dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 7a, the GTR model with gamma correction for intersite rate variation (eight categories) and the covarion model were used. Trees were run as two separate chains (default heating parameters) for 15 000 000 generations, by which time they had ceased converging (the final average standard deviation of the split frequencies was less than 0. 01). The quality of the chains was estimated using built-in MrBayes tools and, additionally, using Tracer 1. 6 package  (Rambaut et al. , 2018) ; based on the estimates by Tracer, the first 6000 generations were discarded for burn-in.
Acknowledgments The research was carried out within the framework of the research topic «Ecological patterns of sustainable functioning of ecosystems and the potential resources of the Volga Basin» AAAA-A17-117112040039-7 of the  Institute of Ecology of the Volga River Basin , a branch of the  Samara Federal Research Centre of RAS. The study in Protected Areas was carried out in accordance with the  Scientific Cooperation Agreements between the Institute of Ecology of the Volga River Basin of RAS  (IEVB of the RAS) and the  National Park «Samarskaya Luka»  and the  Joint Directorate of the Mordovia State Nature Reserve  and the  National Park «Smolny».
Background: Chronic myeloid leukaemia (CML) is a rare disease in children. The frequency and outcome of children evolving to accelerated phase (AP) or blastic phase (BP) under treatment with imatinib is unknown. The aim of the current study is to assess the incidence of progression from CML in chronic phase with imatinib frontline in a paediatric setting and describe the management and outcome of these patients. Patients and methods: In the I-CML-Ped Study database (www. clinicaltrials. gov, #NCT01281735), 19 of 339 paediatric patients in chronic phase treated with imatinib in the.
Materials and methods Patients were identified from the I-CML-Ped Study (www. clinicaltrials. gov #NCT01281735), a database of patients from 0 to 18 years with CML from 37 centres in 14 countries. The I-CML-Ped Study was set up to assess epidemiology, management and outcome of CML in children and adolescents and recorded the data retrospectively from 2000 until 2010, then prospectively from 2011. This study was approved by the institutional review board of the University Hospital of Poitiers, in accordance with the declaration of Helsinski. A total of 445 patients were enrolled from 2001 to January 2016. At diagnosis, 376 patients were in CML-CP according to the European LeukaemiaNet (ELN) criteria  [11] , and 339 received upfront imatinib treatment. Among the 339 patients in CML-CP with imatinib frontline, 19 patients evolved from CML-CP to CML-AP or to CML-BP and represent the study cohort named as AP/BP cohort (Supplemental Figure ).
CML-BP was defined by the presence of at least 30% of blasts cells in blood or BM or evidence of extramedullary disease  [11]. The myeloid or lymphoid immunophenotype of CML-BP was determined by flow cytometry  [15]. BCR-ABL1 kinase domain (KD) mutation analysis was performed as previously reported in case of suboptimal response or treatment failure. Differences in baseline characteristics between AP/BP cohort and the control cohort were tested using the Fisher's exact test for categorical data or the Wilcoxon rank sum test for continuous variables. OS was estimated using the KaplaneMeier method  [16]. To account for competing events, incidence of progression along time was estimated by the cumulative incidence function with the use of Fine and Gray models. Competitive events were deaths from causes other than progression.
Results In the I-CML-Ped Study database, the cumulative incidence of progression at 1 and 3 years was 3%   Eleven of the 19 patients (58%) received hydroxyurea before the start of TKI. Imatinib was the first-line TKI for all patients. The dose was 260 mg/m 2 for 12 patients and 300e340 mg/m 2 for seven. Three patients received a concomitant additional treatment: triple intrathecal injection (n Z 1) for a retinal leukostasis, mitoxantrone (n Z 1) to reduce the leucocytes count and cytarabine/ interferon (n Z 1) for haemorrhagic retinopathy (Table  2 ). Because of non-achievement of cytogenetic and/or molecular responses, the dose of imatinib was increased in six patients (from 260 to 300 mg/m 2 or from 300 to 400e600 mg/m 2 ), whereas eight were switched to dasatinib (60e80 mg/m 2 ). By comparison, among the 320 patients treated with imatinib frontline who did not progress, 71 (22%) were switched to a 2G-TKI because of poor response.
