dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall principles The data analysis will start after the 3-month follow-up data of the last included patient has been obtained, and after the clinical trial module of the study database has been cleaned and locked. The analyses will be done by a co-investigator (MIB) supervised by the principal investigator (YBWEMR) and an independent epidemiologist/statistician of the Amsterdam Medical Centre Clinical Research Unit. The statistical programming and analysis to produce all summary tables and figures will use the statistical package IBM SPSS statistics version 22 (IBM Corp. , Armonk. NY, USA). In general, variables will be summarised using simple descriptive statistics such as means with standard deviation for continuous symmetric variables, medians and interquartile ranges for continuous skewed variables, and frequencies with percentages for categorical variables.
All analyses will be done according to the intentionto-treat principle, by analysing patients in the groups to which they were allocated by randomisation. The analyses will first be performed blind to treatment allocation, to allow for checking of the data and the proposed summaries/analyses. After the investigation and correction of any isolated or systematic data errors, treatment allocation will be unmasked. The primary outcome will be analysed in the pre-specified subgroups below, irrespective of the presence of statistical significance in the overall analysis. Safety outcomes will be additionally analysed in the as-treated (not per protocol) population.
Intention-to-treat population All randomised patients will be analysed in the treatment group to which they were originally allocated irrespective of non-adherence or deviations from protocol. Occasionally, investigators randomised the same patient twice in the web-based randomisation system because they were not aware of the treatment allocation from the first randomisation; in these cases, the treatment allocation of the second randomisation was used in practice, and will be used in the final analyses, and the first unused randomisation record was removed from the trial database.
Current trial status PATCH trial sites recruited from 4 February 2009 in The Netherlands, from 23 May 2011 in Scotland, and from 28 September 2013 in France, and the last patient was randomised on 8 October 2015. The last follow-up was obtained on 6 January. At the time of writing, the trial data have been cleaned and checked for completeness and internal consistency, blinded to treatment allocation. The database will only be locked after this statistical analysis plan has been date and time stamped in a public repository.
Acknowledgements PATCH was funded by: The  Netherlands Organisation for Health Research and Development (ZonMw)  and  Sanquin Blood Bank  in The Netherlands; Le programme hospitalier de recherche clinique (PHRC) in France; and a  Chest Heart and Stroke Scotland (CHSS)  project grant (Ref. Res10) and  Medical Research Council (MRC) clinician scientist (Ref. G108/613 ) and senior clinical fellowships (Ref. G1002605) in Scotland. PATCH was sponsored by:  Academic Medical Centre, University of Amsterdam  in The Netherlands; Inserm  U1171 ,  CHU Lille ,  Univ Lille  2 in France; and  The University of Edinburgh/NHS Lothian Academic and Clinical Central Office for Research and Development (ACCORD)  in Scotland. PATCH was supported by:  Strokavenir Network  and  Etablissement Français du Sang in France ; and the  Scottish Stroke Research Network  and the  Scottish National Blood Transfusion Service Clinical Governance and Safety Committee in Scotland.
Therefore the question of whether there is a difference in the blood transcriptional signature between pulmonary and extra-pulmonary TB has not been answered. We recently published a 380 gene meta-signature (S1 Table ) of active TB compared to healthy controls  [18] , which represents an attempt to identify the most consistently differentially expressed genes across the published publicly available datasets  [18]. Similar to the individual published studies the 380 gene meta-signature was found to be enriched for immune response pathways including multiple pattern recognition receptors, cytokines, the inflammasome, complement and immunoglobulin pathways  [18]. We herein used this published derived gene set as well as other transcriptional analysis tools to test similarities and differences between pulmonary TB and extra-pulmonary TB in a novel dataset and now report heterogeneity driven mainly by the extent of symptoms.
Microarray Human whole blood RNA was isolated, globin reduced and amplified as described previously  [6, 19]. 750 ng of cRNA was hybridized to Illumina Human HT-12 V4 BeadChip arrays and scanned on Illumina iScan. GenomeStudio was used to perform quality control and generate signal intensity. Microarray analysis was undertaken using GeneSpring GX 13. 0. Per sample normalisation (75 th centile) and per transcript normalisation (median of all samples) was performed. Microarray data were deposited in the NCBI Gene Expression Omnibus (GEO) with series accession number (GSE83456). All data collected and analysed in these experiments adhere to the Minimal Information About a Microarray Experiment (MIAME) guidelines.
