dataesr/scientific-paragraphs-categorization huggingface dataset:
To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massively parallel sequencing of primary ECs between 2014 and 2018 and preoperative CE-CT were included (n = 150). Molecular subtypes of EC were assigned using DNA polymerase epsilon (POLE) hotspot mutations and immunohistochemistrybased p53 and MMR protein expression. TMB was derived from sequencing, with > 15. 5 mutationsper-megabase as a cut-point to define TMB-H tumors. After radiomic feature extraction and selection, radiomic features and clinical variables were processed with the recursive feature elimination random forest classifier. Classification models constructed using the training dataset (n = 105) were then validated on the holdout test dataset (n = 45).
The MSK-IMPACT and IHC data for all study patients were reviewed by a board-certified gynecologic pathologist (D. F. D. ). TMB was computed as the number of nonsynonymous somatic mutations-per-megabase (mut/Mb) using MSK-IMPACT sequencing, targeting exons and selected introns of up to 468 cancer-related genes  29, 30. A universal, pan-solid tumor TMB cutoff to distinguish responders from non-responders to immune checkpoint inhibitors is yet to be defined. We selected a cut-point of > 15. 5 mutations/Mb (i. e. ≥ 16 mut/Mb when rounded up to the nearest whole number). This is a clinically meaningful cut-point that mirrors the enrollment criteria of the MyPathway (NCT02091141), a study of Atezolizumab that is actively enrolling patients with TMB-H advanced solid tumors. Segmentation and image preprocessing.
Details of CE-CT acquisition are provided in the Supplement. Two radiologists (Y. L. and J. N. ) with expertise and experience in gynecologic oncologic imaging manually contoured all tumors in consensus. The radiologists used the Insight Segmentation and Registration Toolkit-Segmentation platform (ITK-SNAP) version 3. 6 (https ://itksn ap. org) to trace the outer margin of each lesion on every tumor-containing image, a process that resulted in radiologist-defined tumor volumes-of-interest (VOI)  35. To ensure consistent extraction of texture features, all CT images and corresponding VOIs were resampled to the uniform voxel size of 1 × 1 × 1 mm 3 using the in-house software wrapper written in C++ around the Insight ToolKit software library  36. In addition to tumor VOI, the peritumoral-rim was generated for each lesion/patient to capture the invasive edge of the tumor and interrogate the environment surrounding the tumor.
Integrated radiomic-clinical classification models. Recursive feature elimination random forest classifiers (RFE-RF) with 2000 decision trees and repeated (n = 10) five-fold nested internal cross-validation were built on the data from the training dataset (discovery cohort; n = 105)  43, 44. All features were ranked according to their importance in the classification using Gini feature importance  44. Gini feature importance measures the overall probability of misclassification when using a given feature in the individual trees of the RF classifier  43. Features with Gini feature importance of > 25. 0 (i. e. , features selected > 10% of the time in the 250 analysis folds) were considered relevant to the classification. Cross-validation accuracy was computed using the cases that were not included in the individual analysis folds.
Integrated radiomic-clinical machine learning classifier can identify MMR-D tumors. The performance of the radiomic-clinical model to distinguish MMR-D from CN-low-like and CN-high-like ECs is summarized in Table  2. For this analysis, ECs of POLE molecular subtype were excluded. We found that our model achieved a cross-validation AUROC of 0. 78 (95% CI 0. 67-0. 88) for the training dataset and a true validation AUROC of 0. 78 (95% CI 0. 58-0. 91) for the test dataset (p = 1. 0) (Fig. 3A ). The radiomic-clinical model identified three peritumoral-rim Gabor edge features as the most relevant features to the classification, i. e. , Gini feature importance > 25. 0 (Supplemental Table  1 ). The above peritumoralrim features remained significant after the adjustment for multiple comparisons (p ≤ 0.
