dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection, management and analysis All data, including the responses to the questionnaires, will be transmitted electronically from the sensors or Open access tablet BLE to the home-based data collection and teletransmission device (EeleoCare) and then transmitted every 6 hours to the Clinikali platform via a secure SSL connection. The usual clinical data are collected and saved in an electronic clinical research file, which will be deidentified by giving each patient a unique identification number. Data collection will be conducted in a predefined standard sequence with the main assessments conducted at inclusion (day 1), day 15, day 29 and day 42 (tables 1 and 2). Alerts are automatically generated and sent to an electronic coordination platform for health professionals, with trackable follow-up.
All AEs and SAEs will be recorded in the patients' medical files. Any unexpected or new SAEs will be declared to the relevant authority (regional pharmacovigilance centre). The following changes to data recording or to a patient's clinical or physical well-being will result in the intervention of an oncologist or a nurse: (1) questionnaire MDASI completed daily by the patient from day -8 to day 43: any symptom score ≥7 (scale of 0-10) and/ or absence of evaluation for 48 hours; (2) PiCADo: I<O over 3 days ≤96% (calculated from data recorded over the last 3 days: wait for another 24 hours to calculate the next index, if ≤96% alert medical staff; (3) temperature ≥2°C compared with the reference temperature for the same patient; (4) temperature ≤2°C compared with the reference temperature for the same patient (mean value measured each min between day -7 and day 0 for each patient); (5) absence of data from the monitor for ≥6 hours, (6) detection of monitor removal for ≥6 hours; (7) increase or decrease in body weight of >5% in 1 week or less compared with the median weight measured from day -8 to day 0 and (8) absence of measurement of body weight for 48 hours.
Patients with missing data will not be included in the calculation of percentages. The incidence of emergency hospital admissions, SAEs and telemonitored alerts will also be described in relation to both the actual chemotherapy dose given during the course preceding the event and the dose intensities over the planned study participation of the patient. All statistical tests will be bilateral with an alpha risk of 5%. The time series data parameters will be transferred to the statistician in charge of the analysis and integrated in the database. The analyses will be performed using SAS V. 9. 4 or later. The sponsor and funder have no role in the design, collection, management, analysis, interpretation of data or writing of the report.
The data are transmitted via BLE to a home-based data collection device the size of a mobile phone (EleoCare; Eleo), and then via a secure web interface (General Packet Radio Service, GPRS) to a centralised server (Altran/Cap Gemini), where a set of automatic programmes compute and visually display the dynamics of 12 circadian parameters, 23 e-PROs and body weight changes in near-real time, which can be visualised by the oncology team for decision-making. The size of the home-based data receiver makes it readily portable should a patient leave home for any reason (work, holiday, etc), ensuring the continuity of data collection. The strength of the study is the e-medicine platform itself, which is designed to collect, analyse and graphically display in near-real time both longitudinal physiological time series and e-PROs from remote cancer patients at risk of SAEs, and to involve the oncology teams in the proactive management of remote patients.
Acknowledgements We thank  Malika Ali ,  Fanny Godereaux ,  Sandrine Noyes ,  Nadine Lubangol ,  Imene Hadj-Naceur ,  Melanie Dehais ,  Qi Huang ,  Agnès Fritsch  and  Gabriele Breda  for their contributions. The authors also wish to thank the patient advisors. Contributors The MultiDom protocol was written jointly by FL and MB, with specific aspects being discussed with AU, AT-B, AA, RB, RT, PH and RA prior to its current final version. The current manuscript was written by FL assisted by Newmed Publishing. All coauthors have contributed to the development of this manuscript and have read and approved its final version.
lividus, other sea urchins, and chordates. We compared the gene order and chromosome organization of P. lividus with that of S. purpuratus and L. variegatus, which diverged $60 mya. 38 We profiled genomewide chromatin accessibility throughout embryonic development. We investigated the expression and regulation of genes that originated at the origin of the echinoids clade. We identified a novel family of Pmar1-related transcriptional regulators specific to euechinoids, expressed in the micromere lineage and capable of converting any cell of the embryo into skeletogenic precursors. By integrating genomic and regulatory datasets, we demonstrated how regulatory changes could be associated with the origin of the novel body plan of urchins and other echinoderms.
