dataesr/scientific-paragraphs-categorization huggingface dataset:
We further excluded experiments where the sound stimuli were paired with a visual object to concentrate on sound discrimination, since these studies potentially tap word-object association learning (N = 5). We also excluded experiments where infants were not typically developing and monolingual (N = 29), and data points whose effect sizes were more than 3 standard deviations from the mean of the meta-analysis (N = 4), since subsequent analyses could be affected by such outliers. Since some of these exclusion criteria overlapped, the final dataset contained N = 152 data points. This dataset was restricted further depending on the analysis, as noted below. Based on our prior work with this database, we know it is necessary to control for whether studies involved a habituation phase, a fixed-length familiarization phase, a conditioning phase, or neither, since effect sizes are markedly different as a function of this methodological aspect.
Peripherality We were able to include 26 data points where discrimination was tested from a less peripheral to a more peripheral vowel, and 19 data points for the opposite directionality. Model comparison showed that there was a significant effect of peripherality (see Table  1 ; Fig. 1 ). We additionally explored the estimated mean effect sizes for the two types of data points by constructing separate models. The intercept for the model with data points testing discrimination from less to more peripheral vowels was higher [b = 0. 902, p. 001, CIL = 0. 674, CIL = 1. 130] than the intercept for the model with data points testing discrimination in the opposite direction [b = 0. 475, p. 001, CIL = 0. 287, CIL = 0. 664]. Meta-analytic results thus support the predictions of the NRV model. We do, however, want to caution that the dataset is relatively small, due to the fact that not many studies report discrimination results split by direction.
Spectral and temporal distance A total of 107 data points had associated information on spectral distance. Adding this predictor significantly increased model fit (see Table  1 , Spectral (all); Fig. 2 ). There were no interactions with nativeness or age. Temporal distance could be obtained for 90 data points. This predictor did not improve model fit, (Table  1 , Temporal (all); Fig. 2 ), and there were no interactions with nativeness or age. We carried out several additional analyses to make sure that our results were not due to biases in data selection or unfair comparisons. To verify that differences in outcomes between our analyses of spectral and temporal distance were not based.
Funnel plot peripherality analysis Residual Value Standard Error of Hedges' g 0. 53 0. 40 0. 26 0. 13 0. 00 q q q q q q q q q q q q q q q q q q -2. 00 0. 00 1. 00 on differences in datasets (107 vs. 90 data points), we conducted subset analyses based only on the 62 data points containing information on both measures. Both analyses on spectral and temporal differences yielded results consistent with the main analysis (Table  1 , sub-overlap results). Finally, although we statistically account for the fact that data points from the same paper could be more similar than data points from different papers, it might still be the case that comparisons drawn from the same paper provide a more powerful measure, as presumably they control for differences in e. g. , laboratory habits. In a second subset analysis, we included papers that provided multiple data points for a given dimension, for instance two vowel contrasts.
Spectral and temporal distance Our analyses overall do not find evidence for an effect of temporal distance on effect sizes, whereas they confirm that spectral distance predicts effect sizes significantly. Our results for the simple spectral distance measure we employed are encouraging, and suggest there is sufficient variability in the dataset to explore the matter further. We implemented distance as the Euclidean separation on F1-F2 space in Bark, but many other instantiations are possible. It would be extremely interesting to assess the predictive value of more linguistically informed representations (see e. g. ,  [19] ), but unfortunately few authors make their stimuli available in their raw form, thus limiting their re-description. Another direction of work we have not explored pertains to the relative importance of different formants, which has been proposed in one word-learning study, potentially in a post-hoc manner, whereby F1 would be perceptually more salient than F2  [20].
Our data are available from metalab. stanford. edu, and thus interested readers can download them and assess for themselves this and hopefully many other hypotheses. In contrast to the robust effects of spectral distance, temporal distances were not a significant predictor in any of the analyses. Might this indicate a true difference between the dimensions, or a chance finding due to confounding factors? To better evaluate this marked difference in outcomes, let us consider to what extent differences in the spectral and temporal variability accessible in our database could contribute to these results. Examining the standard deviations of the respective predictor, we find spectral distance to have a larger standard deviation (sd = 1. 99) than temporal distance (sd = 0. 40). A second way to look at this question is to examine how well controlled the respective other dimension was in the two sets of data.
