dataesr/scientific-paragraphs-categorization huggingface dataset:
A number of limitations of the present study must be noted. First, the small size of the study is a limitation. Indeed, the first 214 consecutive CAD patients from the GENES cohort where included in the present study, which represent 25% of the whole cohort (n = 834). Accordingly, mortality rate was higher in included patients than in the whole cohort (36. 4% versus 29. 1%, data not shown), probably because of the longer duration of follow-up for those first included patients. Clinical and biological characteristics were comparable, between included and non-included patients, except for dyslipidemia treatment, somewhat less frequent in included patients (57. 5% vs 65. 8%, p = 0. 04, Supplementary Table  2 ). This difference might result from difference in statin administration practice between the beginning (year 2001) and the end (2004) of the GENES study.
Patients included in the present study presented a stable CAD that was defined by a previous history of acute coronary syndrome, a previous history of coronary artery revascularization, a stable angina or a documented myocardial ischemia, as assessed by presence of coronary stenosis of ≥50% of luminal narrowing at the coronary angiography. Patients who had presented an acute coronary episode in the seven days prior to the recruitment were not included in the study, because they were considered unstable. In the present analysis, we only took into account the first 214 consecutive patients (i. e. those with CAD) in whom NMR-HDL profile was measured and complete data were available for all the subjects. The minimal sample size (n = 186), increased by 15% to take into account missing data, was calculated to detect a HR of 0.
Assessment of the vital status. Vital status was obtained for each participant through the national database ("RNIPP"), which records, every year, all deaths occurring in the French population (http://cesp. vjf. inserm. fr/svcd). For each patients, vital status was assessed yearly from the year of recruitment until December 31, 2014, with a median follow up of 12. 5 years. All dates and causes of death were obtained for participants who died during the follow-up. Main and associated causes of deaths were provided by the French National Institute of Health Research (CépiDc-INSERM), which systematically collects and codes (using the International Classification of Diseases coding system) data recorded on death certificates. Death from a cardiovascular cause during follow-up was assessed by a committee of four medical doctors, every cardiovascular disease was reported as the main cause of death, or when it was mentioned as an associated cause, if the main cause was a plausible complication of cardiovascular disease.
Data collection. Age, environmental characteristics and information on cardiovascular risk factors were collected through standardized face-to-face interviews, performed by a single physician. Past medical history was collected and checked in the patients' medical files. Presence of dyslipidemia, diabetes mellitus or hypertension was assessed from the subjects' current treatments. Dyslipidemia was defined as treatment with drugs or fasting serum total cholesterol ≥2. 40 g/L. Hypertension was defined as treatment with drugs or systolic blood pressure ≥160 mmHg or diastolic blood pressure ≥95 mmHg. Diabetes was defined as treatment with drugs or fasting blood glucose ≥7. 8 mmol/L. Smoking status was classified as current smokers, past smokers having quit for more than 3 years and patients having never smoked.
Assessment of CAD severity and extension and estimation of cardiac function. All stable CAD patients enrolled had a coronary angiography even without evidence of active chronic ischemia. Coronary artery stenoses of ≥50% luminal narrowing were considered significant. Extent of coronary artery disease lesions was assessed by calculating the Gensini Score, based on data from coronary angiography  [49] [50] [51]. Left Ventricular Ejection Fraction (LVEF) was assessed by contrast ventriculography using an isotopic method, and/or by echocardiography. HDL measurement by nuclear Magnetic Resonance (nMR) spectroscopy. HDL particle concentration and size were measured by NMR spectroscopy using the AXINON lipoFIT-S100 test system (Numares AG, Regensburg, Germany) as previously described  11, 12, 23. Serum samples for NMR spectroscopy were stored at -80 °C and kept unthawed until the day of NMR measurements.
Variables with an entry criterion of p ≤ 0. 20 were used as candidate covariates in a multivariate cox regression model. Variables were maintained in the model with a retention criterion of p ≤ 0. 10 for all-cause of death analyses and p ≤ 0. 05 for cardiovascular mortality. The same covariates were identified for all HDL markers studied. The following variables were introduced into the model as covariates when considering all cause of death: age, smoking, treatment for dyslipidemia, eGFR, LVEF, duration of CAD and Gensini score. For cardiovascular mortality the variables retained were treatment for dyslipidemia, LVEF, duration of CAD and Gensini score. All statistical analyses were carried out using the SAS statistical software package 9. 4 (SAS Institute, Cary, NC). Analyses were two-tailed and p < 0.
