dataesr/scientific-paragraphs-categorization huggingface dataset:
A study on a nationwide scale based on medico-administrative data Because the SNDS does not provide direct information about the circumstances of cancer diagnosis, an IC identification algorithm based on the imaging procedures and date of completion was developed to classify women as OSP group, IC group and PSCS group. An approach using a population -based cancer registry to identify cases would be more robust but registries cover local areas not necessarily representative of the whole France. The main advantage of our study was having access to the SNDS, the national medicoadministrative database recording all outpatient and inpatient care consumption for the entire French population. 30 Defossez et al  34  demonstrated by comparing to the medical record, that 98% of pathways were correctly reconstructed in terms of status sequencing, and 94% of pathways were faithful to the truth within 3 days in terms of intervals, demonstrating the ability of this base to describe care pathways.
Clinical practice points • Since 2004 in France, an organized screening programme (OS) for breast cancer has been in place for 50-74 years women who are not at an increased risk. Despite this, 17% of cancers are interval cancers (IC) i. e. diagnosed within 24 months following an OS mammogram that did not show any cancer. • In literature, cases of IC would appear to be more aggressive at the time of diagnosis and have a poorer prognosis than cancers screened through the OS programme with a greater proportion of lymph node and metastatic involvement. The studies disagree as to the impact on survival. • This study is based on an IC identification algorithm from the national medico-administrative database SNDS recording all outpatient and inpatient care consumption for the entire French population.
Data Analyses All the data analyses were performed using the packages vegan  (Oksanen et al. 2015) , car  (Fox et al. 2020) , multcomp  (Hothorn et al. 2020) , emmeans  (Lenth et al. 2020 ) and lme4  (Bates et al. 2016 ) in R 4. 0. 3. (R Core Team 2020). We evaluated the effects of substrate mixtures, sowing and time on species composition, richness, and cover of plant communities. We also evaluated the effects of substrate mixtures and sowing 5 years after the experiment was set up, in 2020. This was done on species composition, richness, and cover of plant communities, as well as on B. retusum and B. hybridum covers. In 2020, we also assessed the effects of treatments on plant species richness and plant cover for target and non-target species separately (target species being the species found on the reference grassland plots in 2020).
For species composition, we used the data of all species but Brachypodium spp. as the aim was to assess the effect on the plant community independently of Brachypodium cover. As the sum of species cover usually largely exceeds total species cover, we therefore used as cover without Brachypodium the product of the sum of species cover excluding Brachypodium spp. cover by the estimated total plant cover, divided by the sum of all species cover, hereafter called plant cover without Brachypodium. Two indices were used to assess the state of the plant communities compared to the reference ecosystem: (1) The CSII norm Index (normalized Community Structure Integrity Index) measuring the proportion of the abundance of species of the reference community represented in the restored community (it shows the percentage of similarity in terms of composition and abundance of the communities studied compared to the reference community) and (2) The HAI (Higher Abundance Index) measuring the proportion of the abundance of species in the restored community, which is higher than in the reference community (it shows what is "in excess," in terms of species and abundance in the studied communities studied compared to the reference community)  (Jaunatre et al.
Acknowledgments This study was supported by Gagneraud construction funding. The authors thank  P. Bourguet  ( Société des Carrières de la Ménudelle ) for his collaboration. The authors thank  J. -M. Arnal  for the preparation and implementation of the plots, Florian and Pascal for substrate mixtures. The authors are also grateful to students, research assistants, and field ecologists for help with fieldwork:  A. Aurière ,  C. Moinardeau ,  C. Vidaller ,  C. Malik , and  D. Pavon.
This work focuses on the development of a comprehensive numerical methodology for the study of natural circulation systems using molten salts as working fluid, in particular for its application in the design of passive safety systems for Molten Salt Reactors (MSRs). The goal of the methodology is to evaluate the performance of possible 3D natural circulation configurations through the description of their dynamic behavior, with a focus in the identification of bifurcation phenomena and performing a stability analysis. The numerical tool implementing this methodology is based on a CFD-based MATLAB-OpenFOAM coupling and has been evaluated against a numerical benchmark of a differentially heated cavity, showing good agreement with literature results. For a first assessment of the performance of the tool a simple 2D case of a simple natural circulation configuration known as Rayleigh-Bénard convection has been used.
