dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, in LMDZ model state variables not defined in the analysis are set to zero, alongside the aerosols. Sea surface and sea ice cover are also from the ECMWF analysis. All hindcasts are performed out to a leadtime of T+10 d. For both of the models, and all hindcasts, output data is interpolated onto a pressure grid in the vertical, every 25 hPa, from 1000 hPa to 100 hPa. Furthermore, output is also produced using the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP;  Bodas-Salcedo et al. , 2011)  for radar reflectivities from Cloudsat to be compared with the observed aircraft-borne radar reflectivities from the NAWDEX field campaign. We restrict the number of hindcasts to take into account the impact of the overall synoptic situation at the time being largely unpredictable (e.
Acknowledgements. This work is part of the  DIP-NAWDEX (DIabatic Processes in the North Atlantic Waveguide and Downstream impact EXperiment ) project which is funded by the  Agence Nationale de la Recherche (ANR)  under grant number  ANR-17-CE01-0010-01. The funding for IM for running the LMDZ T-AMIP experiments was from the  Eurpean research Council  grant number  694768. The LMDZ simulations were performed using the  HPC  from  GENCI-IDRIS  (grant  0292 ). The airborne measurements and the  SAFIRE Falcon  flights received direct funding from  IPSL, Météo-France ,  INSU-LEFE, EUFAR-NEAREX  and  ESA  (EPATAN, contract  n°4000119015/16/NL/CT/gp ). The authors wish to acknowledge use of the  Ferret program  for analysis and Figs. 1-8 and Figs. S1-S4 in this paper. Ferret is a product of  NOAA's Pacific Marine Environmental Laboratory.
Data availability. Data is available by contacting either D. Flack at david. flack1@metoffice. gov. uk or the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/wcd-0-1-2020-supplement. Author contributions. All authors contributed to the writing and editing of the manuscript as well as the scientific discussions. DLAF produced the first draft and conducted the model analysis. IM performed the IPSL-CM6A simulations and RR the CNRM-CM6-1 simulations. GR and SB designed the study. JD, QC and JP provided the observational datasets.
Quantification of Immunoreactive Cells To estimate the number of ∆FosB-positive cells in the various regions of interest, we used the Image-Pro Plus Software version 10. 0 (Media Cybernetics, Silver Spring, MD, USA). By comparing each brain section with the corresponding section in the Paxinos rat brain atlas  (Paxinos and Watson, 2009) , the outlines of regions of interest which are relatively small in size (PVN, VTA, LC, and PBN) were made under the 20× objective of the Olympus BX61 microscope (Olympus Canada, Richmond Hill, ON Canada). For these regions, we analyzed the actual brain sections. For regions of interest that are relatively large (mPFC, Acb, and IC), sections were first scanned using the TISSUEScope 4000 scanner (Huron Digital Pathology, St. Jacobs, ON, Canada) to obtain high-quality images of sections for subsequent analysis.
∆FosB-expressing neuron quantification was performed automatically. To do this, the Image-Pro Plus software was used to identify objects within the regions of interest. Subsequently, the software was fine-tuned continuously by the experimenter until the majority of the objects considered to be neurons in the region of interest were identified by the software. The parameters used were color, area (in pixels: 90-1,500), and size (length: 10-90; width: 5-60). At this point, the value of each parameter was noted and applied to all sections containing regions of interest for analysis. The software was then used to automatically identify all similar objects and the number of objects identified was considered as the number of neurons obtained. To verify the results obtained with the automatic counting, we also performed manual cell counting on some brain sections.
∆FosB-expressing cells were then identified in all outlined regions of interest (Figure  1B ), as previously described. The Image-Pro Plus software-defined the coordinates of all identified ∆FosB-expressing cells using the parameters Center X and Center Y. The coordinates of all cells were then exported into Excel files. Similarly, GAD or TH mRNA expression obtained by in situ hybridization, which appears as dark silver grains, were also identified based on specific parameters [(in pixels) area: 1-90; size (length): 1-20; size (width): 1-20; Figure  1C ] and their coordinates were exported into Excel files. By using a custom-written MATLAB script, double-labeled cells were identified when there was an overlap of ∆FosB expression and mRNA expression at the same location as shown in Figure  1D.
