dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical analysis was carried out using statistical software (GraphPad Prism 8. 0, Graph Prism Inc. , San Diego, CA). A completely randomized design (DIC) was carried out with the following mathematical model: 𝑌𝑌 𝑖𝑖𝑖𝑖 = 𝜇𝜇 + 𝑥𝑥 𝑖𝑖 + 𝑒𝑒 𝑖𝑖𝑖𝑖 (1) Where, 𝑌𝑌 𝑖𝑖𝑖𝑖 = Bodyweight, nasoanal length, Lee index, primordial, primary, secondary, antral, total and atretic follicles, atresia and activation rate μ = Constant 𝑥𝑥 𝑖𝑖 = Supplementation (Control, fish oil, CLA) 𝑒𝑒 𝑖𝑖𝑖𝑖 = Random error For the characteristics bodyweight, nasoanal length and Lee index, an intercepts and slope comparison of the line was performed by means of a simple linear regression analysis during the 4 weeks of evaluation. For the variables number of primordial, primary, secondary, antral, total and atretic follicles, atresia and activation rate a D'Agostino & Pearson normality test was conducted to test the null hypothesis that the data are sampled from a Gaussian distribution.
All women were given a curative analgesic prescription (CAP) to be used at their discretion. Data including demographic data, obstetrical history including history of previous ToPs, confirmation of pregnancy and MToP therapeutic protocol were collected during this visit  [16]. The second visit was scheduled between 2 and 3 weeks after inclusion to assess MToP success (complete expulsion) as recommended in guidelines in place during the inclusion period  [2]. Complementary data, such as intrauterine surgical procedures, additional misoprostol intake and tolerance, were recorded during this visit  [16]. The highest pain experienced by women prior to any curative analgesic intake (CAI) was assessed by the physicians using a five-point Likert scale (absence, mild, moderated, severe, extreme) during the follow-up visit.
Statistics Results are expressed as mean ± standard deviation (m ± SD) or number with percentage (%) calculated on the number of observed data. Risk factors for pain (expressed as an ordinal variable) were investigated using a multiple ordinal logistic regression with stepwise selection with variables associated with p value \0. 2 on univariate analysis. The final model was run with the selected variables and gestational age (as a continuous variable). Analysis of risk factors for curative analgesic intake was performed with a multiple logistic regression with the same strategy. For statistical analysis, SAS 9. 4 software (SAS Institute, Cary, NC, USA) was used.
Causal relation with other baseline characteristics (history of at least one ToP, number of previous ToPs, history of at least one MToP, number of previous MToPs, history of at least one surgical ToP, number of previous surgical ToPs, number M denotes the number of women with documented data * Percentages are calculated on the basis of the number of women who had at least one curative analgesic treatment. Note that women could have more than one type of analgesic taken, so the sum exceeded 100% of children, duration between mifepristone intake and prostaglandin analogue intake) were investigated but showed no association with the severity of reported pain (data not shown). On multivariate analysis adjusted on gestational age, increasing initial (OR 1. 25, 95% CI 1. 06-1. 47) or total dose (OR 1.
Crystalline structure and elemental composition The XRD patterns of the different biochars@ZnO are reported in the Fig. 4. In the sample AB@ZnO (Fig. 4 ), the peak at 2Ѳ = 37. 0°, 40. 1°, 42. 3°, 55. 7°, 66. 7°, 74. 4°, and 78. 9° were due the planes (010), (  002 ), (  011 ), (  012 ), (  110 ), (013), and (020), respectively. This is a clear indication of zincite mineral with chemical formula O 1 Zn 1 having hexagonal crystal system  [83]. The structures of MOPB@ZnO  [84] , SBB@ZnO  [84] , and CRPB@ZnO  [85]  also fall in this category. The elemental composition of the different biochar samples was studied by XPS, and the data were treated by Avantage software. The atomic composition % is reported in Table  3. It is very clear that the pristine biochar contains Zn, S, N, C, O, Si, Ca, Na, Mg, and Cl (Table  3 ).
