dataesr/scientific-paragraphs-categorization huggingface dataset:
Western blots Proteins were prepared from cell preparation by lysis in cold RIPA buffer (50 mM Tris pH 8, 150 mM NaCI, 1% NP40, 0. 5% deoxycholate) supplemented with inhibitors. Protein lysates were separated by gradient 4%-15% SDS-PAGE (BioRad) and transferred to a nitro-cellulose membrane (Amersham Protran 0. 2µm NC). Membranes were blocked with 2, 5% non-fat milk (w/v) in TBS with 0. 05% Tween and incubated with the appropriate primary antibodies at the appropriate dilution and followed by incubation with secondary antibodies conjugated to a fluorophore. Fluorescence was revealed using a scanner (Amersham Typhoon). Primary antibodies from Cell Signaling: ATM D2E2 (CST 2873S) rabbit at 1/1000, ATR E1S3S (CST 13934S) rabbit at 1/1000, mTOR (CST 2972S) rabbit 1/1000, p53 1C12 (CST2524S) mouse 1/1000; PRP8 (sc-30207, Santa Cruz) rabbit 1/200; from ABCAM: EFTUD2 (ab72456) rabbit 1/2000, GAPDH (ab8245 6C5) mouse 1/10000; and from Sigma: NOP58 (HPA018472) rabbit 1/100, RPAP3 (SAB1411438) rabbit 1/1000, Tubulin (I2G10) mouse 1/500, TRRAP 2TRR-1B3 (MABE1008) mouse 1/1000.
Acknowledgments We thank  Solange Morera  and  EMBL  facility for help with anti-RPAP3 antibody generation. The ES cells used for this research project were generated by the trans- NIH Knock-Out Mouse Project (KOMP)  and obtained from the  KOMP Repository  (www. komp. org). NIH grants to Velocigene at  Regeneron Inc  ( U01HG004085 ) and the  CSD Consortium  ( U01HG004080 ) funded the generation of gene-targeted ES cells for 8500 genes in the KOMP Program and archived and distributed by the  KOMP Repository at UC Davis  and  CHORI (U42RR024244 ). We thank the  CIGM team in Institut Pasteur  for technical support in microinjection experiments and animal husbandry. We thank the in-house animal facility, RAM, MRI for their excellent support. We acknowledge the  "Réseau d'Histologie Expérimentale de Montpellier" (RHEM)  facility supported by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , the  European Regional Development foundation  and the  Occitanian Region  for processing our animal tissues.
Data and sample availability statement Mouse sperm for the different strains is available upon request to pradet@crbm. cnrs. fr. Transcriptomic analysis of R2TP subunits in human samples (Figure  6A ) used the UCSC Xena platform for public and private cancer genomics data visualization and interpretation https://xenabrowser. net/  (Goldman et al. , 2018) , The data concerning the patient biopsies (Figure  6B ) are not publicly available because the database contains sensitive information that could compromise patient privacy. Details about the experimental procedures are available upon request to pradet@crbm. cnrs. fr.
LC-MS and NMR analysis LC-MS analysis were performed on an Acquity H-Class UPLC system (Waters Corp. , Milford, MA), using a Kinetex C18 100 A 100 × 2. 1 mm, 2. 6 µm column (Phenomenex) coupled with an Acquity PDA detector and with a mass spectrophotometer Synapt G2-S HDMS system (Waters Corp. , Milford, MA) with electrospray ionization source operating in high resolution mode. The elution gradient was set as follow: from 95% formic acid 0. 01% and 5% acetonitrile to 100% acetonitrile in 10 min, flow rate was fixed at 0. 4 ml/min. The Synapt parameters were optimized as follow: the sample cone was set at 20 V, the source and desolvation temperature were set at 140°C and 450°C, respectively. Each sample were processed with MassLynx (V4. 1) software. Fractions for NMR were collected with a semi-preparative HPLC using Ultimate 3000 LC system (Thermo) equipped with an autosampler (WPS3000TFC) and then evaporated to dryness.
Study design, population and data source We used data from the SUCCEED survey of community-dwelling volunteers aged 50 years or over consecutively attending an outpatient clinic in a geriatric teaching hospital (1925 beds, of which 178 are short-stay beds) in the Paris area (France)  [8]. Individuals underwent a comprehensive, multidimensional clinical evaluation of cognition, mood, balance, mobility, muscle strength, nutrition, comorbidities, incontinence, hearing, and bone density. For the present analysis, we selected individuals aged 50 to 75 recruited between 2010 and 2015 (Figure  1 ). They were contacted by phone in 2016-2017 for information on adverse health outcomes since the first assessment. All study participants received written information about the use of their personal medical data for the study, and provided oral consent.
