dataesr/scientific-paragraphs-categorization huggingface dataset:
r-project. org/) with RStudio (v. 1. 4. 1106). All reads were uploaded to the European Nucleotide Archive (ENA) (https://www. ebi. ac. uk/ena). Short and long reads from the MMC induction, the genome sequencing experiment, the verification of P-P acquisition, and the sequences gained by the genome and hybrid assemblies are accessible under ENA study number PRJEB52357. Details on read accession numbers, assemblies, and experiments are listed in Table  S5.
ACKNOWLEDGMENTS INCEPTION project ( PIA/ANR-16-CONV-0005 ),  Equipe FRM (Fondation pour la Recherche Médicale ):  EQU201903007835 , Laboratoire d'Excellence IBEID Integrative Biology of Emerging Infectious Diseases  [ ANR-10-LABX-62-IBEID], SALMOPROPHAGE ANR-16-CE16-0029. Sequencing was done at the  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France  and was supported by  France Génomique  ( ANR-10-INBS-09 ) and IBISA. We especially thank  Olaya Rendueles Garcia ,  Marc Monot ,  Goerges Haustant ,  Laure Lemée ,  Valérie Briolat ,  Laurence Ma ,  Elodie Turc ,  Amandine Buffet , and  Matthieu Haudiquet  for providing support, advice, and services regarding DNA isolation, library preparation, and sequencing. Moreover, we are grateful to the strain collection center of the  Institut Pasteur  for great service and for efficiently providing the strains that we used in our study.
Interaction between duration and numerosity On each trial, two circular patches of white dots were presented sequentially. Each patch was generated using a program made from the MATLAB Software. The first and second patches were separated by a constant ISI of 1500 ms. The duration of stimulus presentation and the number of dots were manipulated for each patch. The duration and the numerosity of the second patch changed in the same (increase/increase or decrease/ decrease) or opposite (increase/decrease or decrease/increase) directions in trials defined as congruent or incongruent, respectively. In the duration (DurN) task, participants had to indicate whether the duration of the second patch (S2) presentation was shorter ("b" key) or longer ("n" key) than the duration of the first patch (S1) presentation.
Statistical analyses Linear or logistic mixed effect regression models were performed using JAMOVI 1. 1. 9 (The jamovi project (2019). jamovi. (Version 1. 0) [Computer Software]. Retrieved from https://www. jamovi. org -Gallucci, M. (2019). GAMLj: General analyses for linear models. [jamovi module]. Retrieved from https://gamlj. github. io/) to investigate single trial performance in the different tasks. In each model, participant was included as a random factor on the intercept, the group factor (between-subject) was dummy-coded with the control group as reference category and the other factors (within-subject) were simple-coded. Whenever a fixed effect or interaction was significant, a d-like effect size was computed. For linear mixed effect models, the unstandardized regression coefficient was divided by the residual standard deviation (d = unstandardized regression coefficient / residual standard deviation;  Rouder et al.
(B6) into Eq. (B5) and expressing g k-1 i in terms of H k-1 i and H k-2 i yields H k i = H k-1 i + ∆τ 2 θ r f k + 1 - ∆τ θ r H k-1 i -H k-2 i , (B8) which is equivalent to the second-order Richardson method proposed in  Frankel (1950). Author contributions. LR Original study design, code and algorithm development, Julia at scale implementations, scaling and performance benchmarks, figure creation, manuscript edition. IU Code and algorithm development, dispersion analysis, parametric study, figure creation,.
Acknowledgements. We thank  Boris Kaus ,  Stefan Schmalholz  and  Mauro Werder  for stimulating discussions. We acknowledge the  Swiss Geocomputing Centre ,  University of Lausanne , for computing resources on the Octopus supercomputer and are grateful to Philippe Logean 945 for continuous technical support. LR, IU and SO acknowledge financial support from the  Swiss University Conference  and the  Swiss Council of Federal Institutes of Technology  through the  Platform for Advanced Scientific Computing (PASC) program. This work was supported by a grant from the  Swiss National Supercomputing Centre (CSCS)  under project ID c23, obtained via the  PASC  project  GPU4GEO. IU and YP acknowledge the financial support from the  Russian Ministry of Science and Higher Education  (project No. 075-15-2019-1890 ).
