data on the adult exposome gathered in other projects, would allow the study of how the exposome during different life stage affects disease trajectories spanning the entire life course.
Conclusions The ATHLETE project has a strong focus on the vulnerable early stages of the life course. It will continue the implementation of the exposome in early life by developing a sustainable cohort data infrastructure, improving tools for exposure assessment and statistical analysis, generating longitudinal evidence linking the exposome to child and adolescent health, and translating exposome knowledge into policy. The assembly of data, tools and results in an openly accessible toolbox will lead to great opportunities for researchers, policymakers, and other stakeholders beyond the duration of the project. The results will help us to better understand health damage from environmental exposures and their mixtures from the earliest parts of the life course onward and will highlight opportunities for policy actions towards prevention and enhanced protection, including within the EU's Green Deal and Chemicals Strategy for Sustainability.
ACKNOWLEDGMENTS ISGlobal acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S ) and support from the  Generalitat de Catalunya  through the  Centres de Recerca de Catalunya Program. We acknowledge collaboration with European projects, specifically:  Human Early Life Exposome (HELIX)  ( FP7  grant agreement number  308333 ),  LifeCycle  ( H2020  grant agreement number  733206 ), and  Connecting Europe  and  Canada  in personalized health (EUCAN-Connect) ( H2020  Grant Agreement number  824989 ). The  Born in Bradford (BiB ) cohort is only possible because of the enthusiasm and commitment of the  Children and Parents in BiB. We are grateful to all the participants, health professionals, and researchers who have made BiB happen.
Cleaning the Data Set After the initial annotating, the data set was cleaned for duplicates and semiduplicates. Semiduplicates are tweets in which a few characters differ but the meaning is unchanged. A typical example is a retweeted post with the RT: prefix. Another example is a tweet suffixed (by a user or bot) with a few random characters to avoid being recognized (by Twitter detection algorithms) as a mass posting. To detect semiduplicates, we used a nonnormalized Levenshtein distance of less than 30 for tweets with more than 130 characters. For shorter tweets, the distance was scaled. The validity of the deduplication algorithm was qualitatively evaluated by the annotators. We were aiming for a greedy algorithm that identified too many semiduplicates rather than too few. Although this could slightly affect the size of the training set, it was considered to be of greater importance to prevent tweets that looked too similar from being included in both the training and test data sets.
We have open sourced the Python code that we developed for cleaning and removing duplicates and made it available in our web-based GitHub repository  [9]. As the stance of the tweet should be determined solely based on the content of the text, we deidentified usernames and URLs. Apart from cleaning usernames and URLs, the aim was to ensure that the input to the machine learning algorithm was exactly the same as that presented to the human annotators. In this respect, this served to both preserve anonymity and limit potential bias arising from the annotator's interpretation of the usernames. This also helped fulfill ethical responsibilities as well as the European Union's General Data Protection Regulation guidelines. In all, 3 independent annotators screened all posts for inclusion, excluding posts that were not about the vaccines administered during pregnancy.
Bidirectional Encoder Representations From Transformers The main model was based on the newest (May 2019) Whole Word Masking variant of Google's Bidirectional Encoder Representations from Transformers (BERT)  [11]. When published in late 2018, the model demonstrated state-of-the-art results on 11 NLP tasks, including the Stanford Question Answering Dataset version 1. 1 (The Stanford Natural Language Processing Group)  [12]. BERT is a bidirectional, contextual encoder built on a network architecture called Transformer, which is based solely on attention mechanisms  [13]. The main part of the training can be performed on unsupervised and unlabeled text corpora such as Wikipedia, and the pretrained weights  [14]  are trained solely on this general corpus. We trained the model on a domain-specific corpus to expose the model to the vocabulary that is typically used in vaccination posts.