Data collection Baseline parameters were collected prospectively using a self-administered questionnaire. This was followed by an interview, physical measurements, and performance tests conducted by trained nurses, and then a standardized clinical evaluation by a geriatrician. Hypertension was defined as a mean systolic blood pressure (based on three measurements) ≥140 mmHg, a mean diastolic blood pressure ≥90 mmHg, or antihypertensive treatment. Exhaustion was defined as a "yes" answer to the question: "Did you feel any significant or unusual fatigue over the previous year?". Participants were asked about unintentional weight loss over the previous year, and the Mini Nutritional Assessment Short-Form score was recorded (risk of malnutrition: <12/14, i. e. , <12 out of 14). Mobility and muscle strength were assessed via a daily pedometer count for a week (low activity: <7500 steps/day)  [9] , gait speed over 10 meters (slowness: <1 m/s  [10] , relative slowness: <median speed), dynamometer-measured maximum dominant-hand grip strength (JAMAR®, Sammons Preston, Bolingbrook, IL, USA; weakness: <30 kg in men and <20 kg in women)  [11] , and the completion time in a five-time sit-to-stand test  [12].
Selection of indicators for the latent class analysis We first considered 25 potentially relevant frailty markers used in the SUCCEED survey to assess nutrition, mobility, muscle strength, balance, bone density, cognition, mood, hearing, and incontinence. To select the indicators, we first considered the proportion of missing data and the prevalence. Next, we performed a multiple correspondence analysis to assess links between variables, summarize them as a smaller number of factors, and check that each item contributed significantly to the relevant domain. To characterize the clusters and predict class membership, we considered three active covariates: age, gender, and the reason for attending the outpatient clinic (preventive action or a health complaint).
Latent class analysis We iteratively fitted models comprising up to 8 classes, and selected the model with the best statistical properties. Our one-step approach involved the simultaneous estimation of the LC model of interest and a multinomial logistic regression in which the LCs were related to the active covariates listed above. To deal with missing data, we used the procedure implemented in Latent GOLD software (V 5. 0, Statistical Innovations, Belmont, MA) with a missing-at-random hypothesis. The local dependency between two indicators (bone mineral density and relative slowness) was accounted for by adding a direct effect  [20].
Characterization of the profiles identified To characterize the identified LCs, all individuals were assigned to their most likely class. The distribution of illustrative variables and outcomes was compared across LCs by using the Chi2, Fisher's test, Kruskal-Wallis test or a logistic regression to adjust for the length of follow-up. Post-hoc pairwise comparisons were performed when p was ≤0. 10. All statistical analyses were performed with STATA software (V 14. 1, StataCorp, College Station, TX). P values of two-sided test ≤0. 05 were considered statistically significant. P values from multiple pairwise comparisons were corrected using the false discovery rate method  [21].
Study participants Of the 625 individuals in the database, the 589 aged ≤75 years (median age: 61. 7 years [57. 1-65. 8]) were analyzed. After a median follow-up of 3 years (2-4), outcomes were available for 469 individuals (79. 6%) (Figure  1 ). The baseline characteristics of the study population are summarized in Table  1. Half of the population had one comorbidity and over a quarter had at least one of the following frailty factors: urinary incontinence, weakness, limited ankle dorsiflexion, osteopenia, executive dysfunction or exhaustion. Slowness and shrinking were more scarce (<5%). The nine selected indicators are described in Tables  2  and 3.
Variables Primary outcome was the proportion of patients with a NIV prescription. Secondary outcomes were the association between NIV prescription and patients' outcome and all identified variables associated with NIV prescription. We defined groups as BiPAP+ and BiPAP-if NIV was prescribed or not, respectively. Patients' outcome were hospital length-of-stay and mortality status at Day-2 and Day-8 and in-hospital-mortality. Other data reported were comorbidities according to the Charlson Adjusted Index  [9] , initial clinical and biological patterns, and autonomy status. Initial clinical and biological patterns were checked during the two first hours of care, before noninvasive ventilation when performed. Autonomy status was defined according to the AGGIR scale  [10]  and determined before or during the hospitalization.