Recently, a therapeutic algorithm based on paediatric haematologists' experience has been proposed to treat children in CML-CP once failure or suboptimal response has been detected  [32]. Probably with this new algorithm, some of our patients would have been switched earlier to a 2G-KI. However, it should be taken into account that the I-CML study is an international database and access to certain expensive molecules like the 2G-TKI could be more difficult in some of the countries participating in this study. In adult cohorts, a predominance of myeloid immunophenotype of CML-BP (60e80%) was observed  [7, 33]. In contrast to adult patients with CML, we observed predominantly lymphoid-BP (70%) in children with upfront imatinib treatment. In another report from the I-CML-Study, we have also observed a predominance of lymphoid phenotype of de novo advanced phases of childhood CML  [34].
Acknowledgements The authors gratefully acknowledge  Violaine Goyeau  and  Sophie Zin-Ka-Leu  for the data monitoring. The authors thank the  RHME (Association pour le Recherche sur les Maladies Hematologiques de l'Enfant) , the  SFCE  ( SocieteFrancaise de lutte contre les Cancers et les leucemies de l 'Enfant et de l'adolescent) and « Enfants Cancers Sante» for their support. The authors thank the investigators:  Michael Dworzak ,  Susanne Matthes ,  Thomas Lion ,  Marina Borisevich ,  Natallia Savva ,  Marie-Francoise Dresse ,  Alina Ferster ,  Marlies Bekaert ,  Caroline Piette ,  Veerle Labarque ,  Joris Verlloy ,  Julia Yajima ,  Monica Verissimo ,  Amilcar Cardoso ,  Vitoria Pereira Pinheiro ,  Silvia Regina Brandalise ,  Maria Lucia Lee ,  Gordana Jakovljevic ,  Sophia Polychronopoulou ,  Maria Ampatzidou ,  Frankie Cheng ,  Sarah Elitzur ,  Miriam Ben-Harosh ,  Roula Farah ,  Birgitta Versluys ,  Michel Zwaan ,  Emilia Kaiserova ,  Andrea Hraskova , Adalet Meral Gu ¨nes,  Anne Lutun ,  Catherine Devoldere ,  Valerie Li Thiao Te ,  Isabelle Pellier ,  Martine Gardembas ,  Nathalie Cheikh ,  Pauline Simon ,  Valerie Laithier ,  Sebastien Klein ,  Nathalie Aladjidi ,  Cecile Verite, Celine Icher ,  Charlotte Jubert ,  Liana Carausu ,  Christian Berthou ,  Sophie Haro , ChloeHenry,  Marianna Deparis ,  Jeremy Gaudichon ,  Justyna Kanold ,  Florentina Isfan ,  Eric Dore ,  Claire Briandet ,  Gerard Couillault ,  Dominique Plantaz ,  Anne Pagnier ,  Corinne Armari ,  Claude Eric Bulabois ,  Severine Bobillier-Chaumond, Brigitte Nelken, Anne Lambilliotte, Francoise Mazingue, Benedicte Bruno ,  Eva De Berranger ,  Abou Chah Wadih ,  Christophe Piguet ,  Caroline Oudot ,  Yves Bertrand ,  Cecile Renard, Nathalie Garnier, HerveChambost, Gerard Michel ,  Catherine Curtillet ,  Vincent Barlogis ,  Isabelle Thuret ,  Sandrine Visentin ,  Nicolas Sirvent ,  Stephanie Haouy ,  Pascal Chastagner ,  Ludovic Mansuy ,  Audrey Contet ,  Fanny Fouyssac ,  Cecile Pochon, Aurelie Phulpin ,  Julie Valduga ,  Caroline Thomas ,  Philippe Moreau ,  Fanny Rialland ,  Marie-Laure Couec ,  Estelle Thebaud ,  Nicolas Blin ,  Marilyne Poiree ,  Christine Soler ,  Joy Benadiba ,  Pierre Rohrlich ,  Thierry Leblanc ,  AndreBaruchel ,  Benoit Brethon ,  Jean-Hughes Dalle ,  Julie Lachenaud ,  Mony Fahd ,  Seror Elisa ,  Yakouben Karima ,  Florian Chevillon ,  Guy Leverger ,  Judith Landman-Parker ,  Marie-Dominique Tabone ,  Jean Donadieu ,  Laurence Blanc ,  Claire Pluchart ,  Gregory Guimard ,  Virginie Gandemer ,  Sophie Taque , Celine Chappe,  Fabienne Toutain ,  Aude Marie Cardine ,  Cecile Dumesnil ,  Jean-Louis Stephan ,  Claire Berger ,  Sandrine Thouvenin-Doulet ,  Audrey David ,  Catherine Paillard ,  Alexandra Spiegel , Genevieve Plat,  Marie-Pierre Castex ,  Anne Isabelle Bertozzi ,  Marlene Pasquet ,  Pascale Blouin ,  Mathilde Jehanne ,  Stephane Vanderbecken , and  Yves Reguerre.
