dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Results are expressed as mean ± SEM. N are indicated for each group in the order presented on the histogram. No statistical method was used to predetermine sample size. Sample size was chosen based on prior experience for each experiment, to yield adequate power to detect specific effects. Statistical analysis were performed with Statistica software (StatSoft, Tulsa, OK) or GraphPad Prism 7 (La Jolla, CA). We used paired or unpaired two-tailed Student t test to compare two groups and repeated-measures or one-way ANOVA and Tukey's post hoc test to compare three groups. For each analysis, normality of residues and homoscedasticity were assessed, as well as sphericity for repeated-measures ANOVA. If any conditions of application was not fulfilled, we used non-parametric tests. Two groups were compared by the Mann-Whitney or Wilcoxon test and three independent groups were compared by a Kruskal-Wallis test followed by post-hoc comparison of mean ranks.
Acknowledgments We thank  Drs. N. Robil  and  P. de la Grange  from  Genosplice Technology®  for bioinformatics analysis of RNAseq data. We thank  Profs. Haan ,  Bjørbaek and Shoelson  for sharing JAK2ca and SOCS3 plasmids. We thank  Drs. S. Alves  and  N. Cartier  for sharing their IDE antibody. We thank  L. Vincent ,  K. Bastide ,  P. Woodling  and  J. M. Hélies  for their help with mouse housing and transfer to Bordeaux;  C. Garin  and  C. Dudeffant  for help with the APP/PS1dE9 colony. We are grateful to  F. Aubry  for help with initial AAV vector cloning and FACS experiments;  D. Cheramy  and  Dr. E. Diguet  ( Servier ) for sharing their expertise and equipment for MSD® kits;  Dr. L. Juricek  and  Dr. C. Jan  for help with pilot experiments and  Prof. N. Déglon  for technical input at the initial part of the project.
Funding This study was supported by  CEA ,  CNRS  and grants from the  French National Research Agency  (grants #  2010-JCJC-1402-1 ,  2011-BSV4-021-03  and  ANR-16-TERC-0016-01  to C. E. and  2011-INBS-0011  NeurATRIS to P. H. ), from  Fondation Vaincre Alzheimer  (grant #  FR-15015 , to C. E. ),  Laboratory of Excellence in Medical Genomics: GENMED  (to J-F. D. ) as well as  CNRS  and  INSERM  (to S. H. R. O. and A. P) and  Association France Alzheimer  and  Fondation de France  (Prix Spécial 2012 to G. B. ). C. E. and A. P. received support from the  Fédération pour la Recherche sur le Cerveau. K. Ceyzériat ,  L. Ben Haim  and  O. Guillemaud  are recipients of a doctoral fellowship from the  CEA.
Data sources The SNIIRAM (Système National d'Information Inter-Régimes de l'Assurance Maladie) is the French national information system that contains individual, exhaustive, and linkable but anonymous data on healthcare use for approximately 77% of the French population. [26] It aggregates data from (1) The hospital discharge abstract database (Programme de Médicalisation des Systèmes d'Informations [PMSI]), which collects main and associated diagnoses (secondary events and comorbidities) encoded using the World Health Organization International Classification of Diseases and related health problems 10th revision (ICD-10), and procedures performed during hospital stays (in all public and private hospitals), using the common classification system for medical procedures (Classification commune des actes médicaux [CCAM]).
[29] [30] [31] [32]  (2) The reimbursement data for out-of-hospital care (consultations, procedures, drugs); (3) The codes for long-term illnesses (LTIs), including coronary heart disease, that entitle patients to coverage without coinsurance under France's national health insurance program. [33] rious control procedures are regularly conducted to ensure the quality of these data. The reliability of the SNIIRAM, including at first only the hospital database  [27] [28] [29] [30] [31] 34]  and recently the whole database,  [35, 36]  has been established in recent studies. The EGB database (Échantillon Généraliste de Bénéficiaires) is an on-going representative, cross-sectional sample of the SNIIRAM, using a systematic 1/97th random sampling method. [26] The data for this study were extracted from the EGB database.