Acknowledgements A. M. K. and M. M. C. would like to thank the  French Government  for funding AMK's contribution through a fellowship granted by the  French Embassy in Egypt  ( Institut Français d'Egypte). We thank all the technical officers of ITODYS for XPS, Raman, XRD, and FESEM analysis. We are also grateful to  ETS Laurent Mace  ( Huitres Creuses de Normandie ,  F-50560 Gouville-Sur-Mer ) for providing algae samples.
Reanalyzed ChIP-seq data for the CP190 and dCTCF insulator proteins in the S2 cell line are indicated below. 38  histone modifications could be detected. In S2 cells, the inactive BX-C was fully covered by H3K27me3, with no peaks of H3K4me3 at any of the Hox gene promoters (Figures  1C, S1B , and S1C). In genital discs and the Sg4 and S3 cell lines, the active Abd-B gene and its surrounding cis-regulatory domain were enriched for H3K4me3. Interestingly, the distributions of H3K4me3 and H3K27me3 over the active Abd-B cis-regulatory domain were not identical. In Sg4 cells, the entire region spanning iab-7 to iab-9 was devoid of H3K27me3 and enriched for H3K4me3, with the Fab-7 and C BEs acting as boundaries (Figure  1C , arrows, and Figures  S1B  and S1D ). In contrast, in S3 cells the H3K27me3 mark was present at iab-7 and the telomeric part of iab-9.
The pan-cellular nature of our qRT-PCR-based analysis in the heterogeneous genital disc precluded a distinction between a global reduction of Abd-B expression and reduced numbers of Abd-B-expressing cells. To overcome this limitation, we performed single-cell RNA sequencing (RNA-seq) analysis on pools of larval imaginal discs (see STAR Methods for details). Promoter choice was determined by sequencing the 5 0 end of the mRNA molecules in the single-cell RNA-seq libraries. After quality filtering, we obtained data from 8,242 WT cells and 5,582 Fab-7 deletion cells (Figure  5B , top). Although fewer Fab-7 1 cells were recovered, similar numbers of unique mRNA molecules and annotated transcripts were detected in both genotypes, thus confirming comparable data quality (Figures  S5B  and S5C ).
Between 88 ng and 1 mg total RNA was used for the all the RT reactions. cDNA was amplified using SsoAdvanced Universal SYBRGreen Supermix (BioRad) and 0. 5 mM of corresponding primers. With the exception of the Abd-B C promoter, primers were designed over intron boundaries to prevent amplification from genomic DNA using the online version of Primer3 software (https://primer3. ut. ee/). Primers were annealed at 55 C or 60 C and Real-time PCR was performed using a LightCycler 480 instrument (Roche). mRNA abundance was normalized to the Gapdh2 and Act42A housekeeping genes using the DDCt method. 61 Primer sequences for RT-qPCR amplification are listed in Table  S2.
Calibrated RT-qPCR for absolute quantification of Abd-B isoforms To determine absolute mRNA abundance of Abd-B isoforms, external calibration was performed using a plasmid containing all Abd-B isoform-specific RT-qPCR amplicons. The plasmid was assembled using the NEBuilder HiFi DNA Assembly kit (NEB) into a linearized pBlueScript backbone using larval cDNA as a template. Primer sequences for Gibson assembly are listed in Table  S2. Primers were annealed at 55 C or 60 C and qPCR was performed using the SsoAdvanced Universal SYBR Green Supermix (BioRad) on a LightCycler 480 instrument (Roche). mRNA abundance for the different isoforms was normalized over the reference plasmid and a primer set to the common exon of all isoforms. Experiments were performed on biological duplicates (n=2). Primer sequences for RT-qPCR amplification are listed in Table  S2.