Expression analysis Molecular Distance to Health (MDTH), was calculated using methodology previously described  [20] , for defined groups of genes (group of genes defined in legend of Figures) relative to a control group (detailed in legend of Figure). Additionally, the MDTH was calculated using the same methodology but now using 3409 transcripts, which represent the transcripts from 38 annotated modules  [21]. Z-scores were derived from the respective MDTH's and calculated relative to the control group. Modular analysis was undertaken using log2 transformed normalised data  [21]. Differentially expressed genes identified from new dataset for TB and sarcoidosis involved groups being compared to the healthy control cohort (transcripts filtered which were not significantly detected from background in at least 10% of samples, low expressed transcripts filtered (less than 2 fold change from median in 10% of samples), followed by statistical testing (independent t-test with Benjamini Hochberg multiple testing correction (q-value <0.
Testing the meta-signature in a new dataset reveals differences in the transcriptional response of pulmonary and extra-pulmonary patients A new cohort of extra-pulmonary and sarcoidosis patients was recruited together with an existing bank of pulmonary TB and healthy controls formed the dataset for analysis (Cohort details; S2 and S3 Tables). There was no difference in gender frequency  (Fig 1A)  between groups however there were differences in the group composition with regard to ethnicity and age, with the sarcoidosis group being significantly older than the other groups and tending to have less patients of Indian subcontinent background (Fig 1B  and 1C ). Total white cell count was The previously defined 380 meta-signature genes  [18]  (S1 Table, mapped to 687 Illumina transcripts, of which 113 transcripts were excluded as they were not significantly detected from background in 10% of samples) were used for analysis in this new dataset.
Hierarchical clustering of the dataset (healthy controls, pulmonary and extra-pulmonary TB patients only) revealed that the 380 meta-signature genes (S1 Table ) were able to separate healthy controls from the majority of both pulmonary and extra-pulmonary TB patients, although a small number of TB patients clustered together with the healthy controls (Fig  2A ). Using the most consistently identified genes from the meta-analysis as potential biomarkers (15 genes which were identified in 15 or more meta-analysis data-sets, S1 Table ) and calculating a molecular score for these genes, there was a greater sensitivity in identifying pulmonary TB patients than extrapulmonary TB patients from healthy controls (Fig  2B ).
Transcriptional signatures in TB reflect symptom status of individuals There was a significant difference in the molecular distance to health (MDTH) of both pulmonary TB and extra-pulmonary TB patients as compared to healthy controls (Fig  3A ). Receiver operator curves for extrapulmonary and pulmonary cohorts against healthy controls using MDTH derived from the most consistently identified genes (15 genes identified in at least 15 of the meta-analysis datasets  [18] -representing the most robustly identified genes in that analysis, healthy controls used as control group for MDTH) as potential diagnostic biomarker. Both prediction results were validated using k-fold cross validation with k equal to 10 with 1,000 iterations. The mean AUC for the EPTB and PTB validation results are 0. 865 (95% confidence interval: 0.
Overlap in expression profiles between sarcoidosis and extrapulmonary TB patients Analysis of all the TB patients from this new dataset identified 927 genes (S4 Table ) as being differentially regulated compared to healthy controls and 883 genes (S4 Table ) differentially regulated between sarcoidosis patients and healthy controls (Fig  5A ). Overlapping these two gene lists revealed that 709 genes were commonly expressed between the two conditions compared to healthy controls, of which 15 had been previously identified from the meta-analysis as those most consistently expressed in TB (Fig 5A  , S4 Table). Nine of the top 10 upregulated genes by fold change of TB compared with healthy controls were also found within the top 10 upregulated genes by fold change of sarcoidosis compared with healthy controls (Table  1 ).
Acknowledgments With thanks to all of the patients who volunteered to participate in this study. We thank  Abdul Sesay  and  Deborah Jackson ,  Advanced Sequencing Facility  at  The Francis Crick Institute , for help with microarray samples. We thank  Fotini Rozakeas  ( MRC-NIMR ) for help with recruitment of volunteers. We thank  Lúcia Moreira-Teixeira  for critical review and  Krzysztof Potempa  for analysis of data and critical review of the manuscript and  Leona Gabrÿsova  for graphics help.