Dating the Oceanian genetic diversity The ages of coalescence for 13,459,472 SNPs were obtained using GEVA  (Albers and McVean, 2020)  on a dataset of 569 phased whole genome sequences previously described  (Brucato et al. , 2021)  and representing a large worldwide panel of genetic diversity ( ). We used this approach as it requires no prior knowledge about demographic histories or selection events to infer the dates of genetic variants present in large-scale datasets. It is robust to the frequency and sequencing errors, but it relies on the assumptions of a single origin for each allele and a fixed recombination rate. In this absolute dating approach, our ages of coalescence are significantly correlated with the ages of coalescence previously obtained from genomes also included in our dataset (SGDP dataset: ρ = 0.
87, p < 10 -16 , )  (Albers and McVean, 2020; Mallick et al. , 2016). In Oceanian populations (Figure  1A ), we defined the ages of coalescence for 1,790,625 Oceanian-specific SNPs ( ). We defined Oceanian-specific SNPs as variants absent in the rest of the dataset, which includes several East Asian (n = 55) and West Island Southeast Asian genomes (n = 68, ), to limit the influence of recent Asian genetic ancestry found in modern Oceanian genomes as seen in PCA and ADMIXTURE analyses (between 22 and 76%, and ). Table  S1  Figure  S1  Data S1 Table S1.
Distribution of ages of coalescence in Oceanian genomes (A) Map of analyzed populations. Red dots localize the three studied Oceanian regional groups in Wallacea (Flores Island), New Guinea (Papua New Guinean southeast coast), and the Bismarck Archipelago (New Britain). Black dots represent other groups included in our dataset ( ). (B) Cumulative distribution of ages of coalescence estimated with GEVA for Oceanian-specific SNPs binned by frequency. (C) Density plot of ages of coalescence for SNPs specific to each Oceanian regional group. (D) Density plot of ages of coalescence for SNPs present in archaic fragments in New Guinean genomes. The relative proportion of archaic ancestry, estimated with Skov's HMM method, is calculated as (Denisovan ancestry -Neanderthal ancestry)/(Denisovan ancestry + Neanderthal ancestry).
Acknowledgments This work was supported by the  French Ministry of Research  grant  Agence Nationale de la Recherche  ( ANR PAPUAEVOL 20-CE12-0003-01 ), the  French Ministry of Foreign and European Affairs  ( French Prehistoric Mission in Papua New Guinea ), and the  French Embassy in Papua New Guinea. We are grateful to the genotoul bioinformatics platform  Toulouse Occitanie  ( Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12 )for providing help and computing and storage resources. We acknowledge support from the  Labex TULIP, France. MA was supported by the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 16-0030 ). MM was supported by the  European Union  through the  Horizon 2020 research and innovation program  under grant no.
Software and algorithms GATK (Poplin et al. , 2018) https://github. com/broadinstitute/gatk/releases BCFtools v. 1. 4 (Li, 2014) https://github. com/samtools/bcftools SHAPEIT v. 2. r79033 (Delaneau et al. , 2012) https://mathgen. stats. ox. ac. uk/genetics_software/shapeit/s hapeit. html#download The table layout displayed in this section is not how it will appear in the final version. The representation below is solely purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
Dataset We compiled a dataset of 239 Oceanian whole genome sequences from publicly available data: the Simons Genome Diversity Project (SGDP) dataset  (Mallick et al. , 2016) , data from New Guinea  (Brucato et al. , 2021; Jacobs et al. , 2019; Malaspinas et al. , 2016) , data from Indonesia  (Jacobs et al. , 2019)  and data from the Bismarck Archipelago  (Brucato et al. , 2021; Vernot et al. , 2016)  (Table  S1 ). In the present study, the term 'Oceania' is used to include populations from Wallacea, New Guinea, Australia and the Bismarck Archipelago. Our dataset also includes genomes from other continental groups including Africa, East Asia, Europe, South Asia and America  (Jacobs et al. , 2019; Mallick et al. , 2016)  (n = 330, Table  S1 ), to compare our results to a previous report  (Albers and McVean, 2020).