ChIP-seq sequencing reads were filtered using FastQC filtering (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), followed by mapping onto the Drosophila genome (dm6) using bowtie2 (version 2. 3. 0) with filtering for multiple alignments. 55 Low quality reads (<30) and PCR duplicates were removed using SAMtools. 56 Bedgraphs were generated using BAMCoverage. 57 Published CP190 and dCTCF ChIP-seq data in S2 cells,  38  whole embryo  39  and larval central nervous system  40  were analyzed using the same strategy.
After lysis, cell aliquots were stored at -80 C. After pooling of imaginal discs, the experimental procedure as outlined for cell lines was followed. 4C-seq sequencing reads were sorted, aligned, and translated to restriction fragments using the C4CTUS tool, which is a standalone version of the 4C-seq module within the former HTSstation online data analysis service. 54, 65 Reads from different biological samples were demultiplexed using their Illumina Index and reads from different viewpoints were demultiplexed using the first 18 bases sequenced (viewpoint-specific primer). After removing the sequence of the viewpoint-specific primer, the remainder of the reads were mapped onto the Drosophila genome (dm6). Reads mapping to the viewpoint, the directly neighboring ''undigested'' fragment and fragments 2 kb up-and downstream were excluded during the procedure.
Hi-C For Sg4 cells, Hi-C was performed on 15 million cells using the Arima Hi-C kit (Arima Genomics), following the manufacturer's instructions. Hi-C material was sequenced on the Illumina NovaSeq 6000 system (150 bp paired end reads). Hi-C data from S2 R+ cells were obtained from the GEO repository GSE99107. 44 i-C reads were mapped to the Drosophila genome (dm6) using HiC-Pro (version 2. 11. 1) and bowtie2 (version 1. 1. 2). 55, 58  Default settings were used to remove duplicates, assign reads to their restriction fragments and filter for valid interactions. Hi-C matrices at 5kb resolution were generated from the valid interactions and normalized with the Iterative Correction and Eigenvector decomposition method (ICE) in the HiC-Pro tool. TADtool was used to call TAD boundaries with a window size of 51kb and a cutoff of 47 (S2 cells) or 75 (Sg4 cells).
Pools of discs were dissociated in 0. 05% Trypsin EDTA (Thermofisher Scientific) for 8 minutes at 37 C and resuspended in PBS (without MgCl 2 ) with 0. 04% BSA to a concentration of approximately 1000 cells / ml. Reverse transcription and sequencing library preparation were done using the Chromium Next GEM Single Cell 5' kit (10X Genomics), according to the manufacturer's instructions. 5'-single-cell RNA-seq libraries were sequenced on the Illumina NextSeq 550 system (75 bp paired end reads). FastQ files were analyzed using the Cell Ranger software (10X Genomics, version 3. 1. 0), including alignment, filtering and quantitation of reads on the Drosophila genome (dm6) and generation of feature-barcode matrices. For the Sg4 and S2 cell lines, the clusters were obtained using k-means clustering (k = 3) using the Cell Ranger software.
Further visualizations were generated using the Loupe browser (10X Genomics, version 4. 2. 0). For the imaginal discs, all downstream analyses were performed using the Seurat tool (version 4. 0. 1). 60 Cells with UMI count lower than 350 and mitochondrial genes above 5. 5% were excluded from the analysis. After log-normalization, the vst method was used to select the top 2,000 variable features. WT and Fab-7 1 imaginal disc cell data were then integrated using the dataset with the highest number of cells and the first 10 dimensions as a reference. Clustering was performed with the functions FindNeighbors and FindClusters, using the first 10 dimensions from PCA, resulting in the identification of 14 clusters. Identification of the clusters was done using the FindConservedmarkers function in Seurat.