Statistical analysis Data analyses were performed using SPSS 23. 0 (IBM Corporation, Armonk, NY, USA). Descriptive data are reported as median and interquartile range or number and proportion, or adjusted odds ratios and 95% confidence intervals, as appropriate. Univariate analysis was conducted using Mann-Whitney U-test, the log-rank or chi-squared tests, as appropriate. A multivariate Cox regression model was used to analyse Day-8 mortality associated factors. Multivariate models were developed to adjust for relevant variables and from the univariate association (p<0. 20). Statistical significance was defined as p<0. 05 for two-tailed tests. The protocol was approved by the ethic committee of the Société de Réanimation de Langue Française.
Statistical analysis The results are presented as means and standard deviations for continuous variables and as proportions for discrete variables. We used the D'Agostino-Pearson test to assess the normal distribution of our data and then used parametric statistics. We used t-tests to compare continuous data, as appropriate, and t-tests for paired data to evaluate the significances of differences in continuous data before and after surgery. We used Spearman's correlation coefficient test to explore the relationships between values. Snellen visual acuities were converted into logarithm of the minimum angle of resolution (logMAR) units for analysis. Corrected P values < 0. 05 were considered statistically significant. Statistical analysis was performed with SPSS for Windows version 20. 0 (SPSS, Inc, Chicago, IL).
METHODS This is a prospective cohort follow-up study, conducted between June 8 th 2015 and August 1 st 2017 as an ancillary of the LBBB-TAVI study (NCT02482844)  12. The study was approved by the local ethics committee and the National Health Authority (ANSM: 2015-AOO271-48) and the written informed consent of each patient was obtained and archived.
Aortic regurgitation after TAVI was considered to be significant if at least moderate. The change in LVEF was calculated as the difference between the measurement at 6-12 months minus the pre-implantation measurement (ΔLVEF = LVEFpostTAVI -LVEFpreTAVI). The variation in LVEF values are presented in LVEF percentage points. One operator's analysis were for the analysis. The second operator's analysis were used to evaluate inter-operator reproducibility. Left ventricle EF was obtained with the best loop acquired. Data were analyzed consecutively in groups of 10 patients.
Sample size and Statistical analysis Sample size were calculated according to Cohen's definitons of effect-size bounds as: small (ES: 0. 2), medium (ES: 0. 5) and large (ES: 0. 8: grossly perceptible and therefore large), we estimated that 40 patients in each group would be sufficient to detect a 5% absolute difference in LVEF for an effect size between 0. 6 and 0. 7 with a two-sided type I error of 5% and a statistical power greater than 80%. Statistical analyses were performed using Stata software, Version 13 (StataCorp, College Station, TX, US). Tests were two-sided, with a Type I error set at α=0. 05. Continuous data are expressed as mean ± standard deviation (SD) or median [interquartile range] according to the statistical distribution (assumption of normality assessed using the Shapiro-Wilk test).
Résumé Introduction : L'application PELICAN (« Partager Efficacement en Laboratoire les Informations des Comptes rendus ANatomopathologiques ») est un outil permettant de générer des comptes-rendus standardisés et de renseigner automatiquement une base de données, utilisée au CHRU de Nancy depuis 2014 en neuropathologie tumorale. L'objectif de cet article était d'illustrer l'utilisation de cette application pour les méningiomes, avec une première exploitation statistique. Matériels et Méthodes : L'étude inclut les cas de méningiomes enregistrés dans l'application PELICAN jusqu'en juillet 2018. L'application PELICAN est un fichier Microsoft Excel comportant un logiciel écrit en Visual Basic pour Applications, utilisé par le pathologiste pour élaborer le compte-rendu. Les principales données cliniques ont été recueillies à partir de la fiche de recensement du Registre de l'Hérault.
Le suivi était renseigné de façon systématique pour les méningiomes atypiques. Résultats : 295 méningiomes ont été analysés dont 250 méningiomes de grade I, 42 méningiomes de grade II et 3 méningiomes de grade III. Dans les méningiomes de grade II, on constatait une proportion significativement plus élevée d'hommes (p=0,002) et d'infiltration de la dure-mère (p<0,001), une augmentation significative de l'indice Ki-67 (p<0,0001) et une diminution significative de l'expression des récepteurs à la progestérone (p<0,001). Dans les méningiomes atypiques, un indice Ki-67 de plus de 20% était corrélé significativement à une survie sans progression plus courte (p=0,032). Conclusion : L'application PELICAN est un outil d'utilisation simple, permettant de générer des comptes-rendus standardisés tout en alimentant une base de données, ouvrant de très intéressantes perspectives d'un point de vue épidémiologique et scientifique.