Study design and setting In France, the EGB database provides a representative sample of the French population and aggregates data on hospital discharge abstracts and reimbursement data for out-of-hospital care (consultations, procedures, drugs). We conducted a nationwide, population-based, retrospective study in France based on the French EGB database (including hospital and out-of-hospital care). All elderly patients hospitalized in France from January 2011 to December 2013 with a main diagnosis of AMI were identified in the EGB database using validated algorithms (ICD-10 codes I21, I22, I23). [37] Among them, we included all patients admitted from home to acute inpatient hospitals (≥2 day stays). We considered rehospitalizations in short-term units only. We did not include rehospitalizations in long-term care facilities, such as aftercare and rehabilitation ward and long-term acute care.
Variables For each patient, sociodemographic and medical data were collected at the index hospitalization: age, sex, place of residence, LTI status, length of stay, AMI type (segment elevation myocardial infarction [STEMI] versus non-ST-segment elevation myocardial infarction [NSTEMI]), stay in intensive care units, cardiac procedures performed (angioplasty with or without stent implantation, coronary artery bypass graft and other procedures on coronary arteries), and discharge destination. Main and associated diagnoses were used to analyze patients' comorbidities and complications. Comorbid conditions were estimated with the Charlson Comorbidity Index (CCI), an independent predictor of mortality and recurrent AMI within 30 days after AMI. [38] ongestive heart failure, acute and chronic kidney failure, diabetes, and atrial fibrillation were comorbidities of interest that we analyzed separately from the CCI.
To take this variability into account, we performed multilevel logistic regression models to test the association between individual and neighborhood-level variables and 30-day rehospitalization, taking into account the hierarchical structure of our data. We considered a 2-sided P <. 05 as statistically significant. The results are presented as odds ratio (OR) and associated 95% confidence intervals. A forest plot showing the factors included in the multivariate regression analysis is also given. All analyses were performed using SAS, version 9. 3 (SAS Institute, Cary, NC).
Results We identified a total of 891 patients aged ≥65 years, hospitalized in France with a main diagnosis of AMI in the 2011 to 2013 EGB database and included 624 in this study (Fig. 1 ). Among these, 137 (22. 0%) were rehospitalized within 30 days after discharge. The mean time between discharge and rehospitalization was 11. 9 (9. 4) days. Rehospitalization diagnoses were cardiovascular for 56. 2% with mostly chronic ischemic heart disease (19. 7%) and heart failure (16. 1%) (Supplementary appendix 1, http://links. lww. com/MD/C288). Patients' baseline characteristics are shown in Table  1. The mean age was similar between rehospitalized and nonrehospitalized patients: 79. 2 (7. 7) and 78. 1 (8. 0) years, respectively (P =. 15). Although the CCI and the AMI type did not differ significantly between the 2 groups, rehospitalized patients had a higher prevalence of congestive heart failure (P =.
Discussion In this nationwide population-based study from a representative sample of the French population, we found that 22. 0% of elderly patients (≥65 years) hospitalized for AMI were rehospitalized within 30 days after discharge. Regarding early postdischarge ambulatory care, we found that patients who purchased lipid-lowering drugs within 7 days after discharge were less likely to be rehospitalized within 30-days after discharge, but that the early ambulatory medical follow-up did not influence rehospitalization. No study on rehospitalization after AMI has been published in France. Thus, we are unable to shed any comparative perspective on the evolution of rehospitalization rates for patients initially admitted for AMI. In the American national readmission database, the all-cause rehospitalization rate within 30 days after AMI was 14.
Elderly patients could have continued taking the same medications they had before hospitalization and the failure to purchase may thus differ from the failure to follow treatment. Unfortunately, we were unable to distinguish prescribed medications from those purchased or to assess patients' adherence. It would also have been interesting to distinguish antiplatelet and anticoagulant agents: the use of antiplatelet agents is recommended consistently after AMI, whereas anticoagulants are restricted to clinically indicated situations, such as atrial fibrillation. This distinction would require access to additional clinical data not available in our administrative database. Further research is needed to deepen our understanding of the role of treatments in 30-day rehospitalizations. Postdischarge ambulatory care may be an opportunity to reduce 30-day rehospitalizations and improve patients' outcomes.