ACKNOWLEDGMENTS We would like to thank  Christophe Lenglos  for providing the MATLAB scripts for the analyses of deltaFosB, glutamate decarboxylase 65, and tyrosine hydroxylase mRNA expression in this study. This manuscript has been released as a pre-print at https://www. biorxiv. org/content/10. 1101/821355v1(RQA, SC, JC, GG, ET, and IT).
Data Collection Demographic clinical and biological data, as well as ECG, CV risk factors, and history were collected upon admission. The GRACE risk score was also calculated  [17]. The following biological parameters were collected: C reactive protein (CRP), hemoglobin level, plasma N-terminal-pro Brain natriuretic peptide (NT-proBNP), creatinine, and estimated glomerular filtration rate (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration formula (CKD-EPI). The troponin Ic peak was obtained from three blood samples taken every 8 h within the first 24 h following admission, using the conventional method (Dimension Vista luminescent oxygen channeling (LOCI™) troponin I assay, Siemens). The left-ventricular ejection fraction (LVEF) was estimated using echocardiography. Coronary angiography and reperfusion procedures, i.
The quality of the model was evaluated using C-statistics (0. 81, 95% confidence interval: 0. 76-0. 87) and Nagelkerke R 2 (0. 37). The patients with and without PCI were then paired 1:1 on this PS using a caliper width of 10% of the standard deviation of the PS logit. Kaplan-Meier curves and the log rank test were performed to compare one-year CV mortality of patients with and without PCI. We used the SPSS 13. 0 software package for all analyses.
Discussion Acute infection, particularly pneumonia, and MI are both leading causes of hospitalization and mortality and their association, referred to as post-infectious MI, is emerging from the recent literature  [1] [2] [3] 20, 21]. The present observational study from a multicenter cohort of MI hospitalized in CCU addresses, for the first time, one of the key issues for the acute management of post-infectious MI, namely, PCI, focusing on situations for which such a procedure is relevant, i. e. , patients with angiography-proven significant coronary stenosis. Whether acute invasive strategies should be performed in patients presenting post-infectious MI was not thoroughly addressed. In a large cohort of patients with sepsis from the American National Inpatient Sample database, the rate of post-infectious MI was 4.
5%. Among these patients, only 10% underwent an invasive (i. e. , PCI and/or CABG) vs. conservative management strategy. In contrast with our results, revascularization was associated with half-reduced in-hospital mortality, after propensity-score matching  [5]. Moreover, in the Taiwan Health insurance coding database, among patients with a first MI diagnosis, 7% had concurrent acute infection. Invasive management was also associated with an improved long-term prognosis  [6]. However, in these retrospective studies on coding databases, adjustment for potential CV confounders was limited. Thus, access to invasive procedures after MI could indicate a high level of care, introducing a major bias in prognosis evaluation  [22]. In our study, only MI patients hospitalized in a CCU were included and P = 0.
5%. Among these patients, only 10% underwent an invasive (i. e. , PCI and/or CABG) vs. conservative management strategy. In contrast with our results, revascularization was associated with half-reduced in-hospital mortality, after propensity-score matching  [5]. Moreover, in the Taiwan Health insurance coding database, among patients with a first MI diagnosis, 7% had concurrent acute infection. Invasive management was also associated with an improved long-term prognosis  [6]. However, in these retrospective studies on coding databases, adjustment for potential CV confounders was limited. Thus, access to invasive procedures after MI could indicate a high level of care, introducing a major bias in prognosis evaluation  [22]. In our study, only MI patients hospitalized in a CCU were included and prognostic confounders were prospectively collected.
Statistical analysis The results are presented as mean ± standard deviation for normally distributed variables and as median (interquartile range) for nonnormally distributed variables. When investigating the change from baseline variables, a paired sample t-test was used for normally distributed data and Wilcoxon signed-rank test for not-normally distributed data. A X 2 was used for among-group comparisons or a Fisher's exact test if the expected cell count was less than five. A two sided P-value of <. 05 was considered statistically significant. Statistical analyses were performed using Prism (GraphPad Software Inc. , Version 8, San Diego, CA) and SPSS (IBM Switzerland, Version 25, Switzerland).