Acknowledgements This work was supported by the  French National Research Agency  ( ANR , # ANR-16-CE18-0014-01 ),  European Regional Development Fund (ERDF ,  Fonds Européen de Développement Régional)  and " La Région Occitanie " (Project  THERANOVASC  n°  ESR_R&S_DF-000094/2018-003303/18009464 ).
After blocking for 1 h with 5% nonfat dry milk, membranes were incubated with the primary antibodies overnight at 4 °C: ABCA1 (1:500), ABCG1 (1:500), SR-B1 (1:500), LXRα (1:1000), LXRβ (1:1000), or actin (1:5000). After washing, membranes were incubated with a secondary antibody at room temperature for 1 h. Protein bands were visualized with the Fuji LAS 3000 CCD camera system and quantified using the Multi Gauge software (Fujifilm). To measure ABCA1 protein stability, THP-1 cells were seeded and loaded with unlabeled cholesterol as described above, and then treated with soraphen A (0. 1 µM) or solvent vehicle (0. 1% DMSO) for 24 h. Cells were lysed in ice-cold NP-40 lysis buffer containing 1% protease inhibitor at different time points (0, 0. 5, 1, 2, 3, 4, and 6 h) after treatment with the protein synthesis inhibitor cycloheximide (CHX, 100 μM).
Soraphen A activates LXRα and LXRβ, which accounts for increased ABCA1 expression ABCA1 gene expression is regulated by the nuclear receptors LXR, RXR, and PPARγ  [12]. We, therefore, performed luciferase reporter gene assays with the respective receptors in HEK293 cells. Soraphen A increased LXRα and LXRβ activity concentration-dependently (Fig. 4a ). Soraphen A did not alter RXRα activity (Fig. 4c ), but decreased PPARγ activity (Fig. 4d ). In addition, soraphen A did not change LXRα and LXRβ protein expression in cholesterol-loaded THP-1-derived macrophages significantly (data not shown). To further corroborate the effect of soraphen A on LXRα and LXRβ in HEK293 cells, co-treatment experiments with the LXR antagonist GSK2033 were performed. As shown in Fig. 4b , GSK2033 blocked LXR activation by soraphen A or by the positive control GW3965 (LXR agonist).
Acknowledgements We thank  Dr. Rolf Müller  and the  Helmholtz Centre for Infection Research (HZI)/Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)  and the  Department of Pharmacy at Saarland University, Saarbrücken, Germany , for providing Soraphen. This work was supported by the  Austrian Science Fund  ( P25971-B23 ,  2014 ), the  Vienna Anniversary Foundation for Higher Education (Hochschuljubiläumsstiftung der Stadt Wien)  ( H-297332/2014 ), and the  Austrian Federal Ministry of Science, Research and Economy  ( GZ402. 000/00014-WF/V/6/2016within  the  Sino-Austria  project).
ACKNOWLEDGMENTS This study was supported by the  Fondo de Investigaciones Sanitario (FIS) ,  Instituto de Salud Carlos III  ( PI14/01126  and  PI17/01019  to  Juan Fortea ,  PI13/01532  and  PI16/01825  to  Rafael Blesa ,  PI18/00335  to  María Carmona-Iragui. ,  PI18/00435  to  Daniel Alcolea ,  PI14/1561  and  PI17/01896  to  Alberto Lleó ,  PI17/00279  to  Amanda Jiménez , and  FI18/00275  to Víctor Montal) and the  CIBERNED program (Program 1 ,  Alzheimer Disease  to  Alberto Lleó  and SIGNAL study, www. signalstudy. es);and partly jointly funded by  Fondo Europeo de Desarrollo Regional ,  Unión Europea , Una manera de hacer Europa. This work was also supported by the  National Institutes of Health (NIA.
The degree of tubular dilations was automatically quantified using NIS Elements AR software, version 3. 00 (Laboratory Imaging Ltd. ), as previously described  (18). The extent of interstitial fibrosis was also quantified using NIS Elements AR software. Staining of all kidney samples with picrosirius was performed simultaneously, and red intensity above a defined threshold was defined as fibrosis. At least 10 random selected fields (original magnification, ×200) across the corticomedullary junction were analyzed per each kidney section, and the results were expressed as percentages of the total area of the selected fields. Immunohistochemistry. Four-micrometer sections of paraffin-embedded kidneys were submitted to heat-mediated antigen retrieval if required and incubated with primary antibodies against Ki-67 (1:100, ab16667, Abcam, Inc.