BG4 ChIP-seq and G4-seq data We downloaded BG4 ChIP-seq data for HaCaT, K562 and HEKnp cell lines from Gene Expression Omnibus (GEO) accession numbers GSE76688, GSE99205 and GSE107690  [19, 21, 28]. Data were mapped to hg19. We downloaded processed qG4 ChIP-seq (similar to BG4 ChIP-seq) peaks mapped to hg19 for breast cancer  [20]. We downloaded processed G4-seq peaks mapped to hg19 from GEO accession number GSE63874  [9]. We used G4-seq from the sodium (Na) and potassium (K) conditions.
Active G4 sequences We defined positive DNA sequences (active G4 sequences) as having the propensity to form both in vitro and in vivo G4s as follows. We only kept BG4 ChIP-seq peaks overlapping with G4-seq peaks. We then used the 201-bp DNA sequences centered on the BG4 ChIP-seq peak summits. As negative (control) sequences, we used sequences randomly drawn from the human genome with sizes, GC, and repeat contents similar to those of positive DNA sequences using genNullSeqs function from gkmSVM R package (https://cran. r-project. org/web/packages/gkmSVM).
DNA motifs from DeepG4 The first layer of DeepG4 contained kernels capturing specific sequence patterns similar to DNA motifs. For a given kernel, we computed activation values for each positive sequence. If a positive sequence contained activation values above 0, we extracted the sub-sequence having the maximum activation value. The set of sub-sequences was then used to obtain a position frequency matrix (PFM) by computing the frequency of each DNA letter at each position for the kernel. Each kernel PFM was then trimmed by removing low information content positions at each side of the PFM (threshold > 0. 9). PFMs whose size were lower than 5 bases after trimming were removed. PWMs were next computed from PFMs assuming background probability of 0. 25 for each DNA letter as done in JASPAR. Because many PWMs from DeepG4 were redundant, we used the motif clustering program matrixclustering from RSAT suite (http://rsat.
G4 predictions with DeepG4 Using DeepG4, we obtained excellent predictions of active G4s from HaCaT cells on the test set (Figure  3A ; AUC = 0. 956; 95% confidence interval of AUC: [0. 953, 0. 960]). Prediction performance of DeepG4 on an independent dataset with the same cell line (HaCaT) also showed very high accuracy (AUC=0. 95, CI95% : [0. 946, 0. 954]; Figure  3B ). We then evaluated the ability of DeepG4 trained on one cell line (HaCaT) to predict G4s in another cell line. We found that DeepG4 could predict reasonably well active G4s in HEKnp cell line  [19]  (AUC=0. 892, CI95% : [0. 861, 0. 923]; Figure  3C ) and in K562 cell line too  [29]  (AUC=0. 92, CI95% : [0. 917, 0. 924]; Figure  3D ). We then compared DeepG4 with state-of-the-art G4 prediction algorithms, previously shown to perform very well for in vitro G4 predictions.