Funding: AOG and this work is supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001126 ), the  U. K. Medical Research Council  ( FC001126 ) and the  Wellcome Trust  ( FC001126 ). SB was jointly funded by the  UK Medical Research Council (MRC)  and the  UK Department for International Development (DFID)  under the  MRC/DFID  Concordat agreement ( MR/J010723/1 ). GS was supported in part by the  Department of Health  via the National Institutes of pulmonary and extrato reflect the extent of symptoms of disease. Health Research comprehensive Biomedical Research Centre award to Guy's and St Thomas' National Health Service Foundation Trust  in partnership with King's  College London. This study received financial support from  SILA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Images were acquired on a LSM 510 META (Carl Zeiss) or a TCS SP5X (Leica) confocal microscope and the images were processed with ImageJ and Inkscape softwares. Expression and preparation of recombinant thioredoxins. E. coli BL21 cells (Agilent Technoligies #230134) were transformed with the different pET15b constructs and their expression were induced using 1 mM IPTG (isopropyl b-D-thiogalactoside) for 2 h at 37 °C. Cell lysis and protein purification were performed with the Protino Ni-NTA agarose kit (Macherey-Nagel) according to the manufacturer instructions. Before incubation with sperm nuclei, the recombinant thioredoxins were reduced for 3 h at room temperature with 10 mM DTT and desalted with PD-10 Desalting Column containing Sephadex G25 Medium (GE Healthcare) to remove excess DTT. Protein elution was performed using 0.
Decondensation of sperm nuclei with recombinant thioredoxins. Seminal vesicles from virgin males were dissected as described above. Slides were incubated at room temperature in 1X PBS 1%, Triton X100 for 30 min, air dried and then incubated 3 times for 30 min in Potassium Phosphate Buffer containing the desalted thioredoxins. Slides were quickly washed in 1X PBS. Before quantification, slides were incubated for 15 min at room temperature in 1 mM mBrB (Millipore #596105), quickly washed and mounted in mounting medium (Dako) containing propidium iodide (Sigma). Imaging and fluorescence quantification of sperm nuclei. Sperm nuclei were imaged on a TCS SP5 X confocal microscope (Leica) or on a LSM 800 confocal microscope (Carl Zeiss). The images were analysed using the ImageJ 1. 48 software to measure the total quantity of fluorescence.
5) for 10 min, in 100% HB for 10 min, and finally in HB containing 100 mg ml À 1 tRNA and 50 mg ml À 1 heparin, for 1 h at 65 °C. Then, the probe was added before overnight incubation at 65 °C. The ovaries were then washed twice with HB at 65 °C, and once with1:1 PBST:HB at 65 °C. DIG-labelled RNA probes detection was performed using anti-DIG AP antibody (Roche #11093274910; 1:100) and NBT/BCIP colorimetric detection was carried out according to manufacturer instructions (Roche). Ovaries were mounted in mounting medium (Dako) and were imaged using a Z1 microscope (Carl Zeiss). Data Availability. All relevant data are available from the authors on request.
Acknowledgements We are grateful to  Helen Salz  for her generous gift of the dhd mutant stock. Additional stocks were kindly provided by  Christina Rathke ,  John Belote ,  Barbara Wakimoto  and the  Bloomington Drosophila Stock Center. We also thank  Katja Becker  for the gift of the anti-Trx-2 antibody and  Gerard Benoit  for his critical reading of the manuscript. Confocal microscopy was performed at the  Centre Technologique des Microstructures.
The manuscript was prepared by RL and reviewed by all other authors. Competing Interests: SP is an employee of Sanofi Pasteur. AJP has previously conducted studies on behalf of Oxford University funded by vaccine manufacturers but no longer does so. AJP chairs the UK Department of Health's (DH) Joint Committee on Vaccination and Immunisation (JCVI); the views expressed in this manuscript do not necessarily reflect the views of JCVI or DH. MDS has conducted clinical trials on behalf of Oxford University sponsored by vaccine manufacturers. MDS does not accept any personal payments from vaccine manufacturers: grants for support of educational activities are paid to an educational/administrative fund held by the Department of Paediatrics, Oxford University. RL and MDS have received financial assistance from vaccine manufacturers to attend scientific meetings.