Base-calling and per-sample gVCF files were generated using GATK HaplotypeCaller (reads mapping quality ≥20)  (Poplin et al. , 2018). Multisample VCF files were obtained with CombineGVCFs and GenotypeGVCFs. Genotype calls were subsequently filtered for base depth (≥8x and ≤400x) and genotyping quality (≥30) using BCFtools v. 1. 4  (Li, 2014). We removed sites within segmental duplications, repeats and low complexity regions based on publicly available masks (http://hgdownload. soe. ucsc. edu/goldenPath/hg19/database/genomicSuperDups. txt. gz; http://software. broadinstitute. org/software/genomestrip/node_ReferenceMetadata. html). Only biallelic sites with high quality variant calls in at least 99% of samples were retained, leading to a whole genome dataset of 569 individuals typed for 41,250,479 SNPs (Table  S1 ).
We used a mutation rate of 10 -8 and an effective population size of 10,000  (Albers and McVean, 2020; International HapMap et al. , 2007). All parameters were set to the defaults. The analysis was performed in batches of 5,000 SNPs. Results were further filtered using the provided estimate. R script with the scaling parameter set as 10,000. Results based on the 'joint clock model' were analyzed in our study. We performed Spearman's and Pearson's correlation tests between all of our estimated ages of coalescence and those provided on the Atlas of Variant Age database for the human genomes in the SGDP dataset (https://human. genome. dating). For each of the six continental (Oceania, Africa, Europe, East Asia, South Asia, America) and three regional groups (Wallacea, Papua New Guinea, Bismarck Archipelago), we estimated the cumulative distribution of ages of coalescence of SNPs according to their frequency in the respective group, as in  (Albers and McVean, 2020) : below 1%, 2.
Signals of selection We computed three different genomic scans for signals of selection in a target population compared to a reference population. This constitutes our relative dating approach. The indexes XP-EHH and XP-SL were computed with Selscan v. 1. 3. 0 ( Szpiech and Hernandez, 2014) using the three regional groups, defining New Guinea or the Bismarck Archipelago as the target population and Wallacea as the reference population. Since human genetic diversity west of the Wallace line is largely dominated by Asian ancestry  (Hudjashov et al. , 2017; Lipson et al. , 2014) , the Wallacean population on Flores Island is the best proxy in our dataset of modern genomes for a genetic signature prior to the initial settlement of north Sahul. Previous reports showed that secondary gene flows from New Guinea to Wallacea occurred during the last 20,000 years  (Brucato et al.
Archaic genetic ancestry Archaic genetic introgression in individual genomes of each of the three regional groups (Wallacea, New Guinea, the Bismarck Archipelago) was estimated using Skov's HMM method  (Skov et al. , 2018). This does not use an archaic genome as a reference but instead defines introgressed segments that contain a high density of variants found in the target genome but are absent in an unadmixed modern human reference. African genomes from our dataset (n = 38) were used as the unadmixed modern human reference set, following recommended guidelines  (Skov et al. , 2018) , although we note that recent studies have found archaic genetic introgression in some Sub-Saharan African genomes  (Chen et al. , 2020; Durvasula and Sankararaman, 2020). Maximum likelihood estimates of the parameters were computed using the Baum-Welch algorithm and introgressed segments were identified using posterior decoding.
9 mg hydroxyapatite (HA)/cm 3 , 315. 7 mg HA/cm 3 , 337. 5 mg HA/cm 3 , or 359. 2 mg HA/cm 3. All analyses were performed using the Scanco software. JCI Insight 2022;7(21):e161214 https://doi. org/10. 1172/jci. insight. 161214 RNA sequencing. Total RNA from E14. 5 hearts was isolated as described below and submitted to Cofactor Genomics for total RNA sequencing. Four samples were submitted per experimental group. Each RNA sample was obtained from an individual mouse. RNA samples were processed as follows and sequenced in an Illumina platform. Briefly, rRNA probes (Ribo-Zero, Epicentre) were hybridized to total RNA for removal of rRNA from the sample. Ribo-depleted RNA was then fragmented prior to cDNA synthesis using random primers. Double-stranded cDNA was end-repaired and A-tailed to prepare for adaptor ligation.
