dataesr/scientific-paragraphs-categorization huggingface dataset:
But these seeds are defined once for all and can be used for all MR follow-up exams at M i. Therefore, one perspective would be to prevent the definition of new seeds for each new patient and consider the pre-existing seeds of the cohort. Multiple strategies could for instance be considered. First, we could use the pre-defined seeds of the closest pre-segmented patient of the database, e. g. the one with similar height and weight, or with the closest coarse segmentation of body envelopes. A second strategy would be to build a mean seed-based atlas for each organ of interest, considering all pre-defined seeds of pre-segmented patients and transport this atlas on each new patient. Another strategy would be to benefit from the whole seeds database and integrate it in a machine-learning approach to identify the position of newly defined seeds or to select the pertinent ones for each new patient, considering voxels intensity and anatomical features for instance.
A. 2. Results on clinical data The contributions in this section are twofold. In section A. 2. 1, we propose further results than the ones detailed in  (Risser et al 2018)  to show that the seedbased rFM segmentation of clinical data is more robust, accurate and quicker with the proposed FM regularization, compared to the complete manual delineation of regions by clinical experts. In section A. 2. 2, we evaluate the two registration approaches considered for the longitudinal pipeline. Details on the MR dataset of CLL patients considered for the experiments are given in section 4. 2. 1. A. 2. 1. Validation of whole-body rFM segmentation at M0. As mentioned earlier, the semi-automatic rFM segmentation was validated at M0 for the delineation of 12 anatomical structures disseminated over the whole-body MR.
Inclusion and exclusion parameters We performed a retrospective study in the ED of TUH during the period between 5th January and 11th May, 2018. Data concerning patients over the age of 18 with a diagnosis coded as F 60. 3 in the CIM-10 handbook  [17]  was extracted using the Urqual® software currently employed in the ED. We excluded patients with a diagnosis of intellectual disability (F70-F79), neurodevelopmental disorders (F80-F89), psychosis (F20-F29) or a neurodegenerative condition (G30-G32). This was done to standardize our analysis, and because these disorders, in our view, lead to specific behavioral problems that do not match the criteria for a diagnosis of BPD. Patients admitted in for justice requisition were also excluded from the analysis. The diagnosis was made by the resident psychiatrist or senior psychiatrist.
In these structures the follow-up is short (6 weeks), based on brief therapy rationale in order to guarantee a turn over and thus the availability to welcome new patients. At the end of the follow-up the patient is referred to other care structures. This project is progressively built with the patient. We determined the type of referral from the data and specified whether the patient had already been treated within this particular structure or not. We also established whether the crisis structure was accessible at the time of the attendance at the ED, as these wards can close to new admissions when the maximum number of patients they are able to treat is reached. -Information on readmissions at 1 month was also collected, including the reasons for the readmission clustered by suicide attempt, suicide ideation, or other.
Discussion In our study, one third of the patients with a diagnosis of BPD who attended at TUH's psychiatric ED experienced an inconsistent referral based on the current care recommendations. Despite the retrospective data collection, the sociodemographic data of those in our sample was comparable to that obtained prospectively for BPD patients in the FRENCH CRISIS cohort. The majority of our patients were single women with a mean age of around 30  [20] , while the main reasons for the attendances at TUH's ED were mostly suicide attempts and suicide ideation, which is consistent with the relevant literature  [21]. Our main finding was that the consistency of a referral was largely explained by the type of care structure to which the patients were oriented. Indeed, the referrals judged to be inconsistent mostly involved non-specific referrals.
Sex determines the transcriptional signature of peritoneal macrophages The difference in macrophage replenishment prompted us to assess the wider effects of sex on the imprinting of peritoneal macrophage identity and function. We therefore first performed populationlevel RNASeq on peritoneal F4/80 hi CD102 + macrophages FACS-purified from unmanipulated 10-12 week old male and female mice (Supplementary Figure  4 ). To limit potential confounding effects of estrous cycle, the stage of each female mouse was confirmed by vaginal cytology and samples pooled to include cells from all stages of the cycle. Furthermore, to limit the effects of circadian influence, mice in each biological replicate were euthanised at the same time each day. Unbiased clustering was then used to group the populations based on sex, with sex explaining 81% of the variance within the datasets (Figure  4a ) and differential gene expression analysis revealed that 486 mRNA transcripts were differentially expressed (>1.