We found that 19% (20/107) of data points in the spectral analysis contained, in addition to spectral changes, a length difference that could be phonologically contrastive. In contrast, 60% (52/86; note that featural distance could not be coded for 4 data points that had a temporal distinction) of data points in the temporal analysis contained, in addition to a temporal difference, a quality difference that could be phonologically contrastive (such as backness or height). Thus, if anything, the spectral set was more controlled for temporal variation than the temporal set was for spectral variation. Third, we inspected whether changes were greater for spectral versus temporal contrasts in our dataset. Indeed, 90% (96/107) of data points in the spectral analysis actually contained one or more featural changes, while this was the case for only 41% (35/86) of data points in the temporal set, which more often contained within-category variation.
Drawing from our own dataset, the maximal spectral difference is provided by the point vowel contrast [a-i], spanning 6 bark; whereas the maximum temporal difference found here corresponds to a ratio of duration 2 (i. e. , double the length), corresponding to a lexically contrastive length contrast in Japanese. Together with the various subset analyses we have carried out, these conceptual considerations lead us to conclude that the difference in meta-analytic effects measured here for spectral versus temporal differences may indeed be due to actual differences in perceptual effects across these two dimensions, although further work with more carefully controlled data could conclude otherwise.
Acknowledgements and the  Agence Nationale pour la Recherche  [ ANR-14-CE30-0003   MechELex ,  ANR-10-IDEX-0001-02   PSL *,  ANR-10-LABX-0087 IEC]. under the  Marie Sklodowska-Curie  grant agreement No  659553 , This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the.
Physical therapy use increased (43% vs 64% of people had at least one physical therapy session during the year, with an average of 47 vs 84 sessions/person during the year), while specialist consultations decreased (29% of people consulted an ophthalmologist at least once during the year before admission vs 25% after admission; 27% vs 21% consulted a cardiologist). Hospitalization rates were lower during the year following institutionalization (75% vs 40% of people were hospitalized at least once during the year), together with a lower emergency admission rate and a higher day admission rate. Conclusions: Analysis of the new French reimbursement database specific to SNH shows that nursing home admission is associated with a reduction of some forms of outpatient care and hospitalizations.
Analysis of trends before and after nursing home admission would require specific prospective studies, which would be more difficult and expensive to perform. In France, SNH transmit the lists of their residents to national health insurance, which can be linked to data of the National Health Insurance Information System, which comprises information concerning reimbursed or free health care use. The objective of this observational study was to compare, by linking these two databases, disease status and health care use of national health insurance general scheme beneficiaries between the year before nursing home admission and the year after nursing home admission.
Data sources In France, there were about 7500 SNH with 600,000 residents in 2011. SNH are available to dependent elderly people 60 years and older and admission is based on the choice of the person and not mainly on the dependency level. Nevertheless, there are very few persons between 60 and 64 years-old in SNH. The RESID-EHPAD tool allows these SNH to automatically transmit the lists of their residents with admission and discharge dates to French national health insurance. Information concerning these institutions is also collected, which determines the types of health care covered by the lump sum paid to the nursing home by French national health insurance: partial or global budget payment system, presence or absence of a nursing home pharmacy. RESID-EHPAD data are available with a history of 33 sliding months and can be linked to the SNIIRAM database by means of a common identifier.
The Système National d'Information Interrégimes de l' Assurance Maladie (SNIIRAM) [National Health Insurance Information System] database anonymously and comprehensively collects individual data for all health care reimbursed over a three-year period plus the current year to the beneficiaries of the various health insurance schemes  [8]. It does not contain any information concerning clinical results related to prescriptions or examinations. However, it contains information concerning costly long-term diseases (ALD) status, validated by a national health insurance physician at the request of the attending physician, allowing exemption of copayment, can provide information about the nature of the diseases treated. An anonymous and unique identification number for each beneficiary allows this information to be linked to the data collected during hospital stays in the various types of health care institutions by the Programme de médicalisation des systèmes d'information (PMSI) [Hospital discharge System].
