dataesr/scientific-paragraphs-categorization huggingface dataset:
H-W. H. and J-Z. H. are supported by the project (DP210100332) from  Australian Research Council. S. A. is funded by  ANID   FONDECYT 1170995  and  ANID ANILLO ACT192027. MB is supported by a  Ramón y Cajal  grant from  Spanish Ministry of Science  ( RYC2021-031797-I ). The contribution of TG and TUN was supported by the  Research Program in Forest Biology, Ecology and Technology  ( P4-0107 ) and project  V4-3098  of the  Slovenian Research Agency. T. P. M. would like to acknowledge contributions from the  National Research Foundation of South Africa  and cities involved in the South African survey. J. D. and  A. Rey  acknowledge support from the  FCT  ( IF/00950/2014  and  SFRH/BDP/108913/2015 , respectively). JPV is thankful to SERB ( EEQ/2021/001083 ) and  DST  ( DST/INT/SL/P-31/2021 ) and  BHU-IoE  ( 6031 )-incentive grant for research and development.
The light pattern, intensity and duration, as well as the microscope itself, were controlled using NIS Elements software (Nikon). Microtissues were maintained at 37 °C and 5% CO2 in a top-stage incubator (Oko Lab). Confocal images were obtained with a Leica laser scanning microscope (LSM SP8, Leica) equipped with a plan apochromatic 40x/ 1. 30 objective (Leica). Actin and collagen orientations were evaluated from confocal z-stack images with the Orientation-J plug-in (http:// bigwww. epfl. ch/demo/orientationj/) 50 in Image J. This plugin computes the structure tensor for each pixel in the image by sliding a Gaussian analysis window (variance σ = 2 pixels) over the entire image. From the structure tensor are extracted both the orientation and coherency properties of the region of interest.
Force measurement and strain measurement The contraction force generated by individual microtissues before, during and after light stimulations was assessed from the deflection of the cantilevers. This deflection d was determined by comparing the position of the top of the cantilevers to their initial position (i. e. before seeding the cell/collagen mixture). To this end, brightfield images were taken every 30 s and the displacement of the top of the cantilevers was tracked using a custom MATLAB script. Briefly, the displacement of the sharp contrast created by the edge of the cantilever heads was tracked over time by auto-correlation of the interpolated line profiles, allowing subpixel resolution. Tracking results were visually checked and faulty tracking discarded. Based on the linear bending theory, the resulting force F applied on a cantilever was inferred from its deflection and its spring constant k as follows: F = k.
8f-h )  2, 4, 6, 9, 11, 26. No measurable change in cross-sectional area was observed over the duration of light-stimulation experiments, in agreement with the almost inexistent displacements along the y-axis (Figs. 1 2 3 4 ). Consequently, the cross-sectional areas were considered unchanged over the duration of stimulation. Of note, the cross-sectional areas were linearly correlated with the tissue width squared w² (Supplementary Fig. 8h , R² = 0. 80), which allowed estimation of the tissue cross-section when microtissues could not be fixed immediately after experiments. The displacement fields were determined from the brightfield images using a particle image velocimetry (PIV) algorithm implemented as a Matlab toolbox (https://pivlab. blogspot. com/)  51. Briefly, small sub images (interrogation areas) of an image pair consisting of the reference image at t = 0 and the image of interest were crosscorrelated in the frequency domain using FFT to derive the most probable particle displacement vector in the interrogation areas, with its x and y components u and v, respectively.
Statistics and reproducibility For each line graph, the mean represents the mean±standard deviation of n microtissues (n is defined in the caption of each figure). For each box plots, the box extends from the 25th to 75th percentiles, the median is plotted as a line inside the box and the whiskers extend to the most extreme data point that is no more than 1. 5 times the interquartile range (IQR) from the edge of the box (Tukey style). Significances were assessed with Prism (GraphPad). Exact P-values are provided in the source data file. The thank P. and I. Wang for technical support, L. Vallon, S. de Beco and M. kindly providing the opto-RhoA fibroblasts, G. Chagnon and Briot for the mechanical characterization of PDMS, as well as P. for helpful discussions.