PVD acts as chief and principal investigator for vaccine trials conducted on behalf of the University of Antwerp, for which the University obtains research grants from vaccine manufacturers; speakers fees for presentations on vaccines are paid directly to an educational fund held by the University of Antwerp. CV acts as principal and sub-investigator for clinical trials conducted on behalf of the KU Leuven, for which the University obtains research grants; CV has received financial assistance from vaccine manufacturers to attend scientific meetings. Speakers fees for presentations on vaccines are paid to a charity fund held by the Department of Pediatrics, KU Leuven. This does not alter our adherence to PLOS ONE policies on sharing data and materials. either without adjuvant or with 30 μg alum.
It is recommended that antibody persistence is also evaluated to help guide re-immunization practices, however there are no formal immunogenicity criteria provided. Only data relating to HI antibodies are presented here. Analyses were performed by the Sanofi Pasteur Biostatistics Department, Marcy I'Etoile, France, using SAS software version 8. 2 (SAS Institute, Cary NC, USA)) and were independently validated by the Oxford Vaccine Group, University of Oxford, using SAS version 9. 3 (M. V. ).
Acknowledgments The authors would like to thank the participants who took part in the study without whom this study would not have been possible. In addition this study has been made possible by the efforts of the trial and laboratory staff at each center. Antibody titration was performed at the laboratories of the  UK Health Protection Agency  led by  M. Zambon. The Oxford Vaccine Group is supported by the  NIHR Oxford Biomedical Research Centre. AJP is a Jenner Institute Investigator and  James Martin Senior Fellow.
Patients All consecutive adult patients treated for shock with a continuous norepinephrine perfusion through a central venous catheter, and who had at least one CNIP, were included in the study. Invasive arterial blood pressure was continuously monitored (Intellivue MP monitor, Philips Medical Systems, the Netherlands) and recorded on a PC-based data acquisition system supported by Intel-liSpace Critical Care and Anaesthesia (ICCA) software (Philips Medical System, the Netherlands). No change in haemodynamic management for cardiovascular dysfunction occurred in our ICU during the study period. The following data were collected for each patient: gender, age, type of admission (i. e. medical or surgical), type of shock, organ failures according to the Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II), number of CNIPs during ICU stay, length of stay in the ICU, and outcome.
Automated changeovers of norepinephrine infusion pumps Changeovers of vasoactive drug's syringes were performed up to 2 h before the end of the ongoing infusion. As previously described, the automated relay procedure that was provided by the smart pumps consisted of two associated channels, which infused the drug, one after the other, at the same dose  [5]. Accordingly, when the concentration of the vasoactive drug changed in the course of the CNIP, the smart pump automatically modified the flow rate of the new infusion pump to maintain a constant dose of norepinephrine. Syringe pumps were connected to data acquisition system with automatic storage of flow rates and doses. CNIP-induced haemodynamic instability was defined as a variation of MAP > 25 %, compared to the baseline, occurring within 30 min after the changeover.
Statistical analysis Categorical variables are expressed as counts and percentages and continuous variables as medians and interquartile ranges (IQRs), as appropriate. Comparisons of categorical variables were performed using two-sided Fisher's exact tests. Continuous data were compared using the Mann-Whitney U test or Friedman's test, as appropriate. Factors associated with CNIP-induced haemodynamic instability were analysed by two-level mixed effects logistic regression (MELR) modelling. This model is derived from the generalised linear mixed model, and it allowed the estimation of both fixed effects (i. e. factors related to the syringe changeover) and random effects (i. e. related to the patient)  [5, 7, 9]. The outcome was CNIP-related haemodynamic instability, as defined above. The two levels of the model were the characteristics of the changeover (Level 1) and of the patient (Level 2).