Regulatory landmarks during sea urchin development To determine which TFs control successive phases of sea urchin development, we employed a footprinting approach to detect the most likely occurrences of TF binding events in the ATACseq signal at successive stages  75  (Figure  7 ). Then, by using reconstructed gene families, we identified 815 TFs in P. lividus and assigned 568 of them to TFBSs derived from the JASPAR database, which were later employed to analyze the footprint of the ATAC-seq signal (Figure  7D ; Table  S10 ) that we compared with their gene expression across developmental stages (Figure  S7A ). To ascertain the validity of the regulatory interactions deduced from our chromatin profiling data, we also Article reconstructed, for each stage, the global gene-regulatory network (GRN) by leveraging chromatin accessibility in OCRs, the assignment of OCRs to genes, and gene expression.
76 The resulting networks represent genes as vertices and regulatory interactions as edges and can be interrogated to highlight a specific subnetwork. This analysis recovered, for instance, the endomesoderm specification GRN (Figure  7C )  11  and the skeletogenic GRN (Figure  S7E )  12  described previously for S. purpuratus, validating the idea that the OCRs we identified are indeed cis-regulatory modules that bind key TFs of these GRNs. While a small number of loci appear to be transcribed during cleavage stages (Figure  S7A ), we found that the onset of large-scale zygotic genome activation is taking place at the end of cleavage, in the early blastula stage, as pointed out previously  77  (Figures  7D  and S8B ). Gene expression clustering (WGCNA) identifies small sets of genes specifically expressed in each of the cleavage stages, such as G-protein receptors (Oprx1, 16-cell), ionic channels (Sc5a2, 60-cell), Toll-like receptors (2-cell), or homeodomain genes (Figure  S6D ).
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: SLIDINGWINDOW:10:20),  120  we mapped the reads to the reference genome using HISAT2 v2. 2. 1  121  and obtained a transcriptome assembly for each sample using StringTie v2. 1. 4 122 by providing our gene models as a reference annotation file. We obtained 175 GTF files corresponding to the individual assemblies which were merged to obtain a reference GTF using the merge option from StringTie. The merged GTF was used to identify the candidate lncRNAs using FEELnc software  99  in a three steps pipeline: i) filter out transcripts shorter than 200ncl, monoexonic and overlapping protein-coding exons, ii) discard transcripts with coding-potential and iii) classify the candidate lncRNAs according to their relative position in the genome.
Synteny and gene family reconstruction To compute pairwise synteny comparisons, we used mutual-best-hits based on MMSeqs2 (MMSeqs2/12-113e3) comparisons between proteomes after selecting for the longest protein for each locus. After reindexing of gene coordinates, we used Fisher's exact test to determine mutual enrichment of orthologues between chromosomes or scaffolds. To estimate the relationship between gene order and divergence time, we estimated as the fraction of orthologues located in blocks of 2 or more consecutive genes in the same order with no more than one interspersed gene (Figure  3 ). Corresponding python and R codes upload to https://github. com/paracentrotus/ urchinpaper. We used Broccoli (v1. 2) for gene family comparisons using the species specified in Table  S5  ,  101  and inferred clade specific gains and losses by comparing the content of each family with that of a reference species tree using the ete3 library.
Gene expression analyses We extracted RNA for successive embryonic stages and several organs (Table  S3 ) using Trizol reagent (Invitrogen). Strand-specific RNA-seq libraries were prepared using the TruSeq RNA Library (Illumina) and sequenced in a 2x150bp layout with an average of 64. 6M reads per sample. Reads were aligned to the genome using STAR (v2. 5. 2b) at an average rate of 65. 64% of unique mapping. 107 A transcriptome was assembled for each sample using Stringtie (v1. 3. 3b)  108 and sample-specific transcriptome assemblies were merged using Taco. 109 The reads from all the samples were also assembled de novo using Trinity. 94 ene expression was quantified from reads aligned using STAR (v2. 5. 2b) and using featureCounts from the Subread package (v1. 6. 3)  110 and counts converted in FPKM.
We also evaluate the coverage of OCRs for both strands using featureCounts to evaluation their level of transcription on both strands. We used WGCNA (v1. 7. 0) clustering for the full set of embryonic stages and organs. After filtering out genes with limited variance and counts, then, the 'softpower' parameters were estimated and set at 13, and clustering was run with a 'signed' network type. 60 For all clustering analyses, FPKM were calculated and replicates if available were merged to obtain a single gene expression value. Then, we used mfuzz (v2. 54. 0) clustering to compare subsets of 8 embryonic stages in sea urchin, amphioxus and zebrafish using datasets for the two later as described in. 25 After filtering genes with low expression or limited variability, expression values were normalised, the fuzzifier parameters (m) was estimated and the optimal number of clusters was determined by computing with minimal distance between cluster centroids (Dmin) for various numbers of clusters.