Indexed adaptors were ligated to DNA, and the adaptor-ligated DNA was amplified by PCR. Library size and quality were assessed on an Agilent Bioanalyzer, and library yield was quantified by quantitative PCR using the Kapa Biosystems Library Quantification kit prior to sequencing (single-end, 75 bp fragment size) on the Illumina HiSeq 2000. The number of aligned reads per sample varied between 40 millionand 50 million and represented about 78% of the total reads per sample. Quality control, alignment, clustering, normalization, and expression comparison, analysis, and visualization were performed by Cofactor Genomics (http://cofactorgenomics. com). Raw sequence data in Fastq format were assessed for quality (FastQC, http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and rRNA content.
After clusters are created in this way, they undergo linear normalization by multiplying each sample's locus coverage by the total reads of the lowest read count sample divided by the respective sample's total reads. Mapped reads averaged 35 million per sample. These normalized expression data are the basis for the expression comparison. The comparative expression approach steps through each cluster and compares every possible pairing of samples and generates a log 2 (A/B), where A and B are the 2 normalized average coverage of each sample, A and B. Pairwise P values were determined among the samples using Welch's t test for unequal variance. The resulting comparative expressions were visualized in ActiveSite (Cofactor Genomics), and loci of interest were chosen. P values for differentially expressed genes were calculated using Welch's test, and a q value was calculated that adjusted for multiple testing using the Benjamini-Hochberg method.
We focused our analyses on genes showing an expression level higher than 1 read per kilobase of transcript per million of mapped reads in at least 1 of the experimental groups, a fold change in expression (up or down) > 1. 5, and a q < 0. 05. Fastq files as well as processed files and metadata for this experiment have been deposited in NCBI Gene Expression Omnibus (accession number GSE198416). Disease and functional ontology analyses were performed on selected groups of genes using Ingenuity software (QIAGEN); Functional Disease Ontology annotations (http://django. nubic. northwestern. edu/fundo/ and http://projects. bioinformatics. northwestern. edu/fundo); a service from Northwestern University (70-72); and the Database for Annotation, Visualization and Integrated Discovery (https://david.
For each individual sample, expression was corrected by the expression of control, housekeeping genes (Gapdh or Rn18s), which did not exhibit any significant difference in expression between genotypes. Expression data are shown in arbitrary units and represented as fold increase over the mean value in the control group. The sequences of the primers used for each gene are shown in Supplemental Table  2. RNAscope in situ hybridization and histology. The heads of E14. 5 fetuses were harvested as described above and fixed in formaldehyde for 48 hours, then embedded in paraffin and cut in 5 μm coronal or rostro-caudal sections. In situ hybridization of Dio3 mRNA was performed in selected sections of 2 JCI Insight 2022;7(21):e161214 https://doi. org/10. 1172/jci. insight. 161214 animals per genotype utilizing the RNAscope technique (Advanced Cell Diagnostics, Bio-Techne) following the manufacturer's suggested procedures.
We used the RNAscope Mm-Dio3 probe (catalog number 561641) and the ACD 2. 5 HD Detection kit (RED). As a technical negative control, we use the bacterial probe DapB supplied by the manufacturer, while experimental negative controls included tissue sections from mice lacking the full Dio3 gene (Dio3 DEL/DEL mice). Some tissue sections were counterstained with hematoxylin and mounted with EcoMount (catalog EM897L, Biocare Medical), while other sections were mounted with DAPI Fluoromount-G (catalog 0100-20, Southern Biotech). Bright-field or fluorescent images of the mRNA signal were taken, respectively, with a Zeiss Axioskop 40 microscope or a Leica SP8 confocal microscope utilizing LAS X software. For anatomic reference, adjacent tissue sections were stained with H&E at our Histology Core facility following standard procedures.
Acknowledgments We are grateful to  Terry Henderson ,  Armie Mangoba , and  Patrizia Roy  for technical assistance and to  María Jesús Obregón  for providing the T3 and T4 antibodies. This work was supported by grants  DE028732  and  MH096050  from the  NIH National Institute of Dental and Craniofacial Research  and the  NIH National Institute of Mental Health , respectively. This work utilized the  Molecular Phenotyping Core, Histopathology and Histomorphometry Core, Confocal Microscopy Core , and Mouse Transgenic and  In Vivo Imaging Core at MaineHealth Institute for Research. These core facilities are supported by grants  P30GM106391 ,  U54GM115516 , and  P20GM121301  from the  NIH National Institute of General Medical Sciences. Address correspondence to:  Arturo Hernandez ,  MaineHealth Institute  for Research, Scarborough, Maine 04074, USA.