ACKNOWLEDGMENTS We thank  Sylvina Bouleau ,  Emilie Brasset ,  Fabienne Malagnac , and the members of the  Noordermeer lab  for useful discussion. We thank  Denis Duboule ,  Maria Cristina Gambetta , and  Elissa Lei  for comments on the manuscript. We are grateful to  Francois Karch ,  Jacques Montagne ,  Vanessa Ribes , and  Matthieu Sanial  for sharing of reagents and materials. We thank the  I2BC High Throughput Sequencing Platform  for sequencing experiments and single-cell sequencing library preparation. Work in the Noordermeer lab is supported by the  Agence Nationale de la Recherche  ( ANR-14-ACHN-0009-01 ,  ANR-16-TERC-0027-01 ,  ANR-17-CE12-0001-02 ,  ANR-18-CE12-0022-02 , and  ANR-21-CE12-0034-01 ), PlanCancer ( 19CS145-00 ), and the  Fondation Bettencourt Schueller. L. M. -P. was supported by thesis grants from the  Fondation pour la Recherche Medicale  ( FDT202012010470 ) and the  Universite Ṕaris-Saclay  ( Ecole Doctorale Structure et Dynamique des Syste  `mes Vivants).
1 10X Genomics PN-1000128 Chromium Next GEM Single Cell 5' Kit v2 10X Genomics PN-1000265 Arima-HiC+ for HiC kit Arima Genomics N/A Deposited data Hi-C -S2 cells Szabo et al. , 2018 44 GEO: GSE99107 ChIP-seq dCTCF -S2 cells Ong et al. , 2013 38 GEO: GSE41354 ChIP-seq CP190 -S2 cells Ong et al. , 2013 38 GEO: GSE41354 ChIP-seq dCTCF -whole embryo Kaushal et al. , 2022 39 GEO: GSE180376 ChIP-seq CP190 -whole embryo Kaushal et al. , 2022 39 GEO: GSE180376 ChIP-seq dCTCF -larval central nervous Kaushal et al. , 2021 40 GEO: GSE146752 system ChIP-seq CP190 -larval central nervous Kaushal et al. , 2021 40 GEO: GSE146752 system ChIP-seq H3K4me3 -S2 cells This study ENA: PRJEB52393 Mendeley Data: https://doi. org/10. 17632/ rs4sns574p. 1 ChIP-seq H3K27me3 -S2 cells This study ENA: PRJEB52393 Mendeley Data: https://doi.
Data collection All elbow X-rays performed in patients aged between 0 and 18 years in the event of an acute trauma of the upper limb, between January 1, 2015 and August 31, 2019 in the emergency department were retrospectively gathered from the PACS (Intellispace PACS, Philips). All series were kept, including those with only one view, more than two views and sub-optimal views, because of their frequency in children, especially after an upper limb trauma. A total of 1956 X-rays from 935 patients (485 male and 450 female) were collected and de-identified. The de-identification of data and their retrospective analysis was approved by the institutional board under the reference DEC19-279. All these examinations were performed on the same X-ray device (Fujifilm, Tokyo, JP). Radiographs were randomly divided into a training set (668 patients, average age 10), a validation set (99 patients, average age 10) and an internal test set (168 patients, average age 9).
Statistical analysis Analyses were performed using Prism 9 software (GraphPad, La Jolla, CA). Quantitative data were reported as mean §standard deviation. Qualitative data were reported as raw number and percentage (%). The significance threshold was set at P < 0. 05. To compare radiologists' performances before and after the use of A. I. , Wilcoxon matched-pairs signed ranked test was used for sensitivity, specificity, accuracy and Youden index analysis.
External validation The external test set was composed of 61 patients with normal examinations, and 59 patients with at least one abnormal finding on X-ray. The performances of M1 and M2 on this dataset are reported in Table  3. While M1 showed a moderate change in AUROC values (0. 983 to 0. 916), M2 showed a significant drop in AUROC between internal and external test sets (0. 975 to 0. 793), mostly due to a significant drop in specificity (0. 844 to 0. 475). Fig. 3  shows heatmaps after using M1.