LIMITATIONS This study has the same limitations inherent with any prospectively collected data. However, we tried to reduce this bias by standardizing data collection. The small patient numbers limit the generalizability of the paper; however, as already discussed, the outcomes of nonresponders to conventional CRT who undergo endocardial pacing are sparse. Indeed, the ALSYNC study was the largest published study in the literature and included only 28 nonresponders. 12 It would have been important to determine how long patients had been nonresponders to conventional CRT; however, this was outside the scope of this paper. Biventricular pacing was only estimated at 6 months and cannot be fully relied upon without attaching a 24 h-Holter monitor to look for ectopy or arrhythmia. This monitoring was not undertaken in these studies.
7, 9, 22, 26, 27  Furthermore, patients who are MRD-positive before HSCT may be more likely to relapse and have poorer outcomes after transplantation than those who are MRD-negative before HSCT. 22, [28] [29] [30] [31] [32]  To understand the prognostic implications of MRD in real-world clinical practice, a retrospective study of adult MRD-positive patients who received standard-of-care (SOC) treatment between 2000 and 2014 was recently conducted using European ALL study group databases. 33  Median relapse-free survival (RFS) was 12. 4 months (95% confidence interval [CI]: 10. 0-19. 0), and median overall survival (OS) was 32. 5 months (95% CI: 23. 6-48. 0) from bas ne MRD detection in the historic data set among patients in first CR. 33  Blinatumomab, an engineered bispecific T-cell engager (BiTE ® ) antibody construct that targets CD3-expressing T cells to CD19-expressing B cells to facilitate tumour cell lysis,  34  has been investigated in the setting of MRD.
42  Similarly, other studies evaluating treatment outcomes among ALL patients have also used propensity score adjustment to compare two distinct study populations. 43, 44  Overall, the approach of using historic controls and propensity score statistical methods has provided important context for the effect of novel treatments in rare diseases with unmet need. [43] [44] [45] e objective of this study was to compare RFS and OS between patients with MRD-positive BCP-ALL treated with blinatumomab in a single-arm, phase 2 study  12  and patients with MRD-positive disease from a historic data set who were treated with SOC,  33  employing weighted and propensity score analyses. The primary analysis study population included patients in CR1 only, and sensitivity analyses used data from patients treated in CR1 and later CRs.
| Historic data set Full details of the historic data set are described elsewhere. 33 The study population was assembled from ALL study group databases in Europe that included prospective MRD testing in their treatment protocols. Patients with MRD-positive Ph-negative BCP-ALL in CR were included in the study if MRD was detected at a national reference laboratory by PCR at a level of ≥10 -4 or by flow cytometry at a level of ≥10 -3 ; they were aged ≥15 years at the time of ALL diagnosis; they were diagnosed between 2000 and 2014; they were treated at participating study group sites; a history of ALL treatment was available; and a history of relapse status and disease follow-up after MRD detection was available. Exclusion criteria included the following: presence of extramedullary disease at the time of MRD detection; treatment with blinatumomab within 18 months of MRD detection; and allogeneic HSCT before MRD detection.
The weighting method used was the average treatment effect (ATE), and an exploratory sensitivity analysis was conducted using average treatment effects of treated (ATT) weights. 46 Disproportionate influence of large IPT weights was addressed using stabilised IPTW. Further details on the propensity score analysis can be found in the Appendix S1. Relapse-free survival and OS were analysed using Cox proportional hazards regression models with input data weighted according to the methods already described and including blinatumomab or SOC treatment as an independent variable. A time-dependent covariate for HSCT was included in the models because the clinical use of HSCT had increased in the period between the historic study and more recent blinatumomab study. Further sensitivity analyses were conducted by excluding the HSCT covariate.
| Weighted analysis Patient data from the SOC group of the PAS were stratified by age group, and the outcomes obtained for each stratum were weighted to obtain a pooled estimate according to the proportion of patients observed in each age stratum in the blinatumomab study. Weighted to the blinatumomab study population, median RFS in the SOC group was 7. 8 months (95% CI: 6. 4-12. 4), and median OS was 25. 9 months (95% CI: 17. 0-39. 1), whereas in the blinatumomab group, median RFS was 35. 2 months (95% CI: 18. 9-not evaluable [NE]), and median OS was NE (95% CI: 24. 2-NE) (Table  2 ). Of note, median RFS and OS decreased with increasing patient age in the SOC group, but median was typically not reached in the blinatumomab group (Table  2 ).