Summary: Introduction: PELICAN ("Partager Efficacement en Laboratoire les Informations des Comptes-rendus ANatomopathologiques") is a software which generates standardized reports, and allows to automatically create a database. It has been used in central nervous system tumor pathology at the University Hospital of Nancy since 2014. The purpose of this article was to illustrate the use of this application for meningiomas, with a first statistical evaluation. Materials and methods: The export of data included all cases of meningiomas recorded in the PELICAN application until July 2018. The PELICAN application is a Microsoft Excel file containing a software, written in Visual Basic for Applications, and used by the pathologist to create the report. The main clinical data were collected from the Hérault Register census form.
Follow-up was systematically reported for atypical meningiomas. Results: 295 meningiomas were analyzed, including 250 grade I meningiomas, 42 grade II meningiomas, and 3 grade III meningiomas. Grade II meningiomas were characterized by a significantly higher proportion of men (p=0. 002) and dural infiltration (p<0. 001), a significant Introduction L'application PELICAN (« Partager Efficacement en Laboratoire les Informations des Comptes rendus ANatomopathologiques ») a été initiée en 2014 en neuropathologie dans le cadre d'un projet de l'école ARTEM (ARt, Technologie Et Management), dont l'objectif était la création et l'activation d'une base de données exhaustive des tumeurs du système nerveux central  [1] , avec le soutien de l'ANOCEF (Association des Neuro-Oncologues d'Expression Française) et du Registre des Tumeurs de l'Hérault.
Ce projet répondait à plusieurs problèmes et demandes : l'absence de normalisation des données devant figurer dans les comptes rendus d'anatomopathologie, l'absence de mémorisation structurée des données des comptes rendus d'anatomopathologie, et la nécessité de ressaisie des informations pour les logiciels de tumorothèques et autres bases de données, comme en particulier pour le Recensement national histologique des Tumeurs Primitives du Système Nerveux Central (RnhTPSNC) du Registre des Tumeurs de l'Hérault, base nationale des tumeurs du système nerveux central. Depuis 2014, et jusqu'à la date de rédaction de cet article (juillet 2018), 886 tumeurs du système nerveux central ont été enregistrées dans l'application dont 295 méningiomes (33%). L'application PELICAN comporte différents formulaires spécifiques permettant la génération de comptes-rendus standardisés pour la très grande majorité des tumeurs du SNC.
Infrastructure de l'application et élaboration de l'interface de saisie L'application PELICAN est un fichier Excel comportant un logiciel spécifiquement développé pour les laboratoires, écrit en Visual Basic pour Applications (VBA), ne nécessitant pas d'autre suite logicielle que Microsoft Office. Il gère en complète autonomie une base de données spécifique partageable en mode multi-utilisateurs sur le réseau local du CHRU de Nancy. L'exportation des données se fait par copie du compte rendu sous forme de texte mis en forme et collage dans le logiciel métier du laboratoire (DIAMIC). Le premier écran de saisie est commun à toutes les tumeurs du SNC. L'interface de saisie spécifique aux méningiomes et autres tumeurs méningées a été mise au point en se basant sur la « fiche relecture des méningiomes, tumeurs fibreuses solitaires, hémangiopéricytomes » proposée en 2014 dans le cadre du RENOP (Réseau national de NeuroOncologie Pathologique).
Export des données Les informations statistiques globales ont été extraites du logiciel à partir de l'onglet dédié « statistiques générales ». L'export des données enregistrées a été effectué par le biais de requêtes multicritères dans la partie « analyse des données de la base PELICAN ». Les requêtes ont porté sur l'ensemble des données anatomopathologiques relatives aux méningiomes (groupe 110), et de l'ensemble des données clinico-radiologiques, chirurgicales et données issues des feuilles de recensement. L'exportation des données sélectionnées se fait directement à partir de l'application PELICAN sous forme de table Excel.
Analyse statistique L'analyse statistique a été effectuée à l'aide du logiciel GraphPad PRISM. La comparaison de la répartition entre les groupes (méningiomes de grade I vs. II) des variables qualitatives a fait l'objet d'un test du Khi² ou d'un test de Fisher (en cas d'effectifs < 5), et des données quantitatives d'un test de Mann-Whitney-Wilcoxon. Une p-value inférieure à 0,05 est considérée comme statistiquement significative.