We measure the patient through the questionnaires detailed in section 2. 2. • Consumption monitoring profile: monitor the patients' intervention consumption. i. e. , the type and number of interactions of the patient with the CPAP interventions. We measure these interactions using different logging levels in the mobile application. • Health monitoring profile: monitor the different clinical indicators directly linked to the CPAP therapy. We have the Apnea Hypopnea Index (AHI) and adherence level. The CPAP device itself measures these data. Each monitoring data has the data quality metric. We calculate this metric using the monitoring reliability, the data variety and the data volume. For example, for the patient's perception, we ask the patient to fill up the same questionnaire at a different time to get the reliability of the information by double/checking the data convergence.
Feedback Propagation To propagate we retrieve the feedback that we have a buildup previously. In our approach, we take into consideration that there are multiple experts that will give their validation. Each expert has his own field of expertise, therefore, we have a well-defined boundary for feedback propagation. For example, the doctor will validate the patient profile (clustering). Consequently, we easily identify the different models and dataset that has an impact on patient profiling. The feedback boundary must be clear and well-defined to propagate the feedback properly. As a result, each stakeholder of the HIaaS must have explicit and distinct roles. By means of this feedback boundary, we can easily identify the impacted datasets. We update these datasets according to the feedback and therefore converge with the expert validation.
Implementation To collect the feedback from the different experts, we implement a mobile application called Sleep. py Expert. To deliver the CPAP personalized intervention directly to the patient, we implement another mobile application called Sleep. py. The two apps are part of the HIaaS paradigm because a mHeatlh solution provides the responsiveness and flexibility that the HIaaS model requires. Both applications uses the Flutter 1  framework for developing both mobile application. For the backend and the data management, we use python's Flask 2  framework. Finally, to deal with the different data topologies, we implement a SQL (Microsoft SQL Server 3  ) and a NO-SQL (MongoDB 4  ) databases. The mobile application is available on both iOS and Android.
Sleep. py Expert App There is a dedicated application for the different experts called Sleep. py Expert. This application firstly helps the experts to provide personalized CPAP patient management. Secondly, we collect the experts' verifications and validations through Sleep. py Expert. We detailed this verification and validation mechanism in this section 4. 3. The Sleep. py Expert app also logs how the expert consumes the application through different logging level. We develop 2 main features in the application: 1. Monitoring the different data collected on the patient's health and the patient's environment 2. Provide the most appropriate configurations for the CPAP therapy and collect feedback on this prediction.
Food intake monitoring To provide the diet intervention, we set a maximum daily calorie intake for each patient (figure  9 ). We calculate this maximum calorie intake, we use the patient's sexe, age, height and weight. Each patient self-report their height and weight. To help the patient to track the number of calorie intake, we implement a food scanner (figure  10 ). The patient uses Sleep. py to scan the barcode of the product and then the application automatically calculate the number of calories for 1 portion This intervention gives the patients the convenience to track in real-time his calories intake to achieve his goal.
Human Ethics The different decision support tools present in the HIaaS are finally validated by a human expert. This expert takes all the responsibility for this decision and not the data analytic models. These models are built on using a subset of whole patients managed by Linde Homecare France. We make sure that this sub-dataset reflects the whole patients of Linde Homecare France. All data collected are anonymised before their exploitation in data analytic processes. This anonymisation process complies completely with the General Data Protection Regulation (GDPR).
She conducts research in the field of Product Life Cycle management (PLM), Product development and Sustainable Performance. As the project manager, she is currently a coordinator of SUNSpACe ERASMUS+ and co-leading of SHYFTE ERASMUS+ in the field of Smart farming and Industry 4. 0. Nejib Moalla Full Professor at the University Lumiere Lyon 2 and a member of the DISP laboratory in France. He finished his PhD thesis in 2007 and his habilitation in 2015. He held the responsibility of the project management department between 2008 and 2012. His research activities deal with software and data engineering with finalized innovative applications in the industry. He develops research concepts in software quality, service-oriented architectures, performance-based service reuse, microservice architecture, ontology engineering, knowledge Management, etc.