The following explanatory data were related to the changeover: MAP, heart rate, organ supports, mechanical ventilation, renal replacement therapy, changeover order category (1st, 2nd-5th, >5th), norepinephrine concentration (mg ml -1 ), flow rate (ml h -1 ), doses (μg kg -1 min -1 ), and the change in the infused norepinephrine concentration. The following explanatory variables related to the patient were: gender, age category (<45 years, 45-59 years, 60-74 years, ≥75 years), SOFA score category (<10, 10-14, 15-19, ≥20), SAPS II category (<50, 50-64, 65-80, ≥80), type of admission (medical, surgical), and type of shock (septic, cardiogenic, haemorrhagic, other). Covariates with a p < 0. 15 after univariate MELR analysis were entered in the initial multivariate MELR model. MAP before CNIP was forced in the multivariate analysis.
05). Type and magnitude of incidents are depicted in Table  3. As expected, haemodynamic instability was an increase in MAP in 40/43 (93 %) CNIPs with decrease in norepinephrine concentration, whereas it was a drop in MAP in 13/15 (87 %) CNIPs with increase in norepinephrine concentration. No sustained ventricular arrhythmia and no cardiac arrest were directly attributed to a CNIP-related haemodynamic incident. After univariate MELR analysis, age, heart rate, dose of norepinephrine, and change in norepinephrine concentration were significantly associated with haemodynamic incidents (Table  4 ). Gender, type of admission, type of shock, SAPS II score, SOFA score, MAP, mechanical ventilation, renal replacement therapy, norepinephrine infusion concentration and flow rates, and CNIP order were not linked with the risk of haemodynamic incident (data not shown).
Indeed, it is often necessary in clinical practice to change the concentration of the norepinephrine infusion throughout the administration of the drug. To the best of our knowledge, little data are available on changeover with changes in norepinephrine concentration. In the present work, while CNIP with changes in norepinephrine concentration represented almost 1/6 of the changeovers, they accounted for half of the haemodynamic incidents. We found that the only independent factor associated with CNIP-induced blood pressure instability was the change in the norepinephrine concentration. Contrary to what one might expect, the frequency of CNIP-related incidents was not independently associated with the norepinephrine doses infused into the patients and had no link to the severity of the shock.
To generate this tilt boundary, we follow standard methods. Two adjacent copper crystals are first separately rotated symmetrically about the [100] axis, the lower one by an angle eq/2 and the upper one by an angle þq/2. Then, the extremities of both grains are cut and joined together with respect to coincidence site lattice points to form an initial un-relaxed bicrystal configuration. In the process, atoms may be added or removed in the grain boundary area to minimize crystal incompatibility. In particular, it is carefully checked that there is no missing atom or atom overlapping. This initial bicrystal is a high-energy configuration that needs to self-relax by rearrangement of the atomic positions. To simulate such relaxation, we performed molecular dynamics simulations. The program Atomsk  (Hirel, 2015)  and simulation code LAMMPS (Plimpton, 1995) were used.
Protein quantification was determined with the Lowry protein assay kit (Bio-rad, Hercules, CA, USA). The protein was electrotransferred onto a PVDF membrane. The membranes were blocked for 1 h with 0. 05% Tween-20 and 5% NFDM, and then incubated overnight at 4 °C with the antibodies: AntiPer1 (1:500; Thermo Scientific Inc. ) ß-Actin was used as a loading control (1:5,000; GE Healthcare, Hertfordshire, UK). Protein bands were revealed by using the Pierce ECL western blot substrate (Thermo Fisher Scientific, Madrid, Spain). Respective bands were quantified by densitometry. Image J software 1. 50a and intensity values for PER 1 were normalized with ß-Actin.
Statistical analysis. Values are presented as means ± SEM. Differences between two groups were analyzed by Student's t test. The effect of time and groups differences were measured by one-way or two-way ANOVA, with Bonferroni post hoc test for multiple comparisons, using GraphPad Prism software where appropriate. All statistical tests were performed with a level of significance P < 0. 05. Cosinor Analysis was done using the Acrophase program (R. Refinetti 2004) for fitting cosine functions to the data using a fixed 24 hour period and included the mesor (middle value of the fitted cosine representing a rhythm-adjusted mean), the amplitude (difference between the minimum and maximum of the fitted cosine function), the acrophase (the time at which the peak of a rhythm occurs, expressed in hours) and fitted cosine values to calculate the goodness of the fit by coefficient of determination R 2.