As we have such a large number of short texts, this would have extended our data set. We refer to this data set as the vaccine-tweet data set. We tokenized the tweets using the BERT vocabulary and limited the sequence length to 96 tokens. By limiting the sequence length, we were able to increase the batch size, which is known to have a positive effect on performance. Figure  2  shows the sequence length of the downloaded tweets, showing that this trimming would affect less than one in thousand tweets. Tweets longer than 96 tokens were manually examined, confirming that these were mainly repetitive sequences and that the trimming did not affect the meaning (eg, a statement followed by strings of varying lengths of repeated characters such as. or ????). We have further addressed discourse distribution, labeling, and word balance in the word corpus in the study by Martin et al  [10].
In addition, we acquired a data set with a total of 201,133 vaccine-related news articles from the Vaccine Confidence Project (London School of Hygiene & Tropical Medicine) media archive. The articles were collected by automated keyword searches from several sources, including Google News, HealthMap, and Meltwater. It is an extensive collection of vaccine-related articles in English from both news media and blogs. The search criteria have been developed over the years, which is why they have varied slightly, but they are very similar to the list presented in Multimedia Appendix 2. We refer to this data set as the vaccine news data set. We chose not to pretrain the model on a maternal vaccine-specific data set because we wanted the encoder representations to also be used on other vaccine-related topics.
The benchmark architectures, the neural network, and the long short-term memory (LSTM) networks with and without Global Vectors for Word Representation (GloVe; The Stanford Natural Language Processing Group) word embeddings were all taken from Deep Learning With Python by Chollet  [16]. To verify that the neural network was able to solve other neural network tasks, we tested the network structures on one of the most basic NLP tasks: predicting positive and negative sentiments in IMDb (Amazon, Inc) movie reviews  [17]. The final domain-specific pretraining and fine-tuning were carried out on a Cloud TPU (Tensor Processing Unit) v2-8 node with 8 cores and 64 gibibytes memory and an estimated performance of 180 teraflops (Google Cloud). Domain-specific pretraining was carried out for 2 weeks, but, as shown in Figure  3 , there were no measurable improvements after a few days of running.
For our data set, this only provided an accuracy of 43. 7%. Thus, the network was not able to obtain a better result than simply predicting the overrepresented task Promotional for all data points. Adding pretrained GloVe word embeddings to this structure resulted in a slightly better performance, with a maximum accuracy of 55. 5% on the test data set. However, both approaches overtrained after just a few epochs with data sets of this size. To evaluate the reason for this low accuracy, we tested the same network on the IMDb data set, setting the same number of training examples (N=1633). In this case, the network achieved an accuracy of more than 80% even without the GloVe word embeddings, showing that the low accuracy was related to the difficulty of the maternal vaccine categorization. The modern LSTM model is a recurrent neural network with a memory module.
Main Research Target Our main research target was to investigate whether state-of-the-art NLP models could be improved by using the BERT architecture. Using the original pretrained weights, we achieved an average accuracy of 76. 7% when fine-tuning for 15 epochs. Starting from the original weights, the model weights were pretrained on the larger vaccine news data set for 1 million epochs. At various checkpoints (0 E, 250,000 E, 500,000 E, 750,000 E, and 1,000,000 E), the model was forked and then trained on the smaller and more specific vaccine-tweet data set. At each of the checkpoints, the network was fine-tuned on the manually labeled tweets for 15 epochs, and the average of 10 runs is shown in Figure  4. Using pretraining on domain-specific content, the accuracy reached a peak of approximately 79% when training only on the vaccine news data set.
Acknowledgments PEK was funded by the  European Commission  for the call  H2020-MSCA-IF-2017  and the funding scheme  MSCA-IF-EF-ST for the Vaccine Media Analytics project  (grant agreement ID:  797876 ). HJL, PP, SM, SD, and EK were funded by a grant from  GlaxoSmithKline  to support research on maternal vaccination. The funders had no role in the study design, data collection, analysis, interpretation, or writing of this paper. This research was supported with  Cloud TPUs (Tensor Processing Units)  from  Google's TPU Research Cloud.