scRNAseq analysis reveals dimorphic macrophage heterogeneity We next applied single cell RNA sequencing (scRNAseq) to determine whether the transcriptional differences seen in our population-level data were the result of gene differences at a single cell level or if dimorphism was a reflection of differential subset composition between the sexes. A broad approach was used to capture all CD11b + cells depleted of granulocytes and B1 B cells to allow both CD102 - F4/80 lo MHCII + macrophages and resident CD102 + cells to be examined. These cells were FACSpurified from age-matched 12-week-old male and female mice and droplet-based scRNASeq performed using the 10X Genomics platform. 10,000 sorted cells of each sex were sequenced and following quality control, analysis was performed on 4341 and 2564 cells from female and male respectively.
Samples were acquired using a FACS LSRFortessa or AriaII using FACSDiva software (BD) and analyzed with FlowJo software (version 9 or 10; Tree Star). Analysis was performed on single live cells determined using forward scatter height (FCS-H) versus area (FSC-A) and negativity for viability dyes. For analysis of macrophage proliferation, Ki67 expression was used to determine the frequency of all CD102 + /F4/80 hi cells in cycle, whereas a 2h BrdU pulse before necropsy combined with Ki67 expression was used to identify cells in S phase, as described previously  77. mRNA was detected by flow cytometry using PrimeFlow technology (ThermoFisher) using probes against ApoE (probe type 10; AF568), ApoC1 (probe type 4; AF488) and CXCL13 (probe type 6; AF750) according to the manufacturer's guidelines.
Bulk RNAseq CD102 + F4/80 hi cells were FACS-purified from the peritoneal and pleural cavities of unmanipulated male and female mice. For each population, 25,000 cells were sorted into 500µl RLT buffer (Qiagen) and snap frozen on dry ice. RNA was isolated using the RNeasy Plus Micro kit (Qiagen), at which point triplicates of 25,000 cells for each population were pooled. 10 ng of total RNA were amplified and converted to cDNA using Ovation RNASeq System V2 (Nugen). Sequencing was performed by Edinburgh Genomics using the Illumina HiSeq 4000 system (75PE). Raw map reads were processed with the R package DESeq2 78 to generate the differentially expressed genes, and the normalized count reads to generate and visualize on heat maps generated by the R package pheatmap. Samples with >5% of reads mapped to ribosomal RNA were removed from analysis.
DEG were determined using at least a 1. 5-fold difference and adjusted p < 0. 01, for each of the six pairwise comparisons. Pathway enrichment analysis was performed using the GSEA online database and the R package gskb (Gene Set data for pathway analysis in mouse) which makes predictions between each of the six pairwise comparisons, incorporating in the analysis the statistically significant differences in gene expression. The R package gskb was used to determine the chromosomal location of each of the genes and transcription factors. All R code is available upon request.
Single-cell RNAseq 10K cells for male and female sorted cells were loaded in Chromium 10x in parallel. Libraries were prepared as per manufacturer's protocol and sequenced on Illumina Novaseq S1. Initial processing was done using Cellranger (v2. 1. 1) mkfastq and count (aligned to mouse assembly mm10). Preparation of analysis ready data: For each dataset (filtered data from Cell Ranger pipeline), we filtered out potentially low quality cells using dataset-specifc thresholds based on the trend of the number of genes per cell versus number of housekeeping genes per cell and number of genes per cell versus percentage of mitochondrial genes per cell curves as follows. More specifically, for the female data, we retained 4341 cells that have between 300 and 5000 genes, at least 65 housekeeping genes and percentage of mitochondrial genes over the total number of expressed genes below 2%.
For the male data, we retained 2564 cells that have between 300 and 6000 genes, at least 70 housekeeping genes and percentage of mitochondrial genes over the total number of expressed genes below 2%. Finally, we filtered out genes that were expressed in less than 1% of the cells from each dataset. Clustering analysis of the data: Clustering and data merging using CCA was done using Seurat (v3. 1. 0). We used default parameters and 20 principal components for aligning and clustering the data. We next removed a very small cluster that lay far from all other clusters on the UMAP projection, indicating it could be either contamination or doublets and constructed a phylogenetic tree of the remaining clusters to understand the distances and relationship between them. Clusters that were closely grouped together and did not show unique markers, were merged together.