All the datasets, results and algorithms are available from a github repository (see Subsection Comparisons with other G4 tools and availability). DeepG4 outperformed the other algorithms on 4 out of 5 datasets, with an average AUC of 0. 91. Deep learning algorithms PENGUINN and G4detector, that we retrained for fairer comparison, ranked 2nd and 3rd with average AUCs of 0. 865 and of 0. 840, respectively. In particular, they performed the best on the breast cancer data. Among non-retrained algorithms, PENGUINN ranked 4th with an average AUC of 0. 788, and G4detector ranked 7th with an average AUC of 0. 706. Machine-learning approach Quadron, that was originally trained from in vitro G4s, performed the worst to predict active G4s (average AUC of 0. 585, ranked 14th). Re-trained Quadron yielded greatly improved results (average AUC of 0.
Identification of important motifs from DeepG4 The first layer of DeepG4 convolutional neural network comprised kernels that encoded DNA motifs predictive of active G4s. Hence, we extracted from the first layer the kernels and converted them to DNA motif PWMs to better understand which motifs were the best predictors of G4 activity. DeepG4 identified 900 motifs, many of them were redundant. To remove redundancy, we clustered the motifs using RSAT matrix-clustering program and kept the cluster motifs (also called root motifs in the program) for subsequent analyses. Cluster motifs could be divided into two groups: a group of de novo motifs and a group of motifs that resembled known TFBS motifs. To distinguish between these two groups, we used TomTom program (MEME suite) which mapped the cluster motifs to JASPAR database.
53 C • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • DeepG4 Δscore 1K SNPs SNPs ↘ methylation SNPs ↗ methylation 0 1 2 -1 -2 • • • • • • DeepG4 Δscore B p=0. 003 p=3x10 -5 p=0. 08 1K SNPs SNPs ↘ H3K4me3 SNPs ↗ H3K4me3 0 1 2 -1 -2 E 20b -20b 0 -2 -1 0 1 2 3 20b -20b 0 A C G T ↗G4 score rs7249925 (A>G) ↗ expression of NOSIP (eusophagus) Sequence Predicted effect of SNP D rs3748393 (C>A) 20b -20b 0 Sequence ↘ expression of C16Orf95 (testis) 20b -20b 0 Predicted effect of SNP A C G T ↘G4 score -2 -1 0 1 2 Figure 6. G-quadruplex SNPs are associated with gene expression and chromatin activity. A) SNP effect of g-quadruplex (delta score) depending on SNP effect on gene expression (regression beta). SNPs are from GTEx database  [12].
SNPs from 1000 genomes project (1K SNPs) were added as controls. B) SNP effect of g-quadruplex (delta score) depending on SNP effect on H3K4me3 mark (regression beta). SNPs are from Delaneau et al. [14]. C) SNP effect of g-quadruplex (delta score) depending on SNP effect on DNA methylation (regression beta). SNPs are from Pancan-meQTL database  [18]. D) Example of the SNP rs3748393 that is associated with gene expression decrease and which is predicted to decrease G4 activity. The predicted effect of SNPs for each allele and each position around rs3748393 is plotted as heatmap. E) Example of the SNP rs7249925 that is associated with gene expression increase and which is predicted to increase G4 activity. We first analyzed the link between delta score and gene expression, and found that SNPs increasing gene expression presented higher G4 activity as compared to SNPs diminishing gene expression (p = 9×10 -15 ; Figure  6A ).
There are several limitations of the proposed approach. First, the prediction accuracy of DeepG4 strongly depends on existing datasets that are limited, especially regarding in vivo mapping. Once more techniques for in vivo G4 mapping will be developped, DeepG4 will need to be retrained in order to improve prediction accuracy. Moreover, since DeepG4 was trained based on human data, predictions on non-mammalian genomes are expected to be inaccurate. Second, DeepG4 is limited to predict active G4s but a similar approach could be used to predict any active non-B DNA structure using permanganate/S1 nuclease footprinting data  [27]. Third, DeepG4 only uses DNA sequence as input. Incorporating chro-matin accessibility with DNA-seq (or ATAC-seq) as input to DeepG4 could allow the model predict cell-type specific active G4s.