The operator denoted as D stands for D(U) = (U+ H U)/2, where the superscript H indicates the transpose. The implementation for this model is carried out using the open source library software OpenFOAM ®  [7] , which is based in the Finite Volume Method (FVM) C++ library, used for continuum mechanics and fluid dynamics simulations. The current capabilities of this software allow the resolution of this problem decreasing the numerical development effort.
From Eqs. 4-6 it is important to notice that the linearized system only takes into account the base solution of the system from a single description of the temperature and velocity fields, causing this is method to be only applicable for the cases with a steady-state base solution, since in the case that this exhibits an oscillatory behavior, the linearized system would unable to capture its temporal variation. Due to the high computational cost associated to the direct resolution of the EVP, instead a time-stepping approach is selected  [9]. Moreover, adding a Dynamic Mode Decomposition (DMD) algorithm, the characteristic frequencies are retrieved using the time evolution of the linearized system. A more detailed description of this method can be found in  [10]. The routine for the modal analysis, enclosing both the evolution of the linearized system and the resolution of the EVP, is implemented as a MATLAB-OpenFOAM coupling.
Canopies vapor pressure deficit assessment Vapor pressure deficit (VPD) in the canopies of different density was measured with temperature and relative humidity sensors (TinyTag ultra 2, TGU-4500, Gemini Datalogger Ltd, Chichester, UK), during 15 days of the 2018 high VPD season lysimeter trial in India and 6 days during the 2020 low VPD season field trial in Senegal. In India, the sensors were installed at 53 days after sowing (DAS) (corresponding to flowering time in most of the genotypes) and at 89 DAS in Senegal, corresponding to the last week before grain maturity. Data was recorded every 30 min from 7am to 7pm. Daily average of the VPD was calculated according the following formula: VPD = (100 -%RH x SVP/1000) where SVP (saturated vapor pressure) is 610. 7 x 10 (7. 5 x T°C)/(273. 5+T°C).
(Figure  1C, D ). Relative humidity was also lower by 5% on average of the season and 7% on the 60 first days of the crop cycle in 2019 compared to 2020. Light intensity, collected in a nearby weather station 25km apart, was also 10% higher in 2019 than in 2020 during the initial 60 days of the crop (data not shown). The average of daily VPD in the two lysimeter trials carried out in 2018 in India (summer and rainy seasons), differed and were 2. 1 kPa and 0. 91kPa during the dry and rainy season respectively (detailed data not shown). The average of daily VPD in the lysimeter trials carried out in the dry in Senegal was 1. 9 kPa.
No genotype × density interaction was found in the three-density treatment dataset, although a slight but significant genotype x density interaction effect (p-value = 0. 01) was found in a two-way ANOVA that was ran with D1 and D3 data from 2019. During the 2020 trial, the grain yield significantly decreased in the highest density treatment. Average yield data were the following in the three density conditions: 1. 6 ± 0. 55 t/ha in D1, 1. 53 ± 0. 56 t/ha in D2 and 1. 3 ± 0. 47 in D3. Analysis of variance on yield showed a genotypic effect (p-value<0. 0001) and a slight density effect (p-value= 0. 046) with no genotype-bydensity interaction effect (Figure  2D ). Genotypic means of yield in the three density treatments are presented in the Supplementary Table  2.
High-density increases LAI In the different field trials, the leaf area index measured at two different times after sowing was always larger under higher density for all genotypes. A two-way ANOVA showed a significant density effect but no genotype or genotype-bydensity interaction effect in the two dates of measurement (Figure  3A, B ). In the 2020 field trial in Senegal, LAI measurement made at 43 DAS showed significant differences between different densities and genotypes while no genotype-by-density interaction was found. Strong and significant density and genotypic effect was found for LAI for the two dates of measurement (ANOVA, p-value<0. 0001, Tuckey's multiple comparisons test) but no genotype-by-density interaction (Figure  3C, D ). Genotypic values of LAI measured at the different dates, in the different trials were plotted against the yield, and did not show any significant relation (data not shown).