Integrated radiomic-clinical machine learning classifier can identify TMB-H tumors. As a next step, we assessed the performance of the radiomic-clinical model to differentiate all TMB-H ECs, including ECs of MMR-D and POLE subtypes, from TMB-L tumors (Table  3 ). We found that our model achieved a crossvalidation AUROC of 0. 74 (95% CI 0. 64-0. 84) for the training dataset and a true validation AUROC of 0. 87 (95% CI 0. 73-0. 95) for the test dataset (p = 0. 09) (Fig. 3B ). The radiomic-clinical model selected 21 features as the most important to the classification, i. e. , Gini feature importance > 25. 0 (Supplemental Table  2 ). Of these, 12 features remained significantly associated with TMB status after the adjustment for multiple comparisons (p ≤ 0. 044). Again, the peritumoral-rim features including Gabor edge features were among the most relevant to the classification.
Discussion Here, we demonstrate that CE-CT radiomics can distinguish MMR-D from CN-low-like and CN-high-like ECs and identify TMB-H ECs. Our machine learning classifier was built using clinical variables and both intratumoral and peritumoral-rim radiomic features; the model was subsequently validated on the holdout test dataset. The resultant integrated clinical-radiomic models achieved true validation AUROCs of 0. 78 and 0. 87 for classifying MMR-D and identifying TMB-H ECs, respectively. MMR IHC is a highly sensitive and specific tool to determine MMR status, however, the screening is not universal beyond tertiary centers  48. In view of the  common use of CE-CT to stage ECs at the time of initial diagnosis and recurrence, non-invasive radiomic-based identification of MMR-D and TMB-H tumors may reduce the cost and improve the accessibility of molecular stratification.
Using the color tiles, a deep learning model was trained on TCGA cohorts to identify MSI-H tumors, achieving AUCs of 0. 77-0. 84 depending on the cohort  57. The model was then validated on a multi-institutional cohort with a true validation AUC of 0. 84. Interestingly, the authors found that MSI was associated spatially with poor differentiation and lymphocyte-rich tumor areas, consistent with prior histopathology literature. Golia Pernicka et al. evaluated whether CE-CT-based radiomics can predict MMR-D status in patients with colon cancer  58. Similar to our study, the combined clinical-radiomic model predicted MMR-D colon cancer with an AUC of 0. 80 for the training dataset and 0. 79 for the test dataset. The combined model was slightly superior to the clinical-only (AUC of 0. 74) and radiomic-only models (AUC of 0.
Robust and inexpensive assessment of TMB status is also timely given the ongoing prospective clinical trials which are enrolling patients with pan-solid tumors on the basis of TMB and other molecular classifiers (NCT03668119, NCT0291141)  64. Our findings suggest the potential of radiomics as an adjunct tool to molecular profiling, especially given its possible advantage in the presence of intra-tumor heterogeneity. Our study has several limitations, including retrospective study design, relatively small patient cohort, and the fact that all patients were sourced from a single institution. Notably, many patients in our cohort (36%) were imaged at various outside institutions, suggesting that our models may be generalizable across multiple CT protocols and vendors. Further validation of this approach utilizing large multi-institutional cohorts will be essential to exclude overfitting and confirm the applicability of our results across different institutions/patient populations.
7 The BLUEPRINT consortium  8  was established to characterize the epigenetic state and transcriptional profile of different types of hematopoietic cells. Reference datasets for DNA methylation, histone modifications and gene expression were generated from highly purified cell populations using state-of-the-art technologies, in accordance with quality standards set by the International Human Epigenome Consortium. 9 RNAsequencing data from over 270 samples encompassing 55 cell types have been made publicly available (http://dcc. blueprint-epigenome. eu). A subset of these data has been described previously. 10, 11 Here, we present the analysis of 90 total RNA samples obtained from cord and adult peripheral blood, each consisting of one of 27 mature cell types and 32 small RNA samples, each consisting of one of 11 mature cell types.
Bioinformatic analysis An overview of the bioinformatic pipeline is shown in Online Supplementary Figure  S1. To analyze the expression of known genes and transcripts, we trimmed reads with Trim Galore (v0. 3. 7; parameters "-q 15 -s 3 --length 30 -e 0. 05") and aligned them to Ensembl v75 of the human transcriptome with Bowtie  14  (1. 0. 1; parameters "-a --best --strata -S -m 100 -X 500 --chunkmbs 256 --nofw --fr"). Small RNA-sequencing reads were also trimmed with Trim Galore (v0. 3. 7; parameters "-f fastq -e 0. 05 -q 15 -O 3") and aligned to the miRBase (v21) human mature microRNA (miRNA) with RapMap (v 0. 4. 0) using the parameters "quasimap -c -s -z 0. 9". We used MMSEQ  12  and MMDIFF  13  (v1. 0. 10; default parameters) to estimate gene, transcript and miRNA expression levels, and to identify features that were differentially expressed across cell types.