Discussion L'application PELICAN a été initiée en 2014 en neuropathologie dans le cadre du projet ARTEM, dont l'objectif était la création et l'activation d'une base de données exhaustive des tumeurs du SNC. A ce jour, environ 900 tumeurs primitives du SNC y sont enregistrées. Il s'agit d'une application originale, facilement utilisable en routine, constituant dans notre expérience un gain de temps par rapport aux comptes-rendus classiques, et ne nécessitant pas d'autre suite logicielle que Microsoft Office. Elle est un apport considérable d'un point de vue épidémiologique et scientifique avec la possibilité d'enregistrer, de façon couplée aux données des comptes rendus anatomopathologiques, des renseignements cliniques, y compris les données de survie. Nous avons effectué dans cet article la première exploitation statistique des données enregistrées depuis sa création en 2014.
Cette analyse a portée sur 250 méningiomes de grade I, 42 méningiomes de grade II et 3 méningiomes de grade III. Ces données, exportées sous format Excel, ont été importées dans un logiciel de biostatistiques et ont permis de constater qu'il n'y avait pas de différence significative entre les méningiomes de grade I et ceux de grade II pour la symptomatologie au diagnostic, le score OMS et le délai entre l'apparition des symptômes et la prise en charge chirurgicale. Concernant les méningiomes de grade I, les sous-types histologiques prédominant étaient le méningiome méningothélial, suivi du méningiome transitionnel puis du méningiome fibroblastique, de façon globalement similaire aux données de la littérature, en particulier issues du recensement national  [10]. Des critères histo-pronostiques péjoratifs mineurs étaient retrouvés dans 6 à 12% des méningiomes de grade I et entre 50 et 71% des méningiomes de grade II.
Par ailleurs, cette application pourra être dans l'avenir étendue à d'autres types de tumeur, en dehors du SNC. Par exemple, depuis le premier trimestre 2018, nous utilisons l'application PELICAN pour les pièces d'exérèse de tumeurs pulmonaires. En conclusion, l'application PELICAN est un outil d'utilisation simple, permettant de générer des comptes rendus standardisés, tout en alimentant une base de données facilement exploitable sur le plan statistique, ouvrant de très intéressantes perspectives d'un point de vue épidémiologique et scientifique. Ce logiciel s'intègre de plus parfaitement dans le cadre politique actuel d'harmonisation des pratiques par la standardisation des comptes-rendus, tel que recommandé par les derniers Plans Cancer, et répond aux différentes normes actuelles en terme d'interopérabilité.
PATIENTS AND METHODS This prospective observational nonrandomized clinical study was performed at Quinze-Vingts National Ophthalmology Hospital, Paris, France. Informed consent was obtained from each patient before inclusion in the study according to the tenets of the Declaration of Helsinki, and the study was approved by the Ethics Committee of the French Society of Ophthalmology (Institutional Review Board 00008855). Data on consecutive cases of progressive keratoconus followed from January 2014 to January 2016 were collected and prospectively analyzed. All patients had a complete ocular examination. The criteria for keratoconus diagnosis were the presence of central corneal thinning, conical protrusion, Vogt striae, or Fleisher ring on slitlamp examination and a keratoconic appearance on topographic maps.
). The following topographic data were recorded: maximum K measurement, simulated maximum K measurement, minimum K measurement, simulated astigmatism at the center and in the paracentral 3. 0 to 5. 0 mm, and mean corneal power in the paracentral 3. 0 to 5. 0 mm. At least 3 images were obtained of each examination to ensure quality of the scanning-slit corneal topographer and OCT acquisitions. The images were visually inspected, and the best was selected according to the quality of the keratoscope photographs and OCT scans. Moreover, the repeatability of OCT and topography analyses was first assessed by having 2 experienced operators perform 3 consecutive examinations on a sample of 30 keratoconus cases in both groups. The interobserver agreement for the measurement of epithelial and stromal thickness and maximum K values was assessed by the intraclass correlation coefficient (ICC); the limits of agreement were assessed using the Bland-Altman method.
The area under the ROC curve (area under the curve [AUC]) was calculated for each variable. Interobserver agreement for the measurement of epithelial and stromal thicknesses from OCT images and for the measurement of topographic parameters with the scanning-slit corneal topographer was assessed by calculating the ICC. Corrected P values less than 0. 05 were considered to be statistically significant. Statistical analysis was performed using SPSS for Windows software (version 20. 0, IBM Corp. ).