TRGB distances While the distances to F8D1 and NGC 2976 have previously been determined using various HST data sets (e. g. C98 ;  Karachentsev et al. 2000 ; Dalcanton et al. 2009 ) , we decided to rederive these in a consistent manner with our HSC data set. We use the tip of the RGB (TRGB) method (e. g. Lee, Freedman & Madore 1993 ; Sakai, Madore & Freedman 1996 )  and follow the procedure described in  Okamoto et al. ( 2019 ). We begin by defining regions around the galaxies that are to be used for analysis. For F8D1, we select all stars lying within 6. 6 arcmin of our adopted centre (see Section 3. 2 ) and for NGC 2976 we use stars lying within a radius of 10 arcmin. For each system, we select stars with colours in the range 1. 4 < ( gi ) 0 < 2. 25 and with photometric errors smaller than 0.
Structural analysis of F8D1 Before proceeding to analyse the properties of the stream, we revisit the properties of the main body of F8D1 and update its measured structural parameters. As the stars in the inner parts of the galaxy are poorly detected due to aforementioned issues, a structural analysis using resolved stars is not viable and we need to utilize diffuse light instead. Ho we ver, as mentioned in Section 2. 3 , our implementation of HSCPIPE does not preserve the LSB emission of galaxies, leaving F8D1 with a large fraction of diffuse light missing in the final stack. Instead, we investigate the structure of the main body of F8D1 using the MegaCam LSB images. We use the AUTOPROF software  (Stone et al. 2021  ) to construct the surface brightness profile of F8D1. AUTOPROF provides an end-to-end pipeline for non-parametric profile extraction, including masking, sky determination, centroiding, and isophotal fitting.
C O N C L U S I O N S We have used data from Subaru/HSC and CFHT/MegaCam to revisit the properties of F8D1, a peculiar dwarf satellite companion of M81. Despite being the closest known example of a UDG to the Milky Way, it has been very poorly studied since its disco v ery more than 20 yr ago. Indeed, the complex and bright Galactic cirrus around F8D1 makes analysis of its faint structure very challenging. Using Subaru/HSC observations in the g and i filters, we have mapped the distribution of individual RGB stars on the northeast side of F8D1. We have found evidence for a giant tidal tail emanating from this system. This feature can be traced for o v er 1 deg on the sky, corresponding to 60 kpc at the distance of the galaxy. We have mapped the distribution of stars along and across the stream, finding behaviour consistent with an S-shaped structure that is characteristic of tidal stripping.
AC K N OW L E D G E M E N T S RZ acknowledges a studentship received from UKRI STFC and supplementary funding from the Institute for Astronomy at the University of Edinburgh (2145313). We thank Aaron Watkins for useful discussions on sky subtraction and helpful comments from an anonymous referee. SO acknowledges support from JSPS Grant-in-Aid for Scientific Research (18H05875, 20K04031, 20H05855). NA thanks the Brain Pool program for financial support, which is funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2018H1D3A2000902). The methods described in the paper have been implemented using Python code and several Python libraries: MATPLOTLIB for visualization  (Hunter 2007 ) , NUMPY and ASTROPY for data handling  (Harris et al. 2020 ; Astropy Collaboration 2013 , 2018 ) , and SCIPY for model fitting  (Virtanen et al.
2020 ). We use AUTOPROF for the surface brightness analysis  (Stone et al. 2021 ). This research is based on observations collected at the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan, and on observations obtained with Me gaPrime/Me gaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. We are honored and grateful for the opportunity of observing the Universe from Maunakea, which has cultural, historical, and natural significance in Hawaii. This paper uses software developed for Vera C. Rubin Observatory.
We thank the Rubin Observatory for making their code available as free software at ht tp://pipelines. lsst. io/. The Pan-STARRS1 Surveys (PS1) and the PS1 public science archiv e hav e been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg, and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under grant No.
Statistics Descriptive statistical analysis was performed for all data (mean, SD, SEM, min, max, range). A D'Agostino & Pearson normality test identified parametric and non-parametric data. Depending on the nature of the data, significant differences between groups were calculated using the unpaired parametric t-test (2 samples), ANOVA (≥3 samples), unpaired non-parametric Mann-Whitney (2 samples) or Kruskal-Wallis (≥3 samples) test. If not noted otherwise, data are shown as mean ± SD. All analyses were made using GraphPad Prism ® software version 9. 3. 1 (471) (GraphPad Software, LLC, San Diego, CA, USA).