To compare the evolutionary conservation of gene expression modules, a hypergeometric test was performed on the number of genes belonging to distinct gene families shared between each pair of alternate gene expression modules (accounting for possible many-to-many paralogy relationships between individual genes). Gene ontology enrichments were computed using the topGO package using terms transferring from PFAM annotation and Swissprot best hits (evalue 1e-10). 125 Comparisons of transcriptomic distances across embryonic stages and were computing as Jensen-Shannon divergence and single-copy orthologues inferred using Broccoli as performed in. 25 S. purpuratus data analyzed corresponds to PRJNA81157 and Branchiostoma floridae to PRJNA416866.
After tagmentation the libraries were purified with a Zymo DNA clean up kit then a QPCR was performed on an aliquot of the eluted libraries using the Ad1 and Ad2. x primers and the corresponding Ct was determined for each sample. 126 The libraries were then amplified at Ct+ 2 cycles. The quality of the libraries was checked on a 2% agarose gel to verify the size of the fragments and the nucleosome phasing. Reads obtained for each library were mapped using Bowtie2 (v2. 4. 1) with the parameters '-very-sensitive' and '-k 10'  127  and peak calling for open chromatin region was performed using Generich (v0. 6) available at https://github. com/jsh58/Genrich using ATAC model (-j), keeping unpaired alignments (-y), removing PCR duplicates (-r) and excluding reads mapped to mitochondria (-e MT). At this step, we pooled biological and technical replicates together as Generich analyses each replicate separately and then combined them by summarising p values using Fisher's method.
For enrichment analysis, the number of TFBS in the population of peaks associated the gene of interest (<150kb of the was contrasted with the population peaks associated with other genes in a hypergeometric for all TFBS and subjected to BH correction for multiple testing (e. g. Table  S8 ). Network reconstruction was performed using ANANSE  76  from ATAC-seq aligned BAM and 'gimme. vertebrate. v5. 0' binding motif database assigned P. lividus TFs using gimme motif2factor script. Resulting network was reconstructed using the igraph package in R retaining edges with above 90% quantile. were for plotting and of centrality (degree index).
After PCR, clean-up was performed adding 55 ml of SPRI and eluting in 21 ml of DNA purification buffer. Libraries were then using the Agilent 4200 Tapestation instrument and sequenced. were aligned using bowtie2 with the parameters '-local -very-sensitive -no-mixed -no-discordant -phred33 -I 10 -X 700 -3 75' and coverage calculated for previously defined ATAC peaks using  129  Coverage density heatmaps for ATACseq and Cut&Tag datasets were plotted using Deeptools 'plotHeatmap' function. 130  situ hybridization The of pop2 was retrieved from an EST library while the sequences pop1 and pop3 were obtained from available transcriptomes. For in situ hybridisation the full-length sequence of three genes were cloned pGemT. pop1 and pop2 were linearised with NcoI transcribed with SP6 polymerase. plasmid was linearised with and transcribed with T7.
ACKNOWLEDGMENTS F. M. is supported by a  Royal Society University research fellowship  ( URF\R1\191161 ) and a  BBSRC research grant  ( BB/V01109X/ ). The authors acknowledge support from the  Marine Genomics Network of Excellence (MGE) , the  European Marine Biological Resource Centre (EMBRC) , and the  Coordinated Research Infrastructures Building Enduring Life-science (CORBEL). Proximity ligation Chicago and HiC were supported by  Dovetail Genomics (Cantata Bio). Pacbio sequencing was funded by a grant from the  Agence Nationale de la Recherche (ANR)  to  T. L. (project  ANR-14-CE11-0006-01 ) and by a grant from  CORBEL  to M. I. A. E. K. L. was supported by a fellowship from  EMBRC. We thank  Hiroshi Wada  and  Atsuko Yamazaki  for providing the pmar sequences of Eucidaris tribuloides.