Both models showed a drop in AUROC on the external test set (0. 916 and 0. 793 respectively), which is consistent with a tendency of CNN-based models to overfit on internal test sets  [23]. However, the higher drop of M2 on the external test set raised concerns about its potential generalizability and showed that two models that display close AUROC values on internal data can undergo significantly different changes when exposed to another dataset as demonstrated by Wang et al. on mammogram classification  [24]. The magnitude of these changes is unpredictable as Voter et al. showed in their study  [25]  Radiologists' performances were overall lower than the internal performances of both M1 and M2 in silico. However, even without the help of A. I. , radiologists' performances were superior to those of both M1 and M2 on external test sets, which stresses out that comparison between human and algorithms on the sole internal test sets  should be avoided, since they tend to overclaim the inner performances of the algorithms.
First of all, though pretrained on ImageNet, our model was developed on a dataset of relatively limited size. Indeed, many algorithms focusing on conventional X-rays rely on larger datasets  [10]. Nevertheless, few have focused on the specific condition of elbow trauma in children, due to the lower availability of such data as compared to those in adults  [26]. To compensate for the size of the dataset, conventional techniques of data augmentation were performed, but are weaker to prevent overfitting as compared to new data, which can partly explain the changes observed between internal and external test sets  [27]. Secondly, this study was monocentric and showed that results can be variable when exposing algorithms on an external test set (on another device). This stresses out the urge for multicentric trials in the field of A.
Among hematological malignancies, T-cell acute lymphoblastic leukemia (T-ALL) represents a class of aggressive tumors with dismal clinical outcome in case of relapse or refractory diseases. While the intensification of multi-agent chemotherapy protocols has dramatically improved prognosis, refractory and relapsed cases are clinically challenging due to limited therapeutic options. 1, 2 p53 is a transcription factor and a master tumor suppressor gene frequently altered in cancer. 3 8] [9]  Here, we produce the first comprehensive analysis of TP53 Alt and the associated oncogenetic landscape in an extensive cohort of 476 patients newly diagnosed with T-ALL. We investigated the clinical characteristics linked to TP53 Alt in 476 patients, including 215 adults, and 261 children. Adult patients were enrolled in the GRAALL-2003-2005 trials (GRAALL-2003, NCT00222027; GRAALL-2005, NCT00327678) and pediatric patients were enrolled in the FRALLE 2000 trial (Supplemental Figure  1 ).
Based on DNA availability for molecular analysis, 215 adult patients out of 337 and 261 pediatric patients out of 427 were included in this study. No difference in clinical outcomes was observed between the included patients and the entire cohort (Supplemental Figure  2 -3, Supplemental Table  1 ). Diagnostic peripheral blood or bone marrow samples were collected after informed consent was obtained, according to the Declaration of Helsinki. 2] ACCEPTED MANUSCRIPT / CLEAN COPY Genomic analysis was performed by pan-exon targeted next-generation sequencing (NGS) of DNA extracted from diagnostic samples; DNA libraries were prepared using Nextera XT kit (Illumina) and sequenced on an Illumina MiSeq. The NGS panel included 63 genes known to be mutated in T-ALL (Supplemental Table  2. ). Genetic lesion co-occurrences and mutual exclusions were computed using the DISCOVER R package.
We performed a computational approach previously described for the detection of copy number variants (CNVs) from nextgeneration sequence data,  13, 14  including systematic analysis of the depth of TP53 gene coverage. This is based on variations in depth of coverage of aligned sequence reads, using a locally developed algorithm. The CNVs detected were confirmed by high-resolution CGH and/or MLPA analysis (kit P037-CLL-1 and P038-CLL-2 from MRC-Holland). Diagnostic DNA was hybridized on Affymetrix (Santa Clara, CA) Cytogenetics whole Genome 2. 7M Arrays (CGH-array), according to the manufacturer's recommendations. Data analysis was performed with the Chromosome Analysis Suite (ChAS) software (Affymetrix®). Gene copy number aberrations were compared with the Database of Genomic variants (DGV) http://projects.