MLPA Analysis of TSHR, FOXE1, PAX8, and NKX2-1 To assess CNVs, each exon of these genes was evaluated by MLPA (SALSA ® MLPA ® probemix P319-B1 Thyroid; MRC Holland, Amsterdam, The Netherlands) according to the manufacturer's instructions. The kit do not included probes to analyze CNVs in the NKX2-5 gene. The obtained gene dosages were evaluated using the Coffalyser. Net Software (MRC Holland, Amsterdam, The Netherlands).
Structural Modeling of the Human Forkhead Box E1 (FOXE1) and Thyrotropin Receptor Isoform 1 Precursor (TSHR) Proteins Through protein in silico modeling, we further assessed the potential deleterious effects of the identified missense variants cataloged as being of unknown significance (VUS) or likely pathogenic, and lacking evidence by functional assays. To construct protein models, we used the amino acid reference sequences NP_004464. 2 and NP_000360. 2 for human forkhead box E1 (FOXE1) and thyrotropin receptor isoform 1 precursor (TSH receptor), respectively. Only the sequence spanning amino acids 46 to 156 was considered when we created a model for FOXE1. The 3D model of this amino acid sequence was obtained using the I-TASSER server (http://zhanglab. ccmb. med. umich. edu/I-TASSER, accessed on 8 April 2021)  [22] , which is based in the Local MEta-Threading-Server (LOMETS)  [23]  and looks for structural templates similar to the query amino acid sequence deposited in the Protein Data Bank (PDB); these are then assembled as template fragments (threaded) into a complete protein model.
Once we obtained the best models for both proteins, we subjected them to energy minimization with the Molecular Modeling System UCSF Chimera software  [26]. The new coordinates were manually inspected, evaluated for their geometric quality, and validated with MOLPROBITY (http://molprobity. biochem. duke. edu, accessed on 8 April 2021)  [27]. The electrostatic potential of the TSH receptor model was determined with the PBEQ Solver server (http://www. charmm-gui. org/?doc=input/pbeqsolver&step=0, accessed on 8 April 2021)  [28]. The aminoacyl sequence of the transmembrane region of the TSH receptor model was calculated with PROTTER (https://wlab. ethz. ch/protter/start, accessed on 8 April 2021)  [29] , and later embedded into the membrane structure generated by the VMD software  [30]. Finally, in silico mutagenesis for missense variants of the FOXE1 and TSHR models was performed using the molecular graphics system, PyMOL (Molecular Graphics System, v.
(Gly124Arg) FOXE1 variant as deleterious with high confidence scores. To date, the p. (Gly124Arg) FOXE1 allele (rs774035532) exhibits a very low worldwide allelic frequency (0. 0016%) and has been identified exclusively in four heterozygous Latin American individuals according to gnomAD database (https://gnomad. broadinstitute. org/variant/9-100616566-G-C, accessed on 27 May 2021). Although there have been a few reported cases of Bamforth-Lazarus syndrome, a wide variation in clinical expressivity has been noted. For example, not all described patients have choanal atresia or epiglottic bifidity, and a minority shows a thyroid hypoplasia instead of athyrosis  [37] , as seen in our patient HC-266. Attempts to correlate this variable clinical expressivity with the residual functional activity of mutant FOXE1 protein have not been conclusive  [37] , and functional studies based on gel mobility-shift and transfection assays are needed to corroborate if p.
This complete TSH resistance leads to CH identified by TSH-based newborn screening  [43] , as occurred with HC-324: This patient was diagnosed at 1 month of age through a marked elevation of serum TSH (>150 µUI/, reference value: 0. 4-4-0 µUI/mL) along with severe hypothyroidism (total T3: 10 ng/dL, ref. value: 72-170 ng/dL; free T3: 0. 01 pg/mL, ref. value: 1. 8-6. 0 pg/mL; total T4: 0. 7 mcg/dL, ref. value: 4. 5-12. 5 mcg/dL; free T4: 0. 4 ng/dL, ref. value: 0. 8-1. 9 ng/mL). Thus, it seems plausible to consider the compound heterozygous p. [Asp118Asn];[Trp422Arg] as a potential loss-of-function biallelic TSHR genotype. These missense variants are not currently described as being responsible for TSH resistance in ClinVar (https://www. ncbi. nlm. nih. gov/clinvar, accessed on 8 March 2021), Human Gene Mutation Database (HGMD ® , http://www.