Acknowledgements A. M. acknowledges support from the  University Grenoble Alpes Ph. D. fellowship program. M. B. acknowledges financial support from the  ANR   MechanoSwitch project , grant  ANR-17-CE30-0032-01 , the  ANR   Platfor-Mech project , grant  ANR-18-CE14-0037-02 , the  ANR Inter-s-cal project , grant  ANR-21-CE13-0042-02. G. C. acknowledges financial support from the  ANR   SupraWaves project , grant  ANR-19-CE13-0028  of the French.
After sequencing, raw data were assembled de novo using the CLC genomics 10. 2 program (Qiagen, Les Ulis, France), and the genomes were analyzed online using software available at the Center for Genomic Epidemiology-CGE. 3 The latter included multilocus sequence typing (MLST) with the CGE MLST 2. 0 software to determine sequence types (ST), and acquired resistance gene determinations using ResFinder 4. 1  (25) (26) (27). Similarly, plasmid replicon types and virulence genes were identified using PlasmidFinder 2. 0  (26, 28)  and VirulenceFinder 2. 0. , respectively  (26, 29, 30). Reference plasmids sequence were retrieved from the NCBI database, using a local BLAST algorithm. Reads and/or Contigs carrying carbapenemase genes were mapped to reference plasmids, using the CLC genomics 10. 2 program (Qiagen).
Subsequently, 12 μL of the suspension was spotted onto the IR Biotyper target and let dry as previously described. The relationships between the isolates were analyzed using the Bruker IR Biotyper Software (version 2. 1. 0. 195, Bruker)  (32). An online tool 4 was used to assess the quantitative data of discriminatory power and concordance of the typing methods. Simpson's index of diversity (SID) was used to evaluate the discriminatory power of the typing method, calculating the probability that two unrelated isolates from the test strain set will be clustered into different typing groups. Adjusted Rand index (ARI) with 95% confidence intervals was used to evaluate the concordance of IRBT typing results  (33).
coli isolates (6 bla NDM and 7 bla OXA-48-likeproducers). All the tested isolates were resistant to ceftaroline and ceftobiprole but susceptible to eravacyclin, nitrofurantoin, apramycin, and tigecycline. Resistance to cefiderocol, ceftazidime/avibactam, imipenem/relebactam, and meropenem/vaborbactam was observed among the bla NDM -producing isolates (Table  2 ). Additionally, increased MICs to aztreonam/avibactam (4, 8, and 16 mg/L) were noticed among bla NDM -producing E. coli isolates. The whole genome sequencing of the 33 CR-E isolates was performed. After read assembly, the contigs were submitted to ResFinder 4. 0. In total, 47 different resistance genes were identified (Table  S3 ), which code for resistance determinants to clinicallyimportant classes of antibiotics, including β-lactams, aminoglycosides, tetracycline, quinolones, trimethoprim-sulfamethoxazole, fosfomycin, and sulfonamides.
coli NCTC 9022, accession number LR134237) revealed a four amino-acid insertion in PBP3 after residue 333 in 13 of 14 NDM-5-producing E. coli isolates. Two types of insertions were detected: YRIN (n = 7) and YRIK (n = 6). YRIN insertion was found among isolates with ST167 and ST361, and YRIK insertion was present in isolates with ST648 and ST405. ST90 isolate contained neither YRIN nor YRIK insertions. These 4 AA insertions could be correlated with increased MICs to aztreonam/avibactam (4, 8, and 16 mg/L) and cefiderocol among bla NDM-5 producing E. coli isolates, as compared to similar isolates lacking a 4 AA insertion (0. 06 mg/mL).
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the  Assistance Publique-Hôpitaux de Paris , the transdisciplinary  Health and Therapeutic Innovation object (HEALTHI)  of the  Université Paris-Saclay , through a grant from the  French National research Agency  ( ANR-10-LABX-33 ), and the  ANR-BMBF French -German bilateral  project  Natural-Arsenal  ( ANR-19-AMRB-0004 ). Marwan Osman  and  Dina Daaboul  were supported by the  Atkinson Postdoctoral Fellowship (Cornell University ) and  SAFAR Doctoral Scholarship  ( Lebanese University  and  French Embassy in Lebanon) , respectively.
Research in context Evidence before this study Health care workers (HCWs) are at increased risk for SARS-CoV-2 infection and are a priority group for both primary series and booster COVID-19 vaccination. However, in eastern Europe, COVID-19 vaccination uptake among HCWs has been moderate for primary series vaccine, and very low for booster dose. We searched Pubmed database for peer-reviewed articles on factors associated with COVID-19 vaccine uptake and SARS-CoV-2 infection among HCWs, published until October 1, 2022. Evidence on determinants of COVID-19 vaccination among HCWs largely comes from studies that investigated intention to vaccinate, rather than actual COVID-19 vaccine uptake, and generally suggests that male and older HCWs, physicians, and those vaccinated against influenza are more likely to accept vaccination.