For quantification of CD3 + and F4/80 + renal cells, the degrees of cellular infiltrate were automatically quantified using ImageJ software (NIH) and expressed as the percentage of positive staining per field. At least 10 fields per section were examined at the original magnification of ×200. Detection of 5-ethynyl-2′-deoxyuridine and cell cycle progression studies. 5-Ethynyl-2′-deoxyuridine (EdU) labeling and coimmunostaining with anti-Ki-67 and anti-phospho-histone H3 (Ser10) antibodies were performed according to the manufacturer's instructions. Briefly, after paraffin removal, sections were submitted to heat antigen retrieval and incubated with primary antibody to anti-mouse Ki-67 (1:100, ab16667, Abcam, Inc. ). EdU detection was then performed according to the manufacturer's instructions using the Click-iT EdU Alexa Fluor 488 imaging kit (Invitrogen, Thermo Fisher Scientific).
Sections were finally counterstained with Hoechst and mounted for fluorescence microscopy using a Leica SP8 confocal microscope (Leica Microsystems) and the Leica imaging software. Quantification analysis was performed using ImageJ software on at least 10 randomly selected microscopic fields from the cortex and corticomedullary junction in 4 Nx mice from each strain and at each time point. The overall number of tubular epithelial proliferating cells was determined using Ki-67 staining. EdU incorporation and phospho-histone H3 (Ser10) staining were used to identify the number of cells in S and G 2 /M phases, respectively. Subsequently, the number of tubular epithelial cells in G 1 phase was calculated by subtracting the number of cells in S phase (EdU + ) and G 2 /M phase (phospho-histone H3 + ) from the total number of proliferating cells (Ki-67 + ).
Western blot analysis. Western blot analyses were performed as previously described  (8, 18). Briefly, protein extracts from kidneys were resolved by SDS-PAGE before being transferred onto the appropriate membrane and incubated with primary antibodies against phospho-histone H2A. X (Ser139) (1:100; 2577, Cell Signaling Technology) and GAPDH (1:10000; Mab374, MilliporeSigma) followed by the appropriate Alexa Fluor-conjugated secondary antibody at 1:5000 (Life Technologies). Fluorescence was acquired using a ChemiDoc MP Imaging System (Bio-Rad), and densitometry was performed using Image Lab software 5. 0. Microarray analysis. Raw data were normalized with the MAS5 method using the affy software package in R/Bioconductor and consequently log 2 -transformed. Chips were controlled for quality by an in-house pipeline that detects both technical and biological issues  (43).
Data were deposited in the National Center for Biotechnology Information's Gene Expression Omnibus database (GSE148084). Between-sample normalization was performed using the quantile method implemented in the limma package. Unbiased clustering analysis was performed using coinertia analysis implemented in the made4 package. Differential gene expression was detected by a linear model that considers strain (FVB or B6), surgery (Sh or Nx), time (day 2, 28, or 56), and all interactions among the 3 factors using limma. The quality of the model was checked and confirmed by visual inspection of P value histograms and volcano plots. Gene set enrichment analysis was performed using GSEA with 1000 permutations  (44). A collection of gene signatures from Pathway Interaction Database, Pathway Commons, and Reactome was used.
GSEA results are reported in enrichment scores defined as ES = sign(NES)|log 10 (FDR)|, i. e. , the sign of normalized enrichment score (NES) reported by GSEA (1 if NES > 0, 0 if NES = 0, and -1 if NES < 0) multiplied by the absolute log 10 transformation of the FDR reported by GSEA. Enrichment scores range between -4 and 4, corresponding to cases where NES is negative or positive and P = 0. 0001. Statistics. Data are expressed as means ± SEMs. Differences between the experimental groups were evaluated using 1-way ANOVA followed, when significant (P < 0. 05), by the Tukey-Kramer test. When only 2 groups were compared, Mann-Whitney U test was used. A P value of less than 0. 05 was considered significant. The statistical analysis was performed using GraphPad Prism software. Study approval. Animal procedures were approved by the departmental director of "Services Vétérinaires de la Préfecture de Police de Paris" and by the ethical committee of the Paris Descartes University.