All analyses were stratified on the trial. Variables with a p-value less ACCEPTED MANUSCRIPT / CLEAN COPY than 0. 1 in univariate analysis were included in the multivariable models. Statistical analyses were performed with STATA software (STATA 12. 0 Corporation, College Station, TX). All p-values were two-sided, with p<0. 05 denoting statistical significance. Additional details are included in the Supplemental Methods. The incidence of TP53 Alt at diagnosis in these cohorts was 4% (21/476). TP53 Mut were detected in 9 patients (6 adults and 3 pediatric cases, Figure  1A , Supplemental Tables  3  and 4 ) and TP53 Del were identified in 15 patients (7 adult and 8 pediatric cases), 3 patients harbored both TP53 Mut and TP53 Del. Patients with TP53 Alt did not significantly differ from TP53 wild-type (TP53 Wt ) patients regarding sex, age and central nervous system (CNS) involvement (Table  1 ), but were associated with an immature phenotype (combining IM0 T-ALL; TCRd and TCRg germline, IMd T-ALL; TCRd rearranged but not TCRg and IMg T-ALL; both TCRd and TCRg rearranged)  12  (8/20, 40% vs 81/399, 20%, p = 0.
Acknowledgements The authors thank all the participants of the GRAALL-2003 and GRAALL-2005 study groups, the  SFCE  and the investigators of the 16 SFCE centers involved in collection and provision of data and patient samples, and  V. Lheritier  for collection of clinical data. This work was supported by grants to Necker laboratory from the  CAncer Research for PErsonalized Medicine (CARPEM) ,  Association pour la Recherche contre le Cancer (Equipe labellisée) ,  la Ligue contre le Cancer  ( Equipe Labellisée ),  Institut National du Cancer   PRT-K 18-071. The GRAALL was supported by grants  P0200701  and  P030425/AOM03081  from the  Programme Hospitalier de Recherche Clinique, Ministère de l'Emploi et de la Solidarité, France  and the  Swiss Federal Government in Switzerland. Samples were collected and processed by the  AP-HP "Direction de Recherche Clinique" Tumor Bank at Necker -Enfants Malades.
We repeated this process 10000 times, from which the proportion of positive slopes was used as a p-value. Finally, the mean slope b and the intercept a over these 10000 simulations were used to compute the predicted population density at day t for non-declining population as (Eq. 5) or for a declining population starting to rebound at day c (corresponding to in eqs. 3-4) as (Eq. 6) From this, we could then estimate the day at which a declining and a non-declining population would reach equal population density, assuming exponential rebound: (Eq. 7) All statistical analyses were performed on Rstudio (R version 3. 6) using MASS version 7. 3. 53 package  (Venables and Ripley 2002).
Acknowledgements: We thank S. Fereol for help with the PCD inhibitor assay, P. In the second column, d stands for days and M stands for molar of NaCl. All variables in the last column were treated as categorical, except the Inhibitor concentration treated as a continuous variable. Grey lines represent 10000 linear regressions of growth rate against decline rate, based on simulated samples where each pair of values (for decline and rebound) was randomly drawn from a normal distribution with mean set to the predicted value and standard deviation set to the standard error of estimators for each condition. Height of the 10000 replicates had a negative slope (P = 8x10 -4 ).
Subjects Subjects were all resident medical officers, or medical students, of the department, and were enrolled in the study between November 2020 and January 2021. A questionnaire was performed to ensure the absence of any past medical history among: fracture, sprain, or ankle surgery. Before participation, all the volunteers were informed of the purpose of the study and consent were obtained. For each subject, both ankles were included: forty ankles in total were analysed. In addition, the following demographic data were recorded: age, sex, height, side of the dominant foot, type of foot arch (neutral/ flat/ high), and type of hind foot alignment (neutral/ varus/ valgus). These last two parameters were obtained using a podoscope: which uses the Chippaux-Smirak index (CSI) to classify the type of plantar arch and the standing tibiocalcaneal angle to classify the type of hind foot alignment  [2, 6].