In case of any discrepancies between the self-reported vaccination and IIS or E-vizita, such as the date of vaccination, we relied on the data from the national databases. However, for vaccinations received abroad that had not been registered in IIS or Evizita, we used information reported by the participants. At enrollment, we collected a serological sample from each participant, as previously described. 18 Sera were tested for total IgG and IgM antibodies to SARS-CoV-2 spike protein using WANTAI SARS-CoV-2 Ab ELISA (WANTAI BioPharm, Beijing, China). 19 Cut-off values were determined according to manufacturer guidelines. Study data were uploaded and stored in the REDCap system (Research Electronic Data Capture, Vanderbilt University, Nashville, Tennessee, USA). 20 For the present analysis, we used data collected through 11 June 2022.
Ethics The study was approved by the Ethical Review Boards of WHO (reference number CERC. 0097A) and the Albanian Institute of Public Health (reference number 156). This activity was determined to meet the requirements of public health surveillance as defined in 45 CFR  46. 102(l)  (2) (CDC reference number 0900f3eb81ce0ede). This study has been registered with ClinicalTrials. gov (NCT04811391). All participants provided written informed consent.
7% and 17% respectively),  6  we did not have data on the demographic and occupational distribution of all HCWs in Albania to assess if participants in our study were generally representative of hospital workers in the country. Fourth, we did not collect specific information about potential exposures at the time of SARS-CoV-2 infection, and therefore we could not discern whether infections were occupational or community-acquired. Likewise, information on participant characteristics was only collected at time of enrollment, and therefore we could not control for potential time-varying covariates. However, we believe this limitation is unlikely to have impacted the results significantly: For example, Albanian HCWs rarely change their occupational role, a variable associated with both vaccination uptake and SARS-CoV-2 seropositivity.
Acknowledgements We are grateful to all of the HCWs for their heroic efforts during the pandemic and to our study participants. We appreciate the continued support from the leadership and field teams at the  Tirana, Durres , and  Fier Hospitals. We thank  Gazmend Bejtja  and  Gladiola Kashari Kodra  ( WHO Country Office, Albania ),  Alina Guseinova  and  Diogo Simao Lemos  ( WHO Regional Office for Europe, Denmark ),  Ledia Agolli  ( Southeast European Center for Surveillance and Control of Infectious Diseases, Albania ),  Britni Burkhardsmeier  and  Dominique Richardson  ( Task Force for Global Health, United States ), and  Madelyn Rojas  and  Marta Valenciano  ( Epiconcept, France ) for their contributions. This study was funded by the  Task Force for Global Health (US Centers for Disease Control (CDC)  cooperative agreement #  NU51IP000873 ) and the  World Health Organization, Regional Office for Europe.
Abstract: Aim: To build and externally validate an [ 18 F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [ 18 F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34).
However, [ 18 F]FDG PET images might contain more information than can be assessed visually or using traditional quantitative metrics. Quantitative image analysis using radiomics may provide more insights into disease biology. Radiomics is a form of medical image processing that aims to find stable and clinically relevant image-derived biomarkers for lesion characterisation, prognostic stratification, and response prediction, thereby contributing to precision medicine  [6, 7]. Radiomics consists of the conversion of (parts of) medical images into a high-dimensional set of quantitative features and the subsequent mining of this dataset for information useful for the quantification or monitoring of tumour or disease characteristics in clinical practice. The field of radiomics has gained increasing attention in recent years, bringing about many proof-of-concept studies that demonstrate the feasibility and potential utility of radiomics in a wide range of applications.
However, a lack of external validation is still one of the major limitations of radiomics, hampering its clinical translation. While the field of radiomics has grown and the number of papers has gradually increased over the last decade  [8] , widespread clinical implementation has not yet been established due to a lack of standardisation in the different steps of the radiomic pipeline and validation of the radiomic models. Individual studies show promising results, but they sometimes present spurious model interpretations caused by "cherry picking", i. e. , carrying forward only the best results obtained with a single approach while omitting contradictory results obtained with a slightly different approach  [9]. Therefore, model performance should be validated using a new, independent dataset to assess whether the model is predictive for an entire target population or for a specific subset of patients only, e.