Hospital diagnoses are coded according to the International Classification of Diseases 10th Edition (ICD-10), in the same way as the diagnoses allowing attribution of ALD status. Nevertheless, all health care delivered to SNH residents can be identified in the SNIIRAM database depending on the nursing home's mode of funding, as nursing care is included in the global budget paid to all SNH. For SNH opting for the partial budget payment system, general practitioner and physical therapist fees are reimbursed individually and can therefore be identified. Drug reimbursements can also be identified individually in the absence of a nursing home pharmacy.
Statistical analysis Data concerning the residents' diseases were derived from the beneficiaries' disease and expenditure mapping established for 2012 (before nursing home admission) and 2013 (year of nursing home admission) by CNAM-TS based on SNIIRAM data (Additional file 1). This tool can be used to identify diseases and health states in the population of beneficiaries requiring specific management and consequently able to be detected in the SNIIRAM database  [9, 10]. Algorithms are used to identify 56 non-exclusive groups of diseases, grouped into 13 main categories, based on principal diagnoses, related or significant associated diagnoses in short-stay hospitals and psychiatric hospitals; ALD status; dispensing of specific drugs; and specific procedures. Schematically, for certain cardiovascular and neurovascular diseases, the term "acute" refers to hospitalization during the year with an appropriate ICD-10 code and the chronic phase is defined by attribution of ALD status during the year and/or hospitalization with specific codes during the last 5 years.
). The reason for admission was defined by the principal diagnoses of the stay and only reasons (section and 3-digit code of ICD-10), representing a total of at least 2% of all hospitalizations before or after nursing home admission. The proportions of day-only or emergency hospitalizations for each of these reasons were reported. Emergency hospitalization was defined by hospitalizations from home (or the nursing home) via the emergency room or direct intensive care unit admission. Chi-square test, McNemar's test and t-test were used to compare indices before and after admission and between groups. Use of SNIIRAM data by the CNAM-TS has been approved by decree and the CNIL (French data protection authority). Analyses were performed using SAS 9. 3 software (associated with SAS Enterprise Guide version 4.
The increased prevalence of these diseases following nursing home admission can be  partly explained by an increased risk for onset or deterioration of these age-related diseases. The functional repercussions and dependence associated with these diseases are factors predisposing to institutionalization. This increased prevalence may also reflect more frequent screening or diagnosis at the critical time of nursing home admission or an increased prevalence related to management adaptations related to institutionalization, possibly allowing better identification of these diagnoses in the database (ALD applications, hospitalizations, treatments of certain diseases). However, disease prevalences are probably underestimated, as algorithms mainly based on ALD status or diagnoses coded during hospital stays for most diseases can only identify the most severely ill patients or those patients in situations in which ALD status would be beneficial (outpatient care, no private health insurance, etc.
e. about 69% of all people 65 years and older. People covered by other health insurance schemes according to their occupation probably present different socioeconomic, exposure and health care use characteristics. The target population of new nursing home admissions was limited to those residents who were still alive 1 year after admission in order to assess annual health care use, but to exclude terminal care, which probably comprises higher proportions of certain forms of care, such as hospitalization. As diseases are not directly identified in the SNIIRAM database, multi-source algorithms have recently been developed and have been submitted to sensitivity analyses and expert review  [10]. Although the method used in this study is not subject to the biases of self-reported surveys, it presents the classical limitations of data derived from medical and administrative databases.