Identification of differentially expressed genes between female and male cells: We used Student's t-test as implemented in the Seurat package between equivalent female and male cells to identify differentially upregulated genes between male and female cells. We only retained genes with adjusted p-value based on Bonferroni correction below 0. 05. Genes that were identified as differentially expressed for more than four out of the six clusters were selected as global DE genes and were removed from the cluster-specific differences.
Pseudotemoral orderning: We used monocle (v2. 12. 0) with default parameters to build pseudotemporal trajectories of the female and the male data separately. Functional annotation of genesets: We used DAVID to obtain enriched Gene Ontology Biological Process (GO-BP) and KEGG pathway terms for each extracted geneset. Given a list of genes upregulated in a set of cells (e. g. cluster) as target and a list of all genes observed in the respective dataset as background, we downloaded a list of GO-BP terms (GOTERM_BP_FAT) and a list of KEGG pathway terms that were significantly enriched in the target list as a functional annotation chart. We kept only terms with Benjamini adjusted p-values less than 0. 05 and sorted them by decreasing Fold Enrichment. macrophages FACS-purified from the peritoneal cavity of 10-12 week old mice.
Acknowledgements Flow cytometry data were generated with support from the  QMRI Flow Cytometry and Cell Sorting Facility, University of Edinburgh. mRNA sequencing was performed by  Edinburgh Genomics ,  The University of Edinburgh. Edinburgh Genomics  is partly supported through core grants from  NERC  ( R8/H10/56 ),  MRC  ( MR/K001744/1 ) and  BBSRC  ( BB/J004243/1 ). Servier Medical Art was used for the generation of some of the graphics. This work was funded by the  Medical Research Council UK  ( MR/L008076/1  to S. J. J), with additional support from the  Wellcome Trust  ( IS3-R34  to S. J. J; PhD studentship 203909/Z/16/A to P. L. ) and a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  (Grant Number  206234/Z/17/Z  to C. C. B).
III. DATA ANALYSIS AND RESULTS This section contains the experimental procedure and the acquisition and analysis of the ODR data. We present the procedure as actually performed during a measurement shift at the ATF2 accelerator. After a preparatory phase where the particle beam orbit is optimized and aligned, OTR is used first to measure the beam size at the target location for various accelerator conditions. This allows us to provide a cross-calibration for the actual ODR measurements that are performed under the same beam conditions.
B. OTR calibration The transverse beam size is obtained from ODR patterns that are calibrated against OTR data under the same beam-optics conditions. The reference value provided by OTR is calculated by the procedure presented in Ref. [43]. This allows us to establish a relation between the visibility (which we define in the present section) of the ODR pattern and the transverse beam size. The target is inserted so that the beam intercepts the flat (i. e. , without slits) aluminized part of the target. OTR is therefore emitted and observed by the same optical system as that used for the ODR measurements, through the imaging line. Figure  9  shows the intensity of the vertically polarized OTR recorded when the beam is strongly focused vertically by quadrupole QM14, squeezing σ y. For such a small vertical beam size, the image of the particle beam is dominated by the OTR PSF, showing a two-lobe structure (Figs.
Conclusions: Mature PFS data from ALEX confirmed significant improvement in PFS for alectinib over crizotinib in ALKpositive NSCLC. OS data remain immature, with a higher 5-year OS rate with alectinib versus crizotinib. This is the first global randomized study to show clinically meaningful improvement in OS for a next-generation tyrosine kinase inhibitor versus crizotinib in treatment-naive ALK-positive NSCLC. Clinical trials number: NCT02075840.
3 Nextgeneration TKIs are listed as the recommended first-line therapy for advanced ALK-positive NSCLC in treatment guidelines, with alectinib listed as the preferred option. 4, 5 his recommendation is supported by the superior efficacy reported from the phase III, global, randomized ALEX study (NCT02075840). At the primary data cut-off (9 February  2017), after approximately 18 months of follow-up in both arms, the study met its primary end point and demonstrated significantly improved progression-free survival (PFS) with alectinib versus crizotinib in patients with treatment-naive ALK-positive NSCLC. 6 Median investigator-assessed PFS was not reached (NR) with alectinib and was 11. 1 months with crizotinib. By independent review committee (IRC) assessment, median PFS was 25. 7 months with alectinib and 10.