Materials and Methods 2. 1. Patient Population, Data Acquisition, and Image Reconstruction [ 18 F]FDG PET scans of two multicentric prospective cohorts (European Organisation for Research and Treatment of Cancer (EORTC) study 90,111; Unicancer Predictor) of patients with previously untreated histologically or cytologically confirmed squamous cell carcinoma of the oral cavity, oropharynx, larynx or hypopharynx were retrospectively analysed  [5, 11]. Patients in the treatment arm, who received preoperative afatinib (40 mg/day) for 14 to 28 days (14 days in EORTC cohort, 28 days in Unicancer cohort) and underwent [ 18 F]FDG PET imaging on day 0 (baseline) and day 15 (evaluation) followed by surgery, were included. In the EORTC study (ClinicalTrials. gov Identifier: NCT01538381)  [5] , immunohistochemical staining was performed on 4 µm paraffin-embedded tumour sections, and p16 positivity was determined using a histology score of ≥210 according to Ang et al.
(strong nuclear and/or cytoplasmic staining in ≥70% of tumour cells, Clone E6H4, CINtec ® , Roche, Tucson, AZ, USA)  [12]. PET/CT scanners were required to have EANM Research Ltd. (Vienna, Austria) (EARL) accreditation, and sites had to submit a dummy run scan following the study imaging requirements. PET acquisition was started 66 ± 6 min after the injection of 4. 0 ± 0. 7 MBq [ 18 F]FDG per kilogram of body weight administered as an intravenous bolus. Serum glucose levels were below 7. 0 mM/L. The reconstructed voxel sizes ranged from 2. 73 × 2. 73 × 3. 27 mm 3 to 4. 00 × 4. 00 × 4. 00 mm 3. In the Unicancer study (ClinicalTrials. gov Identifier: NCT01415674)  [11] , HPV16 was detected by single PCR with specific HPV16 primers. Other HPV subtypes were determined using multiplexed PCR on DNA extracted from frozen or formalin-fixed paraffin-embedded tumour tissue samples.
Radiomic Feature Extraction Radiomic feature extraction was performed in PyRadiomics version 3. 0 in Python version 3. 7 (Python Software Foundation, Wilmington, DE, USA)  [16]. For baseline as well as evaluation scans, 105 radiomic features were extracted: 18 first order features, 14 shape features, and 73 texture features (22 grey level co-occurrence matrix (GLCM), 16 grey level run length matrix, 16 grey level size zone matrix, 14 grey level dependence matrix, and 5 neighbouring grey tone difference matrix). In addition, total lesion glycolysis-the product of the mean SUV and the metabolic tumour volume-was calculated. A fixed bin size of 0. 5 g/mL was applied, and images were interpolated to isotropic voxels of 4. 00 × 4. 00 × 4. 00 mm 3 using B-spline interpolation, with grids aligned by the input origin.
Features were standardised (centred around 0, standard deviation of 1) to prevent features with the largest scale from dominating the analysis. Redundancy filtering of the Pearson correlation matrix (r = 0. 9) was performed. Subsequently, supervised feature selection was performed using variable hunting with variable importance  [20] , which was repeated 1000 times, selecting the top two features (i. e. , one feature per ten subjects) ranked in terms of occurrence. A Cox proportional hazards regression model using the selected radiomic features was fitted on the training dataset and validated in the external validation set. In addition, a clinical model that univariately selected the two best-performing clinical characteristics, and a combined model that combined the two radiomic features and the two clinical variables, were fitted.
The metabolic tumour volumes at baseline PET as well as at evaluation PET were significantly larger in the Unicancer cohort compared to the EORTC cohort (Table  1 ). Plotting the Schoenfeld residuals against time showed that all the covariates in the Cox proportional hazards model met the proportional hazard assumption (supplementary Figure  S1 ). When the EORTC dataset served as a training set and the Unicancer dataset as an external validation set, the clinical model alone yielded a C-index of 0. 60 in the validation set for the prediction of overall survival (Table  2 ). The selected clinical characteristics were the age of the patient and N-stage. The radiomic model performed slightly better, with a C-index of 0. 69 in the validation set. Two shape features from the evaluation PET were selected: the sphericity and the maximum 2D diameter of the slice.