The origin of the positions is on top of the wedge, in the symmetry plane of the flow vein. The diverging part of the venturi section thus extends from X * = 0 ; Z * = 0 to X * = 1/tan (8 o ) 7. 1 ; Z * = -1; the two side walls are at Y * = ±0. 5 and the upper flat wall is at Z * = 1. Time series of 3000 images are recorded for each experiment. A background reference image I ref is also recorded with the same cameras setting, while the flow is at rest. The instantaneous images I i are then processed with Python, using the scipy. ndimage and scikit-image libraries  33  in the following way, based on Morphological Image Processing 34 : • The images are converted to matrices of floats, the background is substracted and the result is normalized by the background reference image, giving the i th normalized image of the time series I i = Ii-I ref I ref (see Figure 2(a) ).
C. Numerical highlights and comparison with experimental data The topology of the single-phase flow and its nature as a function of the Reynolds number have been studied numerically  28. The computation was performed with the Computational Fluid Dynamics code StarCCM+ (version 13. 02). It uses a finite volume approach. The computation is three-dimensional; the numerical domain is similar to the experiment with an extrusion of 10h throat upstream of the inlet of the test section and an extrusion of 20h throat downstream of the outlet of the test section. The boundary conditions are a uniform inlet velocity and a uniform static pressure outlet. The domain is meshed with a structured grid. A grid convergence test was made at Re = 1500 with up to 7. 2 × 10 6 cells. The final mesh contains 1. 1 × 10 6 cells, with 51 points in the cross-stream direction and a near-wall cell size of 5 × 10 -3 h throat.
B. Developed cavitation regimes identification in degassed oil Approximately one hundred of movies have been recorded at various Reynolds numbers and cavitation numbers. Only the cases with no spurious air bubbles coming from upstream and with cavities larger than 1 mm have been reported in Figure  6. The data all fall below the numerical inception curves. Four different regimes have been identified. Typical instantaneous side and top views illustrating these four regimes are displayed in Figure  7. The first regime identified with a full circle (•) corresponds to steady cavities. For Re 1000, even at very small cavitation numbers, only steady cavities of this type are observed. The cav-ity viewed from side is quite flat and its bottom part is detached from the divergent bottom wall. For larger Reynolds numbers (Re 1200), the type-1 steady attached cavities are only observed at high cavitation numbers, corresponding to small cavities.
Eddy covariance and biometeorological measurements Our eddy covariance system (Li-Cor, U. S. A. ) was installed on an existing permanent data collection structure operated by the Everglades National Park at Bob Allen keys (water quality station BOBF1). This eddy covariance system consists of a rapidly responding (10 Hz) closed-path infrared gas analyzer (Li-7200RS), a sonic anemometer (Gill Windmaster Pro), and bio-meteorological sensors for net solar radiation (Kipp & Zonen NR Lite2), photosynthetically active radiation (PAR; Li-190R Quantum Sensor), and precipitation. Wind speed was corrected to a height of 10 m (U 10 ) assuming neutral    et al. (2012). Positive latent and sensible heat fluxes represent heat loss from the water. In order to more comprehensively assess the energy balance, we also estimated the water-column heat storage (J; W m -2 ) for each 30 min interval as: J = ρ × C P o × ΔT=Δt × h ð Þ ð 1Þ where ρ is the average water density for that time period (kg m -3 ), C P o is the specific heat of water (J kg -1 K -1 ), ΔT/Δt is the average rate of water temperature change (K s -1 ), and h is the water depth (m).
Acknowledgments This work was supported by the  US National Science Foundation  through the  Florida Coastal Everglades Long-Term Ecological Research program  under Grants  DEB-1237517  and  DEB-1832229  and additionally through DAAD (57429828) from funds of the  German Federal Ministry of Education and Research (BMBF). We thank  Sara Wilson  and  Mary Zeller  for assistance in the lab and field. Mary Zeller  also generously gave her time in reviewing this manuscript. We also appreciate the assistance of the  National Parks Service , who allowed this equipment to be deployed in the  Everglades National Park , under Permit number ( EVER-2018-SCI-0072 ). Last, we appreciate the helpful comments during review, which significantly improved the final manuscript. This is contribution # 180  from the  Coastlines and Oceans Division  of the  Institute of Environment at Florida International University.