Experimental records and management One-day-old birds were weighed, and the average initial body weight of the birds in each group was calculated. The birds were provided with feed and water ad libitum throughout the experiment. The number of dead birds and the temperature and humidity in the birdhouse were recorded on a daily basis. Management requirements and data were recorded regularly. The birds were vaccinated according to routine immunization practice in the region. The broiler house was kept clean and was disinfected on a regular basis.
Acknowledgements We would like to thank the staff of the  Animal Nutrition and Feed Science of Northwest A&F University  for their help and support during these experiments. This work was funded by the  National Key Research and Development Program of China  ( 2017YFD0500500 ,  20170502200 ), the  Program for Shaanxi Science & Technology  ( 2017TSCXL-NY-04-04 ,  2015KTCQ02-19 ) and the  Program for Yangling Agricultural High-tech Industries Demonstration Zone  ( 2018CXY-10 ). This research was partially supported by a grant from  KEMIN Zhuhai China, Inc.
Filamentous fungi are one of the main causes of food losses worldwide and their ability to produce mycotoxins represents a hazard for human health. Their correct and rapid identification is thus crucial to manage food safety. In recent years, MALDI-TOF emerged as a rapid and reliable tool for fungi identification and was applied to typing of bacteria and yeasts, but few studies focused on filamentous fungal species complex differentiation and typing. Therefore, the aim of this study was to evaluate the use of MALDI-TOF to identify species of the Aspergillus section Flavi, and to differentiate Penicillium roqueforti isolates from three distinct genetic populations. Spectra were acquired from 23 Aspergillus species and integrated into a database for which cross-validation led to more than 99% of correctly attributed spectra.
For P. roqueforti, spectra were acquired from 63 strains and a two-step calibration procedure was applied before database construction. Cross-validation and external validation respectively led to 94% and 95% of spectra attributed to the right population. Results obtained here suggested very good agreement between spectral and genetic data analysis for both Aspergillus species and P. roqueforti, demonstrating MALDI-TOF applicability as a fast and easy alternative to molecular techniques for species complex differentiation and strain typing of filamentous fungi.
For several years now, MALDI-TOF MS has been successfully applied to microorganism identification, from bacteria  (Basile et al. 1998)  to fungi  (Welham et al. 2000)  including food-related fungi  (Quéro et al. 2018 ). In the latter study, a spectral database comprising 619 strains belonging to 136 species of food interest was built and 90 % correct identification at the species level were achieved after external validation. Several commercial instruments and databases are available  (Deak et al. 2015)  for routine identification. Besides identification at species level, MALDI-TOF MS has been shown as a powerful tool to discriminate fungal species complex and cryptic fungal species. As an example,  Al-Hatmi et al. (2015)  were able to correctly identify species of clinical interest belonging to the Fusarium fujikuroi complex, some of which being cryptic species.
For example,  Alanio et al. (2011)  used MALDI-TOF MS for discriminating 10 species of Aspergillus section Fumigati while  Hettick et al. (2008)  and De Carolis et al. (2012) could differentiate A. flavus from A. parasiticus, and A. parasiticus, A. flavus and A. oryzae, respectively. In a more extensive study,  Rodrigues et al. (2011)  could identify 9 species of Aspergillus section Flavi but could not distinguish aflatoxigenic and nonaflatoxigenic isolates of A. Flavus. More recently,  Imbert et al. (2019)  assessed MALDI-TOF MS identification accuracy of Aspergillus cryptic species on a very large dataset of 1477 isolates using the freely available mass spectrometry identification (MSI) platform. After sequencing a subset of 245 cryptic species isolates for confirmation, they showed that, while very good identification (99.
6 %) could be achieved with MALDI-TOF MS at the section level, only 66. 1% of isolates were correctly assigned at the species level indicating that the database needed further improvement for cryptic species. To another extent, MALDI-TOF MS could also be of great interest for differentiating strains from a same species, i. e. for strain typing. In the past 10 years, it has been applied to bacteria of different genera and species such as Salmonella enterica  (Kuhns et al. 2012) , Staphylococcus aureus  (Ueda et al. 2015) , Legionella spp. (Fujinami et al. 2011)  or Arthrobacter spp. (Vargha et al. 2006)  and could be in some cases as effective as traditional typing methods such as multi-locus sequence typing (MLST) or Pulsed-Field Gel Electrophoresis (PFGE). In a recent study,  Kern et al. (2014)  showed the ability of MALDI TOF MS to differentiate Lactobacillus brevis isolates at the strain level, and correlations could also be made between spectra classification and strain physiological properties.