Moreover, we found that nearly half of the patients were rehospitalized within 7 days, which is consistent with other studies. [40, 53] This early risk of rehospitalization emphasizes the importance of continuity of care. This post-discharge period must be anticipated with transitional care programs. The engagement of primary care professionals starting from the early postdischarge period may be important to prevent rehospitalizations. [54] he strengths of this study include its original focus on postdischarge ambulatory care in everyday practice and the use of a representative sample of the French population, available from a nationwide exhaustive database combining hospital and ambulatory care data. The mean age and sex distribution of our sample were consistent with other studies in France  [49]  and elsewhere.
[23] Regarding socioeconomic status, the mean deprivation index was similar to that of the general French population. [43] Moreover, the validity of the EGB database for studying STEMI has been demonstrated  [55]  and the same proportion of percutaneous coronary intervention was observed in the French main registry data (FAST-MI 2010). [56] ome limitations must be acknowledged. The use of an administrative medical database limited the collection of socioeconomic data. Clinical information, as well, was restricted: information on patients' left ventricular ejection fraction or on the extent of the coronary lesions would have enabled us to perform analyses adjusted for the AMI severity. Moreover, coding may sometimes be incomplete or incorrect and some comorbidities or procedures may have been underestimated.
These problems are, however, unlikely because of the seriousness of the disease and the use of validated algorithms. Coding quality is checked by medical information professionals in each hospital to correct inaccurate diagnoses and to complete recorded comorbidities. Indeed, each French hospital's budget depends on the medical activity described in the PMSI database. Finally, some variables were too few in number and could not be considered for the analysis of post-discharge ambulatory care. To conclude, our study suggests that a high proportion of elderly patients in France are rehospitalized within 30 days after discharge for AMI. The purchase of lipid-lowering drugs prescription within 7 days after discharge was associated with a reduced risk of 30-day rehospitalization. We can postulate that the poor discharge planning and insufficient care coordination between hospital and community-based professionals in France hamper strategies to reduce rehospitalizations.
Acknowledgments The authors thank the project committee members from the  Directorate  for healthcare provision at the  Ministry of Health (Adeline Townsend ,  Adrien Dozol  et  Agnès Solomiac  [bureau R5 « Évaluation, méthodes et modèles »,  Direction Générale de l'Offre de Soins])  and the national agency for the management of hospitalization data ( Véronique Sauvadet ,  Florence Pinelli ,  Nathalie Rigollot ,  Laëtitia Chossegros ,  Eric Ekong ,  Philippe Demey  et  Catherine Le Gouhir [Agence Technique de l'Information sur l'Hospitalisation]). The authors also thank  Suzanne Rankin  for reviewing the English.
Di Paolo and De Camilli 2 reported the existence of both v 4→34 and v 34→4. Consistency of the Model with Data. As described in the Methods section, model equations were formulated according to Biochemical Systems Theory (BST)  23, 24. Initial parameter estimates were derived from the literature and from the BRENDA database  25. The parameter values were subsequently optimized with a genetic algorithm such that the model matched reported phosphoinositide steady-state levels (Supplementary Table  ST1 ) and dynamic phenomena reported in the literature (Fig. 3a, c  and Supplementary Table  ST2 ). This model successfully mimics steady-state levels and 11 out of 13 observed phenomena. The observations that were not replicated are: 1) when the PI levels are reduced, the drop in PI(4,5)P 2 levels is not as evident as reported in the literature (Figs  3a  and 2 ) the knockout of myotubularin MTMR2 effects are only partially replicated (Fig.
7 ). Both our previous dataset  13  and results from the new siRNAs validation tests included in the present study (Supplementary Table  ST10 ) are not as conclusive about the SIOSS phosphatases, as for most of the phosphatases tested, which could be a consequence of the unspecific activity that characterizes phosphatases. For example, synaptojanins catalyse several reactions in the pathway and perturbing them would probably cause unexpected side effects. PI4K. A PI4K knockout affects the fluxes v 0→4 and v 0→45. It decreases PI(4,5)P 2 in 59% (Fig. 7 ). Accordingly, the model suggests that PI4K should be classified as an ENaC activating gene, which is in line with our previous observations  13. An undesirable side effect within these model predictions is the change of the PI(3,4)P 2 concentration, a lipid involved in clathrin-coated vesicle formation and activation of AKT.