Data and code availability The pop1, pop2 and pop3 transcripts have been deposited to NCBI under the accessions Genbank: ON325581, Genbank: ON325582, Genbank: ON325583. The genome (Genbank: GCA_940671915. 1) and sequencing reads have been deposited to NCBI under the accession PRJEB25800. The RNA-seq and ATAC-seq have also been deposited to NCBI Gene Expression Omnibus (GEO) under the accession GEO: GSE202034. Other data files including genome assembly and annotation are available on zenodo (https://doi. org/10. 5281/zenodo. 7459274). Code underlying the analyses is available on github: https://github. com/paracentrotus/genome. METHOD DETAILS DNA extraction and sequencing DNA was extracted from sperm from a single male individual collected in Naples (Italy). Sperm cells were concentrated by centrifugation, washed repeatedly, and subsequently embedded in 2% low melting agarose.
The captured photo sets were then processed using the commercial software Agisoft Metashape (v. 1. 7. 2 build 12070) to produce a final 3D orthomosaic and DEM of the reef sequence (see  Boyden et al. , 2022, for details). In order to calculate the paleo waterdepth of the fossil reef at Lembetabe (and therefore derive a paleo RSL estimate at this location), we analyzed the mean depth distribution of the modern backreef directly offshore Lembetabe (considered as a modern analog of the fossil reef following the methodology of  Rovere et al. , 2016a). To do this, WorldView 2 (v. 28. 4, May 2020) satellite imagery for the Lembetabe reef was processed using the SPEAR Relative Water Depth toolset within ENVI ® (https://www. l3harri sgeospatial. com/Software-Technology/ENVI) to generate a bathymetric DEM.
, 2016)  with four intermediate configurations inbetween. Lastly, in order to investigate deviations relative to present day sea level, we set the excess eustatic sea level during the LIG (assumed to last from 129 to 118 ka) to 0 m. Varying both Earth and ice history leads to 576 different GIA predictions. To calculate the probabilistic distribution in time and space of the RSL corrected for GIA, we implemented a Monte-Carlo approach (1 million permutations), as described in Rovere (2022) (https://doi. org/10. 5281/ zenodo. 7231482).
Higher initial ratio [ 234 U/ 238 U] than the seawater value indicates diagenetic processes that might imply addition of 234 U that would produce an older age (we obtained ages of 139 ± 0. 5 ka, significantly older than the age range of all others samples). Excluding this sample, the U-series ages on the Lembetabe section fall between 115. 0 ± 0. 4 ka and 129. 5 ± 0. 5 ka and the initial d 234 U i range is reduced, with an average of 145. 9 ± 9. 1‰ (2SD) (Table  1 ). The initial d 234 U of this smaller dataset appears homogeneous and in good agreement with marine value  (Andersen et al. , 2010; Chutcharavan et al. , 2018). Most of the samples have high 230 Th/ 232 Th activity ratios implying low 232 Th concentrations (Table  1 ) and hence little evidence of any inherited 230 Th. However, a few other samples have higher concentrations of 232 Th than typical concentrations for surface corals  (Shen et al.
2  for location). On average, the fossil reef stands at an elevation of 1. 86 ± 0. 56 m amsl (Table  2 ). Regarding the relic shore platform, GPS measurements indicate that the paleo-shoreline (i. e limit between the paleo-beach and the paleo-platform) stands at 2. 68 ± 0. 10 m amsl (Fig. 6 ). All the elevations measured with dGNSS in Lembetabe are available in Boyden et Rovere (2021) (https://doi. org/10. 5281/zenodo. 5727117).
We used global databases from  Hibbert et al. (2016)  (based on dated fossil corals and depth distribution compiled by the International Union for the Conservation of Nature -IUCN) and from the Ocean Biodiversity Information System (OBIS) to determine the living depth range of each coral genera/species dated (Table  2 ). We were able to reduce the probable depth range of the fossil reef to 10e15 m thanks to a few genera/species which are most commonly found living within this depth range (i. e Acropora, Dipsastraea gr. pallida, Favites, and Platygyra, Table  2 ). Using the median depth, the paleo-depth is even reduced to less than 5 m (Table  2 ). As well, a shallow environnement is suggested from the OBIS database with a few genera/species particularly abundant between 0 and 10 m (i. e Plesiastrea, Dipsastraea, and Favites, Table  2 ).