Our approach has relevant limitations. All analyses were post hoc and exploratory in nature. The small study population limits the power to detect differences between the two groups; however, the CIs reported and the extensive sensitivity analyses performed can serve as a guide to the significance and robustness of our results. Data completeness and quality varied between the two studies, and only covariates that were reported in both data sets were included in the propensity score models; relapsed vs persistent MRD is a potentially relevant factor that was not recorded in the blinatumomab study. 12 Unlike randomisation, propensity score analysis does not produce a balance between treatment groups with respect to all possible confounding variables. RFS did not appear to change dramatically over time in the historic study, although there was a difference in the proportion of patients who received HSCT over the historic study period, increasing from approximately 30% to 40% from 2000-2004 to 2005 onwards (P =.
Patients and Methods Patients who received AIH-LT, liver transplantation for primary biliary cholangitis (PBC-LT), and liver transplantation for primary sclerosing cholangitis (PSC-LT) were identified from the ELTR database. To compare these groups with a common nonautoimmune liver disease leading to LT, we also identified patients receiving liver transplantation for alcohol-related cirrhosis (AC-LT). Only patients with LT for chronic liver disease were included in the study, and patients with fulminant AIH were therefore not included. Patients with more than 1 diagnosis (including overlap syndromes) were also not included in the present study. A study request was reviewed and approved by the ELTR data committee. Created in 1986, the ELTR prospectively collects LT data from 174 centers in 32 European countries, and it ensures data quality and validity by an annual audit and by cross-checking between the main national and international organ sharing organizations.
The ELTR questionnaire includes data on indications for LT, donor and recipient characteristics, technical aspects of LT, initial immunosuppression, patient outcome, and cause of death or graft loss. (3) he ELTR does not obtain individual consent from patients for inclusion of their data because the French national ethics committee (Commission Nationale de L'Informatique et des Libertes), which was consulted because the ELTR is based in Paris, considered it not mandatory. All ELTR data are anonymized. (18) Only patients with a confirmed diagnosis and without concurrent chronic liver disease were included. Available clinical variables were retrieved from the database. All statistical analyses were carried out with R, version 3. 6. 1 (R Foundation for Statistical Computing, Abbreviations: AIH, autoimmune hepatitis; AIH-LT, liver transplantation for autoimmune hepatitis; AC, alcohol-related cirrhosis; AC-LT, liver transplantation for alcohol-related cirrhosis; CI, confidence interval; CNI, calcineurin inhibitor; DBD, donation after brain death; DCD, donation after circulatory death; ELTR, European Liver Transplant Registry; HR, hazard ratio; IQR, interquartile range; LDLT, living donor liver transplantation; LT, liver transplantation; PBC, primary biliary cholangitis; PBC-LT, liver transplantation for primary biliary cholangitis; PSC, primary sclerosing cholangitis; PSC-LT, liver transplantation for primary sclerosing cholangitis; sHR, subdistribution hazard ratio; UNOS, United Network for Organ Sharing.
Fatal inFectiOns aFter lt Because infections accounted for most deaths in patients after AIH-LT, they were explored in greater detail. The most frequent causes for death in patients after AIH-LT due to infections specified according to the ELTR database code were bacterial infections (n = 77, 13. 8% of all deaths), followed by pulmonary infections (further cause not specified) and fungal infections. A detailed description of the infectious disease leading to death stratified by primary disease is given in Fig. 3A. Patients after AIH-LT showed an increased risk for death caused by bacterial infections compared with patients after PBC-LT (HR, 1. 52; 95% CI, 1. 30-1. 77; P = 0. 007) and AC-LT (HR, 1. 87; 95% CI, 1. 64-2. 14; P < 0. 001) and of death from pulmonary infections compared with patients after PBC-LT (HR, 1.