Acknowledgements Work supported by  UID/MULTI/04046/2013  centre grant (to BioISI) and  DIFFTARGET PTDC/BIM-MEC/2131/2014  grant (to MDA), both from FCT, Portugal. DO is a recipient of a PhD fellowship from  BioSys PhD  programme (Ref:  SFRH/BD/52486/2014 ) and IU of SFRH/BD/69180/2010, both from FCT, Portugal. This work was supported in part by the grants  MCB-1517588  (PI: EOV) of the  U. S. National Science Foundation. The funding agencies are not responsible for the content of this article. The authors are also grateful to  Luís Marques (BioISI)  and to staff from  EMBL, Heidelberg (Germany ), from  ALMF-Advanced Light Microscopy  ( Beate Neumann ,  Christian Tischer ) core facility for technical assistance.
Code availability. The R code is available in GITHUB at the following: https://github. com/dolivenca/ MK15_phosphoinositide_pathway_model. siRNA knockdown validity test. To confirm model predictions, selected phosphoinositide pathway hits identified in a large scale siRNA screen  13  were validated with an independent round of siRNA knockdown assays. Human alveolar type II epithelial A549 cells (ATCC, Cat no. CCL-185) were transfected with 2 or 3 different siRNAs targeting phosphoinositide pathway enzymes. After transfection the FMP/Amiloride live-cell assay  13  was applied to measure ENaC activity. Detailed methods and analysis are described in the Supplementary Information.
| Statistical analysis For each country, the statistical analysis was limited to seasons with 50 or more influenza reported cases and at least 20 weeks of data reporting in order to enhance the reliability of results  9. We determined the proportion of influenza cases that were caused by each virus type, subtype and lineage in each country and season, and determined the median values for each country (ie across seasons) and for the whole region (ie across all seasons and countries). Medians were compared between groups of countries using the Mann-Whitney nonparametric test. We aimed to determine the typical timing and seasonality of the primary and secondary epidemic peaks in each country using the epipoi software  21. The epipoi method first divides the weekly number of reported influenza cases by the maximum value in the same season: this is done to standardize all season-specific time series in each country, so that all seasons are given the same weight in subsequent analysis.
The peak and amplitude of the primary and secondary peaks were calculated for all influenza cases and separately for influenza A and B epidemics (for the latter analysis, for each country we only included seasons with 50 or more influenza cases caused by that virus type). For more details on how the PAF is fitted to the raw data and the timing and amplitude of the peaks are calculated using the epipoi software, the reader can refer to the paper by Alonso and   McCormick where the software is presented  21. Finally, we aimed to detect whether seasonal influenza epidemics spread along geographical gradients in countries of the Middle East and North Africa. For this purpose, we fitted a linear regression model for each season using the longitude and latitude of the country's centroid as independent variables, and the timing of the typical (ie obtained from the PAF) primary epidemic peak (with weeks numbered consecutively from July of 1 year to June of next year) as the dependent variable 23.
A similar analysis was conducted in parallel also for Europe and Central Asia (ie countries included in the south-west Europe, northern Europe, eastern Europe and Central Asia Influenza Transmission Zones  24  ) to test whether the spatial timing of influenza epidemics in the same season is comparable in these two world areas (the Middle East and North Africa vs Europe and Central Asia). Analyses were conducted using Stata version 14 (Stata Corp, College Station, TX) and epipoi 21. Maps were obtained using freely available software (http://mapchart. net/). All tests were two-sided and considered as statistically significant when the P-value was. 05 or less.
| RE SULTS A total of 70 915 laboratory-confirmed influenza cases from nineteen countries in the Middle East and North Africa between 1 July 2011 and 30 June 2016 were contained in the FluNet database. After applying the exclusion criteria, 70 532 cases from 83 seasons in seventeen countries were included in the analysis (there were no seasons with 50 or more reported influenza cases in Azerbaijan and Syria) (Figure  1 ). The number of seasons per country that were included in the analysis ranged from one (2014-2015 in Armenia and 2015-2016 in Afghanistan) to six (eleven countries) (Table  1 ). Influenza cases were unevenly distributed across countries, and over 50% of overall cases were contributed by only three countries (Turkey, Qatar and Egypt). The median number of influenza cases per season and country was 440 (interquartile range [IQR]: 202 -994).