Statistical analysis In total, 170 PD or death events were required to achieve 80% power of the log-rank test to detect a target HR of 0. 65 (corresponding to an increase in median PFS from 10. 9 months with crizotinib to 16. 8 months with alectinib) at a two-sided alpha level of 5%. Comparison between the treatment groups with respect to PFS was based on a stratified log-rank test at a 5% level of significance (twosided). PFS and OS were pre-planned analyses, while assessment of OS data up to 5 years was an exploratory analysis. The KaplaneMeier method was used to estimate median PFS and OS for each treatment arm with 95% CIs. A stratified Cox proportional-hazards regression model was used to estimate the treatment effect, expressed as an HR (alectinib versus crizotinib) with a 95% CI. The median duration of survival follow-up was calculated as the time from randomization until last follow-up for all patients.
Updated OS data Updated OS results are based on a data cut-off of 29 November 2019, providing an additional 12 months of follow-up from the final PFS data cut-off. The median duration of survival follow-up for OS was 48. 2 months (range 0. 5e62. 7) with alectinib and 23. 3 months (range 0. 3e60. 6) with crizotinib. Overall, 113 patients had died [51/152 (33. 6%) with alectinib versus 62/151 (41. 1%) with crizotinib]. OS data remain immature with 37% of events recorded (stratified HR 0. 67, 95% CI 0. 46e0. 98). Median OS was NR with alectinib and was 57. 4 months with crizotinib (95% CI 34. 6eNR) (Figure  2A ). The 5-year OS rate was 62. 5% (95% CI 54. 3e70. 8) with alectinib and 45. 5% (95% CI 33. 6e 57. 4) with crizotinib (Table  1 ). At present, 53 patients (34. 9%) in the alectinib arm and 13 patients (8.
ACKNOWLEDGEMENTS The authors would like to thank the patients, their families and the study centers. They also acknowledge the help of  Inna Vetluhina  with statistical analyses during the conduct of the study. Third-party medical writing assistance, under the direction of the authors, was provided by  Fiona Fernando, PhD , contract medical writer at Gardiner-Caldwell Communications, and was funded by  F. Hoffmann-La Roche Ltd. ACEA Biosciences ,  AstraZeneca ,  Boehringer-Ingelheim ,  Bristol-Myers Squibb (BMS) ,  Celgene , Chimed, Cirina,  Fishawack Facilitate ,  Ignyta ,  Janssen ,  Lilly ,  Merck Serono ,  Merck Sharp & Dohme (MSD) ,  Novartis ,  OncoGenex ,  Pfizer ,  Roche/ Genentech , SFJ Pharmaceutical, Takeda and Vertex; and research funding from  AstraZeneca ,  BMS ,  Clovis Oncology ,  MSD ,  Novartis ,  Pfizer ,  Roche , SFJ Pharmaceutical and XCovery.
Transcriptomic Profiling Showed a Two-Phase Process with Induction of Neuronal Differentiation followed by Induction of the Myogenic Program A global analysis of DEGs between the three RNA Seq datasets (D8, D17, D30) revealed a list of 2229 DEGs between D8 and D17, and 281 genes between D17 and D30, with an overlap of 302 genes common to the three time points, i. e. , corresponding to genes induced at an early step of differentiation and maintained over time (Figure  3A ). For this group, the fifteen most significant associated GO terms corresponded to skeletal muscle function and development or axonogenesis (Figure  3B ). Then, an analysis of significantly overrepresented biological pathways between D8-D17 and D17-D30, revealed two phases in the differentiation process. Between D8-D17, enriched pathways corresponded to axon development and synapse formation, but also cell cycle regulation, consistent with cell cycle exit and final differentiation (Figure  3C ).