Discussion The present study analyzed longterm patient and graft survival in patients with AIH-LT from a validated international multicenter database and compared the outcomes with patients who received LT for PBC, PSC, and AC. This study represents a follow-up analysis of the authors' previous report from 2010,  (13)  on which it largely expands in terms of population size and observation time. 6) (7) (8) (9) (10) (11) (12)  The inclusion of large cohorts transplanted within the past 20 years and uniformly compiled variables within a validated database allowed for a meaningful and statistically sufficiently powered analysis of outcome predictors. Because more than 170 centers from 32 European countries contribute to the prospective ELTR database, the present results are representative for outcomes of adult and pediatric AIH-LT patients in Europe.
Original article | 875 6) (7) (8) (9) (10)  The 2 studies analyzing 10-year follow-up after AIH-LT reported survival rates of 86% (n = 42) and 73% (n = 73). (4, 12) he varying results may be explained by the lack of standardization of care for AIH-LT patients, the small population sizes, the mainly single-center cohorts, and the wide time span covered with changing medical practices and outcomes over time. In the present study comprising 2515 patients with AIH-LT, the survival rates were 79% after 5 years, 71% after 10 years, and 60% after 15 years of follow-up. Thus, an improved 5-year survival probability was observed compared with the authors' previous analysis from 2010,  (13)  where an estimated 5-year survival of 73% after AIH-LT was reported for patients from the ELTR database. This is in accordance with the observation of general constant improvement of outcomes after LT.
(16)  A recent large single-center study (n = 1767) on patients who received LT for various underlying diseases showed an estimated 15-year survival of approximately 60%. (22) The present study observed a similar outcome in AIH-LT patients with 60% survival after 15-year follow-up and comparable patient survival in AC-LT. Similar to the authors' previous report,  (13)  survival in AIH-LT was inferior compared with PBC-LT. Direct comparisons of outcomes between patients receiving AIH-LT and PSC-LT are scarce. In a previous report on LT recipients for AIH and PSC from the United Network for Organ Sharing (UNOS) database, patients with both underlying diseases had similar outcomes after 5 years. (15) However, this group included pediatric LT recipients only. In a recent UNOS study including 4153 patients after AIH-LT, AIH was associated with higher risk of death and graft loss compared with PBC and PSC.
AIH-LT patients are known to be at risk for graft rejection and subsequent graft loss. (12, 15, 31) However, data on longterm outcomes in AIH-LT with rejection are scarce. In the present study, patients with AIH-LT had an increased risk for death and graft loss due to rejection not only compared with patients with the nonautoimmune primary liver disease AC but even more so in comparison to patients with PBC and PSC. Also, there is evidence for crosstalk between autoimmune and alloimmune responses in patients after LT for autoimmune liver diseases. (31) Patients with AIH-LT may have a particular predisposition for immunoreactivity against the liver. The present study has obvious limitations related to a registry study. Not all variables with a potential impact on outcome after LT were available in the ELTR database, such as duration and dose of immunosuppressive treatment, screening for infections, histological and biochemical parameters, and cytomegalovirus status.
These people share some common cultural, traditional, environmental, and lifestyle factors and a few gene clusters in their genomes. However, they have diverse health care systems with extremely variable national economies, ranging from low-income to high-income countries. Furthermore, most registries have been devoted to one syndrome or the other; no registry has concurrent data for AMI and AHF. These syndromes can occur concomitantly, but more frequently, they occur independently. In the last decade, several separate AMI and AHF registries were initiated in some countries of the MENA region  [9] [10] [11] [12] [13] [14] [15]. However, to date, there is no single registry that includes all or most countries in the region. This pilot study aimed to describe the overall study design of the Program for the Evaluation and mAnagement of the Cardiac Events registry for the MENA region (PEACE MENA Registry) and to identify the clinical characteristics, management, in-hospital outcomes, and 30-day mortality rate of patients admitted for AMI and/or AHF in the MENA region.
Statistical analysis Data are expressed as the means ± standard deviation (SD) for normal distributions and the median and interquartile range (IQR) for non-normal distributions. Differences between groups were assessed with the chi square or Fisher's exact test for categorical variables. The student t-test was used for continuous, normally distributed variables, and the Mann-Whitney U test was used for skewed variables. P-values<0. 05 were considered significant. All analyses were performed with SPSS software.