The differences from the proposed clinical trial and thus limitations of the ValRun were: (a) iridectomies were performed by a team member and not by the surgeon, (b) iris biopsies were cut through a larger corneal incision, which is different from surgical practice, (c) four biopsies were taken from one eye whereas for the proposed clinical trial one biopsy will be taken from the patient's eye. The data presented here is derived from IPE cells isolated up to 8 days post-mortem from donor eyes, which may be not as vital as cells isolated from a patient's biopsy. Our data shows that viability of IPE cells isolated from post-mortem donor eyes is 31. 92 ± 19. 04% whereas Lappas et al. determined viability of patient-derived IPE cells to be 75. 45% [21]. For the proposed clinical trial, the results of sterility analyses will be available only after transplantation of tIPE, which poses a risk; however, this risk is acceptable since the percentage of contaminations was low during the ValRun, the customized biocontainer ensures a closed manufacture and transport circuit; additionally, post-operative fundoscopies allow identification of early signs of infection and initiation of appropriate treatment.
Acknowledgments: A special thanks goes to  Joost van den Berg  and  AmBTU  for the production of the plasmid mixture. Another special thanks goes to the team of the  LTTC (Dept. of Diagnostics, University Hospitals of Geneva, Geneva, Switzerland), Alain Conti and Gregg Sealy (Dept. of Ophthalmology, University Hospitals of Geneva, Geneva, Switzerland)  for their support and excellent technical assistance, respectively.
Frequencies and percentages are presented for categorical variables and a 2-sided 95% exact confidence interval (CI) for ORR. The chi-squared test was used to compare proportions between subgroups. For time-toevent variables, the Kaplan-Meier method was used for descriptive summaries. Log-rank test and Cox proportional hazards model were used to compare survival distributions between subgroups. Statistical analyses were performed using SAS (version 9. 4).
Selinexor, with its novel mechanism of action, ease of oral administration, and ability to produce rapid and durable responses in patients with heavily pretreated disease, may help to fill this important unmet clinical need. Combination therapy studies with selinexor (NCT04442022, NCT04607772) are ongoing to determine optimal dosing and response rates/durability; these regimens are highly likely to be substantially more active than single-agent selinexor. Institutional Review Board Statement: The institutional review board approved the protocol (Protocol Number:KCP-330-009), and the study was done in accordance with the International Conference on Harmonisation Good Clinical Practice guidelines and the principles of the Declaration of Helsinki.
02 M EDTA pH 8. 0, 0. 5 mg/mL proteinase K, 0. 5% SDS) for 30 min incubation at 55°C, a phenol chloroform extraction and an ethanol precipitation. DNA was visualized by agarose gel electrophoresis (0. 7% agarose, 50V, overnight at 4°C) and quantified using QuBit. Genome sequencing, assembly. Phages were sequenced by the Biomics platform at the Pasteur Institute. The sequencing library was prepared using a TruSeq Illumina kit, 2 × 75 bp paired end, and sequenced on a NextSeq550 Illumina sequencer. After reads trimming done with Trimmomatic v0. 39 (LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:36)  (Bolger, Lohse et al. 2014 ), de novo assembly was perfomed using SPAdes v3. 15. 2 (--careful --cov-cutoff auto -k 21,33,55,77 -m 10). Vector contamination contigs were excluded using the UniVec Database and one-contig phage genome was manually decircularized.
The syntaxic annotation was done with PHANOTATE v1. 5. 0  (McNair, Zhou et al. 2019). Large terminase subunit was identified using DIAMOND v2. 0. 8. 146 (blastp, default parameters)  (Buchfink, Reuter et al. 2021 ) on previous annotated vibrio large terminase and using HMMSCAN (HMMER v3. 3. 2, default parameters with evalue≤10 -3 ) with PFAM profiles (PF03354. 17, PF04466. 15, PF05876. 14, PF06056. 14) (Mistry, Chuguransky et al. 2021). The phage genome was manually ordered starting from the gene for the large terminase subunit.