Nevertheless, these comprehensive data are derived from homogeneous data collection, but only concern individual reimbursements, requiring selection of subpopulations according to the nature of the study. This database only concerns diseases and health states managed by the health care system and therefore identified by the patient and by nursing staff. This study cannot reliably assess the correlation between health care use and disease status, as the precise impact of the disease, its degree of severity and the level of dependence are unknown. However, concordances were observed between the prevalences of diseases before and after nursing home admission, and health care professional consultations and reasons for admission. One of the limitations of the databases analyzed in this study is the absence of information concerning dependence and family support.
Conclusion Linking of RESID-EHPAD and SNIIRAM databases now allows us to study and monitor trends in the characteristics of nursing home residents, their disease status and their health care use. The data currently available in databases cannot be used to evaluate the relevance of the various types of health care use. Nevertheless, this study demonstrates marked changes in health care use following nursing home admission. Nursing home admission is associated with a reduction of some forms of outpatient care and hospitalizations. Hospitalization remained frequent, but with shorter stays and less frequent emergency admissions, and a higher proportion of hospitalizations without overnight stays. Specialist consultations decreased, dental consultations remained globally stable and physical therapy use markedly increased.
A collection of XML documents has been used to build ontologies  23 24  , while a collection of HTML or MediaWiki documents has been processed to build taxonomies  25. The second category of approaches bears on specific documents or parts of them, for which the layout defines a precise semantics, such as dictionaries and thesaurus  26  or specific and well localized textual structures such as tables, categories  27 28  or infoboxes  29  from Wikipedia pages. Any of these textual structures can also be made explicit thanks to a markup language. Finally, as our study focuses on the Wikipedia corpus, the extracted relations could be exploited to enrich DBPedia. Several tools, called "extractors" have been developed to analyze each type of structured data in Wikipedia. Morsey and colleagues 7 developed 19 of such extractors that produce a formal representation of entities and relations identified within various structural elements from Wikipedia: abstracts, images, infobox, etc.
In BabelNet, missing lexical entries in resource-poor languages have We divided the set of disambiguation pages into two sets: a reference corpus composed of 20 pages, and a training corpus composed of the remaining pages (5904 pages). The two corpora have been pre-processed: (i) the plain text has been extracted from these pages, with the help of a WikiExtractor 5  ; (ii) this plain text has been annotated with Part-Of-Speech and lemma (using TreeTagger); and (iii) it has been annotated with terms (including both single words and multiword expressions) corresponding to labels of concepts in BabelNet for the training corpus (resp. with the set of manually annotated relations for the reference corpus).
Right: total capillary length of the network is expressed as the mean percentage of control values corresponding to cells treated with vehicle ±SEM. Data shown from three independent experiments (n = 3) each performed in duplicate. (E) GSK-343 and panobinostat do not induce apoptosis in ECFCs as measured by fluorescence-activated cell sorting (FACS) after 7-AAD and annexin V staining. (F) GSK-343 and panobinostat reduce serum starvation-induced apoptosis in ECFCs as measured by FACS after 7-AAD and annexin V staining. ECFCs were grown in serum-containing EGM-2 medium or serum-depleted EBM-2 medium. (E and F) Data expressed as mean percentage of control values corresponding to cells treated with vehicle ±SEM from three independent experiments (n = 3), each performed in duplicate. (G) GSK-343 and panobinostat decrease ECFC proliferation as analyzed by bromodeoxyuridine (BrdU) incorporation.
Combined Treatment of ECFCs with Epigenetic Drugs Activates Multiple Pro-angiogenic Pathways Simultaneously To better understand the mechanism through which the combined treatment of GSK-343 and panobinostat increases the pro-angiogenic properties of ECFCs, we measured changes in gene expression by RNA sequencing (RNA-seq) immediately upon drug treatment (i. e. , 72 hr incubation with the EZH2 inhibitor GSK-343 at 5 mM followed by 2 hr incubation with the HDAC inhibitor panobinostat at 10 nM). This experiment, performed using two independently generated ECFC clones from two distinct cord blood donors, identified 553 upregulated genes and 329 downregulated genes (p < 0. 05) (Figure  2A ). RNA-seq results were confirmed by qRT-PCR for all genes tested (Figures 3A, S4, S5A, and data not shown).