The combined model, including both the selected clinical features and the selected radiomic features, performed similarly, with a C-index of 0. 71 in the validation set. In contrast, when the Unicancer dataset served as a training set and the EORTC dataset as an external validation set, the clinical model alone yielded a validation C-index of 0. 67 (Table  3 ). The selected clinical characteristics were again the age of the patient and N-stage. The radiomic model performed better, with a validation C-index of 0. 79, selecting two shape features from the evaluation PET: the sphericity and the maximum 2D diameter of the column. The combined model performed slightly better, with a validation C-index of 0. 82. In the sham experiment, no model yielded a C-index different from 0. 5 (range: 0. 47-0.
This might be attributed to the fact that the recommended minimal number of voxels for the texture analysis of 64 voxels per VOI was not reached in around one third of the VOIs  [9]. For cubic regions, this corresponds to 4 × 4 × 4 mm 3 voxels, i. e. , ~4 cm 3. Consequently, in terms of tumour size, HNSCC are not the most suitable lesions for PET radiomic analysis. Another possible explanation for the selection of two shape features might be the heterogeneity of the datasets. We investigated two multicentre studies that included scans from four and eleven different centres, respectively. While the EORTC centres were EARL-accredited, no such standardisation and/or harmonisation was performed in the Unicancer cohort. Radiomic features are highly impacted by image acquisition and reconstruction settings  [38].
A possible explanation might be that the EORTC validation set has less complexity, which might be due to the heterogeneity in the Unicancer dataset. This explanation might be confirmed by the fact that similar features were selected in both the EORTC and Unicancer model and by the fact that the model trained by the EORTC dataset shows a higher C-index than the model trained by the Unicancer dataset. Our study has several strengths and limitations. The main strength of our work is the external validation, which most radiomic models lack. We also showed that model development and validation is possible in small datasets in the case of a strong biological signal. A limitation of this study is that we chose not to assess low-dose CT radiomic features since we already included over 300 baseline, evaluation, and delta features in two small datasets of 20 and 34 patients, respectively.
Analysis of data We performed a retrospective analysis of neuroblastoma patient data that was gathered as part of standard of care. In addition, for methodological comparison of HPLC-FD and UPLC-MS/MS, catecholamine metabolite measurements in banked urine samples from neuroblastoma and non-neuroblastoma patients were analysed (n = 39, see Supporting Methods). Catecholamine metabolite concentration was expressed relative to creatinine excretion. A catecholamine metabolite was considered to be elevated when the concentration exceeded the upper limit (95th percentile) of the age-specific reference range for 24-hour urine and for spot urine samples. 3  Age-dependent reference values for catecholamine metabolites in 24-hour urine were previously published. 3.
XylT-I/KO embryos show frontonasal dysplasia with flat midface (yellow cercle) and skull dysplasia with narrow rib cage (red square). E In situ mRNA hybridization analysis of XylT-I expression assessed by RNAscope on sections of proximal humerus growth plate in XylT-I/KO and wild-type embryos at E18. 5. F Sections of proximal humerus growth plate of wild-type and XylT-I/KO embryos at E15. 5, E16. 5 and E18. 5 stained with Alcian blue which show strong reduction in GAGs in XylT-I/KO except in hypertrophic zone (HZ). Data are expressed as mean ± S. D. *P < 0. 05, Student's t test. Representative images from three independent experiments are shown (n = 3). Scale bar: 100 μm. G Synthesis level of PG-GAG chains in wild-type and XylT-I/KO primary chondrocytes analyzed by 35 S-sulfate incorporation into GAG chains.
As expected, the intercolumnar collagen fibers run parallel to each other in the growth plate of control mice. In contrast, they are randomly oriented in XylT-I/cKO mice (Fig. 7D ), indicating alterations in the organization of intercolumnar collagen fibrils in XylT-I/cKO mice growth plate. These data indicate that XylT-I supports the organization of collagen fibrils in the growth plate and suggest that loss of columnar organization of chondrocytes may result, in part, from disorganization of intercolumnar collagen fibrils network. We showed above that prehypertrophic and hypertrophic zones were severely reduced in XylT-I/cKO mice compared with control mice, suggesting accelerated chondrocyte maturation. To determine whether maturation was proceeding normally in XylT-I/cKO mice, we examined markers of chondrocyte maturation in the growth plate by in situ hybridization.