Glacial isostatic adjustment GIA driven sea-level peaks early in the deglaciation and subsequently decreases due to continental levering and ocean syphoning (Fig. 7 b ). GIA models with a fast deglaciation (those with an MIS 6 maximum at 136. 5 ka) generally lead to a higher sea level than those with a slower deglaciation. Note that the modeled interglacial ends at 118 ka, which drives the steep drop-off of sea level after this time. We therefore used the GIA correction at 118 ka for data younger than 118 ka. This prediction does not include excess (i. e. more than present) ice melt. Accounting for the gravitational and deformational effects leads to a GIA or fingerprint signal associated with this excess melt  (Hay et al. , 2014). For 1 m of sudden ice melt from the West Antarctic Ice Sheet, RSL in Lembetabe would be expected to rise by 1.
Finally, according to sea-level reconstructions, GMSL was about 10 me20 m below the modern sea level during MIS 5a (Waelbroeck Elevations for RSL index points, i. e LIG corals. For each coral sample taken from the fossil reef (FR) flat at Lembetabe, we show the calculated paleo-RSL (PRSL). Coral genus (and species whenever possible) was determined using the taxonomic classification of  Veron (2000)  with modifications by  Huang et al. (2014b Huang et al. ( ,a, 2016)). When available, depth range and median depth for each coral genus encountered within the fossil reef are indicated. These are taken from the summary of empirically-derived global depth distributions in the fossil coral database from  Hibbert et al. (2016). Coral abundance (in percentage) between 0 and 10 m of depth extracted from OBIS database https://obis.
By using monthly rainfall data from the Global Historical Climatology Network, Dewar et Richard (  2007 ) calculated that the   1  and 2. b) Background GIA signal for Lembetabe (calculated from 576 models), which we consider here to be the amount of sea level change during the interglacial that is purely driven by GIA (i. e. global mean sea level during the last interglacial is set to zero in these calculations). In other words, any difference between the data in a) and the predictions of interglacial sea level in b) must be driven by global mean sea level change. Fig. 8. Kernel density estimate showing the probability density distribution in time and space of RSL at Lembetabe, corrected for GIA. The colorscale is related to the probability density, with a lower probability for darker areas and a higher probability for yellow areas.
Furthermore, applied to our dataset, this subsidence rate would imply an LIG peak sea level of 24 m, which is inconsistent with ice constraints and the higher end of LIG GMSL estimates  (Dutton et al. , 2015a (Dutton et al. , , 2015b)). To remain within the "classic" LIG GMSL estimate (i. e. , 5e10 m above present;  Gulev et al. , 2021) , our location (LIG peak sea level ¼ 3. 2 m) would have to be subsiding (since 125 ka) at a much lower rate, between À0. 014 and À0. 05 mm/yr. Taking into account, instead, the GMSL estimate of  Dyer et al. (2021)  (i. e 1. 2e5. 3 m above present), the tectonic vertical land motions would entail uplift (0. 016 mm/yr) or slight subsidence (À0. 017 mm/yr). However, we remark that these rates are (obviously) not independent from the LIG sea-level record and should be then used with caution.
As the rates mentioned above are based on the modern elevation of the LIG sea-level indicators, they might also include the effects of mantle dynamic topography (DT), which is the vertical crustal motion driven by mantle flow. In the northern tip of Madagascar,  Stephenson et al. (2019)  studied a set of emerged Pleistocene terraces (including the LIG terrace), standing at various elevations over a distance of at most 80 km. By using geophysical data including a seismic tomography model, they suggested an asthenospheric anomaly (about 125 km deep) for the origin of the spatial gradient of uplift observed. The models of  Stephenson et al. (2019)  show DT-driven uplift also for the SW part of Madagascar. DT simulations by  Austermann et al. (2017)  predict 1. 96 ± 1. 4 m (1s) of uplift since the LIG at Lembetabe.
2 and 4. 2 m). While correcting for vertical land motions carries several uncertainties and unknowns, independent MIS 11 data seem to indicate a slight subsidence, possibly between À0. 004 and À0. 02 mm/yr. Applying these rates to our peak sea-level estimate (2. 2e4. 2 m, reported above), we would obtain a peak LIG sea level between 2. 7 and 6. 7 m. Overall, this value is in broad agreement with the upper end of the peak LIG sea-level range calculated by  Dyer et al. (2021) (1. 2e5. 3 m)  and slightly lower than the one obtained from the Seychelles (7. 6 ± 1. 7 m,  Dutton et al. , 2015b). Note, however, that peak sea level in these studies is reported to occur at the beginning of the LIG rather than the end, which is suggested by our data. Our results fall on the low end of the oftencited LIG range (e.