hgmd. cf. ac. uk/ac/index. php, accessed on 8 March 2021), LOVD 3. 0 (https://databases. lovd. nl/shared/genes/TSHR, accessed on 8 March 2021), or TSH Receptor Mutation Database (https://tsh-receptor-mutationdatabase. org/list. html, accessed on 8 March 2021), and to the best of our knowledge, they have not been previously reported as being responsible for any TSHR-related disorder. The p. (Asp118Asn) and p. (Trp422Arg) variants display extremely low worldwide allelic frequencies (0. 00039%: https://gnomad. broadinstitute. org/variant/14-81554332-G-A and 0. 0015%: https://gnomad. broadinstitute. org/variant/14-81609666-T-C, respectively; accessed on 27 May 2021) and they have been identified exclusively in heterozygous Latin American individuals. The crystallographic structure (ranging from amino acids 24 to 257) deposited in PDB (code: 2XWT) and our 3D protein model (Figure  2 ) indicate that p.
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: ClinVar: https://www. ncbi. nlm. nih. gov/clinvar/,accessed on 8 March 2021; dbSNP: https://www. ncbi. nlm. nih. gov/snp/,accessed on 8 March 2021; Genome Aggregation Database (gnomAD) v. 2. 1. 1:https://gnomad. broadinstitute. org/,accessed on 27 May 2021; LOVD: https: //www. lovd. nl/,accessed on 15 March 2021; OMIM: https://www. omim. org/,accessed on 8 March 2021; NCBI: https://www. ncbi. nlm. nih. gov/gene,accessed on 8 March 2021; Protein Data Bank (PDB): http://www. wwpdb. org/,accessed on 8 April 2021; UNIPROT: https://www. uniprot. org/,accessed on 8 April 2021. The data presented in this study are available on reasonable request from the corresponding author. The clinical and molecular data of patients and their relatives are not publicly available due to restrictions to preserve their confidentiality, which was part of the signed informed consent of each patient.
Acknowledgments: M. A. A. -O. and A. G. -d. A. wish to dedicate this article to the memory of  QFB Mario Arturo Maldonado Solís  ( 1968-2021 ), a visionary and enthusiastic professional committed to Mexican children's health through carrying out high-quality newborn screening programs. I. S. -V. acknowledges the  CONACYT post-graduate scholarship  ( CVU 519519 ,  Programa de Maestría y Doctorado  en  Ciencias Médicas ,  Odontológicas y de la Salud, UNAM ). The authors gratefully acknowledge the patients and parents for their commitment. They also thank  Martínez-Cruz , V. ,  Moreno-Rojas ,  R. ,  Urióstegui-Rojas ,  A. , and  Alejandre-López ,  J. L. for their assistance with the recruitment and patient sampling processes, as well as data collection and technical support.
Patients in Paris were not in the acute phase of BD at the time of scanning. Individuals with BPD and ADHD were recruited from the outpatient Emotional Dysregulation Unit for BPD and ADHD of the Geneva University Hospital. BPD diagnosis was established with the SCID for DSM-IV Axis II Personality Disorders  [46] , and ADHD diagnosis with the Diagnostic Interview for ADHD in Adults (DIVA 2. 0), by trained clinicians as part of the standard procedure of these specialized programs. Some patients were under psychotropic medication for comorbidities, as reported in Table  1. Participants were instructed not to take psychostimulants on the day of the study data acquisition. Control participants were recruited via local databases as well as through web advertisement and were matched with patients in terms of age, sex, level of education, and handedness.