This choice of methodology allowed us to obtain regularized transcript and gene-level posterior estimates of expression and the corresponding measures of posterior uncertainty, which could then be accounted for in the modeling of differential expression. For guided transcriptome assembly, we used STAR (v2. 4. 1c; parameters "--runThreadN 8 --outStd SAM --outSAMtype BAM Unsorted --outSAMstrandField intronMotif") to align trimmed reads to build GRCh37 of the reference human genome. We sorted the bam files by coordinate and indexed them with samtools (v 1. 3. 1). 15 We performed guided transcriptome assembly for each sample using StringTie7 (v 1. 3. 4; parameters "-p 8 --rf -G Ensembl_75. gtf -v -l BPSTRG"). We also used StringTie to combine these transcriptomes into a single merged transcriptome, which we then compared to the annotations in Ensembl 75 using Gffcompare.
Differential expression of microRNA We applied the same differential expression modeling described above to the small RNA data for which biological replicates were available (MK, NEU, MONO, M1, M2 and CD4TC samples). Of 2,588 miRBase-annotated  26  miRNA, 603 had a posterior probability of differential expression >0. 8, of which 573 were classified as cell typespecific. The mean expression of miRNA was strongly associated with having at least one validated target among the 29,920 validated miRNA-messenger RNA (mRNA) interactions in the mirecords, mirtarbase and tarbase databases  27  (P<2 x 10 -16 , effect size = 0. 16, logistic regression). For example, 46 of the 50 miRNA (92%) having the greatest mean expression had at least one validated target, while only 458 (18. 2%) of the remaining 2,508 miRNA had a validated target.
The miRNA with the greatest expression in their assigned cell type (Online Supplementary Table  S8 ) have been previously linked to relevant cellular functions in that cell type. For example, hsa-miR-21-5p (the most highly expressed M1-specific miRNA) is involved in resolution of wound inflammation  28  and macrophage polarization; 9 hsa-let-7g-5p, hsa-miR-26a-5p, hsa-miR-150-5p and hsa-miR-146b-5p (the most highly expressed CD4TC-specific miRNA) are important modulators of CD4 + T cells;  30, 31  and hsa-miR-126-3p (the most highly expressed MK-specific miRNA) plays a role in MK/PLT biogenesis. 32, 33 However, using the existing databases of miRNA-mRNA interactions, we did not find any correlation between the expression of miRNA and the expression of their targets, which is consistent with miRNA being only one of a diverse set of molecular players in transcriptional regulation of hematopoietic cells and is in agreement with the results of other studies showing that miRNA induce translational repression without mRNA destabilization.
To avoid the assembly of artefactual sequences originating from pseudogenes, we used a conservative approach that filtered out intronless transcripts and transcripts intersecting any of the transcripts present in Ensembl 75, GEN-CODE 19 or RefSeq  40  (Online Supplementary File 3). This unified filtered transcriptome contained 645 multi-exonic transcripts originating from 400 novel genes. Using the expression values of the subset of 368 novel genes having a log expression >0 in at least one sample, we were able to cluster the samples by cell type (Figure  4A ), suggesting that these novel genes might play a role either in the determination of cellular identity or in performing cell typespecific functions. The vast majority (348 out of 400) of the novel multiexonic genes had a coding potential below the standard CPAT20 threshold (0.
Circular RNA in mature hematopoietic cells CircRNA are single stranded RNA molecules of which the ends are covalently joined by a backsplice mechanism. Some circRNA have been shown to regulate transcription  41  or act as miRNA sponges,  42, 43  but the majority of circRNA have no known function. Peripheral blood contains thousands of circRNA. 44 We identified backsplice junctions in the total RNA-sequencing dataset using five methods  [43] [44] [45] [46]  and excluded backsplices detected by fewer than three of these methods in order to mitigate methodological biases. In addition, we excluded backsplices overlapping known segmental duplications,  47  multiple genes or Ensembl 75-annotated readthrough transcripts. We thus obtained a list of 91,866 consensus backsplices (Online Supplementary Table  S9 ).