This has made it possible to conduct comparative studies of influenza epidemiology and seasonality in tropical and subtropical regions such as Central and southern America  5, 6  and the Asia-Pacific region  7, 31. Despite these improvements, there are still some important gaps in the quality of influenza surveillance data available in the FluNet database which may be seen as limitations of the present study and should be addressed in the coming years to have a better understanding of the epidemiology of influenza in this area. No information is available in the FluNet database or the FluNet website on the national surveillance systems of the participating countries: this limits the interpretability of the results, especially the direct comparability of the data as one does not know whether the data are coming from community and/or hospital settings.
In addition, the robustness of results on the timing of influenza epidemics may be somewhat less strong for countries that provided data for only four (Bahrain, Iraq) or five (Morocco) seasons, compared to countries for which data were available for all of the six seasons. The number of points in our spatial analysis was limited (also because of a lack of data for some countries, such as Libya, Sudan and Saudi Arabia) in relation to the very large longitudinal extension of this region: these made the β coefficients for the season-specific gradients of timing sensitive to the influence of a few points. In some countries, the population is unevenly distributed throughout the territory and concentrated in well-defined areas (eg coastal regions for countries of North Africa), but for some other countries, it would be helpful to have surveillance data at regional level, which is, however, not available in the FluNet at present.
Unlike influenza A, only a very small percentage of influenza B cases were characterized. It is usually very challenging to make reliable predictions on which lineage (Victoria or Yamagata) will dominate each influenza season, and the two lineages also differ in terms of their demographic characteristics  9, [32] [33] [34] [35]. A better knowledge of the patterns of influenza B strain circulation may be helpful to estimate the effectiveness of vaccination campaigns and optimize influenza prevention interventions. Overall, completeness of information within the FluNet database could still be improved; in addition, data validity is uncertain, as no validation with an external source of data has been carried out to date. In conclusion, we found that the epidemiology of influenza is substantially uniform in countries of the Middle East and North Africa.
surveillance data are entered into the FluNet database by collaborating National Influenza Centres and other WHO influenza reference laboratories around the world. The FluNet database contains the weekly number of respiratory specimens that were collected and processed in each country, and the number of those that tested positive for influenza type A and B, A subtype (AH1, A[H1N1]pdm2009, AH3, AH5, not subtyped) and B lineage (Victoria, Yamagata, not determined). A preliminary check of the FluNet database revealed that most countries in North Africa and the Middle East had no or very little influenza surveillance data before the 2009 pandemic. Therefore, we.
Comparison of the spatial timing of influenza epidemics in Middle East and North Africa and in Europe and Central Asia in six consecutive influenza seasons (from 2010-2011 to 2015-2016). The WHO FluNet database, 2010-2016 This includes all countries in the following influenza transmission zones: south-west Europe, northern Europe, eastern Europe and Central Asia (see [WHO Influenza Transmission Zones]). The spatial timing of influenza epidemics in the Middle East and North Africa varied greatly between seasons, both according to the longitude and to the latitude, and in most cases, there was no correspondence with what was occurring in Europe and Central Asia in the same season. A notable exception was the 2015-2016 season,.
TA B L E 1 Laboratory-confirmed influenza samples reported in each country by virus type, subtype and lineage. The WHO FluNet database, 2010-2016 influenza epidemics did not coincide with those observed in Europe and Central Asia, except for the east-to-west gradient in 2015-2016 and the lack of any gradient in 2011-2012. Also, we detected a No. of Influenza (any Country south-to-north latitudinal gradient in the 2012-2013 and 2015-2016 Population (million) Latitude a Longitude a seasons b type) A(H3N2) Afghanistan seasons in the Middle East and North Africa region, while a latitudi-33. 3 33° N 65° E 1 74 13 Algeria nal gradient (also in the south-to-north direction) was observed in 40. 3 28° N 3° E 6 1832 503 Armenia Europe and Central Asia only in 2013-2014. As the geographical gra-3. 1 40° N 45° E Bahrain 1.