The system sends a link to the e-mail address to download the requested data. If part or the entire downloaded data will be published, it is only necessary to indicate its source (http://www. emodnetbathymetry. eu/) in the published document. The high-resolution data can be found at the institutional archive at OGS (https://snap. ogs. trieste. it/cache/index. jsp). For the relevant data included in the area of interest described in this paper, the DOI are: DOI: 10. 6092/SNAP. F56B827CFFB9F0E99603C6EF4074DE73; DOI: 10. 6092/SNAP. B2F9E5768DCC2AA0922F2496792 0D9AB. The computation code for the SELEN4 program used in this study is freely available from the following URL: (https://www. geosci-modeldev-discuss. net/gmd-2019-183/).
Geophysical data High-resolution swath bathymetry (Fig 2 ) and seismic profiles used in this study have been acquired during a series of geophysical surveys conducted on 2016 and 2018 in the NW sector of the Sicilian Channel onboard the R/V OGS Explora. Multibeam bathymetry has been realized with a keel-mounted Reson 1 SeaBat 8111, operating at a frequency of 100 kHz and illuminating a swath on the sea-floor that is 150˚across track and 1. 5˚along track. To ensure the best data coverage, every single swath was superimposed to the adjacent one for about one third. The resulting Digital Terrain Model, with a nominal resolution of 5 x 5 m, was visualized using the GlobalMapper 1 software and the freely available GMT 1 (Generic Mapping Tools). The high-resolution seismic profiles (Chirp profiles) have been acquired along the Multibeam swaths with a keel-mounted Benthos Teledyne 1 Chirp III DSP-665, characterized by sweeps ranging from 2 to 7 kHz.
By definition, ESL represents the globally uniform amount of sea-level change that would be observed for a rigid, non-gravitating Earth with fixed shorelines, i. e. , ESL = (ρi/ρw)(Vi(t)/Ao) where ρi and ρw are the densities of ice and water, Vi(t) is the ice volume at time t and Ao is the present-day area of the surface of the oceans. The total ESL (solid line in  Fig 5)  reaches a maximum of ~140 m at 21,000 yrs B. P. (LGM), and vanishes approximately 1,500 yrs B. P. We note that during the last 7,000 yrs, the ESL variation is only due to the late contribution from the melting of the Antarctic ice sheet. The spatial distribution of the ice sheets and their time evolution in model ANU have been assimilated in our SLE solver SELEN 4 , an open-source program that simulates the GIA process in response to the melting of the late-Pleistocene ice sheets  [48].
pneumoniae, this corresponded to a 3-h exposure to bacteria. All samples were cryopreserved at -80 °C prior to protein quantification using the Simple Plex TM Ella multiplex microfluidic platform (Protein Simple, CA, USA). A 4-plex custom panel was used to quantify interleukin (IL)-6, IL-8, angiopoietin (Ang)-2, and soluble tumor necrosis factor receptor (sTNFR)-1. Low-abundance proteins (Ang-2) were tested at a 1:10 dilution, and high-abundance proteins (IL-6, IL-8, sTNFR-1) were tested at a 1:1000 dilution. The same dilutions were used for perfusate and airspace samples. Assays were performed according to manufacturer's protocol, as described previously  [44]. Raw data were analyzed using the SimplePlex Explorer software.
Funding Dr. Leligdowicz  was supported by the  CIHR Banting Postdoctoral Fellowship  and the  University of Toronto Clinician Scientist Training Program (CSTP) ;  Dr. Ross  by  NIH   T32DK007573-23 ,  Dr. Nesseler  by research grants from the  Société Française d'Anesthésie-Réanimation  ( SFAR, Paris, France ), the  Association Chirurgicale pour le Développement et l'Amélioration des Techniques de Dépistage de Traitement des Maladies Cardiovasculaires  ( ADETEC, Paris, France ), and the  Journées Rennaises d'Anesthésie-Réanimation  (JRAR) association (Rennes, France); and  Dr. Matthay  by  NHLBI HL140026 ,  HL51856 ,  HL134828 , and  UCSF Nina Ireland Fund. None of the funding bodies had a role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.