This cell type-specific summary of AP ranged from 1. 02% in resting HUVEC to 12. 45% in PLT, which is the only anucleated cell type in our dataset (Online Supplementary Figure  S4A , Online Supplementary Table  S10 ). Elevated AP in PLT is consistent with the absence of steady-state tran-scription in PLT and the lower rate of decay of circRNA relative to linear molecules. 51 e performed differential expression analysis of circRNA between all pairs of functional categories of cell types (Online Supplementary Table  S2 ). We identified 5,993 statistically significant differences in circRNA expression, comprising 929 distinct backsplices (<2%) that were differentially expressed in at least one pairwise comparison. These circRNA originated from 698 genes, of which 678 were protein-coding and 20 were non-coding.
is a HSST trainee supported by NHS Health Education England. NF is funded by the NIHR Cambridge Biomedical Research Centre. FP is supported by the Fundação Carlos Chagas Filho de Amparo à Pesquisado Estado do Rio de Janeiro (FAPERJ; E-26/203. 229/2016). NANJ is a recipient of a scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES; Finance Code 001). The work by DS was supported in part by an Isaac Newton fellowship to MF. MF is supported by the British Heart Foundation (FS/18/53/33863).
Funding The work was funded by a grant from the  European Commission   7th Framework Program  ( FP7/2007-2013 , grant  282510 ,  BLUEPRINT ) to XE, PF, JHAM, MY,  HGS  and  WHO. WHO is an  NIHR  senior investigator and receives funding from  Bristol-Myers Squibb , the  British Heart Foundation , the  Medical Research Council  and the  NIHR. OGI , FJM, AF, JMM, LC and PF are funded by the  Wellcome Trust  ( WT108749/Z/15/Z ) with additional funding for specific project components such as GENCODE from the  National Human Genome Research Institute of the National Institutes of Health.
research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. www. thelancet. com/infection Vol 20 July 2020 depended on their vote at the first round of the 2017 presidential election (figure): those who had voted for a far left or far right candidate were much more likely to state that they would refuse the vaccine, as well as those who abtained from voting. These early results are not entirely surprising. When this dimension has been studied, researchers have often found a connection between political beliefs and attitudes to vaccines.
The border response elaborated from January 2020 has opened no new horizon, other than that of falling back on ever smaller local communities, forming so many localised heterotopias. More than ever, the social sciences must tackle the question of pandemics and emerging zoonoses, inasmuch as these are also the result of the policy choices of governments  (Craddock & Hinchliffe 2014). Notes 1 Phylodynamic analysis proposes a precise chronology of the spatial diffusion of the virus on the basis of its marginal genetic variations; see David Larousserie (2020). 2 See our 'Frontières' for a visualization of progressive border closures around the world: https://www. youtube. com/watch?v=mv-OFB4WfBg. The data were extracted from government declarations regarding travel restrictions, the closure of land, sea and air borders and information distributed by embassies around the world.
4 In a cohort of 297 092 patients, within 30 days after discharge from ambulatory surgery unit, 1. 5% were readmitted to hospital and 9. 8% presented to emergency department but were not readmitted. 9 urther improvements are needed in the quality of ambulatory surgery. In a series of 285 incidents in a quality management system database during a period of 5 years, 40% were found to be preoperative (cancellation/ rescheduling, skin preparation, anticoagulation problem, failure to fast), 15% intraoperative (wrong operative site, technical, anaesthetic incident), and 46% postoperative. 10 The low frequency of major complications in ambulatory surgery supports the improvement of procedures regarding the speed of recovery using patient's perceived recovery as outcome. 11 3] [14]  Eligibility criteria can vary, according to medical and surgery selection criteria and also psychosocial and environmental criteria.