Myogenic and Neuronal Specification and Long-Term Persistence of PAX7+ Cells Between D17 and D30, notch pathway inhibition and activation of neuronal differentiation facilitated skeletal muscle differentiation, as evidenced by progressive activation of myogenesis and overrepresentation of genes specific to skeletal muscle differentiation and development (Figure  3F ). At the individual gene level, the expression of embryonic/fetal Myosins was followed by the progressive increase of adult isoforms (MYH2 and MYH7). We also noticed expression of NCAM1 encoding the CD56 cell surface marker between D8 and D17 (Log2FC: -3. 92, padj: 3. 75e -8 ) and a steady expression between D17 and D30 (Log2FC: 1. 077), indicating a stable proportion of precursor cells over time. Besides NCAM1, the expression of other myogenic progenitors markers such as CD29 (ITGB1), CD82 (TSPAN1) and CXCR4 was also detectable in our RNA Seq datasets during the differentiation process.
Funding: This study was funded by  Association Française contre les Myopathies  ( AFM, TRIM-RD program ) and  Region Provence Alpes Côte d'Azur (Cladimus project ,  2018-06436 ). CB was the recipient of a fellowship from  The Algerian Ministry of Higher Education and Research and Fondation pour la Recherche Médicale. C. D. and C. L. are recipient of a fellowship from the  French Ministry of Research and Higher education. K. M. was the recipient of a fellowship from  AFM-Telethon. The project leading to this publication has received funding from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "investissement d'avenir programme "  AMX-19-IET-007 through Marseille Maladies Rares (MarMaRa) Institute.
In a second step in the long-term, the ICOPE project is intended to collect large population data for the development of "Nomograms" of optimal aging in older adults. Several actions to implementation of the ICOPE program in the community have been scheduled by WHO (2), and one is the ICOPE App, a free-of-charge App (Android/Apple Store https://www. youtube. com/watch?v=gLva4ReV9KA. The first version of the ICOPE App is available and allows a selfadministered screening of individuals' intrinsic capacity. New functions of the App are developed and include comprehensive geriatric assessment, sharing on-line information, and receive individual guide and support from primary care. The implementation of the ICOPE App would mitigate the current gap between geriatricians and family physicians and those between the family physicians and the patients.
Connected to a secure medical database, the ICOPE STEP 1 is performed every 4-6 months by professionals or seniors themselves. If a deterioration in one or more domains of intrinsic capacity is identified, an alert is generated by an algorithm which allows the health professionals to intervene quickly  (11). In conclusion, the ICOPE App applies to the "Actionresearch philosophy"  (12)  to bridge the gap between research and clinical practice, and to provide better care for older community-dwellers, especially during this exceptional and dramatic period of pandemic and containment, after which the world of tomorrow will no longer look like the one before.
showed that the same species were isolated from the intestinal carriage and clinical samples in all patients experiencing both carriage and HLAC-E infections  [18]. Finally, these data were collected retrospectively; the risk of bias is high. However, digitalized laboratory information systems limit this risk. The clinical utility of adding HLAC-E detection and reporting to ESBL-E intestinal carriage screening should be evaluated prospectively, to establish its real impact on empiric antimicrobial therapy, carbapenem overuse, and outcome. To conclude, we found that the addition of systematic identification and reporting of HLAC-E to current ESBL-E intestinal carriage screening could help to predict the absence of 3GCR-E in respiratory samples of severe surgical ICU patients. This result could help to improve the appropriateness of empirical antimicrobial therapy in ICU patients with suspected HAP.
0983) and 34% of patients (24/71, P ¼. 0009) with endoscopic or histologic improvement alone after induction, respectively. CONCLUSION: Data from the UNIFI program of ustekinumab in patients with UC treated with ustekinumab indicated the achievement of histo-endoscopic mucosal healing after induction therapy is associated with lower disease activity at the end of maintenance therapy than either histologic or endoscopic improvement alone. ClinicalTrials. gov.
Variables with P <. 05 in the univariate analysis were included in the multivariate analysis, with variables P <. 05 in the multivariable analysis being considered statistically significant. Odds ratios (ORs) and 95% confidence intervals (CIs) are presented. All analyses were performed with SAS software, version 9. 4 (SAS Institute, Cary, NC) and Array Studio software, version 10 (Qiagen, Cary, NC).