Capillary-like Network Formation Assay ECFCs, pretreated with epigenetic drugs or vehicle as indicated, were seeded into 96-well plates previously coated with 50 mL of phenol-red-free, growth factor reduced Matrigel (Corning) at a density of 5,000 cells/well in duplicates. After 24 hr, cells were stained with 5 mg/mL calcein (BD Bioscience) for 5 min at 37 C and photographed using an Axiovert S100 microscope (Zeiss) (C) EZH2 and HDAC inhibitors upregulate multiple transduction pathways in ECFCs. Schematic representation of cell-signaling molecules and processes that are upregulated (red) or downregulated (green) upon treatment of ECFCs with the GSK-343 and panobinostat combination. **p < 0. 01; *p < 0. 05. See also Figure  S6. and AxioVision 4. 6 software at 2. 53 magnification. Total tube length was quantified using AngioQuant software as described  (Fraineau et al.
Cell Migration Assay by Gap Closure ECFC migration was measured by gap closure assay using the Radius 96-well cell migration assay (Cell Biolabs) according to the manufacturer's instructions. Briefly, 2 3 10 4 ECFCs, pretreated with epigenetic drugs or vehicle, were seeded into a 96-well plate containing a hydrogel plug and allowed to adhere onto the plate. The plug was removed providing a cell-free area, and cell migration into the gap was monitored by taking pictures every 3 hr using an Axio Observer. A1 microscope (Zeiss) and AxioVision 4. 6 software at 103 magnification. Once confluent, cells were labeled with crystal violet (Cell Biolabs). The cell-free area was measured using MATLAB software, which provides a simple graphical user interface through which a user can select an image to calculate the area automatically or manually.
Chromatin Immunoprecipitation Analysis of histone modifications was performed by native chromatin immunoprecipitation (ChIP) as described  (Brand et al. , 2008). Cofactor binding was assessed by crosslink ChIP as described  (Palii et al. , 2014). ChIPed DNA was purified by phenol-chloroform extraction, precipitated with ethanol, and specific DNA sequences were amplified with PerfeC TaSYBRGreen SuperMix (Quanta Biosciences) using a Rotor-Gene Q real-time PCR system (QIAGEN). ChIPed DNA quantity was calculated compared with a genomic DNA standard curve using Rotor-Gene Q Series Software 1. 7 (QIAGEN) with R 2 > 0. 96.
Statistical Analyses Data obtained from at least three independent experiments each in duplicate or triplicate were expressed either as mean values or percentages of control values ± SD or SEM. When indicated, statistical significance between data groups was determined by nonparametric Kruskal-Wallis test followed by Dunn's post hoc test, or parametric one-or two-way ANOVA followed by Dunnett or Bonferroni post hoc test using GraphPad Prism 5 software. Use of the non-parametric Kruskal-Wallis test or ANOVA was determined depending on their distribution assessed by the Shapiro-Wilk normality test. Data are considered to be significantly different at p < 0. 05. The p values are depicted with asterisks in the figures as follows: ***p < 0. 001; **p < 0. 01; *p < 0. 05; ns, non-significant; ND, non-determined.
ACKNOWLEDGMENTS We thank  Y. Le ,  D. Yevstafiev , and  C. -P. Chaturvedi  ( OHRI ) for experimental help;  S. Lavictoire ,  I. A. J. Lorimer ,  A. M. Gagnon ,  A. Sorisky , and  W. L. Stanford  ( OHRI ) for providing antibodies; and  F. J. Dilworth  ( OHRI ) for critically reading the manuscript. RNA sequencing was performed at the  Institut de Recherches Cliniques de Montreal (Canada). This project was funded with grants from the  Canadian Institutes of Health Research (CIHR)  ( MOP-82813  to M. B. ); the  Department of Medicine, University of Ottawa  (to D. S. A. ); and the  Heart and Stroke Foundation of Canada  ( GIA-000225  to E. J. S. ). S. F. is supported by a fellowship from the  Cushing Fund.