Variables A 4-part questionnaire containing 25 questions was drawn up, according to current recommendations for ambulatory surgery and to the available literature, since there was no validated tool including all the variables that we were interested to collect. In the first part, demographic and socioenvironmental characteristics were collected according to literature review and French recommendations for ambulatory surgery. 16 The second part contained data related to surgery, including American Society of Anesthesiologists classification of physical status. 17 he third part included data collected to better describe the profile of outpatients: level of education, lifestyle, with children and their respective age, professional activity and health insurance. The fourth part contained environmental characteristics and variables related to organisation after discharge from the ambulatory unit.
Open access The questionnaire was filled in the postoperative period in the ambulatory surgery unit and before the patient's discharge. One month after discharge, data were collected from the patient's medical records in order to assess the primary endpoint: severe postoperative complications defined as conversion to conventional hospitalisation or to emergency department visit within 30 days after discharge from ambulatory unit, delay chosen according to the literature. 9 Data sources/measurement Data related to surgery were collected from medical records. All other variables were collected from patients' declarations in face-to-face interviews with the primary investigator. Secondary endpoints were patients' socioenvironmental characteristics.
These data were collected on the day of surgery and reflect the sum of failures to either schedule a patient escort or an escort who did not present at the time of discharge. Since it was an observational and not an interventional study, patients were discharged even if they declared that they did not have an escort home or that they would be alone on the first night after surgery. Chung et al  27  showed in Toronto (Ontario) that 60% of failures were due to escorts not presenting at the time of discharge. Nevertheless, Chung et al  27  found a much lower rate of patients without an escort (0. 2%, n=60/28391). There are several risks associated with discharging patients without an escort after ambulatory anaesthesia and surgery, requiring greater awareness in this context. 28 Regarding the first period after surgery, 82.
Indeed, quail and chicken are close relatives (Order: Galliformes; Family: Phasianidae), they share on average 95% homology at the gene sequence level (GenBank) and quails are susceptible to most chicken diseases  (Barnes, 1987). Similar to chicken, quail lay about one egg per day, and their smaller size makes them more compatible with the frequently limited space of animal facilities. A decisive advantage of quail over chicken is that they reach sexual maturity in 6 weeks. Thus, their life cycle is significantly shorter than that of chicken (26 weeks), but also of mice (8 weeks) or zebrafish (12 weeks). As no reliable technique for the culture of quail PGCs has yet been described, transgenesis in this species has mainly relied on the infection of blastoderm stage embryos (or circulating  Zhang et al.
Additional resources As an aid to transgenic quail research, QuailNet integrates various resources:. The 3D quail anatomy portal, which contains 3D models of quail embryos covering a range of development stages from embryonic day E1 (HH7) to E11 (HH40). When available, links to the chicken embryo gene expression database GEISHA  (Antin et al. , 2014)  is provided on the gene result page. Useful protocols that relate to data generated with transgenic animals. Altogether these genomic, molecular genetics, and bioinformatics resources aim at elevating the quail to the rank of genetic laboratory animal model of reference for avians.
Embryo clearing, staining and imaging Embryo preparation for whole mount immunostaining and 3DISCO clearing was performed as described in  Belle et al. , 2017. Imaging of stained embryos was performed (for the E3 quail embryo) on a Zeiss Lightsheet Z1 microscope equipped with 5X Plan-Neofluar objectives and (for the E6 quail wing) on LaVision Biotec Ultramicroscope II. Image rendering was performed with an Arivis software suite. The following antibodies were used: anti-GFP chicken polyclonal (Abcam), anti-RFP mouse IgG1 monoclonal (Abcam), anti HNK1 mouse IgM monoclonal (Developmental Studies Hybridoma Bank).
Acknowledgements The authors thank  Terry wise ,  Chris Darcy  and  Mark Tizard  from  CSIRO AAHL (Geelong Australia ) for their expertise in direct injection and transgenic chicken breeding. The authors acknowledge  Monash Micro Imaging ,  Monash University , for the provision of instrumentation, training and technical support. The  Australian Regenerative Medicine Institute  is supported by grants from the  State Government of Victoria  and the  Australian Government. We thank the  Faculty of Medicine and Health Science and Monash Technology and Research Platforms  for their financial support. MQTF was supported by  Faculty Strategic Grants Schemes  from  Monash University  to OS and CM. CM and MJD were supported by grants from  Stem Cells Australia (CSA)  and the  Association Francaise contre les Myopathies (AFM).