Discrimination of Aspergillus section Flavi species complex Two approaches were used to evaluate the power of MALDI-TOF MS to discriminate Aspergillus species from the Flavi section. First, a non-supervised approach was used in which the distances between all spectra were determined using the classical multidimensional scaling function (cmdscale) of Matlab (2014, The Mathworks Inc. , USA). The calculated distances between spectra were based on the absence or presence of peaks and their intensities. These distances were then visualized using a multidimensional scaling (MDS) graphic. Secondly, a supervised approach was used after building a spectral database with 61 strains as previously described  (Girard et al. 2016; Quéro et al. 2018). Briefly, peak lists were binned and a predictive model was established using the Advanced Spectra Classifier (ASC) algorithm developed by bioMerieux (Marcy l'Etoile, France).
In case more than 4 species were retained or if no significant match was found, it was considered as a nonidentification result. Spectra acquired from 7 species, i. e. , A. bertholletius, A. coremiiformis, A. cerealis (ex A. korhogoensis), A. mottae, A. oryzae, A. pseudocaelatus and A. pseudonomius were not included in the database as the number of tested strains was not sufficient. Indeed, the ASC algorithm used for database construction requires a minimum of 2 strains per species. Performances of this database were then evaluated using the same set of isolates by crossvalidation, i. e. , internal validation, as described previously  (Quero et al. 2018). The spectral data were randomly split into 5 subsets, 4 subsets used as learning phase and the last one used to validate the identification performances (or in this case, the attribution to the right group).
Discrimination of Penicillium roqueforti isolates Prior to similarity comparison of spectra and database construction, a two-step calibration was applied on all spectra, involving a linear calibration step to correct any possible global mass shift and a quadratic calibration step on the spectra entire mass range. For linear calibration, specific MS peaks of P. roqueforti spectra were used. To do so, MS spectra of P. roqueforti isolates recorded in the bioMérieux commercial database (version 3. 2) were screened for specific MS peaks, which were then searched in the spectra acquired in the present study. Thirteen specific MS peaks with masses ranging from 3704. 84 to 14431. 15 Da were found. Then, only MS peaks present in more than 70% of isolates belonging to two of the three genetic populations were kept which led to the selection of 9 MS peaks with a mass of  3704.
These selected masses which included 42 MS peaks (data not shown) were then used to build a quadratic model between theoretical and observed masses, allowing a good alignment of spectra. A spectral database in which the spectra derived from P. roqueforti isolates were assigned to one of the three genetic populations identified by  Gillot et al. (2015)  was then implemented, using the ASC algorithm. Performances of this database were first evaluated by cross-validation as described above and database performances were also evaluated by external validation using 21 strains not used to build the database. Spectra derived from these strains were used to challenge the database to assess whether they could be correctly assigned to the correct genetic population.
Spectra of the 16 species which were represented by at least two strains were integrated into the bioMérieux spectral database and identification performances were assessed by crossvalidation (Table  2 ). Overall, more than 99% of spectra (271/272) were assigned to the correct species. Only one spectrum of A. novoparasiticus was not identified, and one spectrum of A. avenaceus was attributed to both A. avenaceus and A. flavus. These results show that, even if the species of Aspergillus section Flavi tested here are closely related, their spectra are sufficiently different to yield correct identification when implemented into a spectral database and analyzed with the ASC algorithm. Results obtained here are of particular interest, especially for A. parasiticus and A. sojae whose spectra were perfectly separated and identified during cross-validation.
arachidicola, A. flavus, A. luteovirescens (ex A. bombycis), A. minisclerotigenes, A. nomius, A. novoparasiticus, A. parasiticus, A. pseudotamarii, A. sergii, A. transmontanensis) from non-aflatoxin producing ones (A. avenaceus, A. caelatus, A. leporis, A. sojae, A. tamarii). To our best knowledge, this is the first time that as many species of the Flavi section are studied and correctly identified by MALDI-TOF MS analysis. Indeed, several papers focused on the identification of Aspergillus section Flavi species of clinical interest such as A. flavus, A. oryzae, A. nomius or A. tamarii and though a separate clustering of species was obtained using hierarchical analysis, no commercial databases were able to accurately identify them  (De Carolis et al. 2012; Li et al. 2017; Masih et al. 2016; Park et al.