Genome annotation. The tRNA were identified with tRNAscan-SE v2. 0. 9  (Chan and Lowe 2019). The functional annotation used multiple approaches. First we used HMMSCAN (evalue≤10 -3 ) with PVOGS profiles  (Grazziotin, Koonin et al. 2017). We also used DIAMOND blastP against first the Nahant collection genomes and after against others bacterial RefSeq (version 212) and phage GenBank genomes (2022-24-06, with 30% identities and 50% coverage)  (Kauffman, Brown et al. 2018 , Buchfink, Reuter et al. 2021 ). Finally we added InterProScan v5. 52-86. 0 (evalue≤10 - 3 ) and eggNOG v5. 0. 2 annotation results  (Jones, Binns et al. 2014 , Huerta-Cepas, Szklarczyk et al. 2019). Clustering, lifestyle and comparative genomic. We clustered phage using VIRIDIC v1. 0r3. 6 (default parameters)  (Moraru, Varsani et al.
2020). Intergenomic similarities were identified using BLASTN pairwise comparisons. Viruses assignment into family (≥50% similarities) genera (≥70% similarities) and species (≥95% similarities) ranks follows the International Committee on Taxonomy of Viruses (ICTV) genome identity thresholds. Phage lifestyle was predicted using BACPHILIP version 0. 9. 3-alpha and lysogeny associated genes was searched using InterProScan (v. 5. 52-86. 0) based on integrase (IPR002104), resolvase (IPR006119), replicative transposases (IPR004189), and transcriptional repressor domains (IPR010982, IPR010744, IPR001387, IPR032499). Absence of prophage integration was also checked using BLASTn searches between both phage and bacterial genomes (evalue ≤0. 01)  (Hussain, Dubert et al. 2021). All comparative genomic analysis was done using MMseqs2 version 13-45111 for protein clustering  (Steinegger and Soding 2018) , FAMSA version 1.
ACKNOWLEDGEMENTS We warmly thank  Sylvain Gandon ,  François Blanquart  and  Martin Polz  for fruitful discussions on the manuscript. We thank  Sabine Chenivesse ,  Sophie Le Panse , the staff of the station  Ifremer Argenton  and  Bouin , the  ABIMS (Roscoff)  and  LABGeM (Evry)  platforms for technical assistance. We thank all members of the "GV team" for support with field sampling. We thank  Zachary Allouche ,  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09 ) and IBISA. This work was supported by fundings from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  884988 ,  Advanced ERC Dynamic)  and the  Agence Nationale de la Recherche  ( ANR-20-CE35-0014 « RESISTE » ) to FLR.
Data Sources and Compilation of a Summary of Product Characteristics Data collection was based on European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) from the European Medicines Agency (EMA) and the World Organization for Animal Health (WOAH)'s data collection templates  (24, 25). Invoices and statement of accounts were requested for 3 calendar years  (2016, 2017, and 2018). All financial documents were scanned using a mobile application during a site visit done over 3 days in December 2019. Financial documents originated from cooperatives, feeding mills and one veterinary practice. In addition, a physical stock inventory of the feeding mill located on the farm, as well as the medicine cupboard and fridges were done once during the same visit, to verify the exact composition of the VMPs containing an antibiotic or a vaccine, as identified from the scanned invoices.
The inventory check was also done to ensure that the correct product information sheet was retrieved, to determine the active ingredient present and its strength when compiling the SPC for each VMP (Tables  1, 3 ). After the site visit, every purchase of a VMP, containing an antibiotic or a vaccine, was recorded as an observation in an electronic database from the scanned invoices. The following information was captured from the invoices: (i) product name, (ii) purchasing date, (iii) quantity purchased, (iv) product packaging size (100 mℓ, 1 kg etc. ,), (v) purchasing price in South African Rand (ZAR) excluding value added tax (VAT) and (vi) the invoice number. A SPC sheet was compiled for every antibiotic or vaccine identified as mentioned above (Tables  1, 3 ). If the composition of the VMP could not be verified from the stock take, the concentration of the antibiotic active ingredient as listed in MIMS IVS-Index for Veterinary Specialties were used  (26).